Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensing throughput optimization in cognitive fading multiple access channels with energy harvesting secondary transmitters

The paper investigates the problem of maximizing the expected achievable sum rate in a fading multiple access cognitive radio network when secondary user (SU) transmitters have energy harvesting capability, and perform cooperative spectrum sensing. We formulate the problem as maximization of throughput of the cognitive multiple access network over a finite time horizon subject to a time averaged interference constraint at the primary user (PU) and almost sure energy causality constraints at the SUs. The problem is a mixed integer nonlinear program with respect to two decision variables, namely, spectrum access decision and spectrum sensing decision, and the continuous variables sensing time and transmission power. In general, this problem is known to be NP hard. For optimization over these two decision variables, we use an exhaustive search policy when the length of the time horizon is small, and a heuristic policy for longer horizons. For given values of the decision variables, the problem simplifies into a joint optimization on SU transmission power and sensing time, which is non-convex in nature. We present an analytic solution for the resulting optimization problem using an alternating convex optimization problem for non-causal channel state information and harvested energy information patterns at the SU base station (SBS) or fusion center (FC) and infinite battery capacity at the SU transmitters. We formulate the problem with causal information and finite battery capacity as a stochastic control problem and solve it using the technique of dynamic programming. Numerical results are presented to illustrate the performance of the various algorithms.
Integrated Learning System for Object Recognition from Images Based on Convolutional Neural Network

There has been an increase in the use of image processing for object recognition. However, traditional methods are not suitable in real-time system because they cannot satisfy human performance. Recently, deep learning with Convolutional Neural Network came to be known as a solution for image recognition. In fact, there are many great result with deep learning in object recognition. However, it needs a number of images to learn. In other words, it is necessary to manage images and categories. This paper proposes integrated object recognition system which manages and learns images. This system collects images automatically in classified categories and learns images in high accuracy. And multiple On-Board computer can share proposed learning system.
A new anti-swing control law for overhead crane systems

In this paper, a novel anti-swing control method is proposed for underactuated overhead crane systems, which is based on an improved damping signal constructed to suppress the payload swing. Specifically, to increase the anti-swing performance of overhead crane systems, an improved damping anti-swing signal introduced into the trolley motion is investigated based on a swing-related storage function. Consequently, a novel anti-swing control method is designed straightforwardly, and the equilibrium point of the overall closed-loop system is proven to be asymptotically stable by Lyapunov techniques and LaSalle's invariance theorem. Numerical simulation results are provided to demonstrate the feasibility and flexibility of the proposed control method as well as its robustness against parameter variations and uncertain disturbances.
Reliably Tracking Partially Overlapping Neural Stem Cells in DIC Microscopy Image Sequences

Automated tracking of individual cells in populations aims at obtaining fine-grained measurements of cell behaviors, including migration (translocation), mitosis (division), apoptosis (death), shape deformation of individual cells, and their interactions among cells. Such detailed analysis of cell behaviors requires the capabilities to reliably track cells that may sometimes partially overlap, forming cell clusters, and to distinguish cellular mitosis/fusion from split and merge of cell clusters. Existing cell tracking algorithms are short of these capabilities. In this paper, we propose a cell tracking method based on partial contour matching that is capable of robustly tracking partially overlapping cells, while maintaining the identity information of individual cells throughout the process from their initial contact to eventual separation. The method has been applied to a task of tracking human central nervous system (CNS) stem cells in differential interference contrast (DIC) microscopy image sequences, and has achieved 97% tracking accuracy.
Does physical attractiveness and sex impact decisions in a threat detection task

The current study was conducted to investigate whether gender and physical attractiveness of face primes had any effect on subsequent categorization of weapons and non-weapons by participants. In Study 1 participants were required to rate a set of face photos of men and women on their level of attractiveness and also how threatening they perceived them to be. These photos were used for Study 2 as primes paired with weapons and non-weapons. One hundred and ten University of Canterbury students were required to categorise weapons from non-weapons after primed faces appeared on a computer screen. Adapted from the ‘shooter paradigm’ and priming studies, this aimed to discover participant’s stereotypes when threat is involved. Specifically, it aimed to assess people’s susceptibility to perceive a non-threatening item as a threat. Three possible theories are presented to explain the findings – the halo effect, the evolutionary intra-sexual competition theory, and the arousal theory. The results of study 1 indicated that there is a negative correlation between physical attractiveness and threat, such that as a target’s physical attractiveness increases their perceived level of threat decreases, suggesting the existence of an overall halo effect (attractive people are non-threatening). The results of study 2 revealed an impact of female primes (in particular attractive female primes) on males’ misclassification of nonweapons. There was no effect found of the primes on females’ classifications. An arousal theory is used to explain this differential response. The results are discussed in terms of implications for the realworld and limitations of the study are outlined and suggestions are made regarding future research.
Modeling Successful Performance in Web Search

Several previous studies have measured differences in information search success of novices and experts. However, the definitions of novices and experts have varied greatly between the studies, and so have the measures used for search success. Instead of dividing the searchers into different groups based on their expertise, we chose to model search success with Task Completion Speed, TCS. Towards this goal, 22 participants performed three fact-finding tasks and two broader tasks in an observational user study. In our model, there were two variables
Using Dynamic Semantic Network to Construct Knowledge Base

Knowledge Base building is a key branch of modern Artificial Intelligence research. Current methods in this field mainly focus on computational efficiency, decidability, data exchange, and machine readability. In this paper, a novel human like knowledge representation method called Dynamic Semantic Network (DSN) is proposed. With the inspiration of high level human brain cognitive models, DSN aims at building more practical and abundant knowledge base. The inference and learning mechanism on DSN are latterly studied. And the construction issues are also discussed.
High speed/accuracy visual servoing based on virtual visual servoing with stereo cameras

This paper presents high speed and high accuracy visual servoing system. The algorithm has three major improvements, which can be implemented in practical applications; Firstly high-accuracy pose estimation by using stereo cameras, secondly real time implementation issues with non-real-time image processing platform and thirdly a consideration for industrial position controller. To resolve the issues, position-based visual servoing (PBVS) is adopted and appearance model based virtual visual servoing (VVS) is applied for pose estimation. VVS approach does not compute the stereo matching but directly compares the OpenGL rendered image and camera image for each camera; estimate the position/orientation using VVS independently for each camera; and provides a theoretically optimal compromise among those estimates. To enhance estimation accuracy, a hybrid method of stereo trigonometry for position estimation and weighted least squares for orientation estimation is proposed to combine the information from the stereo cameras. Operation speed is increased by using graphic processing unit (GPU) acceleration and an on-line trajectory generator which can accommodate the variable cycle of the image processing and the fixed cycle of a common robot controller. Finally, some experimental results illustrate the effectiveness of the proposed framework.
Estimating bias and variance from data

The bias-variance decomposition of error provides useful insights into the error performance of a classifier as it is applied to different types of learning task. Most notably, it has been used to explain the extraordinary effectiveness of ensemble learning techniques. It is important that the research community have effective tools for assessing such explanations. To this end, techniques have been developed for estimating bias and variance from data. The most widely deployed of these uses repeated sub-sampling with a holdout set. We argue, with empirical support, that this approach has serious limitations. First, it provides very little flexibility in the types of distributions of training sets that may be studied. It requires that the training sets be relatively small and that the degree of variation between training sets be very circumscribed. Second, the approach leads to bias and variance estimates that have high statistical variance and hence low reliability. We develop an alternative method that is based on cross-validation. We show that this method allows far greater flexibility in the types of distribution that are examined and that the estimates derived are much more stable. Finally, we show that changing the distributions of training sets from which bias and variance estimates are drawn can alter substantially the bias and variance estimates that are derived.
Identification of anovulation and transient luteal function using a urinary pregnanediol-3-glucuronide ratio algorithm.

The sensitivity and specificity of a urinary pregnanediol-3-glucuronide (PdG) ratio algorithm to identify anovulatory cycles was studied prospectively in two independent populations of women. Urinary hormone data from the first group was used to develop the algorithm, and data from the second group was used for its validation. PdG ratios were calculated by a cycles method in which daily PdG concentrations indexed by creatinine (CR) from cycle day 11 onward were divided by a baseline PdG (average PdG/Cr concentration for cycle days 6-10). In the interval method, daily PdG/CR concentrations from day 1 onward were divided by baseline PdG (lowest 5-day average of PdG/CR values throughout the collection period). Evaluation of the first study population (n = 6) resulted in cycles with PdG ratios > or = 3 for > or = 3 consecutive days being classified as ovulatory; otherwise they were anovulatory. The sensitivity and specificity of the PdG ratio algorithm to identify anovulatory cycles in the second population were 75% and 89.5%, respectively, for all cycles (n = 88); 50% and 88.3% for first cycles (n = 40) using the cycles method; 75% and 92.2%, respectively, for all cycles (n = 89); and 50% and 94.1% for first cycles (n = 40) using the interval method. The "gold standard" for anovulation was weekly serum samples < or = 2 ng/ml progesterone. The sensitivity values for all cycles and for the first cycle using both methods were underestimated because of apparent misclassification of cycles using serum progesterone due to infrequent blood collection. Blood collection more than once a week would have greatly improved the sensitivity and modestly improved the specificity of the algorithm. The PdG ratio algorithm provides an efficient approach for screening urine samples collected in epidemiologic studies of reproductive health in women. ImagesFigure 1. AFigure 1. BFigure 1. CFigure 2. AFigure 2. B
Do Early Diagnosis and Glucocorticoid Treatment Decrease the Risk of Permanent Visual Loss and Early Relapses in Giant Cell Arteritis

AbstractTo determine the incidence of permanent visual loss (PVL) in giant cell arteritis (GCA) and the GCA relapse rate during glucocorticoid (GC) tapering.This prospective, longitudinal single secondary/tertiary rheumatology centre study was conducted between September 2011 and September 2014 in Slovenia. Predetermined clinical and laboratory tests were performed at 12, 24, 48, 96, and 144 weeks after diagnosis.Sixty-eight GCA patients (72.1% female), with a median (IQR) age of 73.2 (67.3–76.1) years and a symptom duration before the diagnosis of a median (IQR) 30 (14–70) days were included. Thirty-nine of 68 patients had symptoms for less than 31 days (14 (10–28) days–early GCA) and 29/68 for 31 days or longer (90 (60–120) days–late GCA). Four (5.9%) patients presented with PVL (1 early GCA). The median (IQR) follow-up was (IQR) 104 (53–126) weeks. GCA relapsed in 17/39 (43.6%) and 14/29 (48.3%) in early and late GCA, respectively. The median (IQR) time to the first relapse was 24.8 (13.6–46.5) weeks (early GCA 14 (13–34) weeks; late GCA 25 (22–48) weeks, P = 0.117), at the methyl-prednisolone dose of 6.0 (4.0–12.0) mg. The patients who relapsed had significantly higher levels of inflammation parameters at the baseline (including ESR, CRP, serum amyloid A, haptoglobin, and fibrinogen). An early GCA diagnosis and prompt GC treatment decreased the PVL rate in comparison to historic controls, but seem to have no impact on the frequency of relapses, which are predicted by the high baseline levels of the biomarkers of inflammation.
Bundeli Folk-Song Genre Classification with kNN and SVM

While large data dependent techniques have made advances in between-genre classification, the identification of subtypes within a genre has largely been overlooked. In this paper, we approach automatic classification of within-genre Bundeli folk music into its subgenres; Gaari, Rai and Phag. Bundeli, which is a dominant dialect spoken in a large belt of Uttar Pradesh and Madhya Pradesh has a rich resource of folk songs and an attendant folk tradition. First, we successfully demonstrate that a set of common stopwords in Bundeli can be used to perform broad genre classification between standard Bundeli text (newspaper corpus) and lyrics. We then establish the problem of structural and lexical similarity in within-genre classification using n-grams. Finally, we classify the lyrics data into the three genres using popular machinelearning classifiers: Support Vector Machine (SVM) and kNN classifiers achieving 91.3% and 85% and accuracy respectively. We also use a Naive Bayes classifier which returns an accuracy of 75%. Our results underscore the need to extend popular classification techniques to sparse and small corpora, so as to perform hitherto neglected within genre classification and also exhibit that well known classifiers can also be employed in classifying ‘small’ data.
Transitivity effects on children's sentence comprehension

On a propose a des enfants francophones âges de 5 ans d'interpreter des phrases actives et passives variant selon plusieurs parametres de transitivite (actionnalite, ponctualite et plausibilite). Des requetes en designation formulees a la voix active et a la voix passive furent utilisees pour obtenir les reponses des enfants
Polarização M1 e M2 da linhagem U-937 de macrófagos em meio de soro de pacientes com transtorno bipolar

Bipolar Disorder (BD) is a severe and highly incapacitating psychiatric disorder which is associated with the presence of medical comorbidities. The progression of BD is related to an important cognitive deficit and also to biological and clinical manifestations that lead to treatment resistance and worse prognosis. Immune disturbances have been widely observed and investigated in BD patients. Chronic inflammatory responses induce neuroinflammation, mainly by pro-inflammatory microglial activation, and result in biochemical and functional impairment. Macrophages are the first line of defense of the immune system and exhibit cell plasticity. As well, microglia represents the resident macrophage of the central nervous system been responsible for its protection. Both cells can be stimulated to polarize into two different phenotypes, mainly proand anti-inflammatory, maintaining the homeostasis under physiologic and pathologic conditions. Therefore, we aimed to investigate macrophages phenotypical response when submitted to BD patients plasma in different episodes, which is considered a pro-inflammatory environment, and healthy controls plasma. Subjects included healthy controls (n=5), remitted BD patients (n=8), manic patients (n=5) and depressive patients (n=5). The mRNA expression of chemokynes and cytokines from U937 cells treated with BD patients plasma were different from those submitted to healthy controls plasma. Higher mRNA expression of IL-1β was observed in those cells submitted to manic and depressive BD patients plasma when compared to euthymic patients. Also, depressive BD patients plasma induced higher expression of TNF-α compared to euthymic patients. However, chemokynes expression, such as CXCL9 and CXCL10, were reduced in depressive BD patients. Inflammatory cytokines such as IL-1β and TNF-α in bipolar manic and depressive patients demonstrate higher amounts of IL-1β mRNA that euthymic patients and depressive patients induced higher amounts of TNF-α mRNA levels than the patients in euthymic U937. Since the expression of CXCL9 and CXCL10 chemokines in plasma from patients with depressive TB, proved less significant expression in the group of manic patients when compared to controls and euthymic bipolar patients.
The image processing system for art specimens: Nephele

In our paper we introduce comprehensive solution for processing and archiving information about artwork specimens used in the course of art restoration - Nephele. The information processing based on image data is used in the procedure of identification of pigment and binder present in the artwork, which is very important issue for restorers. Proposed approach geometrically aligns images of microscopic cross-sections of artwork color layers - image registration method based on mutual information, and then creates preliminary color layer segmentation - modified k-means clustering. The archiving part of the Nephele enables creating database entries for painting materials research database, their storage, and creating text-based queries. In addition to these traditional database functions, advanced report retrieval is supported; based on the similarity of image data, comparing either the ultraviolet and visual spectra images (using co-occurence matrices and color similarity functions), or the electron microscopy images (using features computed from the wavelet decomposition of the data).
The effects of stress on eating patterns and the effects of stress and attachment on eating behaviour and food preference in stress under-eaters and stress-eaters

This article-based thesis examined the effects of stress and attachment style on eating. The first article explored the effects of stress on the eating patterns of undergraduate students. Results demonstrated that stress leads to reported unhealthy changes in eating habits. Specifically, stress led to increased reported preference for take-out food options. Findings showed that gender influenced stress-related snacking and loss of control over eating. The second article examined the influences of stress and attachment on eating in self-reported stress under-eaters and stress-eaters. The results demonstrated that attachment style uniquely influences the eating of these two distinct groups and that stress and attachment have differential effects on eating. These findings add to the paucity of research investigating the effects of stress on eating from a naturalistic approach. These results also identify various idiosyncrasies of these two novel groups and highlight the role of attachment in disordered eating behaviour.
Evaluation of B+Tree-Based Multi-dimensional Range Search Algorithm for P2P Networks

A context-aware service that uses sensing data has attracted attention, along with the development of wireless technology and sensor technology. To provide these services, the sensing data sharing system in P2P networks needs to cope with a vast amount of data. However, existing algorithms do not respond to varying the number of sensing data types. In addition, most existing algorithms cannot execute reverse key resolutions because their search algorithms need to include specific data as the key in the query. To address these issues, we propose a multi-dimensional range search algorithm in P2P networks that uses a B+tree for an efficient search with an arbitrary number of sensing data types.
Usefulness of visual evaluation of the anterior thalamic radiation by diffusion tensor tractography for differentiating between Alzheimer’s disease and elderly major depressive disorder patients

Background and objective Many surveys of neural integrity of the cerebral white matter with psychiatric diseases on diffusion tensor imaging have recently been performed, but these mainly utilize fractional anisotropy (FA) and the apparent diffusion coefficient (ADC) values, and the results were inconsistent and not fully applied clinically. In this study, we investigated the possibility of differentiating between Alzheimer’s disease (AD) and elderly major depressive disorder (MDD) patients in whom early-stage symptoms are difficult to diagnose, by visually evaluating cerebral nerve fascicles utilizing diffusion tensor tractography. We also measured and evaluated FA and ADC values at the same time. Subjects and methods The subjects included 13 AD patients (age: 69.5 ± 5.1 years), 19 MDD patients (65.8 ± 5.7 years), and 22 healthy control (HC) subjects (67.4 ± 4.8 years). Images were acquired using a 1.5T magnetic resonance imaging device and analyzed by diffusion tensor tractography analysis software. Results Depiction of the anterior thalamic radiation (ATR) tended to be poor in AD patients unlike in MDD patients and HC subjects. The FA values in the left superior longitudinal fasciculus and fornix (FX) in AD patients were significantly different from those in MDD patients and HC subjects. The ADC values in the bilateral ATR and left superior and inferior longitudinal fasciculi, left inferior fronto-occipital fasciculus, and FX in AD patients were significantly different from those in MDD patients and HC subjects. Conclusion Visual evaluation of the ATR in combination with the FA values of the left superior longitudinal fasciculus and FX and ADC values of the bilateral ATR, left superior and inferior longitudinal fasciculi, left inferior fronto-occipital fasciculus, and FX is useful for differentiating between AD and MDD patients, which further suggests that it may become a useful auxiliary diagnostic tool.
Practical Parallel List Ranking

Parallel list ranking is a hard problem due to its extreme degree of irregularity. Also because of its linear sequential complexity, it requires considerable effort to just reach speed-up one (break even). In this paper, we address the question of how to solve the list-ranking problem for lists of length up to 2·108 in practice: we consider implementations on the Intel Paragon, whose PUs are laid-out as a grid.