Miracle-dz/UniFAR-FTUs huggingface dataset:
Equivalent-Current-Dipole-Source-Localization-Based BCIs with Motor Imagery

This chapter will propose a new paradigm for single-trial-electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) with motor imagery (MI) [1] tasks. Among such BCIs, the sensorimotor rhythm (SMR)-based ones, when using common spatial patterns (CSPs), require features over broad frequency bands, such as mu, beta and gamma rhythms [2]. Therefore, very high-dimensional feature vectors and continuous-valued patterns necessary for spatio‐ temporally checking the features [3,4] could yield an enormous amount of data and much computational time [5]. So, various data reduction such as downsampling [6,7] and optimal EEG channel configuration [8,9,10] have been investigated for the BCIs.
EVALUATION OF THE NOMOPHOBIA’S PREVALENCE AND ITS IMPACT ON SCHOOL PERFORMANCE AMONG ADOLESCENTS IN MOROCCO

Social networks are a new form of addiction to technology and are beginning to take place in the moroccan society in the last decades, especially among children and adolescents. Furthermore the Nomophobia is a new form of addiction to new generations of mobile phones. Because of the importance of their speed spread and their influence on the person's future and interpersonal relationships, we conducted a study to calculate the degree of Nomophobia in adolescent population. It is through the establishment of a questionnaire for a sample of 541 adolescents including 298 girls and 243 boys of young Moroccans and a test on Nomophobia NMP-Q. The statistical result stated that 69.1% of girls and 63% of boys have Nomophobia in a moderate and severe state, and that the Smartphone is more solicited than the laptop with a negative correlation between school performance and the score of Nomophobia. Thus, poor school performance and mental disorders in adolescents can be explained by taking Nomophobia.
A novel approach to cartoon style rendering of an image with an approximated crayon texture

We present an efficient approach to cartoon style rendering of an image with an approximated crayon texture. The algorithm generates a crayon texture map, with two crayon textures (illuminated color texture and shadowed color texture) combined into one. The crayon texture is generated using crayon stroke generation technique. The texture is then mapped onto the image in cartoon style using per-vertex mapping concept. This algorithm results in an improved transition boundary between the two texture colors, comparable to the normal cartoon shaded image without crayon texture. If the image generated by this algorithm is animated, then the transition boundary would move logically with the object as compared to our earlier algorithm (described in the text), where transition boundary changed drastically with slight movement of an object in the image. The present approach can be used to generate variations in cartoon shading easily and efficiently. We have also attempted to approximate the effect of distribution of wax in the resulting image.
A Content Analysis for Evaluating RFID Applications in Supply Network Management

Traditionally, a supply network is a sequence of different and multiple numbers of processes interconnected each other in order to satisfy all capacities and demand requirements imposed by customers with minimum cost to the network. Radio Frequency Identification (RFID) technology could potentially improve supply network management by guarantying more visibility and real time communications across actors. Currently, RFID has playing an important role in coordinating information in several industrial contexts: in brief, its main advantage is to improve the quality and the speediness of information sharing among different partners in a network. Moreover, RFID capability could be effectively applied to replace traditional approaches (i.e. barcodes) to store and retrieve item data. On the other hand, RFID application is affected by some criticisms from both technological and economical point of view, as all emerging ICT technologies. In recent years, several studies have been developed in order to analyze benefits of RFID applications; few papers are focused on analyzing evaluation frameworks for RFID applications in complex supply networks. The proposed content analysis aims to support in evaluting potential operational benefits of RFID technology in different supply networks. The analysis proposed has been developed according to the well-know Supply Chain Operations Reference (SCOR) which allows integrating business process reengineering, benchmarking, and process measurement into a cross-functional framework. The content analysis has been developed according to current literature about RFID applications; finally, it is proposed a standardized guideline for evaluating potential benefit of RFID technology in supply networks.
Mirror, mirror on the wall, who’s the least biased of them all? Dangers and potential solutions regarding bias in forensic psychological evaluations

When psychological evaluators are asked to provide their expert opinions in legal proceedings, they are expected to do so in an objective and unbiased way. The statutory requirements regarding the admissibility of expert testimony in many countries often cite objectivity and reliability as standards. However, as is true in many realms of human decision-making, the field of forensic psychological assessment is fraught with bias. In this article, we discuss several lines of research that have investigated bias in forensic psychological evaluations. We also discuss emerging lines of research involving methods to measure and reduce bias. We conclude with a call for structured self-monitoring as an important strategy for forensic evaluators to mitigate bias in their work.
Hora: Online Failure Prediction Framework for Component-based Software Systems Based on Kieker and Palladio

Predicting failures in large systems at runtime is a challenging task as the systems usually comprise a number of hardware and software components with complex structures and dependencies. The state-of-the-art techniques approach the task of failure prediction either by creating one separate prediction model for each crucial parameter, or by aggregating parameters of all components in order to build one prediction model. However, both approaches are not suitable for large-scale dynamic systems. In this paper, we propose our envisioned approach Hora—an online failure prediction approach for large-scale component-based systems. Our approach creates a prediction sub-model for each component and combines them using component dependencies obtained from an architectural model, e.g., PCM or SLAstic, into a model that can predict failures of internal and external services at runtime. The framework is built on the Kieker monitoring framework and other supporting tools, e.g., Weka, R, ΘPAD, which allows suitable prediction techniques to be employed for different components of the system.
Incomplete Cholesky decomposition based kernel principal component analysis for large-scale data set

Kernel principal component analysis (KPCA) is a popular nonlinear feature extraction method. It generally uses eigen-decomposition technique to extract the principal components. But the method is infeasible for large-scale data set because of the storage and computational problem. To overcome these disadvantages, an efficient iterative method of computing kernel principal components is proposed. First, the Gram matrix is transformed into the two triangular matrices using incomplete Cholesky decomposition. Then each column of the triangular matrix is treated as the input sample for the covariance-free algorithm. Thus, the kernel principal components can be iteratively computed without the eigen-decomposition. The proposed method uses less than half of original storage capacity and also greatly reduces the time complexity. More important, it still can be used even if traditional eigen-decomposition technique cannot be applied when faced with the extremely large-scale data set. The effectiveness of proposed method is validated from experimental results.
ATP Released from Astrocytes Mediates Glial Calcium Waves

Calcium waves represent a widespread form of intercellular communication. Although they have been thought for a long time to require gap junctions, we recently demonstrated that mouse cortical astrocytes use an extracellular messenger for calcium wave propagation. The present experiments identify ATP as a major extracellular messenger in this system. Medium collected from astrocyte cultures during (but not before) calcium wave stimulation contains ATP. The excitatory effects of medium samples and of ATP are blocked by purinergic receptor antagonists and by pretreatment with apyrase; these same purinergic receptor antagonists block propagation of electrically evoked calcium waves. ATP, applied at the concentration measured in medium samples, evokes responses that are qualitatively and quantitatively similar to those evoked by those medium samples. These data implicate ATP as an important transmitter between CNS astrocytes.
Neutrinos in the Family Replicated Gauge Group Model

We present a discussion of the neutrino mass problem in the anti-grand unification theory based on three family replicated copies of the Standard Model gauge group (SMG). We consider two versions of the theory, with and without right-handed neutrinos, and present order of magnitude fits to the 17 measured quark-lepton mass and mixing angle variables. In the model without right-handed neutrinos, based on the gauge group $SMG^3\times U(1)_f$ and an isotriplet Higgs, we obtain a good 4 parameter fit, but with a vacuum oscillation solution to the solar neutrino problem. In the second model, based on the gauge group $(SMG\times U(1)_{B-L})^3$, we obtain a good 5 parameter fit using the usual right-handed neutrino see-saw mechanism to generate an LMA-MSW solution to the solar neutrino problem. The CHOOZ mixing angle bound is easily satisfied in the first model and is close to the predicted value in the second model.
Hierarchical neural net architectures for feature extraction in ASR

This paper presents the use of neural net hierarchy for feature extraction in ASR. The recently proposed Bottle-Neck feature extraction is extended and used in hierarchical structures to enhance the discriminative property of the features. Although many ways of hierarchical classification/feature extraction have been proposed, we restricted ourselves to use the outputs of the first stage neural network together with its inputs. This approach is evaluated on meeting speech recognition using RT’05 and RT’07 test sets. The evaluated hierarchical feature extraction brings consistent improvement over the use of just the first level neural net.
Diffuse Optical Tomography of Cerebral Blood Flow, Oxygenation, and Metabolism in Rat during Focal Ischemia

Diffuse optical tomography (DOT) is an attractive approach for evaluating stroke physiology. It provides hemodynamic and metabolic imaging with unique potential for continuous noninvasive bedside imaging in humans. To date there have been few quantitative spatial-temporal studies of stroke pathophysiology based on diffuse optical signatures. The authors report DOT images of hemodynamic and metabolic contrasts using a rat middle cerebral artery occlusion (MCAO) stroke model. This study used a novel DOT device that concurrently obtains coregistered images of relative cerebral blood volume (rCBV), tissue-averaged hemoglobin oxygen saturation (Sto2), and relative cerebral blood flow (rCBF). The authors demonstrate how these hemodynamic measures can be synthesized to calculate an index of the oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen consumption (CMRo2). Temporary (60-minute) MCAO was performed on five rats. Ischemic changes, averaged over the 60 minutes of occlusion, were as follows: rCBF = 0.42 ± 0.04, rCBV = 1.02 ± 0.04, ΔSto2 = −11 ± 2%, rOEF = 1.39 ± 0.06 and rCMRo2 = 0.59 ± 0.07. Although rOEF increased in response to decreased blood flow, rCMRo2 decreased. The sensitivity of this method of DOT analysis is discussed in terms of assumptions about baseline physiology, and the diffuse optical results are compared with positron emission tomography, magnetic resonance imaging, and histology observations in the literature.
Approximate linear phase Hilbert transformer

The IIR realization of the Hilbert transformer require less computations than FIR realizations, but the main disadvantage of the minimum phase IIR realization is the nonlinearity of the phase characteristic. In this paper, we show that using all-pass IIR subfilters from the minimal phase solution it is possible to design an IIR Hilbert transformer which exhibits simultaneously approximate constant amplitude response and approximate linear phase.
Opportunity for compute partitioning in pursuit of energy-efficient systems

Performance of computing systems, from handhelds to supercomputers, is increasingly constrained by the energy consumed. A significant and increasing fraction of the energy is consumed in the movement of data. In a compute node, caches have been very effective in reducing data movement by exploiting the available data locality in programs. Program regions with poor data locality, then effect most of the data movement, and consequently consume an ever larger fraction of energy. In this paper we explore the energy efficiency opportunity of minimizing the data movement in precisely such program regions, by first imagining the possibility of compute near memory, and then partitioning the program’s execution between a compute core and the compute near memory (CnM). Due to the emergence of 3D stacked memory, a CnM implementation appears more realistic. Our focus is on evaluating the partitioning opportunity in applications and to do a limit study of systems enabled with CnM capabilities to understand and guide their architectural embodiment. We describe an automated method of analyzing the data access pattern of optimized workload binaries, via a binary-instrumentation tool called SnapCnM, to identify the beneficial program regions (loops) for CnM execution.We also perform a limit study to evaluate the impact of such partitioning over a range of parameters affecting CnM design choices. Our results show that compute partitioning a small (<10%) fraction of a workload can improve its energy efficiency from 3% (for compute-bound applications) to 27% (for memory-bound applications). From the study in this work we discuss the important aspects that help to shape the future CnM design space.
Research on Network anomaly Detection Based on Clustering and Classifier

In this paper, we propose a method to find the anomalous behaviors in network traffic. We map the network connection records into different feature spaces typically of high dimension according to their protocols and services. In training, we perform clustering to group training data points into clusters, from which we select some clusters as normal and known-attack profile according to a simple, but effect, criterion. For those training data excluded from the profile, we use them to build a specific classifier. The classifier has two distinct characteristics: one is that it regards each data point in the feature space with the limited influence scope, which is served as the decisive bounds of the classifier, and the other is that it has the "default" label to recognize those novel attacks. We present a novel classification algorithm, influence-based classification algorithm, to deal with ambiguous data. Our system is tested on the KDD Cup 1999 data. Results show that it is superior to other data mining based approaches in detection performance, especially in detection of PROBE and U2R attacks
A Hybrid Approach for Overlay Multicast

Existing overlay multicast (also called application layer multicast) protocols can be categorized into tree-first and mesh-first approaches by the type of overlay they build. These two approaches complement each other in terms of performance and scalability. In this paper, we propose and evaluate hybrid overlay multicast protocol (HOMP), which is specifically designed for low-delay, many-to-many applications with large receiver sets. HOMP constructs and maintains a hybrid overlay composed of a core mesh and many attached trees for data transmission, thus it shows some properties of both mesh-first and tree-first approaches. Our simulation results reveal that HOMP is a promising application layer overlay construction technique
3D MRA coronary axis determination using a minimum cost path approach

A method is introduced to automatically find the coronary axis based on two or more user‐defined points, even in the presence of a severe stenosis. The coronary axis is determined by finding a minimum cost path (MCP) in a feature image in which the tubular‐like structures are enhanced. The results of the proposed method were compared with manually drawn central axes to estimate the accuracy. In 32 3D TFE‐EPI acquisitions of patients and volunteers, 14 right coronary arteries (RCAs), 15 left anterior descending arteries (LADs), and eight left circumflex arteries (LCXs) were manually tracked twice by two operators to determine a reference axis and to assess the inter‐ and intra‐user variability. On average, the maximum distance to the reference axis, based on only two user‐defined points, is less than 1.5 mm; the average distance is around 0.65 mm, which is less than the average in‐plane resolution. The results of the method are comparable to those of the manual operators. Magn Reson Med 47:1169–1175, 2002. © 2002 Wiley‐Liss, Inc.
Geocookie: A Space-Efficient Representation of Geographic Location Sets

A new approach is presented that allows a user agent to compute a compact browser cookie-like character string (called Geocookie) for a third party, whereby for any given geographic location the third party can either infer that the location is definitely not covered by the Geocookie, or it can infer that the location is probably covered by the Geocookie, depending on whether the user agent has or has not included the location beforehand. The approach extends the concept of a Bloom filter and combines it with Geohashes, thus making it possible to store information about visited geographic locations in the filter. Geocookies can be used in many different scenarios for location-based queries and location-based services, whenever a user agent wants to inform a third party about a set of visited locations such that the third party can compile a result that either favors or excludes these locations. In contrast to existing approaches such as session cookies that are mapped to server-sided stored location trajectories, Geocookies provide a compact and privacy-preserving structure which does not reveal the actual set of all visited locations, but provides a one-way check function which can be used by the third party to evaluate given locations against the Geocookie. In addition, Geocookies provide plausible deniability in case of location matches. This paper introduces a formal definition for Geocookies together with a discussion on practical applications and embedding into HTTP headers.
A Survey on Mobility Models for Wireless Networks

User-centric wireless networks abound around us supported by a variety of networking architectures such as infrastructured hotspots, ad-hoc or mesh architectures. Common to all of these is an increasing node movement frequency which strongly impacts network efficiency, but in order to better understand such impact, there is the need to capture with some level of accuracy mobility patterns of the wireless nodes. Hence, a series of mobility models have been developed. This survey gives insight into the most recent trades and evolution of mobility models, highlighting operation and analysis for each of them. The survey also gives insight into the applicability of mobility models, beyond the common simulation application environment.
Temporal Trends and Spatial Variability of Vegetation Phenology over the Northern Hemisphere during 1982-2012

Satellite-derived vegetation phenology has been recognized as a key indicator for detecting changes in the terrestrial biosphere in response to global climate change. However, multi-decadal changes and spatial variation of vegetation phenology over the Northern Hemisphere and their relationship to climate change have not yet been fully investigated. In this article, we investigated the spatial variability and temporal trends of vegetation phenology over the Northern Hemisphere by calibrating and analyzing time series of the satellite-derived normalized difference vegetation index (NDVI) during 1982–2012, and then further examine how vegetation phenology responds to climate change within different ecological zones. We found that during the period from 1982 to 2012 most of the high latitude areas experienced an increase in growing period largely due to an earlier beginning of vegetation growing season (BGS), but there was no significant trend in the vegetation growing peaks. The spatial pattern of phenology within different eco-zones also experienced a large variation over the past three decades. Comparing the periods of 1982–1992, 1992–2002 with 2002–2012, the spatial pattern of change rate of phenology shift (RPS) shows a more significant trend in advancing of BGS, delaying of EGS (end of growing season) and prolonging of LGS (length of growing season) during 2002–2012, overall shows a trend of accelerating change. Temperature is a major determinant of phenological shifts, and the response of vegetation phenology to temperature varied across different eco-zones.
Enhancing UnitMarch with Alternative Local Search Methods and Unit Propagation Delft University of Technology

This paper describes our efforts to improve the UnitMarch [4] SAT solver by combining the ideas of other SAT solvers with the UnitMarch algorithm. GSAT and WalkSAT are used as an inspiration for improving UnitMarch. We also use a combination of GSAT and WalkSAT, called RandomSAT. The idea behind our modification of UnitMarch is taking large steps towards a solution with an additional algorithm and then using UnitMarch for smaller steps. With this approach we attempt to combine the best of both. The paper also describes preprocessing using unit propagation, which showed to be a significant improvement.