Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Class EEG-Based Motor Imagery Classification Using Phase-Space Reconstruction Technique

Over the last few decades, brain signals have been significantly exploited for brain-computer interface (BCI) applications. In this paper, we study the extraction of features using event-related desynchronization/synchronization techniques to improve the classification accuracy for three-class motor imagery (MI) BCI. The classification approach is based on combining the features of the phase and amplitude of the brain signals using fast Fourier transform (FFT) and autoregressive (AR) modeling of the reconstructed phase space as well as the modification of the BCI parameters (trial length, trial frequency band, classification method). We report interesting results compared with those present in the literature by utilizing sequential forward floating selection (SFFS) and a multi-class linear discriminant analysis (LDA), our findings showed superior classification results, a classification accuracy of 86.06% and 93% for two BCI competition datasets, with respect to results from previous studies.
Identity-based key agreement protocol employing a symmetric balanced incomplete block design

Key agreement protocol is a fundamental protocol in cryptography whereby two or more participants can agree on a common conference key in order to communicate securely among themselves. In this situation, the participants can securely send and receive messages with each other. An adversary not having access to the conference key will not be able to decrypt the messages. In this paper, we propose a novel identity-based authenticated multi user key agreement protocol employing a symmetric balanced incomplete block design. Our protocol is built on elliptic curve cryptography and takes advantage of a kind of bilinear map called Weil pairing. The protocol presented can provide an identification (ID)-based authentication service and resist different key attacks. Furthermore, our protocol is efficient and needs only two rounds for generating a common conference key. It is worth noting that the com munication cost for generating a conference key in our protocol is only O(n√n) and the computation cost is only O(nm2), where n implies the number of participants and m denotes the extension degree of the finite field Fpm. In addition, in order to resist the different key attack from malicious participants, our protocol can be further extended to provide the fault tolerant property.
Online brain imaging by QR factorization of normalized regressor functions

This paper presents an online framework for an effective real-time brain imaging application. The idea is to estimate the activity parameters as coefficients of a linear model by QR factorization of normalized regressor functions. The recursive form of the normalization process makes the proposed strategy computationally efficient and applicable for online utilization. The t-statistics is used to compute the statistical significance of the estimated activity parameters. The results obtained by performing online physiological experiments verify the effectiveness of the proposed methodology by providing the brain activity for the range of measuring optodes. The technique has potential applications in real-time brain imaging. Furthermore, this method can be extended to real-time brain computer and brain machine interface systems.
Viewpoints Combined Classification Method in Image-based Plant Identification Task

The image-based plant identification challenge was focused on tree, herbs and ferns species identification based on different types of images. The aim of the task was to produce relevant species for each observation of a plant of the test dataset. We have elaborated a viewpoints combined classification method for this challenge. We have applied dense SIFT for feature detection and description; and Gaussian Mixture Model based Fisher vector was calculated to represent an image with high-level descriptor. The chosen classifier was the C-support vector classification algorithm with RBF (Radial Basis Function) kernel, and we have optimized two hyperparameters (C from C-SVC and γ from RBF kernel) by a grid search with two-dimensional grid. We have constructed a combined classifier using the weighted average of reliability values of classifier at each viewpoint. The results show that our combined method exceeds our best classifier among the list of classifiers constructed for different viewpoints.
Guarantees for Customers of Incentive Anonymizing Networks

We raise and propose solutions to the problem of guaranteeing that a user of incentive remailing services for anonymization cannot lose money if he does not get full service, i.e., if his message does not reach its destination. Applications such as voting over the Internet or reviewing of articles require anonymous delivery of messages. An anonymizing technique was proposed several decades ago by Chaum and is based on a group of volunteer agents called mixnet. However, mixnets are not yet widely known and used today, and one often mentioned reason is the lack of incentives for volunteers. A recently proposed solution is based on adding digital coins to messages, such that each volunteer can extract only the digital coin designated as a payment for her. However, registered volunteers can sabotage the system by extracting and using their coins without performing their task — which consists of forwarding anonymized messages. The main improvement we propose is to guarantee that no money is lost by the user without getting his message at the destination. This is an essential property for a viable service. Solutions described are based on handshaking mechanisms where each volunteer gets her payment (or key to decrypt the payment) from the agent to which she is expected to forward the message, or from the destination using a public board or a reply message. This ensures that a volunteer gets her financial support only if she fulfills her task. We discuss how techniques for nonrepudiation of receipt of a message, together with reputation systems, can address the remaining problems.
Design and simulation of laser diode with sampled grating structure by the split-step traveling wave approach

The traveling wave model is commonly used in the simulation of semiconductor laser diodes and photonic integrated circuits. When the laser diode is cascaded with other passive elements of relatively large optical dimension such as a sampled grating DBR section, the conventional approach by tracing the forward and the backward traveling waveform along the entire passive section in the time domain becomes extremely time consuming or even becomes impossible to be implemented. To overcome this difficulty, based on the split step approach proposed, the improved traveling wave model is applied to simulate the wavelength-tunable lasers with sampled grating DBR structures and some design issues are discussed.
Feature warping for robust speaker verification

We propose a novel feature mapping approach that is robust to channel mismatch, additive noise and to some extent, non-linear effects attributed to handset transducers. These adverse effects can distort the short-term distribution of the speech features. Some methods have addressed this issue by conditioning the variance of the distribution, but not to the extent of conforming the speech statistics to a target distribution. The proposed target mapping method warps the distribution of a cepstral feature stream to a standardised distribution over a specified time interval. We evaluate a number of the enhancement methods for speaker verification, and compare them against a Gaussian target mapping implementation. Results indicate improvements of the warping technique over a number of methods such as Cepstral Mean Subtraction (CMS), modulation spectrum processing, and short-term windowed CMS and variance normalisation.
AI Planning by Multi-Valued Model-Checking

The subject of this work is planning under uncertainty. Our contribution is three-fold. Firstly, we develop a new framework for representing planning domains with incomplete information. The main idea can be summarised as follows: the use of quasi-boolean algebras as underlying logics in domain specification properly generalises classical planning domains to planning domains which involve uncertainty. We show that our framework unifies some existing approaches to model-based planning under uncertainty. Secondly, we consider the following planning scenario. The information that comprises a planning domain is obtained from multiple, possibly conflicting, sources. The task is to find a plan that is guaranteed to succeed despite the inconsistency in the domain or, if such a plan does not exist, to identify the source of inconsistency that needs to be resolved before a plan can be found. Thirdly, we provide a method for solving the planning problems defined in the new framework, including the above scenario. We show that such problems can be reduced to problems solvable by multi-valued model-checking, a technique that is well-established in the software verification community.
Evolutionary Morphology for Polycube Robots

Recent developments in robot technology have led to modular robots that are assembled from multiple parts. Modular robots have pros and cons. Their shape can be changed to suit a particular purpose and the same parts can be reused, which makes them fault-tolerant and extendable. However, such freedom and flexibility lead to higher cost so as to decide the configuration and actions to meet the purpose. With conventional robots, modular or non-modular, much research has been conducted on the way that a design is determined first and connections with sensory input, internal memory and motor output are acquired through learning. The designs are often based on existing creatures such as insects, dogs and earlier robot models, or parts of such creatures, for example, robot arms. As robot bodies operate in the real world, it makes sense to model them after living creatures that have been successful in nature. In fact, computer simulation patterns akin to living creatures have been created mainly in the field of artificial life, represented by the well-known L-System imitating plants and Dawkins’ biomorph (Dawkins, 1986) imitating insects. Sims’ virtual creatures (Sims, 1994a; Sims 1994b) feature not only a pattern but also a body and nerves, which are actually stylized art rather than robotics. Modular robots, which are easy to configure and recombine, have an advantage in that optimal robot shapes can be achieved, without modeling the existing creatures. Many existing modular robots are based on the block shape. Although automatic design research is not limited to the block shape, the shape has certain advantages: The basic units are easy to make, handle and combine. Furthermore, blocks are similar to cells, which enabled the simulation of biological development in the research on building structures. GOLEM Project (Lipson, 2000; Pollack, 2000) is one of the implementation models of Karl Sims experiments. They evolved frame-type virtual creatures using a linear actuator and fabricated them as a robot in a real world. In the modular robotics, new types of robots have been proposed with the functions such as a self-reconfiguration (Murata et al., 2002; Kurokawa et al., 2003) or self-reproduction (Zykov et al., 2005; Griffith et al, 2005). The LEGO Mind Storm is a famous block-based modular robot, parts of which are so small that half-assembled modules are combined in different placements (Lund, 2001). Ideally, robots can actively change their configuration by automatically recombining modules to fit a particular environment. However, this chapter deals with cube-shaped modular robots that can be manually recombined to achieve significant configurations that can adjust to actual environments using evolutionary morphology. This chapter describes how successfully EC
Building Effective Buyer-Supplier Relationships: Choices and Challenges

The need to reduce uncertainty and maintain flexibility in today’s competitive environment is increasing. Firms are looking for exchange partners who can fulfill not only price, volume and delivery requirements, but also meet higher-order needs for quality, responsiveness, and innovation. This paper reviews literature in management, economics and IT to identify the choices and challenges firms face in building effective buyersupplier relationships. Three types of governance structures are compared and contrasted: electronic hierarchies, electronic markets and cooperative ventures. Issues such as cost, trust and opportunism are explored.
A statistical analysis of the reduction of the wind power prediction error by spatial smoothing effects

We discuss the accuracy of the prediction of the aggregated power output of wind farms distributed over given regions. Our forecasting procedure provides the expected power output for a time horizon up to 48 hours ahead. It is based on the large scale wind field prediction which is generated operationally by the German weather service. Our investigation focuses on the statistical analysis of the power prediction error of an ensemble of wind farms compared to single sites. Due to spatial smoothing effects the relative prediction error decreases considerably. Using measurements of the power output of 30 wind farms in Germany we find that this reduction depends on the size of the region. To generalize these findings an analytical model based on the spatial correlation function of the prediction error is derived to describe the statistical characteristics of arbitrary configurations of wind farms. This analysis shows that the magnitude of the error reduction only weakly depends on the number of sites and is mainly determined by the size of the region. Towards a correction of systematic prediction errors an analysis of the temporal structure of the forecast error is performed. For this purpose the correlation of the errors for consecutive forecasts is analyzed for single sites and ensembles.
Chronic Consumption of a High-Fat Diet during Pregnancy Causes Perturbations in the Serotonergic System and Increased Anxiety-Like Behavior in Nonhuman Primate Offspring

Childhood obesity is associated with increased risk of behavioral/psychological disorders including depression, anxiety, poor learning, and attention deficient disorder. As the majority of women of child-bearing age are overweight or obese and consume a diet high in dietary fat, it is critical to examine the consequences of maternal overnutrition on the development of brain circuitry that regulates offspring behavior. Using a nonhuman primate model of diet-induced obesity, we found that maternal high-fat diet (HFD) consumption caused perturbations in the central serotonergic system of fetal offspring. In addition, female infants from HFD-fed mothers exhibited increased anxiety in response to threatening novel objects. These findings have important clinical implications as they demonstrate that exposure to maternal HFD consumption during gestation, independent of obesity, increases the risk of developing behavioral disorders such as anxiety.
The role of the most recent prior period's price in value relevance studies : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Palmerston North, New Zealand

Numerous value relevance investigations use the Ohlson (1995) model to empirically explore the value relevance of accounting variables such as earnings and goodwill amortisation by employing equity price as the dependent variable, but do not incorporate the most recent prior period’s equity price as an additional explanatory variable. The Ohlson (1995) model and the efficient market literature indicate that, since share prices represent the present value of future permanent earnings in an efficient market, the most recent prior period’s equity price should be a crucial variable for explaining the current price in value relevance models. This thesis therefore outlines how the Ohlson (1995) model incorporates the most recent prior period’s price as a potentially important value relevant explanatory variable, and reformulates the Ohlson (1995) model to demonstrate how the empirical specification of value relevance regression models can be greatly improved by including the most recent prior period’s price as an additional explanatory variable. We revisit the Jennings, LeClere, and Thompson (2001) empirical specification used to study whether goodwill amortisation is value relevant and potentially informative with respect to future earnings to illustrate the improvement to the Ohlson (1995) value relevance model empirical specification. When the model specification is improved by including the most recent prior period’s price as an additional explanatory variable, trailing earnings are shown, using time series, cross-sectional, and returns-based analysis, to be at best marginally value relevant when empirically explaining share prices in value relevance regression models. The thesis also indicates that goodwill amortisation should not be deducted from earnings in accounting statements because the presence of goodwill amortisation is significantly positively (not negatively) related to equity prices. This effect is eliminated when the most
Combining NER Systems via a UIMA-based platform

In this paper, we present a tool aiming at merging named entity annotations provided by different named entity recognition sys-tems. This tool is based on UIMA platform and contains a merging module which uses information about the compatibility of vari-ous annotations and can point out conflicts, and thus yields annotations that are more reli-able than those of any single annotator. This work has been performed as part of the In-fom@gic project
Evolution of Human Receptor Binding Affinity of H1N1 Hemagglutinins from 1918 to 2009 Pandemic Influenza A Virus

The recent outbreak of the novel 2009 H1N1 influenza in humans has focused global attention on this virus, which could potentially have introduced a more dangerous pandemic of influenza flu. In the initial step of the viral attachment, hemagglutinin (HA), a viral glycoprotein surface, is responsible for the binding to the human SIA alpha2,6-linked sialopentasaccharide host cell receptor (hHAR). Dynamical and structural properties, based on molecular dynamics simulations of the four different HAs of Spanish 1918 (H1-1918), swine 1930 (H1-1930), seasonal 2005 (H1-2005), and a novel 2009 (H1-2009) H1N1 bound to the hHAR were compared. In all four HA-hHAR complexes, major interactions with the receptor binding were gained from HA residue Y95 and the conserved HA residues of the 130-loop, 190-helix, and 220-loop. However, introduction of the charged HA residues K145 and E227 in the 2009 HA binding pocket was found to increase the HA-hHAR binding efficiency in comparison to the three previously recognized H1N1 strains. Changing of the noncharged HA G225 residue to a negatively charged D225 provides a larger number of hydrogen-bonding interactions. The increase in hydrophilicity of the receptor binding region is apparently an evolution of the current pandemic flu from the 1918 Spanish, 1930 swine, and 2005 seasonal strains. Detailed analysis could help the understanding of how different HAs effectively attach and bind with the hHAR.
Randomized Prospective Double-Blind Studies to Evaluate the Cognitive Effects of Inositol-Stabilized Arginine Silicate in Healthy Physically Active Adults

Inositol-stabilized arginine silicate (ASI; Nitrosigine®) has been validated to increase levels of arginine, silicon and nitric oxide production. To evaluate potential enhancement of mental focus and clarity, ASI (1500 mg/day) was tested in two double-blind placebo-controlled crossover (DBPC-X) studies using the Trail Making Test (TMT, Parts A and B). In the two studies, healthy males took ASI for 14 and 3 days, respectively. In the first study, after 14 days of dosing, TMT B time decreased significantly from baseline (28% improvement, p = 0.045). In the second study evaluating shorter-term effects, TMT B time decreased significantly compared to placebo (33% improvement, p = 0.024) in a 10-min period. After 3 days of dosing, TMT B time significantly decreased from baseline scores (35% improvement, p < 0.001). These findings show that ASI significantly improved the ability to perform complex cognitive tests requiring mental flexibility, processing speed and executive functioning.
An investigation of the factors affecting the adoption of e-commerce amongst UK-based retailers

A multitude of opinions has been propounded to explain how the Internet can be exploited by commercial organisations. For the most part they are speculative, visionary or promotional. This work seeks to redress the balance by reporting on an empirical research project that has recently been conducted into the adoption of the Internet within the UK retail sector. More specifically, the research presented in this paper reports on the application of multi-variate statistical techniques to a large set of questionnaire responses, with a view to critically evaluating the factors that affect the adoption of the Internet. It has been shown that certain factors are far more significant than others, in influencing the adoption decision. In particular, it has been demonstrated that operating in an appropriate market sector and having a positive view of the viability of the Internet are of particular significance. Furthermore, the importance of developing a coherent and integrated Internet strategy, the need for senior management commitment, and the presence of an appropriate infrastructure and development capability are also highlighted. In conclusion, the paper summarises the implications of these findings for the development of retail strategy.
Ultrasensitive, Label Free, Chemiresistive Nanobiosensor Using Multiwalled Carbon Nanotubes Embedded Electrospun SU-8 Nanofibers

This paper reports the synthesis and fabrication of aligned electrospun nanofibers derived out of multiwalled carbon nanotubes (MWCNTs) embedded SU-8 photoresist, which are targeted towards ultrasensitive biosensor applications. The ultrasensitivity (detection in the range of fg/mL) and the specificity of these biosensors were achieved by complementing the inherent advantages of MWCNTs such as high surface to volume ratio and excellent electrical and transduction properties with the ease of surface functionalization of SU-8. The electrospinning process was optimized to precisely align nanofibers in between two electrodes of a copper microelectrode array. MWCNTs not only enhance the conductivity of SU-8 nanofibers but also act as transduction elements. In this paper, MWCNTs were embedded way beyond the percolation threshold and the optimum percentage loading of MWCNTs for maximizing the conductivity of nanofibers was figured out experimentally. As a proof of concept, the detection of myoglobin, an important biomarker for on-set of Acute Myocardial Infection (AMI) has been demonstrated by functionalizing the nanofibers with anti-myoglobin antibodies and carrying out detection using a chemiresistive method. This simple and robust device yielded a detection limit of 6 fg/mL.