Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Review And Practice Tests For The Fundamentals Of Engineering Exam

Within the past few years, the World Wide Web has proven to be a tool of great potential both as an instrument for education as well as for technology application. This is evident with the increase in distance learning sites available on the Internet. This paper focuses using the Internet by students to prepare for the Fundamentals in Engineering Review Project. This project implements the computer-based, distance learning technology to help students and engineers in industry to prepare for the Fundamentals in Engineering Exam (FIE) in their quest to obtain the Professional Engineering License. A common need associated with the FIE exam is a cost-effective method to obtain an adequate review package [1]. In addition, insufficient time to prepare for the exam serves as another problem. This paper elaborates on the methods and technologies used to counter these problems. In general, by having the review online users are able to access the review site at any given time. This allows users to progress through the review at their own rate. In addition, the information presented can be constantly updated to keep up with current standards. Furthermore, instant feedback can be obtained from the users and any mistakes present can be corrected instantaneously. The integration of multimedia technology such as graphics, movie animations, audio narration, and simulations in the review also enables the users to review in an interactive and innovative environment.
The Research of CPS System Driven by Blockchain

The CPS system senses and recognizes the physical world through numerous node devices and processes the collected data accordingly, then it can achieve the interconnection of the physical world and the virtual network world. However, under the existing conditions, the CPS system has not yet realized the interconnection between nodes and nodes and the physical world. Block chain technology is a distributed ledger in which nodes can freely enter and exit. This article proposes to combine the block chain with the CPS system to improve the efficiency and security of the CPS system. Through theoretical analysis and experiments, it is proved that the combination of block chain and CPS system can effectively improve the scalability of CPS system and improve the ability of CPS system to communicate with the physical world. Through the combination of the public chain and private chain of the block chain, the production speed of the block is improved, and the actual demand of the CPS system is better satisfied.
Prediction of Uplift Capacity For Shallow Foundations Using Genetic Programming

In most geotechnical problems, it is too difficult to predict soil and structural behavior accurately, because of the large variation in soil parameters and the assumptions of numerical solutions. But recently many geotechnical problems are solved using Artificial Intelligence (AI) techniques, by presenting new solutions or developing existing ones. Genetic Programming, (GP), is one of the most recently developed (AI) techniques based on Genetic Algorithm (GA) technique. In this research, GP technique is utilized to develop prediction criteria for uplift capacity of shallow foundations using collected historical records. The uplift capacity formula is developed using special software written by the authors in "Visual C++" language. The accuracy of the developed formula was also compared with earlier prediction methods.
An Efficient Word Alignment Model for Co-Extracting Opinion Targets and Opinion Words from Online Reviews

Objectives: The main objective of this research is to improve the topical relations by extracting the opinion targets as well as opinion words, and achieve the higher performance using word alignment model concept. Methods: Partially Supervised Word Alignment Model (PSWAM) is used for word alignment in existing system. The Latent Dirichlet Allocation (LDA) model is used for discovering opinion word relation extraction in proposed system. Findings: The proposed method achieves high performance in terms of sensitivity and specificity. Application/Improvements: The proposed system is done by using Latent Dirichlet Allocation (LDA) which is used to increase the performance for number of dataset more efficiently.
Design and development of battery management system (BMS) using hybrid multilevel converter

ABSTRACT Batteries that are integrated to the renewable system have to bear a wide range of operational conditions such as varying rates of charge/discharge, depth of discharges, temperature fluctuations and so on. When these batteries are connected in series, after several charging and discharging cycles, the voltage and state-of-charge (SOC) imbalance will occur between different batteries. In the proposed topology, the hybrid multilevel converter is used along with auxiliary battery to get constant output voltage during discharging and store excess power during charging. As a result, the voltage and SOC get balanced and also overcharging and discharging can be avoided. The multilevel output is used to improve the performance of the load and battery lifetime.
Ant Colony Optimization Based Type-2 Fuzzy Force-Position Control for Backhoe Excavator Robot

This paper proposes the design and simulation of Interval Type-2 Fuzzy Logic Control using MATLAB/Simulink to control the position of the bucket of the backhoe excavator robot during digging operations. In order to reach accurate position responses with minimum overshoot and minimum steady state error, Ant Colony Optimization (ACO) algorithm is used to tune the gains of the position and force parts for the force-position controllers to obtain the best position responses. The joints are actuated by the electro-hydraulic actuators. The force-position control incorporating two-Mamdani type-Proportional-Derivative-Interval Type-2 Fuzzy Logic Controllers for position control and 3-Proportional-Derivative Controllers for force control. The nonlinearity and uncertainty in the model that inherit in the electro hydraulic actuator system are also studied. The nonlinearity includes oil leakage and frictions in the joints. The friction model is represented as a Modified LuGre friction model in actuators. The excavator robot joints are subjected to Coulomb, viscous and stribeck friction. The uncertainty is represented by the variation of bulk modulus. It can be shown from the results that the ACO obtain the best gains of the controllers which enhances the position responses within the range of (19, 23 %) compared with the controllers tuned manually.
Assessment of TRMM data for precipitation measurement in Bangladesh

Application of the Tropical Rainfall Measuring Mission (TRMM) can be an efficient method for precipitation measurement in Bangladesh. The TRMM was a joint US−Japan space programme used to measure tropical rainfall with spatial and temporal distribution. This study analyses the reliability of TRMM data by comparing them with the rain gauge data of Bangladesh Meteorological Department at 35 different locations. The comparison is made for the data from 1998 to 2010. Extensive analyses were performed on the basis of different temporal scales (daily, monthly, seasonal and yearly). Both datasets were compared for average, peak and total values. The correlation co‐efficient and bias of TRMM rainfall data with respect to rain gauge values were also determined. However, the rainfall products are also compared at the two most extreme elevation stations to observe the influence of station topography (if any). Results show that TRMM rainfall data closely match with rain gauge data for average values on an annual basis, and the correlation co‐efficient is found to be greater than 0.90. However, significant deviation is found for daily peak rainfall values. Again, a seasonal comparison shows that the TRMM data outputs match with station data except during the dry season. The rainfall data at the highest and lowest elevations show that the TRMM data follow a similar trend to the station data but they underestimate the value to some extent, especially during the wet season. Finally, it is inferred that the TRMM data are reliable in estimating the average rainfall in Bangladesh for hydrological analyses of watersheds.
Resilience Metrics for the Electric Power System: A Performance-Based Approach.

Grid resilience is a concept related to a power system’s ability to continue operating and delivering power even in the event that low probability, high-consequence disruptions such as hurricanes, earthquakes, and cyber-attacks occur. Grid resilience objectives focus on managing and, ideally, minimizing potential consequences that occur as a result of these disruptions. Currently, no formal grid resilience definitions, metrics, or analysis methods have been universally accepted. This document describes an effort to develop and describe grid resilience metrics and analysis methods. The metrics and methods described herein extend upon the Resilience Analysis Process (RAP) developed by Watson et al. for the 2015 Quadrennial Energy Review. The extension allows for both outputs from system models and for historical data to serve as the basis for creating grid resilience metrics and informing grid resilience planning and response decision-making. This document describes the grid resilience metrics and analysis methods. Demonstration of the metrics and methods is shown through a set of illustrative use cases.
Application of Multilevel Structural Equation Modeling in Testing the Effect of Teacher’s and Student’s Goal Orientation and Help-seeking on 7th Grade Students’ Math Performance

The purpose of the present study was to apply the Multilevel Structural Equations Modeling (MSEM) in order to investigate the effect of teachers’ and student’ goal-orientation as well as help-seeking on the 7th grade students’ math performance. For this purpose, 550 seventh grade learners selected by two-stage cluster-sampling method completed Megli et al.’s (2000) Goal-orientation Questionnaire, as well as Rian and Pontrich's (1997) help-seeking Questionnaire. Also, teachers responded to Butler’s (2007) questions about goal-orientation. Data were analyzed using Multilevel Structural Equations Modeling. The results showed that goal-orientation had a significant direct effect on academic performance of individuals and the whole class, and the effect of help-seeking on their academic performance was significant as well. Findings also showed a positive and significant relationship between Mastery Goals and Mathematical Performance, and a negative and significant relationship between the Goals of Tendency toward Performance and Avoidance of Performance with students' Mathematical Performance. Also, considering the teacher's orientation, it was disclosed that there is a direct positive relationship between the Mastery Goal and the Functionalism with the Educational Mastery Functions, and between the Function Escaping Goal and Work Avoidance with Functional Teaching Methods. Moreover, teachers with Mastering Goals had positive perceptions of helping out when needed, and teachers who had Functional Goals concerned with the rote learning of assignments The fit for Multilevel Structural Equations Model evaluated as fair enough and students' Goal-orientation explained 22% of Performance Variance while this amount was 28 % for Teachers’ Goal-orientation.
A comparative study of CFs, LBP, HOG, SIFT, SURF, and BRIEF techniques for face recognition

In recent years, developing Surveillance Systems (SS) for security has been one of the most active research fields in most applications. These systems used to adjust, enhance, and improve the security. One of these systems, face recognition system plays an efficient and very important tool for several applications despite the existence of different surveillance systems, like hand geometry, iris scan, as well as fingerprints. This is because it is natural, non-intrusive, and inexpensive. For the past two decades, various face recognition methods have been proposed to reduce the amount of calculation and improve the recognition rate. These proposed methods could be categorized into three significant categories: Local feature approaches, Subspace learning approaches, and Correlation filters approaches. In this paper, we discuss and compare some common face recognition algorithms. Our objective of this work is to demonstrate the effectiveness and feasibility of the best methods for face recognition in terms of design, implementation, and application.
Étude de dosimétrie dans des tumeurs chez la souris et chez l’humain, et conception et réalisation d’une interface graphique pour le calcul de la dose absorbée et des interactions des particules

The Monte Carlo (MC) method is widely recognized as an important simulation technique to study the physics of particle interactions in nuclear medicine and radiation therapy. There are different codes dedicated to dosimetry applications and widely used today in research or in clinical application, such as MCNP, EGSnrc and GEANT4. Such codes make the physics easier, but the programming remains a tedious task even for physicists familiar with computer programming. In this paper we report the development of a new interface GEANT4 Dose And Radiation Interactions (G4DARI) based on GEANT4 to simulate absorbed dose and particle tracking in humans, small animals and complex phantoms. The calculation of the absorbed dose is performed based on 3D CT human or animal images in DICOM format, from images of phantoms or from solid volumes which can be made from any pure or composite material to be specified by its molecular formula. G4DARI offers menus to the user and tabs to be filled with values or chemical formulas. We show results obtained in a lung tumor in a digital mouse irradiated with seven energy beams. In conclusion, G4DARI can be easily used by any researcher without the need to be familiar with computer programming, and it will be freely available as an open source.
Learning Person Trajectory Features for Sports Video Analysis

We propose a generic deep model to learn features describing person trajectories. This network uses layers of 1D temporal convolutions over person location inputs. The network can model the patterns of motion exhibited by people when performing different activities. These trajectory features are used in a two-stream deep model that takes as input both visual data and person trajectories for sports video analysis. Our model utilizes one stream to learn the visual temporal dynamics from video clips and the other stream to learn the spacetime dependencies from trajectories. We evaluate our trajectory feature learning model on data from NBA basketball games. We also utilize a dataset from NHL hockey games, which contains broadcast videos and uses state of the art automatic camera calibration, human detection, and tracking algorithms to estimate player positions in world coordinates. Experiments show that person trajectories can provide strong spatio-temporal cues, which improve performance over baselines that do not incorporate trajectory data.
On Testing Safety Properties of synchronous FSM composition

—This paper studies the safety issues of the synchronous composition of Finite State Machines (FSMs). Synchronous FSM composition is widely used when designing and analyzing various aspects in hardware implementation including so-called Trojan subcircuits. When an external input sequence is applied and component FSMs cannot agree on matched internal actions or there are different matching options, the sequence can destruct or block a corresponding device and thus, should be clearly avoided. In this paper, we propose a formal approach for analyzing which external input sequence can induce such situations (if any) and then to derive a finite automaton that represents the set of all possible safe external input sequences.
Spatio - Temporal Context Anomaly Detection for Residential Power Consumption

Monitoring energy consumption and diagnosing abnormal behavior will enable utilities to introduce strategies to improve system resiliency, stability, and to meet energy efficiency targets. The deployment of advanced metering infrastructure (AMI) enables utilities to collect various raw data from its customers and networks. This paper presents contextual anomaly detection algorithm to detect irregular power consumption and visualize anomaly scores using unsupervised learning algorithm and temporal context generated from meter readings. The proposed algorithm computes an anomaly score for each user by considering historical consumption data. The anomaly score for a user is then adjusted by analyzing other contextual variables such as seasonal variation day of the week and other users with the same historical pattern. The implementation of realworld data set provided by power utility company shows a high performance of the proposed algorithm.
LSM Reloaded - Differentiate xVA on your iPad Mini

This article by Brian Huge and Antoine Savine reviews the so called least square methodology (LSM) and its application for the valuation and risk of callable exotics and regulatory value adjustments (xVA). We derive valuation algorithms for xVA, both with or without collateral, that are particularly accurate, efficient and practical. These algorithms are based on a reformulation of xVA, designed by Jesper Andreasen and implemented in Danske Bank's award winning systems, that hasn't been previously published in full. We then investigate the matter of risk sensitivities, in the context of Algorithmic Automated Differentiation (AAD). A rather recent addition to the financial mathematics toolbox, AAD is presently generally acknowledged as a vastly superior alternative to the classical estimation of risk sensitivities through finite differences, and the only practical means for the calculation of the large number of sensitivities in the context of xVA. The theory and implementation of AAD, the related check-pointing techniques, and their application to Monte-Carlo simulations are explained in numerous textbooks and articles, including Giles and Glasserman's pioneering Smoking Adjoints. We expose an extension to LSM, and, in particular, we derive an original algorithm that resolves the matters of memory consumption and efficiency in differentiating simulations together with the LSM step.
MAS-Based Distributed Cooperative Control for DC Microgrid Through Switching Topology Communication Network With Time-Varying Delays

A multiagent system based distributed cooperative control is proposed for a dc microgrid. First, the multiagent system based control scheme is built, where each distributed energy resource unit is associated with a first-level unit control agent to deal with primary control and a secondary-level distributed cooperative control agents to implement secondary control. For the secondary control purposes of global voltage regulation and proportional current sharing, both voltage and current regulators are installed in each secondary-level agent. Their consensus secondary control protocols are designed by using the state information exchanged among only adjacent secondary-level control agents based on a switching topology communication network with time-vary delays. Then these protocols are applied into the associated first-level agent to structure an improved outer-loop droop controller. According to the reference set points corrected by the improved droop controller, an inner-loop voltage/current robust controller is designed in place of the conventional PI control to regulate the voltage and current of distributed energy resource unit. Finally, the validity of the proposed control is testified by means of simulation results.
Framework for embedding physical systems into virtual experiences

Figure 1: System in action We present an immersive Virtual Reality (VR) experience through a combination of technologies including a physical rig; a gamelike experience; and a refined physics model with control. At its heart, the core technology introduces the concept of a physics-based communication that allows force-driven interaction to be shared between the player and game entities in the virtual world. Because the framework is generic and extendable, the application supports a myriad of interaction modes, constrained only by the limitations of the physical rig (see Figure 1). To showcase the technology, we demonstrate a locomoting [11] robot placed in an immersive gamelike setting.
Gamification Science, Its History and Future: Definitions and a Research Agenda

Background. Definitions of gamification tend to vary by person, both in industry and within academia. One particularly popular lay interpretation, introduced and popularized by Ian Bogost, and reiterated by Jan Klabbers, is that gamification is “bullshit” and “exploitationware. ” They describe gamification as a marketing term or business practice invented to sell products rather than to represent a real and unique phenomenon relevant to a nascent game science. However, this view is an oversimplification, one which ignores a growing body of theory development and empirical research on gamification within a post-positivist epistemology. In fact, because gamification is so much more outcome-focused than general game design, current gamification research in many ways has a stronger footing in modern social science than much games research does. Aim. In this article, to address common misunderstandings like these, we describe the philosophical underpinnings of modern gamification research, define the relationship between games and gamification, define and situate gamification science as a subdiscipline of game science, and explicate a six-element framework of major concerns within gamification science: predictor constructs, criterion constructs, mediator constructs, moderator constructs, design processes, and research methods. This framework is also presented diagrammatically as a causal path model. Conclusion. Gamification science refers to the development of theories of gamification design and their empirical evaluation within a post-positivist epistemology. The goal of gamification scientist-practitioners should be to understand how to best meet organizational goals through the design of gamification interventions, drawing upon insights derived from both gamification science and games research more broadly.