Miracle-dz/UniFAR-FTUs huggingface dataset:
In-Heal: Intelligent Healthcare Architecture using AI-based Priority Scheduling Mechanism in vSDN-driven Edge Network

The advancements of Edge Computing, Artificial Intelligence, vSDN (virtualized Software Defined Networking) are significantly boosting up the upcoming smart healthcare systems, but the advanced medical facilities are severely affected by the lack of proper systematic architectures. The chronological up-gradation of the latest technology is not amalgamated according to the existing infrastructure and that is why the fatal cases of medical negligence can not be overlooked, specially during any pandemic or epidemic situation. In this work, we have proposed a novel AI-driven network architecture that will proficiently improve the supervision systems inside smart medical care units. vSDN-driven edge servers are responsible for continuous monitoring of patient inflow and outflow inside any hospital and simultaneously provide the health status of the patients using an AI-based Risk Factor (RF) Scale. An intelligent priority scheduling mechanism is designed to selective filter the health conditions and identify the critical patients with immediate medical assistance. Our analysis shows that the proposed method provides better patient-to-doctor mapping using various available media and significantly reduce the cases of medical negligence inside any medical care facility. This dynamic architecture can be easily up-gradable even for the multi-vendor scenario. Our approach is also efficient enough to control any sudden surge of cases that the entire world has recently witnessed due to COVID-19. We have used random real-time patient flow data to validate our claims.
Engagement and Reasons for Selecting University Studies Using the Clustering Technique

The objective of this work is to discover groupings of university students, characterized by their student engagement, motivation for career selection and academic performance, this through the application of a descriptive data mining technique called low cluster The k-means algorithm. The study was carried out in a university in southern Mexico with students belonging to careers in technology. The UWES-S instrument and a series of questions related to the reasons for choosing university studies were used to measure student engagement. A descriptive data analysis and a heat map were performed, which graphically represents the variables involved and their relationship, then the techniques necessary for the development of clustering were applied. In order to carry out these analyzes, the R language was used. As results, two clusters were found, cluster 1 is identified by students with a high level of student engagement, and the main reason for selecting the career is due to the possibility of work It offers, followed by the academic quality of the career, on the other hand, cluster 2 is characterized by a medium student engagement also by the possibility of work it offers followed by the aptitudes for studies.
Game Dynamics Preferences Are Connected with Experiences Derived from First-Person Shooters

Individual differences in game dynamics preferences may affect the way players react to different types of games. In the present study, 40 inexperienced players played a violent first-person shooter game. The players' preferences for violent game dynamics were scoped before playing. Moreover, the players self-reported their sense of curiosity, vitality and self-efficacy in life in general and during playing. The results show that players who do not like violent game dynamics experience a lower sense of curiosity, vitality and self-efficacy during playing rather than in real life. Instead, there is no evidence for such difference for players who express a neutral or mildly positive preference for violent dynamics, with the exception of a slightly worse sense of self-efficacy in playing versus real life. Those who disliked the dynamics also showed less positive emotion after playing than before playing. Game dynamics preferences also correlated with perceived difficulty of the game and using the gaming pad. The results point out that players who report disliking violent dynamics do not get emotional and motivational benefits from playing and consider playing difficult. For research focusing on games user research, the results indicate game dynamics preferences and their compatibility to the game contents as a relevant measure that may affect players' sense of curiosity, vitality and self-efficacy during playing.
Long-wavelength compact data link modules for 40-300-Mbit/sec applications

Data link components for operation at data rates to 40 Mbit/sec have been in production at AT&T for some years. Recently 50-Mbit/sec components with increased functionality, lower cost, and smaller size were described.1 The data link modules described here extend this family to data rates from 40 to 200 MBit/sec (NRZ).
Fuzzy Logic Based Inverter Controller for Stand-Alone Operation of PV/Battery System

The production of electrical energy using renewable energy sources such as wind and solar has increased due to atmospheric pollution and the shortage of conventional energy resources. This paper focuses on modelling and simulation of photovoltaic system with battery storage for isolated operation. The stand-alone system consists of PV system with MPPT controller, battery storage and inverter with its controller. The entire model has been simulated in MATLAB/Simulink platform. A Fuzzy Logic based inverter controller is implemented to get constant AC voltage and frequency. A control strategy for bidirectional DC-DC converter is necessary to obtain constant DC link voltage in the course of change in supply power or load variations. The performance of PV and battery system for stand-alone operation is verified by considering various operational conditions, such as varying PV power and load scenarios. The simulation results establish the satisfactory working of the system under different operating scenarios.
Time delay compensation in a bilateral teleoperation system

Time delay is a relevant factor in the performance of teleoperation systems. In this work, an experimental 1-DOF testbed has been designed and built. It considers brushless motors as actuators for a master and a slave rotational robots. Architectures torque-velocity and current-velocity are proposed and evaluated in the experimental testbed applying the wave variable approach. Simulations and experimental results shows that is possible represent the proposed architectures with the basic compensators, based on wave variables, which allows the teleoperation system to compensate the time delay effect and maintain stable behavior.
How the Players Get Their Spots: A Study of Playstyle Emergence in Digital Games

How, when, and why do players settle into a particular playstyle when playing a new digital game? Though some aspects of these questions have been addressed in player research (e.g. through player typologies), we are still lacking comprehensive answers that adequately account for the roles of both the player and the game in the manifestation of playstyles. The qualitative study presented here is a middle-ground look into how playstyles emerge when players sit down to play a new digital game. It frames playstyles as an in-game function of the player's ludic habitus - their past experiences, knowledge, and attitudes. The study takes the form of a playtest with ten players, using a custom adventure game/hypertext fiction prototype developed in Twine. The prototype offered two modes of engagement - slower reading of poetic text, and faster-paced exploration and puzzle-solving. The study found that playstyles consolidate at specific moments of discovery (e.g. upon solving an early puzzle), when the player's ludic habitus contextually interprets game design cues and reacts with a player-preferred form of engagement.
A simulation model for the saturable reactor

A simulation model for the saturable reactor, an electromagnetic devices widely used as a protective element for the thyristor circuit, is presented. The model has been simulated using an improved version of SPICE, which also contains the model of the single-core reactor (SCR), and has proved to be fast and accurate on practical cases. Examples given include an industrial chopper for an electric drive; the model of the saturable reactor has improved the overall simulation accuracy of the chopper. >
Self‐centering structural systems with combination of hysteretic and viscous energy dissipations

This paper presents an innovative set of high‐seismic‐resistant structural systems termed Advanced Flag‐Shaped (AFS) systems, where self‐centering elements are used with combinations of various alternative energy dissipation elements (hysteretic, viscous or visco‐elasto‐plastic) in series and/or in parallel. AFS systems is developed using the rationale of combining velocity‐dependent with displacement‐dependent energy dissipation for self‐centering systems, particularly to counteract near‐fault earthquakes. Non‐linear time‐history analyses (NLTHA) on a set of four single‐degree‐of‐freedom (SDOF) systems under a suite of 20 far‐field and 20 near‐fault ground motions are used to compare the seismic performance of AFS systems with the conventional systems. It is shown that AFS systems with a combination in parallel of hysteretic and viscous energy dissipations achieved greater performance in terms of the three performance indices. Furthermore, the use of friction slip in series of viscous energy dissipation is shown to limit the peak response acceleration and induced base‐shear. An extensive parametric analysis is carried out to investigate the influence of two design parameters, λ1 and λ2 on the response of SDOF AFS systems with initial periods ranging from 0.2 to 3.0 s and with various strength levels when subjected to far‐field and near‐fault earthquakes. For the design of self‐centering systems with combined hysteretic and viscous energy dissipation (AFS) systems, λ1 is recommended to be in the range of 0.8–1.6 while λ2 to be between 0.25 and 0.75 to ensure sufficient self‐centering and energy dissipation capacities, respectively. Copyright © 2010 John Wiley & Sons, Ltd.
Feature Selection on MRI Data for Improved Classification of Alzheimer Disease

The analysis of the hippocampus volumetric and cortical thickness measurements of human brain embedded in MRI biomarkers can provide an understanding of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) in neurodegenerative disorders against Normal Controls (NC) class. In this analysis, the hippocampus features of 498 subjects from ADNI data base and 150 subjects from ADNI-1 data base were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) and Ridge Cross Validation models were utilized for feature selection as per its relative relevance for the classification. The selected features from LASSO and Ridge Cross Validation models were utilized for binary classification of AD Vs NC, AD Vs MCI and MCI Vs NC as well as the multiclass AD Vs MCI Vs NC, using various machine learning classifiers for comparison and cross validation of classification accuracy. It is evident from the analysis that, chosen alone features using LASSO and Ridge CV models, depict, consistent and improved classification accuracy as compared to classification accuracy of original features space. This analysis solidifies the method of identification of relevant features as biomarker for detection of AD and MCI subjects through the MRI data for improved diagnosis
Smart vehicle monitoring for detecting anomalies on CAN bus

Modern vehicles are embedded with advanced electric devices known as Electronic Control Unit (ECU). CAN protocol is used in transmitting/receiving messages between the ECUs. CAN bus is a well-known and broadly used protocol in the automotive industry and is evaluated as one of the best standards for vehicular networks. Unfortunately, the CAN bus protocol is vulnerable to frequent attacks due to the lack of a message authentication capability in the protocol. Unprotected CAN communications make the vehicles accessible to a variety of harmful wired/wireless attacks. This makes easier for hackers to break the network and carry out malicious activities. In the proposed system, CAN bus protocol is implemented to monitor the parameters like Vehicle Speed, Seatbelt status, Temperature, humidity, Altitude of the vehicle remotely. This system will be able to overcome the security drawbacks in CAN bus protocol which detects and ignores any irregular or unusual traffic activities such as fuzzy, Denial of Service (DoS), and impersonation attacks using the anomaly detection method.
Graph-based Representation of Audio signals for Sound Event Classification

In recent years there has been a considerable rise in interest towards Graph Representation and Learning techniques, especially in such cases where data has intrinsically a graph-like structure: social networks, molecular lattices, or semantic interactions, just to name a few. In this paper, we propose a novel way to represent an audio signal from its spectrogram by deriving a graph-based representation which can be then employed by already established Graph Deep-Neural-Networks techniques. We evaluate this approach on a Sound Event Classification task by employing the widely used ESC and Urbansound8k datasets and compare it with a Convolutional Neural Network (CNN) based method. We show that such proposed graph-based approach is extremely compact and used in conjunction learned CNN features, allows for a significant increase in classification accuracy over the baseline with more than 50 times less parameters than the original CNN method. This suggests that, the proposed graph-based features can offer additional discriminative information on top of learned CNN features.
Cloud computing for small business: Criminal and security threats and prevention measures

Compared with large organisations, small businesses operate in a distinct and highly resource-constrained operating and technical environment. Their proprietors are often time poor, have minimal bargaining power and have limited financial, technical, legal and personnel resources. It is therefore unsurprising that cloud computing and its promise of smoothing cash flows and dramatically reducing ICT overheads is attractive to small business. Cloud computing shifts the delivery and maintenance of software, databases and storage to the internet, transforming them into Pay-As-You-Go services accessed through a web browser. While providing many benefits, cloud computing also brings many risks for small business, including potential computer security and criminal, regulatory and civil liability issues. This paper, undertaken as a collaborative partnership with the ARC Centre of Excellence in Policing and Security at Griffith University, identifies these risks and offers a perspective on how they might be contained so that the benefits of cloud computing do not outweigh the risks for small businesses in the 21st century.
Path Planning Scheme for Collision Avoidance in Unmanned Aerial Vehicle Traffic Management System

The expectations from Unmanned Aerial Vehicles (UAVs) to contribute in various services are increasing in which group of UAVs will be present in low altitude shared airspace, is controlled and managed by different service providers. In order to provide conflict-free paths for UAVs before its flight operation, techniques like pre-flight conflict detection and resolution (CDR) can be employed. There are many methods and algorithms proposed but an efficient preflight mission planning to avoid collision of paths is proposed in this paper. This can be achieved by automating the process of path planning before the mission using an algorithm designed to avoid collision. We consider the flight path of the ongoing mission and look for an alternate path if they collide.
What You Need to Know About Optical Circuit Reconfigurations in Datacenter Networks

Increasing demand for flexibility in datacenter networks has led researchers to propose various designs of adaptable topologies using optical circuit switching. However, reconfigurations interrupt data transmissions that can diminish the benefit of adapting physical networks. The state-of-the-art lacks detailed models of end-to-end reconfiguration characteristics of involved networking components like end-host NICs and switches. The measurements of five commercially available programmable NICs and switches demonstrate that end-to-end reconfiguration delays indeed exhibit variability across devices and settings. The results suggest, however, that their behavior can be modeled, which opens new opportunities for scheduling algorithms.
Modeling and Analysis of Average Torque Control Strategy on Switched Reluctance Motor for E-mobility

This paper investigates the Average Torque Control (ATC) approach for an electric scooter application using a 250W, Outer-rotor type, 8/6 Switched Reluctance Motor (SRM). It also employs a search algorithm based on a multi-objective function to determine optimum excitation angles to increase SRM drive performance. The MATLAB/Simulink environment is used to model and analyze the SRM drive control system. The simulation results demonstrate the drive system's smooth efficiency with reduced torque ripple over a speed range due to the optimum turn-on and turn-off angle selection. The ATC technique is selected as an ideal alternative for the SRM drive control system against other control strategies due to the wide-speed range application in an electric scooter.
Hierarchical Power Management Model and Analysis in Data Center

Power provision is coming to be the most important constraint to data center development, how to efficiently manage power consumption according to the loads of the data center is urgent. In this paper, we provide the Request-Response Hierarchical Power Management (RRHPM) model for data center, and based on queuing theory, analyse the performance and constraints of two strategies hierarchical structure implement of RRHPM. Numerical results show that the Equal Utilization Strategy has less average response time, can manage more service nodes with the same response time threshold, and require less power management nodes than popular Equal Degree Strategy.
Global Sensitivity Analysis of Variably Saturated Flow and Transport Parameters and Its Implication for Crop Yield and Root Zone Hydrosalinity

AbstractModeling of crop yield and root zone hydrosalinity usually requires a large number of parameters that are often expensive to obtain and have associated measurement errors. Consequently, identifying the most relevant parameters, and their contributions to the uncertainty of the output, might be utilized as a basis to focus research resources efficiently. Global sensitivity analysis (GSA) is a powerful tool that can be utilized to achieve this goal. However, some GSA methods perform better than others, which introduces the risk of mistakenly prioritizing a secondary parameter while neglecting a primary one. This paper evaluates four GSA methods utilized to rank the importance of a total of 18 input parameters with respect to five performance indices using a variable saturated flow and transport model. Some of the major findings of this research are (1) the crop-yield variance is controlled largely by only four of the 18 parameters that were considered in this study; (2) the van Genuchten pore-size p.
Adaptive Perceptual Training in a Virtual Environment

The United States military’s strategic position is evolving, and as a result, the Services are emphasizing the importance of sociocultural pattern recognition, sensemaking in ambiguous urban contexts, and understanding of patterns of life. In fact, military personnel at increasingly lower echelons are expected to possess these nuanced psychosocial perception and decision-making skills. To facilitate training of these complex competencies, the authors are developing a Virtual Observation Platform, an immersive virtual environment designed to adaptively train US Marine Corps personnel in sustained observation, sociocultural pattern recognition, anomaly detection, and other perceptual–cognitive skills. This paper briefly describes the purpose of the system and then covers its adaptive instructional tailoring in detail. The Platform’s adaptive features include information quality/quantity manipulation and instructional scaffolding in the form of communications from a virtual squad (i.e., peers) that are intrinsic to the narrative of the scenario.
Stochastic Modeling to Investigate the Impacts of Wind Power on Thermal Scheduling

This paper proposes the stochastic cost model solution technique for optimal operation of the generators in a wind-thermal scheduling considering the demand and wind power generation uncertainties. The main objective of this paper is to minimize the fuel cost and maximize the profit of generation companies (GENCO’S) with all possible constraints. The cost curve of the generating unit is linearized in a piecewise manner so that a mixed integer programming method can be used to solve the hourly load demand and wind velocity must be forecasted to prevent the errors. The generation scheduling formulations are involved the perspective of a generation company (GENCO). The stochastic model is tested on IEEE-36 unit thermal system in addition with one unit of 50MW wind power system.
Threats and Attacks to Modern Vehicles

As modern vehicles are complex IoT devices with intelligence capable to connect to an external infrastructure and use Vehicle-to-Everything (V2X) communication, there is a need to secure the communication to avoid being a target for cyber-attacks. Also, the organs of the car (sensors, communication, and control) each could have a vulnerability, that leads to accidents or potential deaths. Manufactures of cars have a huge responsibility to secure the safety of their costumers and should not skip the important security research, instead making sure to implement important security measures, which makes your car less likely to be attacked. This paper covers the relevant attacks and threats to modern vehicles and presents a security analysis with potential countermeasures. We discuss the future of modern and autonomous vehicles and conclude that more countermeasures must be taken to create a future and safe concept.
Photogrammetry and 3D laser scanning as spatial data capture techniques for a national craniofacial database

Photogrammetry is a non‐contact, high‐accuracy, practical and cost‐effective technique for a large number of medical applications. Lately, three‐dimensional (3D) laser scanning and digital imaging technology have raised the importance of digital photogrammetry technology to a new height in craniofacial mapping. Under the support of the Eighth Malaysian Development Plan, the Ministry of Science, Technology and the Environment (MOSTE) Malaysia allocated a grant to establish procedures for the development of a national craniofacial spatial database to assist the medical profession to provide better health services to the public. To populate the database with normal and abnormal (malformation, diseased and trauma and burn victims) craniofacial information, it is necessary to evaluate the technology needed to capture the essential data of craniofacial features.