Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospective, Blinded Exploratory Evaluation of the PlayWisely Program in Children with Autism Spectrum Disorder

The purpose of the study was to explore a low-cost intervention that targets an increasingly common developmental disorder. The study was a blinded, exploratory evaluation of the PlayWisely program on autism symptoms and essential learning foundation skills (attention, recognition, and memory skills) in children with a diagnosis of autism, autism spectrum disorder (ASD), pervasive developmental disorder – not otherwise specified (PDD-NOS), and Asperger syndrome (AS). Eighteen children, 1 to 10 years of age, were evaluated using the Childhood Autism Rating Scale, Second Edition (CARS2); the PlayWisely Interactive Test of Attention, Recognition, and Memory Skills; Autism Treatment Evaluation Checklist (ATEC), and the Modified Checklist for Autism in Toddlers (M-CHAT). There were significant treatment effects for the PlayWisely measure on the Yellow Sets that examine recognition; Purple Sets that examine brain region agility and early memory skills; Blue Sets that examine phonemic awareness and recognition; and for the Total Sets, with a similar trend toward improvement in the Green Sets that examine perception and Red Sets that examine attention. No other measures reached statistical significance. The results suggest that PlayWisely can improve recognition, brain region agility, phonemic awareness, letter recognition, and early memory skills in ASD. It was observed by the parents, coaches, and study investigators that the children who were less than 3 years of age showed improvements in autism symptoms; however, the group was too small to reach statistical significance. Future studies are needed to see if this intervention can mitigate autism symptoms in very young children with ASD.
Cancer Therapy : Clinical Multicenter , Phase II Study of Axitinib , a Selective Second-Generation Inhibitor of Vascular Endothelial Growth Factor Receptors 1 , 2 , and 3 , in Patients with Metastatic Melanoma

Purpose: Thismulticenter, open-label, phase II study evaluated the safety and clinical activity of axitinib, a potent and selective second-generation inhibitor of vascular endothelial growth factor receptors (VEGFR)–1, 2, and 3, in patients with metastatic melanoma. Experimental Design: Thirty-two patients with a maximum of one prior systemic therapy received axitinib at a starting dose of 5 mg twice daily. The primary endpoint was objective response rate. Results: Objective response rate was 18.8% [95% confidence interval (CI), 7.2–36.4], comprising one complete and five partial responses with a median response duration of 5.9 months (95% CI, 5.0–17.0). Stable disease at 16 weeks was noted in six patients (18.8%), with an overall clinical benefit rate of 37.5%. Six-month progression-free survival rate was 33.9%, 1-year overall survival rate was 28.1%, and median overall survival was 6.6months (95%CI, 5.2–9.0). Themost frequently (>15%) reported nonhematologic, treatment-related adverse events were fatigue, hypertension, hoarseness, and diarrhea. Treatment-related fatal bowel perforation, a known class effect, occurred in one patient. Axitinib selectively decreased plasma concentrations of solubleVEGFR(sVEGFR)-2 and sVEGFR-3 comparedwith soluble stemcell factor receptor (sKIT). No significant association was noted between plasma levels of axitinib and response. However, post hoc analyses indicated potential relationships between efficacy endpoints and diastolic blood pressure of 90 mm Hg or higher as well as baseline serum lactate dehydrogenase levels. Conclusions: Axitinib was well tolerated, showed a selective VEGFR-inhibitory profile, and showed single-agent activity in metastatic melanoma. Further evaluations of axitinib, alone and combined with chemotherapy, are ongoing. Clin Cancer Res; 17(23); 7462–9. 2011 AACR.
Approximate nearest neighbor search on HDD

Nearest Neighbor (NN) search plays important roles in Computer Vision algorithms. Especially, NN search on immensely large amount of image data set stored on the Internet is getting highlighted. For dealing with such huge data, main memory of a single PC is insufficient. As a solution, we propose an approximate NN search on hard disk drive (HDD) in this paper. This algorithm is based on recently proposed Principal Component Hashing (PCH). In our algorithm “PCH on HDD” (PCHD), the hash bins are represented by the leaf nodes of B+ tree for dealing with the dynamic addition and deletion of the data. Of course, the search time is slower than the original PCH. However, we found some advantages of this approach through the experiments using standard PC and 10000 stored images: 1) the memory consumption is 42 times smaller, 2) the first search time including the cold start-up time is 4.3 times faster (PCH:31.8[s], PCHD: 7.4[s]), 3) and interestingly, the successive searches are accelerated owing to the cache mechanism embedded in the operating system (mean search time decreases from 7.4[s] to 0.64[s]). We also confirmed that our algorithm performs NN search on 1 million image datasets with only 193MB memory consumption; however, PCH cannot, because of the huge memory consumption. These properties reveal that this algorithm is suitable for non-time-critical NN search applications and NN search engine called by web servers, where the search engine starts up in response to occasional queries.
Response Properties and Adaptation of Neurones Sensitive to Image Motion in the Butterfly Papilio Aegeus

Wide-field direction-selective neurones from the optic lobes of the butterfly Papilio aegeus show some properties similar to those displayed by the large neurones of the fly lobula plate. Temporal and spatial frequency threshold tuning curves show that butterfly optic lobe neurones sensitive to different directions of image motion are fed by presynaptic subunits similar to those of the fly. However, unlike fly lobula plate neurones, the butterfly optic lobe neurones show a steep low-spatial-frequency roll-off which persists even at high temporal frequencies. Also exceptional is the temporal resolution of rapid changes in image speed by the butterfly neurones. When the cells are adapted to continuous motion their responses indicate a further increase in temporal resolution. Evidence is provided that in any one state of adaptation the neurones may be thought of as piece-wise linear and, thus, their responses can be predicted by convolution with a velocity kernel measured for that adaptation state. Adaptation to continuous motion results in the cells responding to motion in proportion to the mean motion signal. Motion in the non-preferred direction also appears to adapt the cells. Velocity impulse responses of both butterfly and blowfly neurones were determined with one-dimensional gratings and two-dimensional textured patterns and the results for the two stimuli are shown to be very similar.
A LAYER-DECOMPOSITION WITH DEGREE METHOD FOR SUBGRAPHS RECOGNITION PROBLEM

For two planar undirected graphs $G$ and $H$, the {\it subgraphs recognition problem} (SRP) is to find and list all subgraphs of $G$ which are isomorphic to $H$. In this paper, we introduce the idea of layer-decomposition with degree and present an algorithm based on this idea for SRP. Since subgraphs isomorphic to each other contain their spanning trees with the same decomposition, the SRP can be decomposed into two subproblems: First, find subtrees of $G$ which have the same layer-decomposition as that of $H$. Then, test whether the induced subgraphs generated by these subtrees are isomorphic to $H$. By this scheme, we greatly decrease the complexity to $O(n(\Delta-1)^{k-1}k^2)$, where $\Delta$ is the degree of $G$ and $n, k$ are the orders of $G, H$ respectively.
Bangla news classification using naive Bayes classifier

Web is gigantic and being constantly update. Bangla news in web are rapidly grown in the era of information age where each news site has its own different layout and categorization for grouping news. These heterogeneity of layout and categorization can not always satisfy individual user's need. Removing these heterogeneity and classifying the news articles according to user preference is a formidable task. In this paper, we propose an approach that provides a user to find out news articles which are related to a specific classification. We use our own developed web crawler to extract useful text from HTML pages of news article contents to construct a Full-Text-RSS. Each news article contents is tokenized with a modified light-weight Bangla Stemmer. In order to achieve better classification result, we remove the less significant words i.e. stop - word from the document. We apply the naive Bayes classifier for classification of Bangla news article contents based on news code of IPTC. Our experimental result shows the effectiveness of our classification system.
Neuroscience and Biobehavioral Reviews Review Advances in Functional Neuroanatomy: a Review of Combined Dti and Fmri Studies in Healthy Younger and Older Adults

Structural connections between brain regions are thought to influence neural processing within those regions. It follows that alterations to the quality of structural connections should influence the magnitude of neural activity. The quality of structural connections may also be expected to differentially influence activity in directly versus indirectly connected brain regions. To test these predictions, we reviewed studies that combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) in younger and older adults. By surveying studies that examined relationships between DTI measures of white matter integrity and fMRI measures of neural activity, we identified variables that accounted for variability in these relationships. Results revealed that relationships between white matter integrity and neural activity varied with (1) aging (i.e., positive and negative DTI–fMRI relationships in younger and older adults, respectively) and (2) spatial proximity of the neural measures (i.e., positive and negative DTI–fMRI relationships when neural measures were extracted from adjacent and non-adjacent brain regions, respectively). Together, the studies reviewed here provided support for both of our predictions.
Deciphering the Molecular Basis of Wine Yeast Fermentation Traits Using a Combined Genetic and Genomic Approach

The genetic basis of the phenotypic diversity of yeast is still poorly understood. Wine yeast strains have specific abilities to grow and ferment under stressful conditions compared with other strains, but the genetic basis underlying these traits is unknown. Understanding how sequence variation influences such phenotypes is a major challenge to address adaptation mechanisms of wine yeast. We aimed to identify the genetic basis of fermentation traits and gain insight into their relationships with variations in gene expression among yeast strains. We combined fermentation trait QTL mapping and expression profiling of fermenting cells in a segregating population from a cross between a wine yeast derivative and a laboratory strain. We report the identification of QTL for various fermentation traits (fermentation rates, nitrogen utilization, metabolites production) as well as expression QTL (eQTL). We found that many transcripts mapped to several eQTL hotspots and that two of them overlapped with QTL for fermentation traits. A QTL controlling the maximal fermentation rate and nitrogen utilization overlapping with an eQTL hotspot was dissected. We functionally demonstrated that an allele of the ABZ1 gene, localized in the hotspot and involved in p-aminobenzoate biosynthesis, controls the fermentation rate through modulation of nitrogen utilization. Our data suggest that the laboratory strain harbors a defective ABZ1 allele, which triggers strong metabolic and physiological alterations responsible for the generation of the eQTL hotspot. They also suggest that a number of gene expression differences result from some alleles that trigger major physiological disturbances.
CG-KQML + : An Agent Communication Language and its use in a Multi-Agent System

This paper deals with communication protocols between agents and between agents and users [3]. It presents a new communication model which is based on a careful analysis of speech act theory and on two fundamental principles applied to communication: a) communication is considered as a negotiation process and, b) communication results in an exchange of mental states. Using this model of communication and the conceptual graph formalism for the representational level, we propose a new agent communication language, called CGKQML+ which is an extension of the KQML language. The paper also shows the use of CG-KQML+ in a MAS called POSTAGE which aims at helping users in their correspondence task. In POSTAGE, software agents manage administrative correspondence on behalf of and in cooperation with their users. Users and agents have interactions which respect administrative correspondence rules. A POSTAGE agent is responsible for sending the generated message to the addressee's POSTAGE agent. The paper presents the second version of POSTAGE which is implemented using the Prolog+CG language.
A collaborative framework for intrusion detection (C-NIDS) in Cloud computing

In recent years, Cloud computing has emerged as a new paradigm for delivering highly scalable and on-demand shared pool IT resources such as networks, servers, storage, applications and services through internet. It enables IT managers to provision services to users faster and in a cost-effective way. As a result, this technology is used by an increasing number of end users. On the other hand, existing security deficiencies and vulnerabilities of underlying technologies can leave an open door for intruders. Indeed, one of the major security issues in Cloud is to protect against distributed attacks and other malicious activities on the network that can affect confidentiality, availability and integrity of Cloud resources. In order to solve these problems, we propose a Collaborative Network Intrusion Detection System (C-NIDS) to detect network attacks in Cloud by monitoring network traffic, while offering high accuracy by addressing newer challenges, namely, intrusion detection in virtual network, monitoring high traffic, scalability and resistance capability. In our NIDS framework, we use Snort as a signature based detection to detect known attacks, while for detecting network anomaly, we use Support Vector Machine (SVM). Moreover, in this framework, the NIDS sensors deployed in Cloud operate in collaborative way to oppose the coordinated attacks against cloud infrastructure and knowledge base remains up-to-date.
Nonstationary Panels

Recent years have seen growing interest in nonstationary panels. The initial main motivation for using panel data was to increase the power of unit root tests. Levin and Lins (1992)1 tests and subsequent panel unit root tests con rm the power advantage of using panel data. Levin and Lins and other tests have been applied on a large scale for di¤erent purposes (see the list below) and numerous theories for nonstationary panels have also been developed. These developments resemble those of nonstationary time series in the eighties and nineties when empirical applications and econometric theory had grown hand in hand, satisfying each others needs and posing new problems for each other. Indeed, one who has observed the evolution of the econometric theory of nonstationary time series in those days may realize that the research paradigm of nonstationary panels is similar to that of nonstationary time series during that time, though nonstationary panels have some unique issues such as cross-sectional correlation and ways to handle two indices going to in nity. As with nonstationary time series, interest in the panel unit root test has extended to the panel cointegration test, panel cointegrating regression, panel VAR, and optimal unit root testing as will be discussed later. The purpose of this paper is to survey major developments in the econometrics of nonstationary panels since early nineties, complementing earlier surveys by Baltagi and Kao (2000), Banerjee (1999), and Phillips and Moon (2000). Topics that will be discussed include panel tests for a unit root and stationarity for both independent and dependent panels, panel regressions, tests for panel cointegration, among others. Econometric methods for nonstationary panels have been applied extensively in the empirical literature.2 A partial list of related articles studying the PPP (purchasing power parity) hypothesis includes Frankel and Rose (1996); Oh (1996); Lothian (1997); MacDonald (1996); Wu (1996); Coakley and Fuertes (1997); Papell (1997);
2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops Designing with Midwives: Improving Prenatal Care in Low Resource Regions

This paper describes the early design of a diagnostic system to support prenatal care of rural pregnant women. Our design is based on interviews and feedback from midwives who have worked in developed and developing countries. Based on the interviews we identified key issues in prenatal care and properties of a basic diagnostic system applicable in a low resource setting. The design requirements and challenges were verified with midwives and a concept was developed. Furthermore, we report technical requirements and limitations to implement the proposed diagnostic system in a rural setting.
Predicting Problematic Internet Use in A Sample of Canadian University Students

The growth of Internet users in Canada has been phenomenal. The most recent Canadian Internet Use Survey revealed that 83% of Canadian households had access to the Internet at home in 2012, compared with 79% in 2010 (Statistics Canada, 2013). Doubtlessly, the Internet has become an increasingly important feature of the learning environment for students. Excessive use of the Internet, however, can pose various serious risks for the users. In fact, the negative consequences that can arise from excessive Internet usage have attracted increasing research attention. Studies have demonstrated that academic under-performance, failure to exercise and to engage in faceto-face social activities, negative affective states, sleep deprivation, decreased ability to concentrate, health problems, and family conflicts were the frequently reported consequences of excessive internet use (Gür, Yurt, Bulduk, & Atagöz, 2015; Hetzel-Riggin & Pritchard, 2011; Lam & Lam, 2016; Sari & Aydin, 2014; Snyder, Li, O’Brien, & Howard, 2015; Weinstein & Lejoyeux, 2010). This article presents the findings from a survey that disentangled the determinants of problematic internet use (PIU) in a sample of undergraduate students.
Extremal vacuum black holes in higher dimensions

We consider extremal black hole solutions to the vacuum Einstein equations in dimensions greater than five. We prove that the near-horizon geometry of any such black hole must possess an SO(2,1) symmetry in a special case where one has an enhanced rotational symmetry group. We construct examples of vacuum near-horizon geometries using the extremal Myers-Perry black holes and boosted Myers-Perry strings. The latter lead to near-horizon geometries of black ring topology, which in odd spacetime dimensions have the correct number rotational symmetries to describe an asymptotically flat black object. We argue that a subset of these correspond to the near-horizon limit of asymptotically flat extremal black rings. Using this identification we provide a conjecture for the exact “phase diagram” of extremal vacuum black rings with a connected horizon in odd spacetime dimensions greater than five.
Experimental performance evaluation of passive UHF RFID systems under interference

Performance of passive ultrahigh frequency radio-frequency identification (UHF RFID) systems is determined by the ability of the RFID reader to precisely decode the RF signal backscattered by the RFID tag. For a successful operation, two conditions must be met: (1) the power level of the impinged signal on the RFID tag must be above its threshold to power up its internal circuitry, and (2) the backscattered signal received by the RFID reader must be higher than its sensitivity threshold. The environment in which such systems are deployed has a major effect on these two conditions. In this paper, performance of a passive UHF RFID system is experimentally evaluated for potential deployment in industrial settings where many sources that generate ambient electromagnetic interference (EMI) exist. This interference will inevitably affect the performance of the deployed passive UHF RFID systems. Three different types of EMI are considered in this work: impulsive continuous wave interference and interference with Gaussian and Rayleigh distributions. The performance metric used in this paper is the reading rate per second. The obtained experimental results show that the performance of passive UHF RFID system depends on the nature and the strength of the ambient EMI. The results reported are of particular interest to those who deploy passive UHF RFID systems for industrial applications particularly in situations that already have machines in use that generate unpredictable EMI.
Nonthreshold-based node level algorithm of data compression over the wireless sensor networks

Energy saving is an important issue in wireless sensor networks (WSNs) since the nodes are typically powered by batteries with a limited capacity and the energy is limited. As data communication usually consumes much energy and bandwidth, decreasing energy consumption can be generally achieved by reducing the communication of data, for instance, through data compression. Thus, it is an important research issue that how to decrease energy consumption as well as maximize the lifetime of WSNs and increase the efficiency of data communication through data compression on the WSNs nodes where energy supply, memory space and processing resources are constrained. Considering the basic idea of edge operator in the field of image processing and the characteristic that the data stream collected by the nodes of WSNs is time series data, this paper proposes a nonthreshold-based node level algorithm of data compression over the WSNs. The algorithm, with simple calculating and low complexity, compresses the time series data collected by sensor nodes into many piecewise linear representations by extracting some points named edge-points that can indicate the trends of time series data, and especially dose not require any prior knowledge of the monitored objects as well as any predefined threshold value related to the time series data. The experiments on real public sensor data series show that the proposed algorithm can compress data effectively and reduce the communication of data obviously. Moreover, compared with some other data compression algorithms, the proposed algorithm appears better compression performance, reconstructed error and stability which allows the algorithm being applied to the collected data series with different fluctuation characteristics. Consequently, it can save the energy of wireless communication of senor nodes and prolong the lifetime of WSNs better.
EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface

A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes is mandatory. We propose a new algorithm to select a relevant subset of electrodes by estimating sparse spatial filters. A l1-norm penalization term, as an approximation of the l0-norm, is introduced in the xDAWN algorithm, which maximizes the signal to signal-plus-noise ratio. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 10 sensors, the loss in classification accuracy is less than 5%.