Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Economic Dispatch of Hydro-Thermal-Wind-Photovoltaic Power System Considering Mixed Coal-Blending Combustion

This paper proposes a stochastic economic dispatch (SED) model for hydro-thermal-wind-photovoltaic power system (HTWPPS) considering mixed coal-blending combustion (MCBC) to improve the operational efficiencies of thermal power units (TPUs) with deep peak-regulating operation (DPRO). Firstly, the rations for different types of coal can be acquired by meeting the operational requirements of boilers. Three functions including the cost of coal consumption, polluting gas emission, and the life loss cost for TPUs could be obtained by fitting coal quality. Besides, they would be strictly piecewise linearized, respectively. Consequently, the objective function involving the above three kinds of costs as well as the cost of oil injection can be presented. Secondly, there is a two-stage framework for reducing the coupling of HTWPPS and improving the solving efficiency. In the first stage, the Monarch Butterfly Optimization (MBO) algorithm is employed for obtaining the power outputs of hydro-power units (HPUs) taking the minimum water consumption as the objective. In the second stage, the load curve can be updated according to the results from the first stage. Besides, the Latin Hypercube Sampling method and the backward scene reduction technique are utilized to acquire a set of scenarios of wind power. In this way, the SED model considering MCBC can be achieved. The case studies have been carried out on a simplified provincial power system in China with the help of MATPOWER and CPLEX. The results show that the proposed model can effectively increase the economic and environmental benefits for TPUs.
Interference-aware Multi-User, Multi-Polarization Superposition Beamforming (MPS-Beamforming)

Wireless technology has witnessed a massive amount of attention in communication domain, not only in industrial field, but in academia field as well. Multiple-input multiple-output (MIMO) systems among them has emerged itself as a promising future technology. Few of the properties of MIMO include diversity, spatial multiplexing, and beamforming. Exploiting these properties can lead us to improve the data rate. Among these properties lies polarization diversity which has large potential to improve the performance in wireless technologies. This paper contributes in investigating and implementing interference-awareness to multiple users with the help of dual polarized MIMO schemes at ±45º. Various factors such as Cross-polarization Discrimination (XPD), Signal to Interference noise ratio or SINR have been viewed that utilizes multi-polarization superposition beamforming (MPS-Beamforming) to optimize the system design. Simulation results are provided which shows improvement of the performance in terms of the Symbol Error Rate (SER) for the users. The polarization diversity with beamforming has high potential for the next generation wireless technologies.
Object Motion Deblurring in Single Image Under Static Background

When shooting a moving object, as the object moves too fast or the camera’s exposure time is too long, smears may occur in the image, which would result in motion blur. The blind restoration of object motion blur is a challenging inversive problem. To effectively extract useful information from blurred images, this paper proposes a new method to remove motion blur, which is based on the maximum a posterior (MAP) framework. Firstly, the framework combines guided filtering and automatic GrabCut image segmentation algorithm in order to divide the image into different layers. Afterwards, it uses the image gradient to estimate the blur kernel through an alternating iterative optimization strategy. The iteratively reweighted least squares algorithm (IRLS) is used to optimize the solution of the model. Finally, we use the unsharp masking algorithm to improve the high-frequency components of the image and enhance the edge and details of the image. Therefore, the algorithm can effectively remove the blur caused by the motion of the object, suppress the noise and ringing effect, and recover a higher quality clear image, which can be demonstrated on benchmark problems.
Distance Education Gamification in Social Virtual Reality: A Case Study on Student Engagement

Sustaining students’ interest and engagement in semester-long courses in distance learning settings is a tangible challenge in Higher Education. Social Virtual Reality platforms feature richer, spatial affordances in comparison to 2D, webbased synchronous and asynchronous systems. Gamification is a promising method which adds an additional, affective layer to learning in the direction of the enhancement of motivation. In this article the results of an exploratory study on student active engagement applying gamification in a postgraduate distance education course taught at a UK university are presented. Based on the design principles of a gamified course, student experiences and recommendations to practitioners are discussed.
RNA sequencing identifies transcriptional changes in the rabbit larynx in response to low humidity challenge

Background Voice disorders are a worldwide problem impacting human health, particularly for occupational voice users. Avoidance of surface dehydration is commonly prescribed as a protective factor against the development of dysphonia. The available literature inconclusively supports this practice and a biological mechanism for how surface dehydration of the laryngeal tissue affects voice has not been described. In this study, we used an in vivo male New Zealand white rabbit model to elucidate biological changes based on gene expression within the vocal folds from surface dehydration. Surface dehydration was induced by exposure to low humidity air (18.6% + 4.3%) for 8 h. Exposure to moderate humidity (43.0% + 4.3%) served as the control condition. Ilumina-based RNA sequencing was performed and used for transcriptome analysis with validation by RT-qPCR. Results There were 103 statistically significant differentially expressed genes identified through Cuffdiff with 61 genes meeting significance by both false discovery rate and fold change. Functional annotation enrichment and predicted protein interaction mapping showed enrichment of various loci, including cellular stress and inflammatory response, ciliary function, and keratinocyte development. Eight genes were selected for RT-qPCR validation. Matrix metalloproteinase 12 ( MMP12 ) and macrophage cationic peptide 1 ( MCP1 ) were significantly upregulated and an epithelial chloride channel protein ( ECCP) was significantly downregulated after surface dehydration by RNA-Seq and RT-qPCR. Suprabasin ( SPBN ) and zinc activated cationic channel ( ZACN ) were marginally, but non-significantly down- and upregulated as evidenced by RT-qPCR, respectively. Conclusions The data together support the notion that surface dehydration induces physiological changes in the vocal folds and justifies targeted analysis to further explore the underlying biology of compensatory fluid/ion flux and inflammatory mediators in response to airway surface dehydration.
A Trajectory Classification Model Using Grammar Parsing

Trajectory classification is a hot topic in the field of spatiotemporal data mining. Existing models exert spatial or temporal computation on trajectory data, which require huge efforts and are often time consuming and lack of efficiency. This article proposes a model to classify unknown ship trajectories through a syntax recognition approach. By using the background semantic information in the rasterized sea chart, the model transforms the ship trajectories into symbolic sentences containing both spatiotemporal and semantic information, and reduces their scale. The class feature is expressed as a context-free grammar and the data classification is implemented through syntax parsing. The parsing requires less computation and is more efficient. Experiments are carried out to verify the model’s practicability, and the results show that it is valid and effective.
Color Lane Line Detection Using the Bhattacharyya Distance

In this paper, we propose a color lane line detection algorithm that can be used with moving vehicles. First, to reduce false detection and processing time, we considered the geometric relationship of the camera and the vanishing point. To effectively utilize color images for color line detection, we used the edge detection method based on the Bhattacharyya distance and a morphological operation. The proposed method was tested under various conditions (highway, urban, others; sunny, cloudy, early evening) and the experimental results show promising performance.
Two-Way Relaying Non-Orthogonal Multiple Access With Imperfect Successive Interference Cancellation in Power Line Communications

In this article, we study the application of cooperative non-orthogonal multiple access (NOMA) in power line communication (PLC) where two users exchange information using a two-way relay (TWR). Under the assumption of perfect and imperfect successive interference cancellation (SIC), we analyze key performance metrics for PLC log-normal channels impaired by impulsive noise. In particular, for delay-limited (DL) and delay-tolerant (DT) modes, approximate closed-form expressions are derived for the user outage probability and user ergodic rate. The analytic results are verified via Monte Carlo simulations. Compared with the conventional orthogonal multiple access (OMA) scheme, the simulation results show that our proposed NOMA TWR enhances the user outage probability and the user ergodic rate performance in the low signal-to-noise ratio (SNR) region. However, this gain diminishes as the imperfect SIC error increases and is non-existent beyond a certain level of imperfect SIC. Furthermore, the system performance is maximized by optimizing the power allocation coefficients where the system throughput and ergodic sum-rate of the NOMA TWR network in DL and DT operation, respectively, are shown to approach a ceiling in the presence of imperfect SIC. The energy efficiency (EE) of the proposed system is also analyzed for DL and DT modes.
Analysis and Design of Services in Building Units in the context of a Smart City

The economic development planning and implementation of the modern societies shall be carried out simultaneously by both environmental protection and a natural resources exploitation in a sustainable way. Building units could achieve these goals directly through an energy design and their corresponding energy efficiency. Initially, a building is being simulated through a computational model, based on parameters of the methodology established by the Energy Efficiency Regulation of Buildings (E.E.R.B.). Afterwards, the results are being thoroughly checked and evaluated in the frame of its energy efficiency, as well as its financial data. The conclusions can be deduced through the exploitation of Internet of Things - IoT technology by evolving the building and thus, expanding the city into a smart city.
A 10-Ports MIMO Antenna System for 5G Smart-Phone Applications

This paper presents a 10-ports, hybrid multiple-input multiple-output (MIMO) antenna system for 5G Smartphone applications. The proposed antenna system comprises two types of antenna modules: (1) multi-band module consists of two identical multiband antenna elements, each antenna element in this module covers the 2G bands (GSM 850/900/1800/1900 MHz), 3G band (UMTS 2100 MHz) and 4G bands (LTE 2300/2500), and (2) single-band module consists of eight identical L shape elements, each antenna element in this module covers the C-band (3400-3600 MHz) for 5G mobile application. The overall dimensions of the proposed antenna system are $150\times 80\,\,mm^{2}$ . The proposed antenna system is fabricated and tested. Experimental results show reflection coefficients better than -6 dB and -10 dB for multi-band and single-band modules, respectively, with high isolation levels between the antenna elements in both modules. Moreover, the measured envelop correlation coefficients (ECC) is well below 0.3 and 0.1 for the proposed multi-band and single-bands modules, respectively. In addition, single antenna elements in both modules show good radiation characteristics with maximum peak gain between 2 dBi and 4 dBi. Finally, 43 bps/Hz channel capacity is achieved in the single-band module. With these characteristics, the proposed antenna system can be a good candidate in the modern mobile communication systems.
Ground-Fault Circuit Interrupters: A Standards Perspective

Ground-fault circuit interrupters (GFCIs), ESPEcially class A GFCIs, have been instrumental in reducing electrocution. A trinational North American standard governs the testing and certification requirements for class A GFCIs in Canada, the United States, and Mexico. However, the standard governing the requirements for special-purpose GFCIs (SPGFCIs), which include GFCI classes C, D, and E, is defined only in the United States. On the other hand, installation requirements for GFCIs in Canada and the United States are defined in Part I of the Canadian Electrical Code (CEC) and National Electrical Code (NEC), respectively. The certification and installation requirements for GFCIs have evolved through time. This article covers their development. The physiological effect of current, the theory of operation, and various types of GFCIs are explained. Finally, GFCI application issues and the effect of frequency on the operation of GFCIs are discussed.
Trusted Data Notifications from Private Blockchains#

Private blockchain networks are used by enterprises to manage decentralized processes without trusted mediators and without exposing their assets publicly on an open network like Ethereum. Yet external parties that cannot join such networks may have a compelling need to be informed about certain data items on their shared ledgers along with certifications of data authenticity; e.g., a mortgage bank may need to know about the sale of a mortgaged property from a network managing property deeds. These parties are willing to compensate the networks in exchange for privately sharing information with proof of authenticity and authorization for external use. We have devised a novel and cryptographically secure protocol to effect a fair exchange between rational network members and information recipients using a public blockchain and atomic swap techniques. Using our protocol, any member of a private blockchain can atomically reveal private blockchain data with proofs in exchange for a monetary reward to an external party if and only if the external party is a valid recipient. The protocol preserves confidentiality of data for the recipient, and in addition, allows it to mount a challenge if the data turns out to be inauthentic. We also formally analyze the security and privacy of this protocol, which can be used in a wide array of practical scenarios.
Effects of Lighting Variations in Virtual Learning Environments

The use of virtual reality in education is giving researchers new designing opportunities to create learning environments with characteristics that are difficult or even impossible to implement in the real world. There are plenty of educational, cognitive, architectural and neuroscience theories suggesting that certain environmental aspects can enhance the learning process. Such an example is the variation of lighting conditions, in terms of intensity (high or low luminance) and/or temperature (cold or warm), that have been shown to improve memory, attention and executive functions. In this paper, we investigate the differences between a real and a Virtual Learning Environment (VLE) regarding the effects of the lighting conditions on several cognitive functions while we apply, for the first time, certain lighting theories directly into a University level VLE. The first main outcome of our study is that lighting conditions in VLEs affect students’ cognitive functions in a similar way to traditional/physical learning environments, i.e. high luminance enhance memory, attention and executive functions when compared with low luminance. Our results indicate that if we need to test a cognitive or learning hypothesis, we can test it directly into a virtual environment since it gives us similar results/outcomes with those triggered in real one. Moreover, our results demonstrate that exposure to blue (cold) lighting in VLEs increase students’ scores, in the given pseudoword test, in comparison with their exposure to red (warm) lighting.
Non-intrusive Virtual Machine Analysis and Reverse Debugging with SWAT

This paper presents SWAT — System-Wide Analysis Toolkit. It is based on open source emulation and debugging projects and implements the approaches for non-intrusive system-wide analysis and debugging: lightweight OS-agnostic virtual machine introspection, full system execution replay, non-intrusive debugging with WinDbg, and full system reverse debugging. These features are based on novel non-intrusive introspection and reverse debugging methods. They are useful for stealth debugging and analysis of the platforms with custom kernels. SWAT includes multi-platform emulator QEMU with additional instrumentation and debugging features, GUI for convenient QEMU setup and execution, QEMU plugin for non-intrusive introspection, and modified version of GDB. Our toolkit may be useful for the developers of the virtual platforms, emulators, and firmwares/drivers/operating systems. Virtual machine intospection approach does not require loading any guest agents and source code of the OS. Therefore it may be applied to ROM-based guest systems and enables using of record/replay of the system execution. This paper includes the description of SWAT components, analysis methods, and some SWAT use cases.
Prediction of Early Failure in Electrical Cardioversion of Atrial Fibrillation Using Refined Multiscale Entropy

In the management of atrial fibrillation (AF), electrical cardioversion (ECV) is a common treatment. Although its initial success rate is high, many patients present AF recurrence after some weeks or months. Hence, being able to identify patients at low chance of mid-term sinus rhythm maintenance is important for a rationale therapeutic strategy. To this end, several parameters assessing fibrillatory (f-) waves have been introduced, however, with limited predictive ability. Moreover, the cardiovascular system exhibits nonlinear dynamics at different time-scales that these indices do not account for. Hence, the present work evaluates the ability of the multiscale entropy (MSE) analysis of the f-waves to improve preoperative forecasts of ECV outcome. Both traditional MSE and a refined version (RMSE) were applied to the main f waves component obtained for standard lead V1. As a reference, previously proposed predictors were also computed. Results revealed that RMSE was able to anticipate AF recurrence after 1 month of ECV with an accuracy around 78%. Moreover, a Naive Bayes model combining previous parameters and RMSE indices reported a discriminant ability 10% higher than single metrics. It could then be concluded that analysis of nonlinear dynamics at large time-scales can enhance ECV outcome predictions.
Ankle Joint Torque Prediction Based on Surface Electromyographic and Angular Velocity Signals

Joint torque prediction plays an important role in quantitative limb rehabilitation assessment and exoskeleton robot, and it is essential to acquire feedback or feedforward signal for adaptive functional electrical stimulation (FES) control. The Surface electromyography (sEMG) signal is one of the basic processing techniques to detect muscle activity, and also one favorable technique to estimate joint torque. In order to predict joint torque in a wide range of real time convenient applications, it is necessary to fuse sEMG signals with other convenient physical sensors such as accelerometers and gyroscopes, herein, we use a time delay artificial neural network to predict human joint force of ankle eversion and inversion based on sEMG and angular velocity signals. We testify our method on the data recorded from 8 subjects (5 healthy subjects and 3 patients) who are on isokinetic ankle eversion and inversion. The results show that the mean Cross-correlation coefficients ( $\rho$ ) and the mean normalized root-mean-square deviation (NRMSE %) calculated between the prediction and the real value for isokinetic contraction is 0.966± 0.019 and 7.9% ±0.026. Compared with artificial neural network (ANN) and support vector regression (SVR), the proposed method can predict the joint torque effectively. For the future application, the method has the potential to be employed to predict the ankle moments in real-time application for quantitative lower limb rehabilitation assessment and exoskeleton robot control.
RP-based Voluntary Movement Intention Detection of Lower limb using CNN

Exoskeleton robots and brain computer interface (BCI) have a considerable development. But, there is a problem of relatively lower detection accuracy for voluntary movement intention. Deep learning is an effective method to solve it, hence a RP-based voluntary movement intention detection of lower limb using convolution neural network (CNN) is proposed in this paper. Firstly, the mechanism of readiness potential (RP) was analyzed, its property is an important basis for movement intention detection. Then, the lower limb voluntary movement intention detection system based on electroencephalogram (EEG) was established and six healthy subjects participated in this experiment. The different CNN models were established as a classifier. The experimental results show that the window length of EEG classification data was set as 300ms to get a better detection accuracy and time performance. The average detection accuracy of right leg voluntary movement using AlexNet, VGGNet-16 and VGGNet-19 was 72.1%, 83.0% and 84.2% respectively. Specifically, the results suggested that the detection accuracy was also increased with the increase of CNN network layer. This work demonstrates that VGGNet-based CNN model can detect lower limb voluntary movement intention, it lay a foundation for human-computer interaction of lower limb exoskeleton robot.
RTPDroid: Detecting Implicitly Malicious Behaviors Under Runtime Permission Model

In Android 6.0 and above, Install-time Permission Model is replaced with Runtime Permission Model (RPM) where permission requesting is performed at runtime, rather than at install-time, to protect users' privacy. RPM brings certain benefits to security, but still has drawbacks that are exploitable by malware. The permission could be attained under a reasonable context and then be freely used under another context for executing malicious behavior without notifying users. In addition, RPM may cause bugs when developers forget to add permission checking before using the permission. Motivated by these problems, we propose RTPDroid, an approach to the detection of implicitly malicious behaviors and bugs brought by RPM. In this approach, these implicitly malicious behaviors and bugs are defined formally. Then, notions of user-aware contexts as well as user-aware call graphs are utilized for the detection. Experiments on 221 real-world apps reveal 131 bugs and 174 implicitly malicious behaviors under RPM.