Miracle-dz/UniFAR-FTUs huggingface dataset:
A Two-Level MAC Protocol Strategy for Opportunistic Spectrum Access in Cognitive Radio Networks

In this paper, we consider medium access control (MAC) protocol design for random-access cognitive radio (CR) networks. A two-level opportunistic spectrum access strategy is proposed to optimize the system performance of the secondary network and to adequately protect the operation of the primary network. At the first level, secondary users (SUs) maintain a sufficient detection probability to avoid interference with primary users (PUs), and the spectrum sensing time is optimized to control the total traffic rate of the secondary network allowed for random access when the channel is detected to be available. At the second level, two MAC protocols called the slotted cognitive radio ALOHA (CR-ALOHA) and cognitive-radio-based carrier-sensing multiple access (CR-CSMA) are developed to deal with the packet scheduling of the secondary network. We employ normalized throughput and average packet delay as the network metrics and derive closed-form expressions to evaluate the performance of the secondary network for our proposed protocols. Moreover, we use the interference and agility factors as the performance parameters to measure the protection effects on the primary network. For various frame lengths and numbers of SUs, the optimal performance of throughput and delay can be achieved at the same spectrum sensing time, and there also exists a tradeoff between the achievable performance of the secondary network and the effects of protection on the primary network. Simulation results show that the CR-CSMA protocol outperforms the slotted CR-ALOHA protocol and that the PUs' activities have an influence on the performance of SUs for both the slotted CR-ALOHA and CR-CSMA.
Transmit-power reduction for class-1 Bluetooth-enabled indoor cordless phones

Class-1 Bluetooth devices support a transmission range of about 100m and are useful for indoor cordless telephony with advantages of wider coverage, greater user mobility, and more convenience. To minimize the transmit power of class-1 devices, feedback power control specified in the Bluetooth specification can be used. This paper shows that further transmit-power reduction is possible by reducing the Golden Receive Power Range (GRPR) from the specified value of 20dB. For typical indoor lognormal-shadowing channels, more than 4dB reduction in the mean transmit power can usually be obtained by reducing the GRPR to 10dB. However, using a smaller GRPR increases the frequency of making power-adjustment requests through the Link Manager Protocol (LMP), thereby pre-empting more voice packets and affecting the voice quality. We compute the overhead cost due to power control, defined as the percentage of the total number of packets used for power-adjustment requests, when the GRPR is set at 10dB /spl plusmn/ 6dB, wherein 6dB is the tolerance allowed in implementation.
EFFECT OF DESTINATION IMAGE, PERCEIVED QUALITY, PERCEIVED VALUE TO SATISFACTION AND REVISIT INTENTION IN TANJUNG BENOA BALI

Interesting attractions will make tourists come back to visit and create a positive image of the tourist area. This study aimed to analyze the influence of Destination Image, Perceived Quality, Perceived Value on Satisfaction and Revisit Intention on tourism in Tanjung Benoa Bali.This study uses primary data through questionnaires on 150 travelers who've been to Watersport Tanjung Benoa Bali. The sampling technique of this study was non-probability sampling. Processing and testing the model using Structural Equation Modeling (SEM) with the help of analysis of moment structures (AMOS 16). The results of quantitative research shows that there is influence of destination image on perceived quality, perceived value, satisfaction and revisit intention significantly. The influence of perceived quality significantly to perceived value, satisfaction and revisit intention. Note also that there is the effect of perceived value on satisfaction and revisit intention. In this study also found that there is influence significant satisfaction to revisit intention.
Anisotropic reflection of UV radiation at the top of the atmosphere: Characteristics and models obtained from Meteor 3/TOMS

Reflection of solar radiation usually exhibits a strong dependence on viewing geometry relative to the Sun's position. Such a dependence needs taking into account in remote sensing studies employing satellite measurements made with scanning radiometers. While many investigations have been conducted concerning the angular variation of radiation in the total solar, visible, and IR wavelengths, no study was devoted to the UV radiation whose dependence on viewing geometry may differ from radiation in other spectral regions. On the basis of two and half years of Meteor-3/TOMS data (from January 1992 to May 1994) the angular characteristics of UV radiation reflected at the top of the atmosphere (TOA) were studied extensively. A set of angular dependence models (ADMs) was developed for UV radiation at 340, 360, and 380 nm over 10 scene types. The results show that the angular dependence of UV-reflected radiation is strong and rather different from that for total shortwave (SW) radiation as derived for the Earth Radiation Budget Experiment (ERBE). The largest discrepancy occurs under clear conditions over oceans for overhead Sun and large viewing zenith angles. The discrepancy in TOA mean albedos derived using the two sets of ADM can reach up to 48%. The discrepancy diminishes to about 10% for clear land scenes. Comparisons were also made for regional monthly mean TOA albedos over the whole globe derived from the ERBE SW ADM, TOMS UV ADM, and the assumption of isotropic reflection from an independent data set acquired by Nimbus 7/TOMS in January and July 1991. The differences exhibit an apparent zonal trend, large at high latitudes, moderate at midlatitudes, and small in the tropics.
Utility of a Three-Dimensional Interactive Augmented Reality Program for Balance and Mobility Rehabilitation in the Elderly: A Feasibility Study

Objective To improve lower extremity function and balance in elderly persons, we developed a novel, three-dimensional interactive augmented reality system (3D ARS). In this feasibility study, we assessed clinical and kinematic improvements, user participation, and the side effects of our system. Methods Eighteen participants (age, 56-76 years) capable of walking independently and standing on one leg were recruited. The participants received 3D ARS training during 10 sessions (30-minute duration each) for 4 weeks. Berg Balance Scale (BBS) and the Timed Up and Go (TUG) scores were obtained before and after the exercises. Outcome performance variables, including response time and success rate, and kinematic variables, such as hip and knee joint angle, were evaluated after each session. Results Participants exhibited significant clinical improvements in lower extremity balance and mobility following the intervention, as shown by improved BBS and TUG scores (p<0.001). Consistent kinematic improvements in the maximum joint angles of the hip and knee were observed across sessions. Outcome performance variables, such as success rate and response time, improved gradually across sessions, for each exercise. The level of participant interest also increased across sessions (p<0.001). All participants completed the program without experiencing any adverse effects. Conclusion Substantial clinical and kinematic improvements were observed after applying a novel 3D ARS training program, suggesting that this system can enhance lower extremity function and facilitate assessments of lower extremity kinematic capacity.
Effects of GLP-1 on Forearm Vasodilator Function and Glucose Disposal During Hyperinsulinemia in the Metabolic Syndrome

OBJECTIVE Patients with the metabolic syndrome (MetS) have impaired insulin-induced enhancement of vasodilator responses. The incretin hormone glucagon-like peptide 1 (GLP-1), beyond its effects on blood glucose, has beneficial actions on vascular function. This study, therefore, aimed to assess whether GLP-1 affects insulin-stimulated vasodilator reactivity in patients with the MetS. RESEARCH DESIGN AND METHODS Forearm blood flow responses to acetylcholine (ACh) and sodium nitroprusside (SNP) were assessed in MetS patients before and after the addition of GLP-1 to an intra-arterial infusion of saline (n = 5) or insulin (n = 5). The possible involvement of oxidative stress in the vascular effects of GLP-1 in this setting was investigated by infusion of vitamin C (n = 5). The receptor specificity of GLP-1 effect during hyperinsulinemia was assessed by infusing its metabolite GLP-1(9-36) (n = 5). The metabolic actions of GLP-1 were also tested by analyzing forearm glucose disposal during hyperinsulinemia (n = 5). RESULTS In MetS patients, GLP-1 enhanced endothelium-dependent and -independent responses to ACh and SNP, respectively, during hyperinsulinemia (P < 0.001 for both), but not during saline (P > 0.05 for both). No changes in vasodilator reactivity to ACh and SNP were seen after GLP-1 was added to insulin and vitamin C (P > 0.05 for both) and after GLP-1(9-36) was given during hyperinsulinemia (P > 0.05 for both). Also, GLP-1 did not affect forearm glucose extraction and uptake during hyperinsulinemia (P > 0.05 for both). CONCLUSIONS In patients with the MetS, GLP-1 improves insulin-mediated enhancement of endothelium-dependent and -independent vascular reactivity. This effect may be influenced by vascular oxidative stress and is possibly exerted through a receptor-mediated mechanism.
Manifestations of metacognitive activity during the collaborative planning of chemistry practical investigations

ABSTRACT This paper elaborates a process followed to characterise manifestations of cognitive regulation during the collaborative planning of chemistry practical investigations. Metacognitive activity was defined as the demonstration of planning, monitoring, control and evaluation of cognitive activities by students while carrying out the chemistry task. Inherent in collaborative learning is the social aspect of metacognition, which in this study was evidenced in social cognitive regulation (notably of intra- and interpersonal metacognitive regulations) as groups of students went about planning their practical investigations. Discussions of two of the learning groups (n = 4; n = 3) as they planned the extended practical investigation were recorded, transcribed and analysed for indicators of any inherent metacognitive activity. The process of characterising the manifestations of metacognition resulted in the development of a coding system which specifies not only the regulatory strategies at play but the type of regulation (self or other), the area of regulation (cognition, task performance or behaviour) as well as the depth of regulatory contributions (high or low). The fine-grained coding system allowed for a finer theoretical elucidation of the social nature of metacognition. The implications of this study for metacognition and chemistry education research are highlighted.
A Biased k-Random Walk to Find Useful Files in Unstructured Peer-to-Peer Networks

In this paper, we consider a problem of finding "useful" files matching a given query in unstructured P2Ps. The proposed scheme is a variant of k-random walk, which combines a synchronization mechanism proposed by Lv et al. with a mechanism to evaluate the usefulness of discovered files. In addition, we apply a variant of popularity-biased k-random walk to accelerate the file search in normal k-random walk under uniform distribution. The goodness of the scheme is evaluated by simulation. The result of simulations indicates that the proposed biased k-random walk scheme certainly finds useful files in short time, without significantly increasing the number of message transmissions.
User satisfaction with an Internet-based learning medium

The study is one of the very few attempts to investigate antecedents and consequences of satisfaction with an Internet-based learning medium. By extending McKinney et al.'s (2002) web-customer satisfaction model, the research model postulates a positive link between overall satisfaction and intention to continue to use an ILM. Data collected from 504 undergraduate students are examined through the Structural Equation Modeling approach with Partial Least Squares (PLS). Empirical findings demonstrated that our research model provided a relatively high explanatory power. Moreover, all hypothesized associated were found statistically significance. The implications of this study are noteworthy for both researchers and practitioners.
Modeling combined phonological and lexical change of two linguistic communities through social interaction

In this work, we are presenting a simple model of combined linguistic change at the phonological and at the lexical level. We experiment with the model by using agent-based simulation: two groups of agents that speak initially different languages interact through participation in different social activities. It is shown that the lexicon size initially grows and later shrinks as the agents converge to a monolingual configuration. The final language that emerges is determined by the relative percentage of the two populations initially, since this percentage defines the average frequency of interactions. Thus always the dominant language is the one of the majority, in the sense that the words of the final language are closer to or even copied from the language of the majority. Variations in the structure of interaction between the two groups will lead to a linguistic mix. The addition of a new parameter, called intensity of interaction, and expressing differential attention to different speakers, may revert these tendencies and yield languages where the language of a minority may finally dominate, such as in a realistic master-slave scenario where the dominant language is the master’s language independently of relative size of the two populations.
The Role of Fetal Testosterone in the Development of the “ Essential Difference ” Between the Sexes : Some Essential Issues 1

2 Introduction The Empathizing/Systemizing (E/S) hypothesis developed by Baron-Cohen and colleagues has two main goals: first, to explain the presence of brain, cognitive, and behavioral differences between the sexes; and second, to explain the pattern of symptoms associated with autistic syndromes. These two goals are connected, since Baron-Cohen argues that autism is the expression of an " extreme male brain " (e.g. Baron-Cohen 2002). Briefly, the E/S hypothesis proposes that levels of fetal testosterone (fT) influence brain development in such a way that lower levels of fT (more common in females) result in a 'female brain' that is " predominantly hard-wired for empathy " (Baron-Cohen 2003: 1). Empathizing is defined as " the drive to identify another's mental states and to respond to these with an appropriate emotion, in order to predict and to respond to the behavior of another person " (Baron-Cohen, Knickmeyer, and Belmonte 2005: 820). By contrast, higher levels of fT (more common in males) result in a 'male brain' that is " predominantly hard-wired for understanding and building systems " (Baron-Cohen 2003: 1). Systemizing is defined as " the drive to analyze a system in terms of the rules that govern the system, in order 2 to predict the behavior of the system " (Baron-Cohen, Knickmeyer, and Belmonte 2005: 820). The " extreme male brain " of autism thus manifests as poor empathizing abilities twinned with superior systemizing abilities. The social and political implications of the E/S hypothesis are readily apparent. While Baron-Cohen (e.g. Baron-Cohen 2003) is clear that a person of one sex may have the brain of the other (thus a woman may have a 'male' brain, and vice versa), on average the sexes will differ in their hardwired cognitive predispositions. This implies that gender inequalities are not due solely to gender discrimination or socialization, but at least partially to an " essential difference " (Baron-Cohen 2003), on average, between the sexes. (For an explicit argument of this kind with respect to sex ratios in math and physics, see Baron-Cohen 2007: 169.) Indeed, Baron-Cohen (2003: 185) argues that individuals with a 'female brain' are biologically predisposed toward occupations that, currently at least, are performed mostly by women, and thus " make the most wonderful counsellors, primary-school teachers, nurses, carers, therapists, social workers, mediators, group facilitators or personnel staff. " By contrast, those with a 'male brain' supposedly enjoy a hardwired facility for traditionally male …
Domain visualization using VxInsight® for science and technology management

We present the application of our knowledge visualization tool, VxInsight, to enable domain analysis for science and technology management within the enterprise. Data mining from sources of bibliographic information is used to define subsets of information relevant to a technology domain. Relationships between the individual objects (e.g., articles) are identified using citations, descriptive terms, or textual similarities. Objects are then clustered using a force-directed placement algorithm to produce a terrain view of the many thousands of objects. A variety of features that allow exploration and manipulation of the landscapes and that give detail on demand, enable quick and powerful analysis of the resulting landscapes. Examples of domain analyses used in S&T management at Sandia are given.
Unsupervised discovery of linguistic structure including two-level acoustic patterns using three cascaded stages of iterative optimization

Techniques for unsupervised discovery of acoustic patterns are getting increasingly attractive, because huge quantities of speech data are becoming available but manual annotations remain hard to acquire. In this paper, we propose an approach for unsupervised discovery of linguistic structure for the target spoken language given raw speech data. This linguistic structure includes two-level (subword-like and word-like) acoustic patterns, the lexicon of word-like patterns in terms of subword-like patterns and the N-gram language model based on word-like patterns. All patterns, models, and parameters can be automatically learned from the unlabelled speech corpus. This is achieved by an initialization step followed by three cascaded stages for acoustic, linguistic, and lexical iterative optimization. The lexicon of word-like patterns defines allowed consecutive sequence of HMMs for subword-like patterns. In each iteration, model training and decoding produces updated labels from which the lexicon and HMMs can be further updated. In this way, model parameters and decoded labels are respectively optimized in each iteration, and the knowledge about the linguistic structure is learned gradually layer after layer. The proposed approach was tested in preliminary experiments on a corpus of Mandarin broadcast news, including a task of spoken term detection with performance compared to a parallel test using models trained in a supervised way. Results show that the proposed system not only yields reasonable performance on its own, but is also complimentary to existing large vocabulary ASR systems.
Represent and Infer Human Theory of Mind for Human-Robot Interaction

This abstract is proposing a challenging problem: to infer a human’s mental state – intent and belief – from an observed RGBD video for human-robot interaction. The task is to integrate symbolic reasoning, a ﬁeld well-studied within A.I. domains, with the uncertainty native to computer vision strategies. Traditional A.I. strategies for plan inference typically rely on ﬁrst-order logic and closed world assumptions which struggle to take into account the inherent uncertainty of noisy observations within a scene. Computer vision relies on pattern-recognition strategies that have difﬁculty accounting for higher-level reasoning and abstract representation of world knowledge. By combining these two approaches in a principled way under a probabilistic programming framework, we deﬁne new computer vision tasks such as actor intent prediction and belief inference from an observed video sequence. Through inferring a human’s theory of mind, a robotic agent can automatically determine a human’s goals to collaborate with them.
An analytical MOSFET model including gate voltage dependence of channel length modulation parameter for 20nm CMOS

This paper describes an analytical MOSFET model for 20 nm CMOS. The model includes the gate voltage dependence of the channel length modulation parameter. It is found that the channel length modulation parameter extracted from experimental data has remarkable gate voltage dependence in sub 65 nm region. The dependence has been successfully modeled and included in an analytical MOSFET model. This model can predict the current - voltage characteristics with in good accuracy for n-channel and p-channel MOSFETs down to 20 nm.
Cognitive radio technology: Principles and practice

This position paper first briefly reviews the basic concepts about cognitive radio (CR) technology, and then discusses the differentiating features of CR and the fundamental theories or principles behind CR, especially from the artificial intelligence and machine learning perspective. A survey on the state-of-the-art development on CR testbeds around the world is presented, which indicates the trend of future development. A lot of emerging research topics in various areas are also discussed, which could be further investigated in an innovative CR approach.
The research of end-point prediction based on combination model of electric arc furnace

Because the end-point parameters of an electric arc furnace (EAF) are affected by both quantitative factors and non-quantitative factors, we combine Nonlinear Gray Bernoulli-Markov model with Support Vector Machine (SVM) to produce a Nonlinear Gray Bernoulli-Markov-SVM prediction model for estimating the end-point parameter values of an EAF. The effects from the non-quantitative factors on the prediction values of end-point parameters are reflected by Nonlinear Gray Bernoulli-Markov model; while the effects from the quantitative inputs are reflected by the SVM. The Nonlinear Gray Bernoulli model that reflects non-quantitative factors is established firstly, and then, its prediction values are revised by the Markov chain. Because the effect from the quantitative inputs can not be reflected by Nonlinear Gray Bernoulli-Markov model, the Nonlinear Gray Bernoulli-Markov model is certainly not free from prediction errors from the quantitative inputs. These prediction errors are compensated by the SVM model. Further, combining Differential Evolution (DE) with Particle Swarm Optimization (PSO), a Differential Evolution Particle Swarm Optimization (DEPSO) is proposed. The parameter of Nonlinear Gray Bernoulli Model is optimized by the PSO algorithm, while that of SVM are optimized by the DEPSO algorithm. The final prediction values of the end-point parameters in EAF are thus obtained. Simultaneously, a rolling forecasting mechanism is introduced for improving the prediction precision.
Estimating Cross-Sectionally Dependent Panels with Weak Exogeneity

In this paper we propose moment estimators for cross-sectionally dependent panels where the explanatory variables are weakly exogenous. The methods considered allow N-consistent estimation of dynamic panels with general cross-sectional dependence which no existing techniques can handle. The method appears to work well even for T as low as five. We model CSD by a multifactor error-structure. Observable moments then depend on the parameters of interest and nuisance parameters that are themselves transforms of the unobserved factors.