Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and implementation of virtual driving system fusing driver's cognitive and operating characteristics

Mental fatigue affects driver's alertness and ability to drive safely, which is easy to cause traffic safety problems. The aim of this paper is to establish a driving simulation system based on virtual-reality technology to offer an environment for continuously monitoring driver's drowsiness level. By using 3DS max software for the modeling of car parts, driving scenes and Virtools for the establishment of automobile's motion system, this system can provide a driving simulation experiment platform so as to collect EEG data and other parameters during the driving process. Then, through studying different classification methods of different fatigue status, the SVM algorithm is chosen for the assessment of mental fatigue. So we could explore the relationship between the collected EEG data and driving operation data. At last, we use the EEG feature vectors derived from CSP algorithm as input data and the level of fatigue obtained by using the driving operation data as the output to train the fatigue detection system and test it with corresponding data. The result shows that combined CSP with Wavelet-packet transform algorithm, the fatigue status classification accuracy is 94.2529% (82/87).
A new hybrid routing protocol in WSNs

In view of the limitations of resources of WSNs, the uncertainty of network topology and self-organization character of WSNs, a new hybrid routing protocol is proposed in this paper. This protocol judge the current state of the node and routing selecting through the performance of residual energy of node. Each node in the network communicates with each other by the way of "best effort to transmit data " and "forced to establish adaptive dynamic cluster head". Simulation results show that this protocol has a high production rate of the cluster head and reliable packet delivery rate, and it can effectively reduce the network loads and network energy consumption.
Simulation, modeling, and performance analysis of IEEE 802.16e OFDMA Systems for Urban and Rural Environments

In this paper we investigate the performance of IEEE 802.16-2005 OFDMA system with high order QAM/QPSK mapping in the presence of high SNR values that are found both in Urban and Rural Environments. We consider OFDMA scheme with an AWGN channel and analyze their performance in terms of the BER in the system.
Identification of human skill based on feedforward / feedback switched dynamical model

To realize the harmonious cooperation with the operator, the man-machine cooperative system must be designed so as to accommodate with the characteristics of the operator's skill. One of the important considerations in the skill analysis is to investigate the switching mechanism underlying the skill dynamics. On the other hand, the combination of the feedforward and feedback schemes has been proved to work successfully in the modeling of human skill. In this paper, a new stochastic switched skill model for the sliding task, wherein a minimum jerk motion and feedback schemes are embedded in the different discrete states, is proposed. Then, the parameter estimation algorithm for the proposed switched skill model is derived. Finally, some advantages and applications of the proposed model are discussed.
Blocking mammalian target of rapamycin (mTOR) improves neuropathic pain evoked by spinal cord injury

Abstract Spinal cord injury (SCI) is an extremely serious type of physical trauma observed in clinics. Neuropathic pain resulting from SCI has a lasting and significant impact on most aspects of daily life. Thus, a better understanding of the molecular pathways responsible for the cause of neuropathic pain observed in SCI is important to develop effective therapeutic agents and treatment strategies. Mammalian target of rapamycin (mTOR) is a serine/threonine protein kinase that is well known for its critical roles in regulating protein synthesis and growth. Furthermore, compelling evidence supports the notion that widespread dysregulation of mTOR and its downstream pathways are involved in neuropathic pain. Thus, in this study we specifically examined the underlying mechanisms by which mTOR and its signaling pathways are involved in SCI-evoked neuropathic pain in a rat model. Overall, we demonstrated that SCI increased the protein expression of p-mTOR, and mTORmediated- phosphorylation of 4E–binding protein 4 (4E-BP1) and p70 ribosomal S6 protein kinase 1 (S6K1) in the superficial dorsal horn of the spinal cord. Also, we showed that blocking spinal mTOR by intrathecal injection of rapamycin significantly inhibited pain responses induced by mechanical and thermal stimulation. In addition, blocking spinal phosphatidylinositide 3-kinase (p-PI3K) pathway significantly attenuated activities of p-mTOR pathways as well as mechanical and thermal hyperalgesia in SCI rats. Moreover, blocking mTOR and PI3K decreased the enhanced levels of substance P and calcitonin gene-related peptide (CGRP) in the dorsal horn of SCI rats. We revealed specific signaling pathways leading to SCI-evoked neuropathic pain, including the activation of PI3K, mTOR and its downstream signaling pathways. Targeting one or more of these signaling molecules may present new opportunities for treatment and management of neuropathic pain often observed in patients with SCI.
2011 6th International Conference on Risks and Security of Internet and Systems (crisis) Performance Improvements for Sha-3 Finalists by Exploiting Microcontroller On-chip Parallelism

—As ubiquitous devices, microcontrollers are deployed in a great variety of applications many of which involve communication over insecure channels that require cryptography. Here we investigate the possibility of using on-chip coprocessors from currently available microcontrollers for increasing computational power by employing parallelism. For this we focus on the analysis of SHA-3 finalists in order to identify features that can lead to an efficient parallel implementation with on-chip coprocessors. Experimental results on a Freescale S12X family microcontroller equipped with an XGATE coprocessor are presented. In this two core environment, speedups between 18 and 73 percents are obtained for the five SHA-3 finalists. In our software implementations BLAKE proves to be the best performer, especially for short messages, followed at some range by Grøstl, then by Skein, Keccak and JH.
A Comparative Analysis of Biomarker Selection Techniques

Feature selection has become the essential step in biomarker discovery from high-dimensional genomics data. It is recognized that different feature selection techniques may result in different set of biomarkers, that is, different groups of genes highly correlated to a given pathological condition, but few direct comparisons exist which quantify these differences in a systematic way. In this paper, we propose a general methodology for comparing the outcomes of different selection techniques in the context of biomarker discovery. The comparison is carried out along two dimensions: (i) measuring the similarity/dissimilarity of selected gene sets; (ii) evaluating the implications of these differences in terms of both predictive performance and stability of selected gene sets. As a case study, we considered three benchmarks deriving from DNA microarray experiments and conducted a comparative analysis among eight selection methods, representatives of different classes of feature selection techniques. Our results show that the proposed approach can provide useful insight about the pattern of agreement of biomarker discovery techniques.
Representation of rules for relevant recommendations to online social networks users

In our prior work, we identified rules for use in recommendation algorithms on Online Social Network (OSN) in order to increase the relevance of content suggested to a user. The resulting recommendation algorithms filter out and prioritize event types for OSN users (such as photo posts by friends, status posts, shared content, etc.), and are thereby intended to reduce information overload. This paper proposes a representation of these rules in a requirements model of a OSN. This is interesting, because recommendation rules influence user behavior, which in turn influences future requirements. If there is a recommendation algorithm, then its behavior should be represented also in requirements models of the system. The paper makes two contributions. We define requirements that OSNs should satisfy in order to produce relevant recommendations of event types to users. We investigate whether an existing requirements modeling language (namely, i-star) can be used to model these requirements.
Performance of trajectory plot for serial concatenation of BCH and Convolutional codes

Nowadays, the use of digital communication has been highly demanding. The advanced communication is needed by people to communicate with each other at anywhere, anytime and any place. Therefore, the better performance of communication systems is very important. Extraordinary efforts have been done by many scientists to achieve better performances of digital communication. Over than 50 years, the researchers have been working hard to develop the state of the art of digital communication. Due to this phenomenon, this paper try to acquire well performance of trajectory using serial concatenation of coding. Here, two kinds of channel coding are applied, namely: BCH and Convolutional codes. Furthermore, there are also decoding process using BCJR algorithm which is implemented in the inner and outer decoding scheme for both codes. In addition, several impacts of interleaved in the EXIT chart are going to present using three types of different length of interleaver. As a result, using quite long of interleaver length, is going to produce well-match the trajectory plot and also the EXIT chart. Lastly, outer codes is well-shown by applying a BCH code for decoding process, while Convolutional code is quite good for inner codes.
Gaussian mixture models for affordance learning using Bayesian Networks

Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.
On Recovering the Duffing System Based on a Modified Gradient Algorithm

The paper presents a strategy for recovering the unknown parameters of the Duffing oscillator using only a measurable output signal. The suggested approach employs the construction of an integral parametrization of an auxiliary output, which is calculated by measuring the difference between the output and its respective time delayed version. First, we estimate the auxiliary output followed by the application of a modified gradient algorithm, then we adjust the gains of the proposed linear estimator in order to obtain convergence of the estimation error to zero. Convergence of the proposed scheme is shown using Lyapunov stability
Neural Analog of Arousal: Persistent Conditional Activation of a Feeding Modulator by Serotonergic Initiators of Locomotion

We investigated how a neural analog of a form of arousal induced by a mildly noxious stimulus can promote two antagonistic responses, locomotion and feeding. Two pairs of cerebral serotonergic interneurons in Aplysia, CC9 and CC10, were persistently activated by transient noxious stimuli. Direct stimulation of CC9–10 activated locomotor activity that outlasted the stimulation and enhanced subsequent nerve-evoked locomotor programs. Thus, CC9–10 function both as initiators and as modulators of the locomotor network. CC9–10 also interacted with the feeding circuit but in a fundamentally different manner. CC9–10 did not directly trigger feeding activity or activate feeding command or pattern generating interneurons. CC9–10 did, however, elicit slow EPSPs in serotonergic cells that modulate feeding responses, the metacerebral cells (MCCs). CC9–10 persistently enhanced MCC excitability, but did not activate the MCCs directly. Previous work has demonstrated that the MCCs are activated during food ingestion via a sensory neuron C2. Interestingly, we found that CC9–10 stimulation converted subthreshold C2 mediated excitation of the MCC into suprathreshold excitation. Transient noxious stimuli also enhanced MCC excitability, and this was largely mediated by CC9–10. To summarize, CC9–10 exert actions on the feeding network, but their functional effects appear to be conditional on the presence of food-related inputs to the MCCs. A potential advantage of this arrangement is that it may prevent conflicting responses from being directly evoked by noxious stimuli while also facilitating the ability of food-related stimuli to generate feeding responses in the aftermath of noxious stimulation.
Identifying the root cause of failures in IT changes: Novel strategies and trade-offs

Despite the Change and Problem Management have received significant attention from the academic community in recent years, the developed solutions do not identify the root cause of failures in IT Changes and, in some cases, only detect software failures. To address this, in this paper, we introduce four strategies to identify root cause of problems based on an interactive approach, in which the Diagnosis System questions a human operator. The strategies introduced and evaluated in this paper are built upon a system we have developed previously, but whose root cause identification was more rudimentary. A case study that uses the improved solution is conducted for the purpose of analyzing the diagnostics generated. Thus, it was possible to compare the diagnostics generated by each strategy, identifying any trends.
Video games production networks: value capture, power relations and embeddedness

This paper has two main aims. Firstly to conceptualize the production networks of the video games industry through an examination of its evolution into a multi-million dollar industry. Secondly, to use the video games industry to demonstrate the utility of Global Production Network approaches to understanding the geographically uneven impacts of globalization processes. In particular, three key notions of value, power and embeddedness are used to reveal the most powerful actors in the production network, how they maintain and exercise their power, and how the organization of production is manipulated as a result. It is argued that while hardware production is organized by console manufacturers using truly global sourcing strategies, the production of software is far more complex. In fact, software production networks are bounded within three major economic regions: Western Europe, North America and Asia Pacific. This paper seeks to explain how and why this has occurred. Copyright 2006, Oxford University Press.
CONSTRAINING THE SCATTER IN THE MASS–RICHNESS RELATION OF maxBCG CLUSTERS WITH WEAK LENSING AND X-RAY DATA

We measure the logarithmic scatter in mass at fixed richness for clusters in the maxBCG cluster catalog, an optically selected cluster sample drawn from Sloan Digital Sky Survey imaging data. Our measurement is achieved by demanding consistency between available weak-lensing and X-ray measurements of the maxBCG clusters, and the X-ray luminosity–mass relation inferred from the 400 days X-ray cluster survey, a flux-limited X-ray cluster survey. We find (95% CL) at N200 ≈ 40, where N200 is the number of red sequence galaxies in a cluster. As a byproduct of our analysis, we also obtain a constraint on the correlation coefficient between ln LX and ln M at fixed richness, which is best expressed as a lower limit, rL,M|N ⩾ 0.85(95% CL). This is the first observational constraint placed on a correlation coefficient involving two different cluster mass tracers. We use our results to produce a state-of-the-art estimate of the halo mass function at z = 0.23—the median redshift of the maxBCG cluster sample—and find that it is consistent with the WMAP5 cosmology. Both the mass function data and its covariance matrix are presented.
A Survey on Dynamic Spectrum Access Techniques in Cognitive Radio Networks

The idea of Cognitive Radio (CR) is to share the spectrum between a user called primary, and a user called secondary. Dynamic Spectrum Access (DSA) is a new spectrum sharing paradigm in cognitive radio that allows secondary users to access the abundant spectrum holes in the licensed spectrum bands. DSA is an auspicious technology to alleviate the spectrum scarcity problem and increase spectrum utilization. While DSA has attracted many research efforts recently, in this paper, a survey of spectrum access techniques using cooperation and competition to solve the problem of spectrum allocation in cognitive radio networks is presented.
Pressure and Cortisol Stress Responses Exploring Heart and Soul : Effects of Religiosity / Spirituality and Gender on Blood

The current study investigated gender effects on the influence of self-reported religiosity and spirituality on cardiovascular and cortisol responses to a laboratory stressor among young adults. Participants with higher composite religiosity/spirituality scores, religiosity, levels of forgiveness and frequency of prayer showed lower cortisol responses. Greater composite religiosity/spirituality, religiosity, frequency of prayer and attendance at services were associated with lower blood pressure in males and elevated blood pressure in females. Findings suggest that spiritual and/or religious individuals may experience a protective effect against the neuroendocrine consequences of stress, though cardiovascular benefits may vary by gender. This work represents an important step in the convergence of multiple realms of research by linking physiological measures with indicators of individual belief systems.
Dynamic Load Balancing Strategy for Grid Computing

Workload and resource management are two essential functions provided at the service level of the grid software infras- tructure. To improve the global throughput of these software environ- ments, workloads have to be evenly scheduled among the available resources. To realize this goal several load balancing strategies and algorithms have been proposed. Most strategies were developed in mind, assuming homogeneous set of sites linked with homogeneous and fast networks. However for computational grids we must address main new issues, namely: heterogeneity, scalability and adaptability. In this paper, we propose a layered algorithm which achieve dynamic load balancing in grid computing. Based on a tree model, our algorithm presents the following main features: (i) it is layered; (ii) it supports heterogeneity and scalability; and, (iii) it is totally independent from any physical architecture of a grid.
Multiagent Technology Solutions for Planning in Ambient Intelligence

The coordination of devices during the execution of services in AmI systems is of paramount importance. Effective solutions are based on planning techniques and multiagent technologies. Literature focuses on the analysis of the most appropriate planner and leaves open the technological issues. In this paper we show the limitations of the multiagent state-of-the-art technologies and we design a multiagent system for planning in AmI applications.
Discussion on Microwave-Matter Interaction Mechanisms by In Situ Observation of “Core-Shell” Microstructure during Microwave Sintering

This research aims to deepen the understanding of the interaction mechanisms between microwave and matter in a metal-ceramic system based on in situ synchrotron radiation computed tomography. A special internal “core-shell” microstructure was discovered for the first time and used as an indicator for the interaction mechanisms between microwave and matter. Firstly, it was proved that the microwave magnetic field acted on metal particles by way of inducing an eddy current in the surface of the metal particles, which led to the formation of a “core-shell” microstructure in the metal particles. On this basis, it was proposed that the ceramic particles could change the microwave field and open a way for the microwave, thereby leading to selective heating in the region around the ceramic particles, which was verified by the fact that all the “core-shell” microstructure was located around ceramic particles. Furthermore, it was indicated that the ceramic particles would gather the microwaves, and might lead to local heating in the metal-ceramic contact region. The focusing of the microwave was proved by the quantitative analysis of the evolution rate of the “core-shell” microstructure in a different region. This study will help to reveal the microwave-matter interaction mechanisms during microwave sintering.
Vehicle Image Classification Method Using Edge Dimensions , SVM and Prototype Fabrízia

Automatic vehicle classification is an important task in Intelligent Transport System because it allows the attainment of the traffic parameter called vehicles count by category. Proposed methods of vehicle image classification, obtained from videos of roads traffic, have known limitations, such as strong dependence of detection methods, hard image normalization and low accuracy. This paper presents a vehicle classification method based on edge dimensions of the vehicle image. The method consists of three stages: image preprocessing, training and classification. In the pre-processing stage, the removal of horizontal bands over vehicle edge image is applied. The used features are edge dimensions, such as width and height, and as to the used classification rules there are prototype and SVM. The objectives of the method are acceptable accuracy and less dependence of detection methods. The classification results of distance to the prototype and SVM are compared with NN.
A Security Evaluation Framework Based on STRIDE Model for Software in Networks

Software in networks, which is a special kind of applications in service-oriented computing and ultra-large-scale systems, is a complex software system deploying on network environment. Requirements of networked software pose many security problems owing to the dynamic topology structure and users’ uncertainty. How to evaluate the degree of software security in networks is a challenging problem. In this paper, we present a framework for flexible assessing software to determine how well it can satisfy intended security requirements. On the basis of analyzing the threats which software in network facing, a security evaluation method based on STRIDE model is proposed. According to its own features of networked software and threat classification method of STRIDE model, we design a SN-Security Evaluation Model, in which the dependability-based, vulnerability-based and risk-based approaches are incorporated for the software security estimation. It provides a valuable way to help users to create the threat modeling and evaluating the safety degree for software security. A case study is conducted to verify the framework proposed in the paper.