Miracle-dz/UniFAR-FTUs huggingface dataset:
An OpenViBE-based brainwave control system for Cerebot

In this paper, we develop a brainwave-based control system for Cerebot, consisting of a humanoid robot and a Cerebus™ Data Acquisition System up to 128 channels. Under the OpenViBE programming environment, the control system integrates OpenGL, OpenCV, WEBOTS, Choregraph, Central software, and user-developed programs in C++ and Matlab. The proposed system is easy to be expanded or upgraded. Firstly, we describe the system structures for off-line analysis of acquired neural signals and for on-line control of a humanoid robot via brainwaves. Secondly, we discuss how to use the toolboxes provided with the OpenViBE environment to design three types of brainwave-based models: SSVEPs, P300s, and mu/beta rhythms. Finally, we use the Cerebot platform to investigate the three models by controlling four robot-walking behaviors: turning right, turning left, walking forward, and walking backward.
Comparison of particle swarm optimization and shuffle complex evolution for auto-calibration of hourly tank model's parameters

The famous Hydrological Tank Model is always preferred for runoff forecasting. This main reason is Tank Model not only simple in term of its structures, but able to forecast runoff accurately using only rainfall and runoff data. However, much time and effort are required to calibrate a large numbers of parameters in the model for obtaining better results through trial-and-error procedure. Therefore, there is an urgent need to develop an auto-calibration method. Two types of global optimization methods (GOMs), named as Particle Swarm Optimization (PSO) and Shuffle Complex Evolution (SCE) are selected. The selected study area is Bedup basin, Samarahan, Sarawak, Malaysia. Input data used for model calibration are hourly rainfall and runoff only. The accuracy of the simulation results are measured using Coefficient of Correlation (R) and Nash-Sutcliffe Coefficient (E 2 ). The robustness of the model parameters obtained are further analyzed with boxplots analysis. Peak errors are also evaluated to determine the difference between the observed and simulated peaks. Results revealed that the performance of simple PSO method is slightly better than the famous and complicated SCE method. PSO is able to obtain optimal values for 10 parameters fast and accurate within a multidimensional parameter space that could provide the best fit between the observed and simulated runoff.
An Interference Model of Visual Working Memory

The article introduces an interference model of working memory for information in a continuous similarity space, such as the features of visual objects. The model incorporates the following assumptions: (a) Probability of retrieval is determined by the relative activation of each retrieval candidate at the time of retrieval; (b) activation comes from 3 sources in memory: cue-based retrieval using context cues, context-independent memory for relevant contents, and noise; (c) 1 memory object and its context can be held in the focus of attention, where it is represented with higher precision, and partly shielded against interference. The model was fit to data from 4 continuous-reproduction experiments testing working memory for colors or orientations. The experiments involved variations of set size, kind of context cues, precueing, and retro-cueing of the to-be-tested item. The interference model fit the data better than 2 competing models, the Slot-Averaging model and the Variable-Precision resource model. The interference model also fared well in comparison to several new models incorporating alternative theoretical assumptions. The experiments confirm 3 novel predictions of the interference model: (a) Nontargets intrude in recall to the extent that they are close to the target in context space; (b) similarity between target and nontarget features improves recall, and (c) precueing—but not retro-cueing—the target substantially reduces the set-size effect. The success of the interference model shows that working memory for continuous visual information works according to the same principles as working memory for more discrete (e.g., verbal) contents. Data and model codes are available at https://osf.io/wgqd5/.
An expedition in search of a fifth universal factor: Key issues in the lexical approach

It is argued that the fifth factor of the Big Five Model of personality traits cannot yet claim universal status. In order to identify a fifth factor within the lexical approach it is necessary to make full use of the potentialities of the psycholexical principles. Several flaws in the lexical enterprise are discussed, both regarding the theoretical delineation of traits and the operational - dictionary-related - identification of trait descriptors. Hitherto largely implicit definitions of traits should be made explicit, and agreement should be reached about the theoretical width of the trait domain. Also, in order to obtain cross-culturally comparable results, the procedural steps in the lexical search for trait terms should follow an agreed-upon standard. None of the nominated fifth factors, for instance, Culture, Intellect, or Openness to Experience, has both proceeded from the lexical method and received unquestionable cross-cultural affirmation.
Region-based fully convolutional networks for vertical corner line detection

Region-based Fully Convolutional Network (R-FCN) is recently developed to detect objects and has been successfully used to detect various kinds of semantic objects such as humans and dogs. We investigate the ability of R-FCN for detecting unusual objects in this paper. In detail, based on R-FCN we present an efficient method for vertical corner line (VCL) detection on buildings. Traditionally, VCLs are treated as one kind of image features. In this work, however, they are treated as a class of symbolic objects. Experimental results show that R-FCN, when employed to detect VCLs, could perform potentially better than traditional feature detection algorithms.
Antibodies in the Absence of Neutralizing Antibody-Dependent Cellular Cytotoxicity Cross-Reactive Influenza-Specific

A better understanding of immunity to inﬂuenza virus is needed to generate cross-protective vaccines. Engagement of Ab-dependent cellular cytotoxicity (ADCC) Abs by NK cells leads to killing of virus-infected cells and secretion of antiviral cytokines and chemo-kines. ADCC Abs may target more conserved inﬂuenza virus Ags compared with neutralizing Abs. There has been minimal interest in inﬂuenza-speciﬁc ADCC in recent decades. In this study, we developed novel assays to assess the speciﬁcity and function of inﬂuenza-speciﬁc ADCC Abs. We found that healthy inﬂuenza-seropositive young adults without detectable neutralizing Abs to the hemagglutinin of the 1968 H3N2 inﬂuenza strain (A/Aichi/2/1968) almost always had ADCC Abs that triggered NK cell activation and invitro elimination ofinﬂuenza-infected human blood and respiratory epithelial cells. Furthermore, we detected ADCC in the absence of neutralization to both the recent H1N1 pandemic strain (A/California/04/2009) as well as the avian H5N1 inﬂuenza hemagglutinin (A/Anhui/01/2005). We conclude that there is a remarkable degree of cross-reactivityof inﬂuenza-speciﬁc ADCC Abs in seropositive humans. Targeting cross-reactive inﬂuenza-speciﬁc ADCC epitopes by vaccination could lead to improved inﬂuenza vaccines. The Journal of Immunology , 2013, 190: 000–000.
Individual Action and the Demand for Institutions

In contrast to the recently dominant nonrational approach to institutional structure, the authors posit that new institutional structures under most conditions must have net benefits for performance of the organization or they are significantly less likely to be adopted. Over the past 2 decades, the dedicated new biotechnology firm (NBF) emerged as a new institution—a new form of science-based firm in which venture capital support of basic research is legitimated. The authors show that the most productive “star” bioscientists, one of whom invented the form, played a central role in where and when NBFs were formed and that star involvement determines which ones are most successful. Furthermore, involvement with a NBF furthers the scientific and financial success of star scientists. The authors conclude that the NBF was a rational and successful innovation for its inventor and his imitators and that explains its becoming a new institutional form.
Modeling unstructured document using N-gram consecutive and wordnet dictionary

The main issue in Text Document Clustering (TDC) is document similarity. In order to measure the similarity, documents must be transformed into numerical values. Vector Space Model (VSM) is one of technique capable to convert document into numerical value. In VSM documents was represented by the frequencies of term inside document and it works like a Bag of Word (BOW). BOW has resulted two major problems since it ignores the term relationship by treating term as single and independent. Both problems stated as Polysemy and Synonymity concept which is reflected to the relationship of terms. This study was combined WordNet and N-gram to overcome both problems. By modifying document features from single term into Polysemy and Synonymity concept, it has improved VSM performance. There are four steps in experimental. Text documents selection, preprocessing, applying clustering and cluster evaluation using F-measure. With dataset reuters50_50 obtained from UCI repository the experiment was successful and the result promising.
Quantifying Mosaic Development: Towards an Evo-Devo Postmodern Synthesis of the Evolution of Development via Differentiation Trees of Embryos

Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.
pit/LESS: Stateless Forwarding in Content Centric Networks

We present a novel approach to support stateless forwarding in information centric networks with emphasis on the CCN/NDN architectures by proposing a space-efficient in-packet filter based on the encoded counting bloom filter. Proposed solution avoids the typical pitfalls of using classic Bloom Filters by significantly reducing the false positive transmission attempts, while supporting reliable and resource efficient delivery of content packets to end hosts with minimal overhead. Comparisons to classic Bloom filter (and counting Bloom filter) based implementations suggest multiple times improvement in performance with the proposed solution without loss of support for stateless forwarding along the path.
Towards an integrated conceptual model of security and dependability

It is now commonly accepted that security and dependability largely represent two different aspects of an overall meta-concept that reflects the trust that we put in a computer system. There exist a large number of models of security and dependability with various definitions and terminology. This position paper suggests a high-level conceptual model that is aimed to give a novel approach to the area. The model defines security and dependability characteristics in terms of a system's interaction with its environment via the system boundaries and attempts to clarify the relation between malicious environmental influence, e.g. attacks, and the service delivered by the system. The model is intended to help reasoning about security and dependability and to provide an overall means for finding and applying fundamental defense mechanisms. Since the model is high-level and conceptual it must be interpreted into each specific sub-area of security/dependability to be practically useful.
Low-latency routing for fronthaul network: A Monte Carlo machine learning approach

A fronthaul bridged network has attracted attention as a way of efficiently constructing the centralized radio access network (C-RAN) architecture. If we change the functional split of C-RAN and employ time-division duplex (TDD), the data rate in fronthaul will become variable and the global synchronization of fronthaul streams will occur. This feature results in an increase in the queuing delay in fronthaul bridges among fronthaul flows. This paper proposes a novel low-latency routing scheme designed to satisfy the latency requirements in fronthaul networks with path-control protocols. The proposed scheme formulates the maximum queuing delay by defining competitive links and flows. It selects the set of paths that satisfy the latency requirements with the Markov chain Monte Carlo method using machine learning (MCMC-ML). The initial paths are selected from candidate paths using the learned solutions, and path-reselection is performed with the MCMC method. We confirmed with computer simulations that the proposed scheme can compute routes for all flows that satisfy the delay requirements. We also confirmed that the route computation is accelerated with the learned solutions, even if the flow distribution changes.
Cardiac testing and outcomes in infants after an apparent life-threatening event

Objectives We sought to determine the yield of cardiac testing and to identify predictors of cardiac disease in infants with an apparent life-threatening event (ALTE). Design Retrospective longitudinal cohort study. Setting Paediatric hospital providing primary and tertiary care that is part of an integrated healthcare system. Patients Infants hospitalised for an ALTE from 1999 to 2003. Main exposures Cardiac testing used at time of ALTE and results, and clinical risk factors for cardiac disease. Outcome measures Short-term (during hospitalisation) and long-term (through November 2009) follow-up for any diagnosis of significant cardiac anatomic or rhythm abnormality. Results Study criteria were met by 485 infants (mean age 1.9, SD±2.2 months; 49% boys). Cardiac testing was performed on 219 (45%) patients during ALTE hospitalisation, identifying two patients with significant cardiac disease (cardiomyopathy, ventricular pre-excitation). During 7.7 years of follow-up, three additional significant cardiac diagnoses (ventricular pre-excitation, frequent ventricular ectopy, moderate aortic stenosis) were identified. All cardiac tests had low positive predictive value (PPV). Significant cardiac disease was associated with prematurity (22% vs 80%, p=0.002), but not age, gender, prior ALTE or rescue breaths. Conclusions This longitudinal study of an ALTE cohort revealed significant cardiac disease in <1% of patients. Prematurity was the only clinical predictor identified. ECG was sensitive for identifying significant cardiac disease, but routine testing warrants further investigation because of the low PPV.
Staggered Prices and Trend Inflation: Some Nuisances

Most of the papers in the sticky-price literature are based on a log-linearization around the zero inflation steady state, a simplifying but counterfactual assumption. This paper shows that when trend inflation is considered, both the long-run and the short-run properties of DGE models based on the Calvo staggered price model change dramatically. It follows that results obtained by models log-linearized around a zero inflation steady state are quite misleading. Furthermore, the same is not true for models based on the Taylor staggered price model, which is robust to changes in trend inflation. As a conclusion, the Taylor model is to be preferred, unless one is willing to index nominal variables.
Does Mutational Robustness Inhibit Extinction by Lethal Mutagenesis in Viral Populations?

Lethal mutagenesis is a promising new antiviral therapy that kills a virus by raising its mutation rate. One potential shortcoming of lethal mutagenesis is that viruses may resist the treatment by evolving genomes with increased robustness to mutations. Here, we investigate to what extent mutational robustness can inhibit extinction by lethal mutagenesis in viruses, using both simple toy models and more biophysically realistic models based on RNA secondary-structure folding. We show that although the evolution of greater robustness may be promoted by increasing the mutation rate of a viral population, such evolution is unlikely to greatly increase the mutation rate required for certain extinction. Using an analytic multi-type branching process model, we investigate whether the evolution of robustness can be relevant on the time scales on which extinction takes place. We find that the evolution of robustness matters only when initial viral population sizes are small and deleterious mutation rates are only slightly above the level at which extinction can occur. The stochastic calculations are in good agreement with simulations of self-replicating RNA sequences that have to fold into a specific secondary structure to reproduce. We conclude that the evolution of mutational robustness is in most cases unlikely to prevent the extinction of viruses by lethal mutagenesis.
Factors Affecting the Difficulty of Reading Comprehension Items for Successful and Unsuccessful Readers

AbstractThis study assessed the contributions of various test features (passage variables, question types, and format variables) to reading comprehension performance for successful and unsuccessful readers. Items from a typical standardized reading comprehension test were analyzed according to 20 predictor test features. A three-stage conditional regression approach assessed the predictability of these features on item-difficulty scores for the two reader groups. Two features, location of response information and stem length, accounted for a significant amount of explained variance for both groups. Possible explanatory hypotheses are considered and implications are drawn for improved test design as well as for further research concerning interactions between assessment task features and reader performance.
Mining Web to Detect Phishing URLs

Proliferation of phishing attacks in recent years has presented an important cyber security research area. Over the years, there has been an increase in the technology, diversity, and sophistication of these attacks in response to increased user awareness and countermeasures. In this paper, we propose a novel scheme to automatically detect phishing URLs by mining and extracting Meta data on URLs from various Web services. Applying the proposed approach on real-world data sets, it is demonstrated that Logistic Regression classifier can achieve an overall accuracy of 97.2-99.8%, false positive rate of 0.1-1% and false negative rate of 0.7-6.5% in detecting phishing and non-phishing URLs.
Hamiltonian ABC

Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood estimates, ABC remains useful for relatively low-dimensional problems. We introduce Hamiltonian ABC (HABC), a set of likelihood-free algorithms that apply recent advances in scaling Bayesian learning using Hamiltonian Monte Carlo (HMC) and stochastic gradients. We find that a small number forward simulations can effectively approximate the ABC gradient, allowing Hamiltonian dynamics to efficiently traverse parameter spaces. We also describe a new simple yet general approach of incorporating random seeds into the state of the Markov chain, further reducing the random walk behavior of HABC. We demonstrate HABC on several typical ABC problems, and show that HABC samples comparably to regular Bayesian inference using true gradients on a high-dimensional problem from machine learning.
A Novel Coaxial Magnetic Gear and Its Integration With Permanent-Magnet Brushless Motor

A magnetic geared machine (MGM) is believed to be a promising candidate for high-torque direct-drive application. One of the key issues for developing MGMs is how to resolve the contradiction between the good performance and the complex structure. This paper aims at proposing a novel coaxial magnetic gear (CMG), which will not increase the mechanical complexity after integration with a permanent magnet (PM) brushless machine. The prominent feature of the proposed CMG is the introduction of the stator with modulating teeth, which function as the same as the modulating pole-pieces in the conventional CMG. The integrated MGM can then be achieved by inserting the armature windings into the stator slots. The configuration, harmonic analysis, and torque capability of the proposed CMG are compared with the conventional CMG. The operating principle and electromagnetic performance of the proposed MGM are investigated by dividing it into one vernier PM machine, one PM brushless machine and one proposed CMG. The results show that the developed integrated MGM exhibits good torque capability and power factor.
A Simplified Probability Model of Error of Measurement

A model of variability in measurement which does not employ the concepts of “true score” and “error score” is presented. Reference to an observed score random variable, X, together with the usual axioms of probability, is shown to be a satisfactory basis for derivation of results of the classical test theory which relate observable quantities. In addition, reliability formulas such as the KR 20 and KR 21 are obtained by construction of the observed score random variable over a sample space of outcomes of a testing procedure and assignment of probabilities to outcomes. The approach is consistent with trends in psychological theory toward objectively defined constructs and avoids redundancy in derivations, as well as connotations which arise from reference to “true values” and “errors. ” The present model is shown to be consistent with a relativistic, as opposed to an absolutistic, conception of measurement.