Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel DC excited reluctance generator with segment type rotor core

This paper presents a novel segment type DC excited Reluctance Generator (DCeRG) with segment type rotor core. Conventional proposed DCeRG has simple and robust structure, low cost, and maintenance free for small-scale distributed power sources. In the paper, by using FEM we confirmed that the output power and efficiency of the DCeRG with segment type rotor core are improved in comparison with conventional DCeRG which has salient pole rotor. Furthermore, torque ripple is remarkably reduced by the segment rotor structure.
SMILE: A Similarity-Based Approach for Multiple Instance Learning

Multiple instance learning (MIL) is a generalization of supervised learning which attempts to learn useful information from bags of instances. In MIL, the true labels of the instances in positive bags are not always available for training. This leads to a critical challenge, namely, handling the ambiguity of instance labels in positive bags. To address this issue, this paper proposes a novel MIL method named SMILE (Similarity-based Multiple Instance LEarning). It introduces a similarity weight to each instance in positive bag, which represents the instance similarity towards the positive and negative classes. The instances in positive bags, together with their similarity weights, are thereafter incorporated into the learning phase to build an extended SVM-based predictive classifier. Experiments on three real-world datasets consisting of 12 subsets show that SMILE achieves markedly better classification accuracy than state-of-the-art MIL methods.
Flexible smoothing with B-splines and penalties

B-splines are attractive for nonparametric modelling, but choosing the optimal number and positions of knots is a complex task. Equidistant knots can be used, but their small and discrete number allows only limited control over smoothness and fit. We propose to use a relatively large number of knots and a difference penalty on coefficients of adjacent B-splines. We show connections to the familiar spline penalty on the integral of the squared second derivative. A short overview of $B$-splines, of their construction and of penalized likelihood is presented. We discuss properties of penalized B-splines and propose various criteria for the choice of an optimal penalty parameter. Nonparametric logistic regression, density estimation and scatterplot smoothing are used as examples. Some details of the computations are presented.
Impact of size and decoupling element on some fundamental compact MIMO antennas

The effective bandwidth of fundamental MIMO antennas (ideal MIMO Dipole and ideal MIMO monopole) are studied in this paper. The S11 bandwidth, total efficiency bandwidth and envelope correlation coefficient bandwidth are shown, and effects from antenna sizes, matching and decoupling networks are analyzed. Furthermore, the effective bandwidth of MIMO antennas is defined in this paper as the overlap part of above three bandwidths; it can show a comprehensive performance of the MIMO system.
Making Design Objects Relevant to the Task at Hand

Many problem-solving approaches are based on the assumption that a problem can be precisely defined before it is solved. These approaches are inadequate for dealing with ill-defined problems, which require the coevolution of problem setting and problem solving. In this paper, we describe integrated, domain-oriented, knowledge-based design environments and their underlying multifaceted architecture. The environments empower humans to cope with ill-defined problems, such as design, by supporting an incremental approach to problem setting and problem solving. We focus on the integration of specification, construction, and a catalog of prestored design objects in those environments. The synergy of integration enables the environments to make those objects relevant to the task at hand. Taking architectural design as a domain to illustrate our approach, we describe an operational, prototype system (CATALOGExPLORER) that assists designers in locating examples in the catalog that are relevant to the task at hand as articulated by a partial specification and a partial construction. Users are thereby relieved of the task of forming queries and navigating in information spaces.
Energy Efficient Refrigeration and Flexible Power Consumption in a Smart Grid

Refrigeration and heating systems consume substantial amo unts of energy worldwide. However, due to the thermal capacity there is a potential for st ing “coldness” or heat in the system. This feature allows for implementation of dif erent load shifting and shedding strategies in order to optimize the operation energywise, b ut without compromising the original cooling and indoor climate quality. In this work we investigate the potential of such a strategy and its ability to significantly lower the cost rel at d to operating systems such as supermarket refrigeration and heat pumps for residentia l houses. With modern Economic Model Predictive Control (MPC) methods we make use of weathe r for casts and predictions of varying electricity prices to apply more load to the syste m when the thermodynamic cycle is most efficient, and to consume larger shares of the electri city when the demand and thereby the prices are low. The ability to adjust power consumption a ccording to the demands on the power grid is a highly wanted feature in a future Smart Grid. E fficient utilization of greater amounts of renewable energy calls for solutions to control t he power consumption such that it increases when an energy surplus is available and decreas es when there is a shortage. This should happen almost instantly to accommodate intermitten t energy sources as e.g. wind turbines. We expect our power management solution to render systems with thermal storage capabilities suitable for flexible power consumption. The a ggregation of several units will contribute significantly to the shedding of total electrici y demand. Using small case studies we demonstrate the potential for utilizing daily variation s to deliver a power efficient cooling or heating and for the implementation of Virtual Power Plant s i Smart Grid scenarios.
Web botnet detection based on flow information

Botnets are a combination of cyber attack, infection, and dissemination, and they become one of the most severe threats on the Internet. Cross the Internet, the infected host might launch any kind of attacks such as DDoS (Distributed Denial-of-Service) or Phishing. Comparing with botnets using other command-and-control (C&C) channels, web-based botnets are difficult to detect, because the C&C messages of web botnet are spread over HTTP protocol hiding behind normal flows. Most previous work tackles IRC-based botnet detection, while this study analyzes web botnet behaviors and develops a detection mechanism based on anomaly web flow traffic over an administrative network domain. Web bots exhibit routine and regular web connections which can be used to identify unusual web flow in a network. The experimental results show that the proposed approach can detect web botnets efficiently both in the simulated networks and a real campus network.
TKE-Based Moist Eddy-Diffusivity Mass-Flux (EDMF) Parameterization for Vertical Turbulent Mixing

A new turbulent kinetic energy (TKE)-based moist eddy-diffusivity mass-flux (EDMF) vertical turbulence mixing scheme (EDMF-TKE) is developed, where the nonlocal transport by large turbulent eddies is represented by a mass-flux (MF) scheme while the local transport by small turbulent eddies is represented by an eddy-diffusivity (ED) scheme, which is given by a function of a prognostic TKE. In the scheme, an MF approach is employed for the stratocumulus-top-driven downdrafts as well as for the thermals in the daytime unstable boundary layer. The scheme includes parameterizations for enhanced buoyancy due to moist adiabatic processes in condensation and for TKE interaction with cumulus convection. A scale-aware parameterization is proposed for the grid sizes where the large turbulent eddies are partially resolved. The single-column model (SCM) tests show that both the EDMF-TKE and the current operational NCEP GFS hybrid EDMF scheme (EDMF-CTL) simulate a daytime dry-convective boundary layer well and agree with a benchmark large-eddy simulation (LES). For a marine stratocumulus-topped boundary layer case, the EDMF-TKE better simulates the liquid water and wind speed profiles than the EDMF-CTL compared to the LES. For a stable boundary layer (SBL) case, the EDMF-TKE also agrees better with the LES than the EDMF-CTL, although it tends to produce a deeper SBL compared to the LES. On the other hand, three-dimensional medium-range forecast experiments show that the EDMF-TKE slightly improves forecast skill in the 500-hPa height anomaly correlation and wind vector, while it has a neutral impact on precipitation forecasts over the continental United States.
Towards a Multi-modal Emotion-Awareness e-Learning System

This paper describes an innovative e-Learning project proposal, named MeToo, aiming to enhance existing e-learning platforms by developing tools and services which support the detection and representation of learners' emotions, as well as emotion-based learning adaptation and affective feedback. To this end, MeToo applies novel emotion detection models to rich multimodal data collected using state of the art channels, advanced sensors and novel adaptive interfaces. Via multiple small-scale pilots in formal, informal and workplace learning environments, MeToo system will intend to demonstrate a positive impact of emotion-aware e-learning on decreasing learners drop-out rates, increasing satisfaction and improving learning performance, thus making learning as a whole a better experience. MeToo's approach requires a multi discipline perspective with expertise in e-learning, software development, internet of things, educational psychology, affective sciences, social sciences and neuroscience. To this end, MeToo not only includes experts in the above domains but also specialists with access to primary and secondary schools, university students, corporate learners and open education users. The paper shows the most relevant aspects of the scientific approaches of this project proposal submitted to the Horizon 2020 ICT program of the European Commission.
Text mining for chat message analysis

Instant Messaging (IM) is a peer-to-peer service for remote users to communicate with each others. There are many IM systems such as MSN Messenger and Yahoo Messenger which are used by millions of users everyday. However, IM technology serves as a double edged sword and could be misused for illegitimate information exchange or committing crimes for its anonymity and completely uncontrolled chatting environment. To help enforce legitimate contents communicated in chat environments, IM monitoring systems have been developed for monitoring chat messages. Although most of these systems can provide good monitoring functions, they only provide simple message analysis features such as browsing and simple keyword-based searching of the recorded messages. In this paper, we propose a system, called IMAnalysis, that supports intelligent chat message analysis using text mining techniques. The IMAnalysis system provides functions on chat message retrieval, social network analysis and topic analysis. Chat message retrieval provides general browsing and retrieval support. Social network analysis discovers the social interactions of IM users with their contacts. And topic analysis detects automatically the topics that IM users are involved in.
The role of corporate culture, market orientation and organisational commitment in organisational performance

Purpose – The purpose of this paper is to propose a conceptual framework that explores the relationships among the following constructs: corporate/organisational culture, market orientation, organisational commitment and organisational performance in non-profit organisations. Design/methodology/approach – In line with previous studies in the field, a quantitative research design was adopted. The data collection was performed through a mail survey of a sample of Portuguese non-profit organisations operating in the area of health. Structural equation modelling was used as a means to analyse the hypothesised relationships. Findings – Results have shown that organisational culture impacts on organisational performance. Concerning the market orientation consequences, the study results suggest that higher levels of market orientation result in both high levels of organisational commitment and organisational performance. Results also suggest that organisational commitment does not affect performance in a very si.
Does repetitive task training improve functional activity after stroke? A Cochrane systematic review and meta-analysis.

OBJECTIVE To determine if repetitive task training after stroke improves functional activity. DESIGN Systematic review and meta-analysis of trials comparing repetitive task training with attention control or usual care. DATA SOURCES The Cochrane Stroke Trials Register, electronic databases of published, unpublished and non-English language papers; conference proceedings, reference lists, and trial authors. REVIEW METHODS Included studies were randomized/quasi-randomized trials in adults after stroke where an active motor sequence aiming to improve functional activity was performed repetitively within a single training session. We used Cochrane Collaboration methods, resources, and software. RESULTS We included 14 trials with 17 intervention-control pairs and 659 participants. Results were statistically significant for walking distance (mean difference 54.6, 95% confidence interval (95% CI) 17.5, 91.7); walking speed (standardized mean difference (SMD) 0.29, 95% CI 0.04, 0.53); sit-to-stand (standard effect estimate 0.35, 95% CI 0.13, 0.56), and activities of daily living: SMD 0.29, 95% CI 0.07, 0.51; and of borderline statistical significance for measures of walking ability (SMD 0.25, 95% CI 0.00, 0.51), and global motor function (SMD 0.32, 95% CI -0.01, 0.66). There were no statistically significant differences for hand/arm functional activity, lower limb functional activity scales, or sitting/standing balance/reach. CONCLUSION Repetitive task training resulted in modest improvement across a range of lower limb outcome measures, but not upper limb outcome measures. Training may be sufficient to have a small impact on activities of daily living. Interventions involving elements of repetition and task training are diverse and difficult to classify: the results presented are specific to trials where both elements are clearly present in the intervention, without major confounding by other potential mechanisms of action.
Impact of Cloud Computing Virtualization Strategies on Workloads' Performance

Cloud computing brings significant benefits for service providers and users because of its characteristics: \emph{e.g.}, on demand, pay for use, scalable computing. Virtualization management is a critical task to accomplish effective sharing of physical resources and scalability. Existing research focuses on live Virtual Machine (VM) migration as a workload consolidation strategy. However, the impact of other virtual network configuration strategies, such as optimizing total number of VMs for a given workload, the number of virtual CPUs (vCPUs) per VM, and the memory size of each VM has been less studied. This paper presents specific performance patterns on different workloads for various virtual network configuration strategies. For loosely coupled CPU-intensive workloads, on an 8-CPU machine, with memory size varying from 512MB to 4096MB and vCPUs ranging from 1 to 16 per VM, 1, 2, 4, 8 and 16VMs configurations have similar running time. The prerequisite of this conclusion is that all 8 physical processors are occupied by vCPUs. For tightly coupled CPU-intensive workloads, the total number of VMs, vCPUs per VM, and memory allocated per VM, become critical for performance. We obtained the best performance when the ratio of the total number of vCPUs to processors is 2. Doubling the memory size on each VM, for example from 1024MB to 2048MB, gave us at most 15% improvement of performance when the ratio of total vCPUs to physical processors is 2. This research will help private cloud administrators decide how to configure virtual resources for given workloads to optimize performance. It will also help public cloud providers know where to place VMs and when to consolidate workloads to be able to turn on/off Physical Machines (PMs), thereby saving energy and associated cost. Finally it helps cloud service users decide what kind of and how many VM instances to allocate for a given workload and a given budget.
Unscented Kalman Filter-based adaptive tracking control for wheeled mobile robots in the presence of wheel slipping

A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown longitudinal and lateral slipping. A kinematic model of a tracked wheeled mobile robot (WMR) is established in this paper, in which both longitudinal and lateral slipping is considered and processed as three time-varying parameters. The Unscented Kalman Filter (UKF) with the low pass filter is then introduced for real-time estimation of the slipping parameters online. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov theory. Simulation results show the effectiveness and robustness of the proposed method.
A Study on the Effect of Storytelling Marketing on Brand Image, Perceived Quality, and Purchase Intention in Ecotourism

Stories have been broadly applied to traditional marketing, attempting to arouse consumers’ affection to present empathy on the characters and plots in the stories and change consumer attitudes to achieve the marketing objective. Stories are easily memorized and would appear consonance. Planned storytelling marketing could create affection and infinite business opportunities as well as infinite value of brand to implant in consumers’ mind. From the aspect of storytelling marketing, this study intends to discuss ecotourists’ reaction to stories and understand the effect of emotional appeal to ecotourists by delivering external information to ecotourists. General people are distributed and collected the questionnaire on-site. Total 300 copies of questionnaire are distributed, and 282 valid copies are retrieved, with the retrieval rate 94%. The research results show significant effects of type of storytelling marketing on brand image, purchase intention, and perceived quality. According to the results to propose suggestions, it provides indicative reference for ecotourism businesses applying storytelling marketing to promote brand image and enhance consumers’ perceived quality and purchase intention.
When Pizza Doesn’t Sound as Good as Usual: Restrained Versus Unrestrained Eaters’ Responses to Gluten-Free Menu Items

Increasing demand for gluten-free foods is driving restaurants to add gluten-free menu items. However, it remains unclear how gluten-free labels affect food consumption and overall evaluations of a dining experience. We conducted two studies to fill this gap. Results from Study 1 suggest that unrestrained eaters (i.e., people with low levels of dietary restraint) expect to decrease their food consumption when a menu item bears a gluten-free cue, whereas restrained eaters (i.e., people with high levels of dietary restraint) do not show such an effect. This negative impact of a gluten-free cue on consumption among unrestrained eaters is mediated by expected taste. Results from Study 2 show that when a gluten-free purchase is incentivized with immediate incentives (i.e., price discounts), an increase in dietary restraint leads to a heightened salience of a health goal. Such an effect is attenuated with delayed incentives (i.e., redeemable points). The health goal salience mediates the impact of dietary restraint on anticipated satisfaction with a gluten-free dining experience incentivized with immediate rewards. This research contributes to the hospitality literature and the broader literature on food consumption. Moreover, our findings suggest that managers need to focus on clearly communicating the appealing taste of gluten-free foods and aim at activating a health goal among unrestrained eaters.
SARP: Self Acknowledge Routing protocol for Route maintenance in Mobile Ad Hoc Network

Mobile Ad hoc Networks (MANET) are characterized by multi hop wireless connectivity, infrastructure less environment and frequently changing topology. Wireless links in MANET are highly error prone and can go down frequently due to mobility of nodes. Most reactive routing protocols do not maintain routing information at the node, on link failure typically a route error message is sent to the source and source initiates another route discovery which creates a lot of routing overhead. We present a Self Acknowledge Routing Protocol (SARP) for route maintenance in mobility, where each intermediate node runs a Self Acknowledge (SA) algorithm to recover the broken link to reach the destination efficiently. Main intends of the proto- col is to minimize new route discovery process through route repair and minimize the network overhead. The protocol is implemented over Dynamic Source Routing Protocol (DSR) and the experiment results shows reduction in control overhead and end-to-end delay with an improvement of the packet delivery ratio.
Improving the performance of speech clustering method

Speech clustering is an unsupervised technique that uses the similarity features of acoustic data for creation of clusters. Traditional clustering methods are used in speech clustering and these methods require the knowledge about the number of clusters. Quality of speech clusters are depending on estimation of prior number of clusters. For this reason, this paper addresses the problem of assessment of number of clusters. The proposed method discovers the effective clusters by known number of clusters. Experimental results are conducted for demonstrating the efficiency of proposed methods using real time datasets.
A new secret key agreement scheme in a four-terminal network

A new scenario for simultaneously generating a secret key and two private keys among three Terminals in the presence of an external eavesdropper is considered. Terminals 1, 2 and 3 intend to share a common secret key concealed from the external eavesdropper (Terminal 4) and simultaneously, each of Terminals 1 and 2 intends to share a private key with Terminal 3 while keeping it concealed from each other and from Terminal 4. All four Terminals observe i.i. d. outputs of correlated sources and there is a public channel from Terminal 3 to Terminals 1 and 2. An inner bound of the “secret key-private keys capacity region” is derived and the single letter capacity regions are obtained for some special cases.