Miracle-dz/UniFAR-FTUs huggingface dataset:
Facilitating a Hierarchy of Engagement: Corporate Education in Virtual Worlds

Virtual worlds, where many people can interact simultaneously within the same three-dimensional environment, are productive enabling environments for corporate education. In this chapter, the authors propose a hierarchy of four types of educational engagement, at successively deeper levels of interaction. The authors then show that virtual worlds can be useful platforms for distance corporate education because they can be used to promote engagement at all four levels of the proposed hierarchy. By linking their hierarchy with existing learning theories, they argue that the effectiveness of corporate education can be successfully carried out by using virtual worlds. They also provide an overview of the historical development of virtual worlds, the development of distance education, and a description of technological, institutional, and research challenges needed to be met for distance corporate education to realize its potential. DOI: 10.4018/978-1-61520-619-3.ch011
Learning Context-aware Latent Representations for Context-aware Collaborative Filtering

In this paper, we propose a generic framework to learn context-aware latent representations for context-aware collaborative filtering. Contextual contents are combined via a function to produce the context influence factor, which is then combined with each latent factor to derive latent representations. We instantiate the generic framework using biased Matrix Factorization as the base model. A Stochastic Gradient Descent (SGD) based optimization procedure is developed to fit the model by jointly learning the weight of each context and latent factors. Experiments conducted over three real-world datasets demonstrate that our model significantly outperforms not only the base model but also the representative context-aware recommendation models.
Skeletons and Silhouettes: Comparing User Representations at a Gesture-based Large Display

Mid-air gestures offer a promising way to interact with large public displays. User representations are important to attract people to such displays, convey interactivity and provide meaningful gesture feedback. We evaluated two forms of user representation, an abstract skeleton and a silhouette, at a large public information display. Results from 56 days, with 190 sessions involving 483 detected people, indicate the silhouette attracted more passers-by to interact and, of these, more engaged in serious browsing interactions. By contrast, the skeleton representation had more playful interactions. Our work contributes to the understanding of the implications of these choices of user representation.
BIBO-Stable Recursive Functional Link Polynomial Filters

The preeminent merit of linear or nonlinear recursive filters is their ability to represent unknown systems with far fewer coefficients than their nonrecursive counterparts. Unfortunately, nonlinear recursive filters become, in general, unstable for large amplitudes of the input signal. In this paper, we introduce a novel subclass of the linear-in-the-parameters nonlinear recursive filters whose members are stable according to the bounded-input bounded-output criterion. It is shown that these filters, called stable recursive functional link polynomial filters, are universal approximators for causal, time-invariant, infinite-memory, continuous, nonlinear systems according to the Stone–Weierstrass theorem. To further reduce the filter complexity, simplified structures are also investigated. Even though simplified filters are no more universal approximators, good performance is demonstrated exploiting either data measured on real systems or available in the literature as benchmarks for nonlinear system identification.
Primary User Traffic Estimation for Dynamic Spectrum Access

This paper presents a mathematical analysis of the accuracy of estimating Primary User's (PU's) mean duty cycle u, as well as the mean off- and on-times, where the estimation accuracy is expressed in terms of the Cramer-Rao bound on the mean squared estimation error. For estimating u, we derive the mean squared estimation error for uniform, non-uniform, and weighted sample stream averaging, as well as maximum likelihood (ML) estimation. The estimation accuracy of the mean PU off- and on-times is studied when ML estimation is employed. Besides, the impact of spectrum sensing errors on the estimation accuracy is studied analytically for the averaging estimators, while simulation results are used for the ML estimators. Furthermore, we develop algorithms for the blind estimation of the traffic parameters based on the derived theoretical estimation accuracy expressions.
AUTOMATIC ROAD-SIGN DETECTION AND CLASSIFICATION BASED ON SUPPORT VECTOR MACHINES AND HOG DESCRIPTORS

: This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.
FAULT TOLERANT CONTROL FOR NONLINEAR SYSTEMS DESCRIBED BY TAKAGI-SUGENO MODELS

In this paper the problem of active fault tolerant control (FTC) in noisy systems is studied. The proposed FTC strategy is based on the known of the fault estimate and the error between the faulty system state and a reference system state. A proportional integral observer is used in order to estimate the state and the actuator faults. The obtained results are then extended to nonlinear systems described by nonlinear Takagi-Sugeno models. The problem of conception of the proportional integral observer and the FTC strategy is formulated in linear matrix inequalities (LMI) which can be solved easily. Simulation examples are given to illustrate the proposed method for the linear and nonlinear systems.
Exercising with a humanoid companion is more effective than exercising alone

Engineers developing socially assistive robots for promoting a healthy and active lifestyle are facing several mechanical and interactive design challenges. In this paper we are answering the question whether a humanoid exercising partner capable of working out together with its user makes significant improvement in the user's engagement. We conducted a study with 56 participants doing five isometric exercises in three conditions comparing the differences between working out alone (IC), with an robot instructor (RI) guiding the user, or with a robot companion (RC) exercising co-actively with the user. Our results show that the RC brings a motivational gain to exercise longer compared to all other conditions.
Boosting the Quantile Distribution: A Cost-Sensitive Statistical Learning Procedure

In many metropolitan areas, eorts are being made to count the homeless to ensure a proper provision of social services. Some areas are very large, which makes spatial sampling a viable alternative to an enumeration of the entire terrain. Consequently, counts are manually observed in sampled regions, but they must be imputed in unvisited areas. Along with the imputation process, the costs of underestimating and overestimating may be weighted distinctly depending on one’s perspective and what is at stake. Here, we analyze data from the 20042005 Los Angeles County homeless study using a variant of stochastic gradient boosting that allows for asymmetric costs. Specically, we demonstrate how to boost the quantile distribution, which exhibits a straightforward translation to error estimation costs. Imputed counts and model diagnostics using various cost functions are reported. Practical usage of cost-sensitive imputed estimates are discussed briey.
Overexpression of the pineapple fruit bromelain gene (BAA) in transgenic Chinese cabbage (Brassica rapa) results in enhanced resistance to bacterial soft rot

Bromelain is a crude protein extract obtained from pineapple stems, which comprises a variety of proteolytic enzymes. It exhibits potential therapeutic activities against trauma, inflammation, autoimmune diseases and malignant disorders. In this study, we cloned BAA1 (the gene encoding fruit bromelain) into a plant expression vector that was then used to transform Brassica rapa and overexpress BAA1 under the control of the cauliflower mosaic virus (CaMV) 35S promoter. We demonstrate that constitutive overexpression of BAA1 in B. rapa confers enhanced resistance to the soft rot pathogen Pectobacterium carotovorum ssp. carotovorum. These results suggest that it could be utilized for protecting plants from attack by bacterial pathogens.
Retrieval effectiveness of tagging systems

Social tagging is a widespread activity for indexing user-generated content on Web services. This paper summarizes research on folksonomies and their retrieval effectiveness. A TREC-like retrieval test was conducted with tags and resources from the social bookmarking system delicious, which resulted in recall and precision values for tag-only searches. Moreover, several experimental tag-based databases (i.e., power tags, Luhn-tags) have been tested regarding their retrieval effectiveness. Test results show that folksonomies work best with short queries although recall values are high and precision values are low. Here, a search function “power tags only” greatly enhances precision values.
The introduction of ontology model based on SSO design pattern to the intelligent space for home service robots

In the intelligent space for home service robots, there are many different sensors and actuators, but there is no unite description standard which results in the heterogeneity of devices and hampers the data sharing among them. Fortunately, ontology can integrate data from different sources to eliminate the heterogeneity between the devices. Therefore, we introduce the ontology to the intelligent space and construct the ontology model. Meanwhile, considering the sensors are the symbol devices in the intelligent space, we construct the ontology model based on the SSO design pattern aligned to DUL and integrated with SSN to make the model more universal.
A watermarking system for IP protection by buffer insertion technique

In this paper, we introduce a kind of watermarking system for IP protection (IPP). The copyright is encrypted and then embedded into the design as the watermark in buffer insertion stage. This watermarking technique can identify the design copyright uniquely, and is fit for both ASIC using standard cells and full-custom design. We have evaluated the technique on several testing designs, the inserted results show that the watermarking process achieves 100% success causing little overhead on design performance. Extraction and identification process of the watermark is also analyzed. The watermark embedded is hard to be found out and tampered away, and the technique can be integrated into EDA tools for manufacturing
A Novel Single-stage AC/DC Converter with ZVS Characteristic

A novel single-stage AC/DC converter is proposed in this letter. The converter is designed by integrating a sepic circuit and a flyback circuit, the circuit work in the quasi-resonance state, and the switch is turned on in the zero voltage switching (ZVS) state, which decreased the switching losses of the converter Streszczenie. Opisano nowy konwerter AC/DC. Uklad pracuje w stanie quasi-rezonansu z przelączaniem przy zerowym napieciu. (Nowy konwerter AC/DC z przelączaniem przy zerowym napieciu)
Design of sliding mode controller for linear vernier permanent magnet motor

Urban rail transit has become a convenient way to travel. The linear vernier permanent magnet (LVPM) motor has advantages of low cost and high efficiency, which has a bright future in rail traffic. In this paper, a new controller is designed based on the backstepping method and the sliding mode control (SMC) method, which is applied for the location tracking for LVPM motors. The neural network minimum parameters learning algorithm is adopted to approach the system unknowns. Simulation results show that the backstepping SMC can track the exact location, and improve the robustness and stability.
Negative regulation of myelination: Relevance for development, injury, and demyelinating disease

Dedifferentiation of myelinating Schwann cells is a key feature of nerve injury and demyelinating neuropathies. We review recent evidence that this dedifferentiation depends on activation of specific intracellular signaling molecules that drive the dedifferentiation program. In particular, we discuss the idea that Schwann cells contain negative transcriptional regulators of myelination that functionally complement positive regulators such as Krox‐20, and that myelination is therefore determined by a balance between two opposing transcriptional programs. Negative transcriptional regulators should be expressed prior to myelination, downregulated as myelination starts but reactivated as Schwann cells dedifferentiate following injury. The clearest evidence for a factor that works in this way relates to c‐Jun, while other factors may include Notch, Sox‐2, Pax‐3, Id2, Krox‐24, and Egr‐3. The role of cell–cell signals such as neuregulin‐1 and cytoplasmic signaling pathways such as the extracellular‐related kinase (ERK)1/2 pathway in promoting dedifferentiation of myelinating cells is also discussed. We also review evidence that neurotrophin 3 (NT3), purinergic signaling, and nitric oxide synthase are involved in suppressing myelination. The realization that myelination is subject to negative as well as positive controls contributes significantly to the understanding of Schwann cell plasticity. Negative regulators are likely to have a major role during injury, because they promote the transformation of damaged nerves to an environment that fosters neuronal survival and axonal regrowth. In neuropathies, however, activation of these pathways is likely to be harmful because they may be key contributors to demyelination, a situation which would open new routes for clinical intervention. © 2008 Wiley‐Liss, Inc.
An Accurate Frequency Tracking Method Based on Short Current Detection for Inductive Power Transfer System

Frequency drifting is a common problem in an inductive power transfer (IPT) system. Conventional autonomous oscillation method for maintaining soft switching is challenged due to the drawbacks of feedback delay and disturbance, resonant failure, and requirements of additional start-up circuit. A novel frequency tracking method based on short current detection is proposed for IPT applications. In addition, an instantaneous short current detection method utilizing cheap comparator is proposed. Furthermore, a fast and accurate tracking method is proposed to calculate the frequency mismatch and make a correction. The method can realize accurate frequency correction in several oscillation periods. Furthermore, the method is simple and economic for hardware implementation. Finally, the results of the experiment and comparison results verified the frequency tracking method.
Independent transmission of convergent visceral primary afferents 3 in the solitary tract nucleus 4 5 6 7 8 Abbreviated title : Convergent afferents isolated in NTS

47 Cranial primary afferents from the viscera enter the brain at the solitary tract nucleus (NTS) 48 where their information is integrated for homeostatic reflexes. The organization of sensory 49 inputs is poorly understood despite its critical impact on overall reflex performance 50 characteristics. Single afferents from the solitary tract (ST) branch within NTS and make 51 multiple contacts onto individual neurons. Many neurons receive more than one ST input. To 52 assess the potential interaction between converging afferents and proximal branching near to 53 second order neurons, we probed near the recorded soma in horizontal slices from rats with 54 focal electrodes and minimal shocks. Remote ST-shocks evoked monosynaptic excitatory 55 postsynaptic currents (EPSCs) and nearby focal shocks also activated monosynaptic EPSCs. 56 We tested the timing and order of stimulation to determine whether focal shocks influenced ST 57 responses and vice versa in single neurons. Focal-evoked EPSCs response profiles closely 58 resembled ST-EPSC characteristics. Mean synaptic jitters, failure rates, depression and 59 phenotypic segregation by capsaicin responsiveness were indistinguishable between focal and 60 ST evoked EPSCs. ST-EPSCs failed to affect focal-EPSCs within neurons indicating that 61 release sites and synaptic terminals were functionally independent and isolated from crosstalk 62 or neurotransmitter overflow. In only one instance, focal shocks intercepted and depleted the ST 63 axon generating evoked EPSCs. Despite large numbers of functional contacts, multiple 64 afferents do not appear to interact and ST axon branches may be limited to close to the soma. 65 Thus, single or multiple primary afferents and their presynaptic active release sites act 66 independently when they contact single second order NTS neurons. 67 68 69
The Broadcast Comparison Model for On-Line Fault Diagnosis in Multicomputer Systems

This paper describes a new comparison-based model for distributed fault diagnosis in multicomputer systems with a weak reliable broadcast capability. The classical problems of diagnosability and diagnosis are both considered under this broadcast comparison model. A characterization of diagnosable systems is given, which leads to a polynomial-time diagnosability algorithm. A polynomial-time diagnosis algorithm for t-diagnosable systems is also given. A variation of this algorithm, which allows dynamic fault occurrence and incomplete diagnostic information, has been implemented in the COmmon Spaceborne Multicomputer Operating System (COSMOS). Results produced using a simulator for the JPL MAX multicomputer system running COSMOS show that the algorithm diagnoses all fault situations with low latency and very little overhead. These simulations demonstrate the practicality of the proposed diagnosis model and algorithm for multicomputer systems having weak reliable broadcast. This includes systems with fault-tolerant hardware for broadcast, as well as those where reliable broadcast is implemented in software.
EEG‐Triggered Functional MRI in Patients With Pharmacoresistant Epilepsy

Functional magnetic resonance imaging (fMRI) triggered by scalp electroencephalography (EEG) recordings has become a promising new tool for noninvasive epileptic focus localization. Studies to date have shown that it can be used safely and that highly localized information can be obtained. So far, no reports using comprehensive clinical information and/or long‐term follow‐up after epilepsy surgery in a larger patient group have been given that would allow a valuable judgment of the utility of this technique. Here, the results of 11 patients with EEG‐triggered fMRI exams who also underwent presurgical evaluation of their epilepsy are given. In most patients we were able to record good quality EEG inside the magnet, allowing us to trigger fMRI acquisition by interictal discharges. The fMRI consisted of echoplanar multislice acquisition permitting a large anatomical coverage of the patient's brain. In 8 of the 11 patients the exam confirmed clinical diagnosis, either by the presence (n = 7) or absence (n = 1) of focal signal enhancement. In six patients, intracranial recordings were carried out, and in five of them, the epileptogenic zone as determined by fMRI was confirmed. Limitations were encountered a) when the focus was too close to air cavities; b) if an active epileptogenic focus was absent; and c) if only reduced cooperation with respect to body movements was provided by the patient. We conclude that EEG‐triggered fMRI is a safe and powerful noninvasive tool that improves the diagnostic value of MRI by localizing the epileptic focus precisely. J. Magn. Reson. Imaging 2000;12:177–185. © 2000 Wiley‐Liss, Inc.
A Simple Geometric Model Provides a Possible Quantitative Explanation of the Advantages of Immediate Feedback in Student Learning

Calculus is a known bottleneck for many students studying science and engineering. Various techniques have been developed to enhance the students’ success. A recent study [1] published in the Notices of American Mathematical Society showed that only one factor determines the success of a technique: the presence of immediate feedback. On average, students who receive immediate feedback learn twice faster than students who are taught in a more traditional way, with a serious feedback only once or twice a semester (after a test). The very fact that immediate feedback is helpful is not surprising: it helps the student clear misconceptions and avoid the wrong paths. However, the fact that different techniques involving feedback lead to practically the same learning speed-up is intriguing. To explain this speed-up, we provide a simplified first-order description of a learning process in simple geometric terms. We show that already in this first approximation, the geometric description leads to the observed two-fold speed-up in learning. 1 Immediate Feedback Improves Student Learning: Empirical Data Student understanding is extremely important. One of the main objectives of a course – whether it is calculus or physics or any other course – is to enable students to understand the main concepts of this course. Of course, it is also desirable that the students learn the corresponding methods and algorithms, but understanding is the primary goal. If a student does not remember
Concept Drift Detection Using Online Bayesian Classifier

In data classification the goal is to predict the category of novel instances based on a collection of exemplars whose respective categories are known a priori. The state-of-theart includes various algorithms to solve this problem, including Naive Bayes, Random Forest, Support Vector Machines (SVM), among others. Most of these classifiers consider that the statistical data distribution remains static over time and that one has access to the instances at every momment. In this paper, we focus on the sub-problem known as online classification, where instances arrive sequentially as a flow, and whose stochastic distribution potentially varies as these instances become available. Usually, “traditional” classification mechanisms, as the ones mentioned above, fail to succeed under these circumstances.