Miracle-dz/UniFAR-FTUs huggingface dataset:
Single trial EEG Classification

INTRODUCTION IN JUST 50 YEARS, the computer has revolutionized our lives, and our futures. But one problem remains: the interface between computer and mind. This is the Big Barrier, and it is almost impenetrable today as it was in those early days when we used paper tapes. The race is on to breach the Big Barrier. Researchers in industry and academia are seeking new, more expensive ways to communicate with machines. The idea of using our brains to directly control a machine isn't particularly new. Research into the Brain-Computer Interface, or BCI, began in earnest in the early 70's, when the USA Department of Defense saw the promise of fighter pilots using their minds to directly control their planes. A successful BCI must be bi-directional. Getting information into the brain is relatively easy: we can use the normal sensory channels, such as sight or hearing. But getting the information out of the brain by studying its electrical signature is a harder problem. However, by focusing on very specific areas of brain activity, such as the motor function, it is possible to analyze EEG data using filters, Fourier transforms, and neural networks, to extract some useful signal the noise. BCI research is red-hot. Although still at an exploratory stage, the implications of recent research results are phenomenally exciting. Perhaps one day, the Big Barrier will simply disappear altogether.
Connection-oriented multicasting in wormhole-switched networks on chip

Network-on-chip (NoC) proposes networks to replace buses as a scalable global communication interconnect for future SoC designs. However, a bus is very efficient in broadcasting. As the system size scales up to explore the chip capacity, broadcasting in NoCs must be efficiently supported. This paper presents a novel multicast scheme in wormhole-switched NoCs. By this scheme, a multicast procedure consists of establishment, communication and release phase. A multicast group can request to reserve virtual channels during establishment and has priority on arbitration of link bandwidth. This multicasting method has been effectively implemented in a mesh network with deadlock freedom. Our experiments show that the multicast technique improves throughput, and does not exhibit significant impact on unicast performance in a network with mixed unicast and multicast traffic if the network is not saturated
Polish phones statistics

The phonemic statistics were collected from several large Polish corpora. The paper presents methodology of the acquisition process, summarisation of the data and some phenomena in the statistics. Triphone statistics apply context-dependent speech units which have an important role in speech technologies. The phonemic alphabet for Polish, SAMPA, and methods of providing phonemic transcriptions are described with detailed comments.
Special education and regular class teacher attitudes regarding social behaviors of children : steps toward the development of a social skills curriculum /

s p e c i f i c b e h a v io r mentioned s e v e r a l t imes was lunchroom o r t a b l e manners, ag a in a " b a s ic s o c i a l i z a t i o n " k ind o f b e h a v io r . A l though responses to the open-ended q u e s t io n s are m a in ly p o s i t i v e , some teachers r a i s e ques­ t io n s about word ing o f i tem s , the form o f the q u e s t i o n n a i r e , and whether i t is p o s s ib le in f a c t to teach many o f the s o c ia l b e h a v io rs l i s t e d in the q u e s t i o n n a i r e . L i m i t a t i o n s and Sources o f E r r o r The d a ta con s id e re d in t h i s s tudy were ge ne ra ted by responses on a q u e s t i o n n a i r e , which presumably r e f l e c t e d the a t t i t u d e s and o p in io n s o f the respondents about s o c ia l b e h a v io rs a t a p a r t i c u l a r p o i n t in t im e . There a re a number o f problems a s s o c ia t e d w i t h q u e s t io n n a i r e s such as t h a t used in the p r e s e n t s tu d y . W i th a m a i le d q u e s t i o n n a i r e , f o r example , t h e r e is no easy way to c o n t r o l c o n d i t io n s under which the q u e s t i o n n a i r e is com ple ted . The f a c t t h a t the q u e s t i o n n a i r e was n o t anonymous ( a l ­ though a few respondents d id leave o f f t h e i r names) cou ld c o n t r i b u t e to some d i s t o r t i o n in responses in the d i r e c t i o n o f " s o c i a l l y a c c e p t a b l e " responses. Extreme d i s t o r t i o n s were u n l i k e l y , however, inasmuch as the c o n te n t o f the q u e s t i o n n a i r e items was f a i r l y benign in n a t u r e , and such e r r o r s , I f any, would h o p e f u l l y have d i s t r i b u t e d themselves randomly.
Online incremental clustering with distance metric learning for high dimensional data

In this paper, we present a novel incremental clustering algorithm which assigns of a set of observations into clusters and learns the distance metric iteratively in an incremental manner. The proposed algorithm SOINN-AML is composed based on the Self-organizing Incremental Neural Network (Shen et al 2006), which represents the distribution of unlabeled data and reports a reasonable number of clusters. SOINN adopts a competitive Hebbian rule for each input signal, and distance between nodes is measured using the Euclidean distance. Such algorithms rely on the distance metric for the input data patterns. Distance Metric Learning (DML) learns a distance metric for the high dimensional input space of data that preserves the distance relation among the training data. DML is not performed for input space of data in SOINN based approaches. SOINN-AML learns input space of data by using the Adaptive Distance Metric Learning (AML) algorithm which is one of the DML algorithms. It improves the incremental clustering performance of the SOINN algorithm by optimizing the distance metric in the case that input data space is high dimensional. In experimental results, we evaluate the performance by using two artificial datasets, seven real datasets from the UCI dataset and three real image datasets. We have found that the proposed algorithm outperforms conventional algorithms including SOINN (Shen et al 2006) and Enhanced SOINN (Shen et al 2007). The improvement of clustering accuracy (NMI) is between 0.03 and 0.13 compared to state of the art SOINN based approaches.
Hand-Eye: A Vision-Based Approach to Data Glove Calibration

We describe a new method for data glove calibration that uses computer vision techniques to create a filter for individual customization of input obtained from the glove. Our major observation is that it is possible to create a linear correlation between the hand posture reported by the data glove and the observed posture of the hand itself. We use a feature-based computer vision system independently to extract information about hand posture from video images of a human hand that is using the data glove. We simultaneously collect the glove data for the same posture. Linear regression is used on the combined sets of reported data to establish a filter that customizes the data reported from the glove for an individual user. The filtered glove data is mapped onto a computer-generated image of the hands skeletal structure. We show by comparison that the computer-generated hand image exhibits a posture quite similar to that of the actual hand.
Efficient algorithm for a class of least squares estimation problems

A computationally efficient algorithm for solving least squares estimation problems is proposed. It is well suited for problems with the normal equation matrix factorizable in terms of Kronecker's products. Three classes of identification problems factorizable in this sense are pointed out. Computational complexity of the algorithm and its robustness against round off errors is also discussed. >
The second coming of cold dark matter

While the standard cold dark matter (CDM) model has received numerous marks against it on the basis of a variety of observational data, the prediction of high σv, the pairwise‐velocity dispersion between galaxies, on small (megaparsec) scales was reported as perhaps one of its greatest failings. Here, we reexamine the case of CDM and σv with high‐resolution numerical simulations. The σv statistic was measured in simulations, in artificial galaxy catalogs, and the CfA North Forty redshift survey. In our reanalysis of the CfA data, we found that σv∼500 km/s, significantly higher than the original estimates of Davis & Peebles (1983). This new value, along with our estimate of the velocity bias, leads to a virial measure of the cosmological density parameter, Ω∼0.9, which is more than four times the traditionally cited CfA result. Analysis of the simulations indicates that while the method of Davis and Peebles works well, the σv statistic itself is not generally robust: there is large scatter from catalog to .
CLAU - A Service-Oriented System for Complex Language Alignment: Architectural Aspects

In the last years, parallel corpora have become an effective framework to study how well the linguistic phenomena and, more specifically, annotation schemata can be applied when importing the annotations from one language to the other(s). In the case of automatic import, the evaluation and correction are better to be performed by linguists using specific software. The paper proposes CLAU - a service-oriented interactive application allowing users to import, evaluate, correct, and share XML-based annotations in parallel texts. The design, general architecture, and implementation are discussed. Also, two use cases are presented: temporal annotations in parallel texts and how CLAU facilitates social Web interactions between language scientists.
Weighing the value of changeability in Open Source Software

Software value assessment is not an easy task. Many techniques have been proposed in the past, qualitative and quantitative, based on human evaluation or on measureable metrics. This paper proposes a quantitative technique based on the concept that “modularization is value”, applied to the Debian package database. This (software implemented) technique defines two metrics: change cost (as a function of the dependencies among packages) and integration cost (as a function of the number of maintainers involved). We applied our technique to sets of packages and we show and comment the results we obtained.
Psychopathology and languishing are distinct

ABSTRACT Hall-Simmons and McGrath have proposed that character strengths can moderate the impact that clinical symptoms have on functioning. This notion is reasonable in light of existing evidence. Specifically, we provide secondary analyses from recently published data suggesting that character strengths can moderate the impact of psychopathology on functioning. We argue however that this conceptual work should only the first step in developing a comprehensive model of how flourishing can be promoted and languishing avoided.
Automating the Discovery and Modeling of Open Source Software Development Processes

Introduction The goal of our work is to develop new techniques for discovering, modeling, analyzing, and simulating software development processes based on information, events, and contexts that can be observed through public information sources on the Web. Our domain examines processes in open source software development (OSSD) projects, such as those associated with the Apache Web server, Mozilla Web browser, and interactive development environments like NetBeans and Eclipse. In our previous work, we demonstrated the ability via manual search and analysis methods to discover (fragments of) process workflows in projects like NetBeans [9] by analyzing the content of their web information spaces, including informal task prescriptions, community structure and work roles, overall project organization, product histories, and communications among community members.
A new coverage algorithm for directional movement Wireless Sensor Networks

The coverage is a critical issue in WSNs and considered as a measure of the service quality of WSNs. We proposed a coverage optimal algorithm based on Virtual Distance (VD) to enhance the coverage of WSNs after initial random placement. We define the ideal position of node related to its neighbor, and then the distance vector of one node's real position to ideal position is defined as virtual distance related to the neighbor node. The actual travelled path which is determined by the weighted sum of all virtual distance of sensor. The experimental results show that that VD algorithm enables a remarkably improved coverage of the interested area in various environments.
A system for airport surveillance: detection of people running, abandoned objects, and pointing gestures

The proposed system is focusing on the detection of three events in airport videos: a person running, a person putting down an object and a person pointing with his/her hand. The system was part of the NIST-TRECVid 2010 campaign, the training dataset consists in 100 hours of video from the Gatwick airport from five different cameras. For the detection of a person running, a non-parametric approach was adopted where statistics about tracked object velocities were accumulated over a long period of time using a Gaussian kernel. Outliers were then detected with the help of a kind of tstudent test taking into account the local statistics and the number of observations. For the detection of "object put" events, we follow a dual background segmentation approach where the difference in response between a short term and a long term background model (Mixture of Gaussians) triggers alerts. False alerts are excluded based on a simple modeling of the camera geometry in order to reject objects that are too large or too small given their positions in the image. The detection of pointing gesture events is based on the grouping of significant spatio-temporal corners (Harris) in a 3x3x3 cell called compound features as proposed recently by Andrew Gilbert et al. [10]. A hierarchical codebook is then derived from the training set based on a data mining algorithm looking for frequent items (called transactions). The algorithm was modified in order to deal with the large number of potential transactions (several millions) during the training step.
Memory Predictions are Influenced by Perceptual Information: Evidence for Metacognitive Illusions

Although perceptual information can capture attention and is utilized when judging size or depth, little work has investigated whether such information is used to make memory predictions. The present study examined how the font size of to-be-remembered words influences predicted memory performance. Participants studied words for a later free recall test that varied in font size and made judgments of learning (JOLs) for each item. JOLs were influenced by font size, with larger font sizes given higher JOLs, whereas little relationship was evident between font size and recall. The effect was modified when other, more valid, sources of information (e.g., associative strength) were available when making JOLs, and persisted despite experience with multiple study-test sessions, using a forgetting scale to assess predictions, and explicitly warning participants that font size has little effect on memory performance. When ease of reading was manipulated, such that large font size words were made less fluent, the effect was eliminated. Thus, highly accessible perceptual cues can strongly influence JOLs, likely via encoding fluency, leading to metacognitive illusions.
Against the tide: the role of bacterial adhesion in host colonization

Evolving under the constant exposure to an abundance of diverse microbial life, the human body has developed many ways of defining the boundaries between self and non-self. Many physical and immunological barriers to microbial invasion exist, and yet bacteria have found a multitude of ways to overcome these, initiate interactions with and colonize the human host. Adhesion to host cells and tissues is a key feature allowing bacteria to persist in an environment under constant flux and to initiate transient or permanent symbioses with the host. This review discusses reasons why adhesion is such a seemingly indispensable requirement for bacteria–host interactions, and whether bacteria can bypass the need to adhere and still persist. It further outlines open questions about the role of adhesion in bacterial colonization and persistence within the host.
Application and performance of joint cooperative Cell-ID localization and robust map matching

In this paper two extensions to the well known Cell-ID localization technique for handsets in mobile cellular networks are presented: It is firstly shown how the combination of multiple cross-provider Cell-IDs, which may become available from cooperation of one-hop neighbours in mobile ad-hoc networks (MANETs) or from Dual SIM mobiles phones, can be used to improve the positioning accuracy. Secondly, a robust map matching algorithm for Cell-ID localization is presented. The proposed map matching algorithm is termed robust, as it is capable of dealing with the - compared to GPS - more coarse Cell-ID based position estimates. An implementation of a localization system based on these methods shows, that the positioning accuracy can be significantly improved compared to the simple single Cell-ID technique.
Pengaruh Pengungkapan Informasi Lingkungan Terhadap Kinerja Keuangan

Penelit ian ini merupakan studi mengenai pengungkapan informasi lingkungan dan kinerja keuangan. Penelitian ini bertujuan untuk menganalisis pengungkapan informasi lingkungan terhadap kinerja keuangan. Penelit ian ini dilakukan dengan metode studi kasus pada perusahaan manufaktur publik yang mengikuti PROPER di Indonesia. Unit analisisnya adalah perusahaan peserta PROPER periode 2011-2013. Model persamaan struktural dengan menggunakan program WarpPLS diadopsi untuk menganalisis data. Hasil penelitian ini menunjukkan pengungkapan informasi lingkungan dengan content analysis yang lebih luas memberikan informasi lebih baik, dan pengungkapan informasi lingkungan berpengaruh signifikan terhadap kinerja keuangan .
Female employment following childbirth: differences between native and immigrant women in Switzerland

ABSTRACT This paper analyses how native and immigrant women adapt their labour supply soon after childbirth in Switzerland. The analysis aims to examine whether the heterogeneity in post-birth female employment is explained by differences by women's origin with respect to skills composition, occupational attainment and household financial situation. Using 2010–2015 panel data from the Swiss Labour Force Survey, multinomial models focus on the transitions experienced by employed women from t−1 (one year prior) to different levels of labour-market involvement in t (reference week) after childbirth (which occurs when a woman has a child of less than one year of age in t). Three possible outcomes are considered: same/more working hours, fewer working hours, and withdrawal from employment. Double hurdle models are also used to account for the total amount of hours worked by those women who remain in employment after childbirth. In post-birth employment patterns, a larger positive effect of women's opportunity cost, measured through educational attainment and previous job characteristics, than of partners’ income, is observed, especially for immigrant women.
Accurate range image registration: Eliminating or modelling outliers

Automatic and accurate range image registration is often a prerequisite step for range image analysis and interpretation. Due to occlusion, appearance and disappearance of points in different images, outliers inevitably occur. In this case, various techniques to eliminate and model outliers have been proposed for accurate range image registration. The objective of this paper is to experimentally investigate which of the outlier elimination and modelling is more effective for the evaluation of possible correspondences established, so that a deep insight into how advanced range image registration algorithms will be developed can be obtained. The experimental results based on both synthetic data and real images show that the outlier modelling often outperforms the outlier elimination in the sense of producing more accurate and robust range image registration results.
Frequent pattern mining in mobile devices: A feasibility study

The availability of computational power in mobile devices is key-enabler for Mobile Data Mining (MDM) at user-premises. Alternately, resource-constraints like limited energy, narrow bandwidth, and small screens challenge in adoption of MDM. Currently, MDM is based on light-weight algorithms that are adaptive in resource-constrained environments but a study to evaluate the performance of general algorithms still lacks in the literature. To this end, we have studied six Frequent Pattern Mining (FPM) algorithms and deployed them in mobile devices to evaluate the feasibility and highlighted the associated challenges. The experiments were performed on real and synthetic data sets strictly in android-based mobile device and compared with PC-based setup. The experimental results show that FPM algorithms can leverage MDM after tuning some basic parameters.
A progressive learning method for symbols recognition

This paper deals with a progressive learning method for symbols recognition which improves its own recognition rate when new symbols are recognized in graphics documents. We propose a discriminant analysis method which provides allocation rules from learning samples with known classes. However a discriminant analysis method is efficient only if learning samples and data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each observation to take into account the parasitic effects between the data and the learning samples. We propose also an adaptation to consider the user feedback.
An ontological-based approach to the exploration of Geographic Information Science & Technology Body of Knowledge

Using as a starting point the results attained in previous curricula development initiatives in the GIS&Science domain, a prototype tool for the visualization and exploration of Geographic Information Science & Technology Body of Knowledge (GIS&T BoK) contents is presented. As suggested in the challenges launched for the second edition of the GIS&T BoK, the construction of this knowledge domain visualization tool, aims at stressing the advantages of using an ontological-based approach in the process of creating and exploring the Model Curricula vision. Therefore, one of the objectives of this prototype was the conception of a semantic network allowing for the translation of the fundaments of the GIS&T domain into a meaningful exploration environment and, from there, to evaluate the capacity to support the instructional sequences design for the creation of future exemplar pathways.
Flow-based detection of RDP brute-force attacks

This paper describes a design and evaluation of a network-based detection of brute-force attacks on authentication of Microsoft Windows RDP. The network flow data provides sufficient information about communication of two nodes in network, even though the RDP communication is encrypted. An analysis was based on the network flow data collected in the Masaryk University network and host-based data from logs of a server with opened Remote Desktop Connection. These data helped us to improve the flow detection using the information gathered from the server event log. Despite the fact that RDP is encrypted, flow data gives us a sufficient amount of information to determine whether the connection is an authentication or regular remote desktop session. We implemented the attacks detection as a plugin for the widely used NfSen collector. The plugin is involved in the active defense of the network of Masaryk University.