Miracle-dz/UniFAR-FTUs huggingface dataset:
Cuckoo Optimisation based Intrusion Detection System for Cloud Computing

— In the digital era, cloud computing plays a significant role in scalable resource sharing to carry out seamless computing and information sharing. Securing the data, resources, applications and infrastructure of the cloud is a challenging task among the researchers. To secure the cloud, cloud security controls are deployed in the cloud computing environment. The cloud security controls are roughly classified as deterrent controls, preventive controls, detective controls and corrective controls. Among these, detective controls are significantly contributing for cloud security by detecting the possible intrusions to prevent the cloud environment from the possible attacks. This detective control mechanism is established using intrusion detection system (IDS). The detecting accuracy of the IDS greatly depends on the network traffic data that is employed to develop the IDS using machine-learning algorithm. Hence, this paper proposed a cuckoo optimisation-based method to preprocess the network traffic data for improving the detection accuracy of the IDS for cloud security. The performance of the proposed algorithm is compared with the existing algorithms, and it is identified that the proposed algorithm performs better than the other algorithms compared.
Does studying postgraduate palliative care have an impact on student’s ability to effect change in practice?

Objective To find out from alumni whether their postgraduate course in palliative care had an impact on their ability to change practice Setting Palliative & Supportive Services, Flinders University has delivered postgraduate palliative care courses via the online learning mode of delivery since 2004 Subjects An online survey was administered to alumni asking about such issues as: the impact of learning for practice, and their ability to influence change (Flinders University ethics no: 7154). Seventy-six alumni responded to the survey, and were mostly older female nurses, which is not only a reflection of our student cohort but also of clinical practice. Primary argument In this study, we are examining the relevance of our courses to practice, specifically how alumni report the impact of postgraduate study on both their individual clinical practice and organisational systems. Evidence based practice is the cornerstone of nursing and of education programs globally and while our students are learning best practice they report that they cannot easily translate their new knowledge into practice. Conclusion Clinicians with postgraduate qualifications can be empowered to expand their clinical skills and more, for example, their leadership capabilities, to critically challenge health care systems and act as a role model for others. However, if we are to truly build the capacity of our students and alumni to implement changes in the workplace then we need to also engage them in evidence to practice strategies and change management theory and practice.
Learning about the Literacy Development of English Language Learners in Asynchronous Online Discussions

The present study examined asynchronous online discussions in the online course “English Language Development” to identify themes related to teachers and teacher candidates' learning about the language and literacy development of English Language Learners (ELLs) when they facilitated online discussions. Our overall goal was to determine whether teachers and teacher candidates developed sufficient understanding to effectively educate ELLs. Participants were 29 teachers and teacher candidates who were enrolled in the course in the fall semester, 2011 at a large research university in Indiana. The following research questions were addressed: 1) What do candidates identify as key issues for the literacy development of ELLs when they facilitate online discussions? and 2) In what ways did they discuss these key issues in the online environment? The analysis of teachers and teacher candidates' weekly online discussion postings (n = 154) indicated that student-discussion facilitation can impact the levels of teachers and teacher candidates' understanding about the literacy development of ELLs.
Compass: an expert system for telephone switch maintenance

: GTE's Compass (Central Office Maintenance Printout Analysis and Suggestion System) is an expert system that aids in the maintenance of a telephone switching system. It analyzes maintenance printouts of telephone company central office switching equipment and suggests maintenance actions to be performed. The results from several field trials indicate that the current version of Compass performs well in identifying system faults and in suggesting corresponding maintenance actions. An expanded version of the expert system is under development while the present version is being put into field use.
Vision system measuring remote target based on unmanned aerial vehicle

Point to the low precision issue measuring remote target with vision system directly, the characteristics were analyzed. Firstly, the system platform measuring remote target with unmanned aerial vehicle was built. Secondly, the vision system was calibrated with high precision. Finally, the measuring precision of vision measurement was tested by the experiment for the remote target. Then the feasibility measuring remote target with high precision by this method was evaluated and the some measurements were proposed to improve the brought problems in this system.
1-V 9-bit pipelined switched-opamp ADC

A 9-bit 1.0-V pipelined analog-to-digital converter has been designed using the switched-opamp technique. The developed low-voltage circuit blocks are a multiplying analog-to-digital converter (MADC), an improved common-mode feedback circuit for a switched opamp, and a fully differential comparator. The input signal for the converter is brought in using a novel passive interface circuit. The prototype chip, implemented in a 0.5-/spl mu/m CMOS technology, has differential nonlinearity and integral nonlinearity of 0.6 and 0.9 LSB, respectively, and achieves 50.0-dB SNDR at 5-MHz clock rate. As the supply voltage is raised to 1.5 V, the clock frequency can be increased to 14 MHz. The power consumption from a 1.0-V supply is 1.6 mW.
Erstellung von Steuerungssoftware für automatisierte Materialflusssysteme per Drag & Drop

Logistics Journal : Proceedings, Vol. 2017, Iss. 10 - Automatisierte Materialflusssysteme ermoglichen hohe Durchsatze und kurze Durchlaufzeiten, benotigen jedoch eine individuell erstellte Steuerungssoftware, welche heutzutage uberwiegend manuell erstellt wird. In diesem Beitrag wird fur eine modulare Architektur zur Steuerungserstellung eine Basis-Bibliothek und ein Drag & Drop-Vorgehen zur Steuerungserstellung in einer grafischen Entwicklungsumgebung vorgestellt.
Authoring Plan-based Narratives via a Social Network

One way of interacting with an Interactive Storytelling system is via an authoring system prior to plan-based narrative generation. In the search for a user-friendly authoring method for plan-based storytelling domains we have developed a method that captures important narrative aspects such as characters’ relationships as a way of defining a story. This represents a novel form of high-level authoring for plan-based storytelling which fits specific narrative genres: namely, serial dramas (or soap operas) where social relationships between characters act as a determinant for the narrative events that make up different episodes. The approach is implemented in a demonstration system which makes the dependency explicit: using a visual interface users can set social relationships between virtual characters and generate an episode based on that network. Stories are generated at run-time using a plan-based approach that exercises meta-level control over narrative trajectory via the use of pseudo-landmarks. Thus the system provides authors with a visual mechanism for the specification of key story determinants and observation of their impact on generated narratives. The demonstration system is set in the medical drama genre (in the style of serials such as House, ER and Scrubs). During the demo participants are able to interact freely with the system: setting relationships between virtual characters to “author” an episode of the drama in which the relationships they have set lead to peripeteia in the context of medical story lines; and then watching this episode as it is visualised as a 3D animation.
The quality of attractions and the satisfaction, benefits and behavioural intentions of visitors: verification of a model

The quality of attractions and the satisfaction, benefits and behavioural intentions of visitors: verification of a model The study is an attempt to verify a model of the relations between motivation, quality of attraction, benefits, satisfaction and the behavioural intentions of visitors to tourism attractions. The data for the analysis was collected from 582 visitors during the 10th Archaeological Festival at Biskupin. It was noted that the effect of the quality of the attraction on behavioural intentions leads to the benefits gained by the visitors. Benefits have a stronger total effect on behavioural intentions than visitor satisfaction.
A Wetness Detection Technique Towards Scalable, Array-Based, Fully-Textile Sensing

Fully-textile systems capable of sensing multiple health parameters have the potential to enable novel wearables for a more complete picture of health status in humans. Multiple textile based sensing modalities can be incorporated in an array configuration to extract meaningful physiological information. In this paper, we present a wetness sensing technique embedded in textile fabric to detect the presence of fluids on the skin. The sensing is performed on the area between parallel stitched conductive seam-lines. Impedance measurements performed between pairs of conductive yarns in rows or columns are combined to determine wetness level of a selected fabric area. This technique enables an array-based wetness detection scheme to detect both the presence and extent of fluids within the fabric using an “outside-in” approach which also optimizes fluid detection speed. A custom-designed, modular electronic switching system provides array-based impedance analysis capability to scan multiple areas within the fabric. Overall, the techniques presented here provide a new fully-textile sensing approach to expand the sensor toolkit towards achieving multimodal and ubiquitous sensing in wearable form factors.
Modeling Short-Channel Effects in Core–Shell Junctionless MOSFET

In this paper, we propose a model for estimating short-channel effects (SCEs) in the shell-doped double-gate junctionless (JL) MOSFET. The main emphasis of this paper is to estimate SCEs by effectively capturing source/drain (S/D) extensions beyond the gate edges for different values of undoped core thickness (<inline-formula> <tex-math notation="LaTeX">${T}_{\textsf {core}}$ </tex-math></inline-formula>), shell doping (<inline-formula> <tex-math notation="LaTeX">${N}_{\textsf {d}}$ </tex-math></inline-formula>), gate length (<inline-formula> <tex-math notation="LaTeX">${L}_{\textsf {g}}$ </tex-math></inline-formula>), and gate (<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {gs}}$ </tex-math></inline-formula>) and drain (<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {ds}}$ </tex-math></inline-formula>) biases in subthreshold regime. The threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {th}}$ </tex-math></inline-formula>), drain-induced barrier lowering and subthreshold swing (<inline-formula> <tex-math notation="LaTeX">${S}$ </tex-math></inline-formula>), extracted from transfer characteristics, are in good agreement with the simulation results. Results highlight the utility of shell doping and core thickness to provide an additional degree of freedom to control SCEs. The proposed model can be useful in estimating SCEs and optimizing core–shell JL architecture for low-power applications.
Adaptive Actuator Failure Compensation for Possibly Nonminimum-Phase Systems Using Control Separation Based LQ Design

This paper develops an adaptive actuator failure compensation control scheme for discrete-time and possibly nonminimum-phase systems with unknown actuator failures of characterizations that first, some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants, and second, the actuator failures as system disturbances are not matched by other inputs. Such a failure compensation control problem is solved by using a new control separation design based on a new linear quadratic (LQ) control formulation: One control component is used for disturbance rejection and actuator failure compensation, and one component is used for closed-loop stability and regulation, to achieve the desired control objective. The nominal LQ control solution uses a new cost function to derive the two-component control signal, which has improved performance over a traditional LQ control design. The adaptive actuator failure compensation control problem is solved using a stable adaptive law for parameter estimation, to guarantee the system stability and output regulation performances in the presence of plant and actuator failure uncertainties. Simulation results are presented to verify the satisfactory performance of the proposed adaptive LQ control scheme.
A Probabilistic Economic Assessment Approach for Active Power Curtailment of Photovoltaic Based Distributed Generators

Associated problems with high distributed generation (DG) penetration urge the distribution network operator to execute online solutions. One of the most posed solutions is the DG active power curtailment. However, in many cases, the curtailment is performed based on technical considerations only that may badly affect the DG investment. This paper presents a probabilistic economic approach for assessing the power curtailment of photovoltaic (PV) based DGs. This approach aims to determine the maximum allowable active power curtailment from the PV DG while ensuring profitable DG investment. The proposed approach utilizes Monte Carlo simulations (MCS) and T-Copula method for modeling the PV power while considering the temporal stochastic dependence of the solar irradiances. The probabilistic approach considers the taxes, the capital cost allowance, the escalation rate, and the inflation rate to ensure accurate calculation of the DG project cash flow. Moreover, the presented approach takes into account the variations in PV DG feed-in-tariffs from one interval to another. Several test cases are performed; the results and discussions show the importance of the presented study in guiding the distribution operator to take proper decisions.
Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications

We explore the implementation of vehicle fine-grained type and color recognition based on neural networks in a real-world application. We suggest changes to the previously published method with respect to capabilities of low-powered devices, such as Nvidia Jetson. Experimental evaluation shows that the accuracy of MobileNet net slightly decreases compared to ResNet-50 from 89.55% to 86.13% while inference is 2.4× faster on Jetson.
An Improved Method of 3D Scene Reconstruction Based on SfM

The 3D virtual scenes are widely used in different applications, such as visual simulation, augmented reality and virtual reality and so on. For reducing the time consumption with 3D modeling, we propose an improved method of 3D scene reconstruction based on SfM. By taking the video streaming as input, we put forward a feature similarity determination strategy to extract keyframes, and utilize a dense algorithm to improve the model accuracy. Moreover, the method appends 3D model filtering to remove the redundancy of the result models. We demonstrate our method by testing the time cost and effect of different videos and datasets. Results illustrate that our method is practical and available.
Robust Unit Commitment Considering the Temporal and Spatial Correlations of Wind Farms Using a Data-Adaptive Approach

In robust optimization problems, building a proper uncertainty set for the stochastic variables plays an important role. Due to the restricted mathematical formulations of the uncertainty sets, the results derived from conventional two-stage robust optimization are usually over conservative. In this paper, a novel data-adaptive robust optimization method for the unit commitment is proposed for the power system with wind farms integrated. The extreme scenario extraction and the two-stage robust optimization are combined in the proposed method. The data-adaptive set consisting of a few extreme scenarios is derived to reduce the conservativeness by considering the temporal and spatial correlations of multiple wind farms. Numerical results demonstrate that the proposed data-adaptive robust optimization algorithm is less conservative than the current two-stage optimization approaches while maintains the same level of robustness of the solution.
Robust Allocation of Hardening Resources Against Load Redistribution Attacks in Smart Grids

This paper develops a multi-level game-theoretic framework for determining the optimal hardening resource allocation strategy with the consideration of potential load variations in order to protect modern power systems from load redistribution attacks. After the identification of worst-case load redistribution attacks is modelled as a bilevel attackers-operators problem, a minimax-regret-based optimization problem is presented in the multi-level defenders-attackers-operators interactive framework. Such a complex optimization problem is then equivalently converted into a bi-level mixed-integer linear programming problem. Case studies are also conducted for validating the proposed robust hardening strategy in the single-area RTS-96 system.
Vertical Current Transport in AlGaN/GaN HEMTs on Silicon: Experimental Investigation and Analytical Model

We investigate the vertical leakage mechanism in metal–organic chemical vapor deposition-grown carbon (C)-doped AlGaN/GaN High Electron Mobility Transistors (HEMTs) on 6-in silicon wafer. Substrate bias polarity-dependent <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}_{s}$ </tex-math></inline-formula>, temperature-dependent fitting, and band diagram analysis pointed to the Poole–Frenkel (P–F) type of conduction mechanism for vertical transport in the devices with breakdown as high as 580 V for a buffer of <inline-formula> <tex-math notation="LaTeX">$\textsf {4}~\mu \text{m}$ </tex-math></inline-formula>. Trap activation energy of 0.61 eV was estimated from the P–F fitting which matches well with values reported in the literature. We propose that higher dislocation density leads to shallower traps in the buffer and build an analytical model of dislocation-mediated vertical leakage around this. The variation in leakage as a function of dislocation density at a given field is predicted and is found to be the most abrupt in the range from <inline-formula> <tex-math notation="LaTeX">$\sim 10^{\textsf {7}}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$\sim 10^{\textsf {9}}$ </tex-math></inline-formula> cm<inline-formula> <tex-math notation="LaTeX">$^{-\textsf {2}}$ </tex-math></inline-formula> of dislocation density. This can be attributed to a sharp decrease in trap activation energy in the above range of dislocation density, possibly due to complex formation between point defects and dislocations.
Short-term Load Forecasting Using Deep Belief Network with Empirical Mode Decomposition and Local Predictor

This paper proposes a novel load forecast algorithm framework, which combines the Deep Belief Network(DBN) with Empirical Mode Decomposition(EMD) and Local Predictor(LP). Empirical Mode Decomposition(EMD) is applied to decompose the load curve for denoising. DBNs are graphical models which learn to extract a deep hierarchical representation of the training data. LP is used to select samples based on Euclid distance. These selected samples compose the neighbor set which are used to fine tuned the network. Therefore, Deep Belief Network combined with Empirical Mode Decomposition and Local Predictor (EMD-DBNLP) can be applied to forecast the load and provide more accurate and reliable results. The data used for this experiment is system loads from the AEMO on NSW. The performance of EMD-DBNLP is compared with other algorithms, including Least Squares Support Vector Machines(LS-SVMs), Back Propagation-Artificial Neural Network (BP-ANN), traditional Deep Belief Network(DBN) and Deep Belief Network with Empirical Mode Decomposition(EMD-DBN) respectively. The forecast results demonstrate that the proposed algorithm achieves a better performance than others.
Fabrication and Characterization of Capacitive RF MEMS Perforated Switch

In this paper, we have designed, simulated, fabricated, and characterized a clamped-clamped micro mechanical structure-based shunt capacitive RF MEMS switch. The clamped–clamped micromechanical structure is micromachined using a gold metal thickness of 500 nm. AlN is used as a dielectric material, and it is deposited using the dc sputtering PVD process. In the MEMS technology, particularly in devices fabrication, releasing the membrane is a difficult task, and here, we have presented a novel wet process to release the membrane. Primarily, the S1813 sacrificial layer is etched by using the piranha solution and cleaned with the IPA solution. Critical point drying is done after fabrication to reduce the stiction effect on the switch. Overall, the switch requires the pull-in voltage of 5.5 V for 1.8- $\mu \text{m}$ displacement. In the process of optimization, primarily, the switch is designed and simulated using finite-element method tools. The reliability of the capacitive RF MEMS switches depends on the stiction problem caused by dielectric charging, and the proposed capacitive switch dielectric charging behavior is characterized using the CV curve method.
Optimal Target Tracking Based on Dynamic Fingerprint in Indoor Wireless Network

Indoor target tracking is a crucial application of indoor localization systems. Wi-Fi fingerprint-based target tracking/positioning has been extensively studied due to its deployability under pervasive indoor wireless local area networks. However, the majority of fingerprint-based schemes adopted the static radio map and did not make full use of continuous motion information of the target. Therefore, we build a ubiquitous indoor tracking/localization system that can maintain a dynamic fingerprint database at low cost in this paper. First, we propose an optimal target tracking algorithm exploiting the historical data of the target location information to help improve the accuracy, which adopts a modified particle filter algorithm to reduce the number of samples and computing overhead. Second, we present a database self-update method according to trajectory continuity, which is employed to update fingerprint database dynamically for keeping the system robust. Finally, we tested the optimal target tracking based on dynamic fingerprint algorithm (OTTDF) in a complex laboratory area with diverse target motion conditions and various obstacles, our experimental results indicate that the OTTDF scheme successfully handles complex indoor structure, including different target motion state, signal fingerprint changes caused by obstacles, simultaneously provides better performance in localization cost, and localization/tracking accuracy in indoor wireless network.