Miracle-dz/UniFAR-FTUs huggingface dataset:
Building a Physical Model to Teach Creative Problem-solving Skills in Online and Live Courses

Many faculty, especially new faculty, sometimes feel overwhelmed by having to teach both online and live courses while simultaneously pursuing scholarship and service activities. An effective way for faculty to improve their teaching efficiency is by creating assignments that can be taught in both online and face-to-face classes. An assignment the author has developed involves teaching creative problem solving skills for both online and face-to-face courses, by having students build a physical model of a problem and solution. In today’s competitive work environment, faculty need to provide students with the creative thinking skills that can help prepare them to deal with complex and unstructured problems that they will encounter in the workplace. At the same time, while developing their courses, sometimes faculty lose sight of the possible application of assignments for multiple modes of delivery. Faculty need to recognize the opportunities to create assignments and structure them for both online and face-to-face class delivery. In the paper, the author will describe the major components of the Creative Problem Solving Assignment. The paper will identify the benefits derived from using this assignment to develop creative thinking skills. In the paper, the author will assess the assignment on promoting creative problem solving skills using a pretest/posttest and an assignment evaluation. Also, the author will provide ideas for technology and engineering faculty on how they can develop assignments for both online and face-to-face course delivery. Finally, the author will compare the evaluation results of the assignment between his online and face-to-face classes. Key Terms: Brief, Creative Problem Solving Assignment (CPS Assignment), imagination, online and face-to-face course delivery, physical model, visualization, whole brain thinking.
Opinion Mining on Book Review Using Convolutional Neural Network Algorithm — Long Short Term Memory

The quality of a product can be determined by consumer reviews of the product because consumer opinion is in general honest and sentimental to the product. The process of opinion extraction is called opinion mining. This process is done to determine the tendency of the reviewer to the reviewed object. Deep Learning is a recently developed opinion extraction model. This model is widely used for achieving performance in Natural Language Processing. This work classifies the book review on Amazon.com into positive reviews or negative reviews using a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) algorithms. Simulation results show that the performance of this algorithm is approximately 65.03% for testing data and 99.55% for training data.
Enhancing user access information with spatial data

Abstract Enforcing proper access control is essential for ensuring the security of sensitive resources in an organization. Access control models like the Role-Based Access Control (RBAC) model, its various temporal, spatial as well as spatial and temporal extensions have been proposed for achieving this objective. Though several algorithms have been presented by researchers for implementing RBAC and temporal RBAC, to the best of our knowledge, no attempts have been made so far for deploying a spatial or spatio-temporal RBAC model in terms of either creating suitable datasets or algorithms. For doing this, we require user-permission assignment information containing location constraints that need to be subjected to role mining approaches to derive the spatially constrained roles. In this paper, we focus on bridging this gap by presenting a suitable representation for spatially constrained access information. We name it as Spatial User-Permission Assignment (SUPA) matrix. We also propose an approach for generating SUPAs which can serve as inputs for the spatially constrained role creation procedures. We name our proposed method as Spatial Access Dataset Enumeration (SPADE). SPADE can be easily utilized to generate datasets of varied sizes which can be used for the performance evaluation of different role creation algorithms. As experimental results, we report the structure of sample SUPAs generated using SPADE.
Synchrophasor-Based Dominant Electromechanical Oscillation Modes Extraction Using OpDMD Considering Measurement Noise

A synchrophasor-based approach for security status monitoring is an effective method of assessing power system stability without models, but the accuracy of this method is greatly affected by noise at the same time. This paper proposes an optimized dynamic mode decomposition algorithm to extract electromechanical oscillation modes from the noise-containing data of wide-area measurements. This algorithm effectively improves the ability of the dynamic mode decomposition technique to resist noise in the observed data by using variable projection and a finite-difference style approximation method. The proposed method provides a reliable extraction of the spatial relationships (mode shape), dynamic trends (frequency and damping ratio), and dominant modes using the derived energy relationships in the measured data. The performance of the proposed method has been investigated using simulated data from a simplified 14-generator system and measured data from a real system.
A Cross-layer Collaborative Handover Authentication Approach for 5G Heterogeneous Network

The next fifth generation mobile network (5G) is going to have a massive impact on almost new frontier technological applications in everything from smartphones to future vehicles, with highly heterogeneous, broadband access everywhere, faster speeds, lower latency and higher throughput. As moving to 5G, security architecture must also evolve, especially to seek for more efficient and secure handover authentication solutions. In this work, we design our security mechanism based on physical layer and upper layer cryptographic techniques, combining the advantages of these techniques to achieve a faster and more reliable authentication objective for 5G HetNets. In the cross-layer authentication solution, we firstly execute the handover authentication in physical-layer. Kolmogorov-Smirnov (K-S) hypothesis is adopted by exploiting the physical layer attributes of wireless channels. If it pass the initial authentication, then we implement the EAP/AKA authentication to achieve a more reliable security objective. Finally, the numerical simulation results and discussions are presented to show the feasibility of the proposed approach.
Combating Alarm Fatigue: The Quest for More Accurate and Safer Clinical Monitoring Equipment

As the demand for health-care services continues to increase, clinically efficient and cost-effective patient monitoring takes on a critically important role. Key considerations inherent to this area of concern include patient safety, reliability, ease of use, and cost containment. Unfortunately, even the most modern patient monitoring systems carry sig nificant drawbacks that limit their effectiveness and/or applicability. Major opportunities for improvement in both equipment design and monitor utilization have been identified, including the presence of excessive false and nuisance alarms. When poorly optimized, clinical alarm activity can affect patient safety and may have a negative impact on care providers, leading to inappropriate alarm response time due to the so-called alarm fatigue (AF). Ultimately, consequences of AF include missed alerts of clinical significance, with substantial risk for patient harm and potentially fatal outcomes. Targeted quality improve - ment initiatives and staff training, as well as the proactive incorporation of technological improvements, are the best approaches to address key barriers to the optimal utilization of clinical alarms, AF reduction, better patient care, and improved provider job satisfaction.
Fixation-Related EEG Frequency Band Power Analysis: A Promising Methodology for Studying Instructional Design Effects of Multimedia Learning Material.

During the last decade the combined recording of eye-tracking data and electroencephalographic (EEG) data has led to the methodology of fixation-related potentials analysis (FRP). This methodology has been increasingly and successfully used to study EEG correlates in the time domain (i.e., event-related potentials, ERPs) of cognitive processing in free viewing situations like text reading or natural scene perception. Basically, fixation-onset serves as time-locking event for epoching and analysing the EEG data. In this article the methodology of fixation-related frequency band power analysis (FRBP) is proposed and conceptually outlined to study cognitive load and affective variations in learners during free viewing situations of multimedia learning materials (i.e., combinations of textual and pictorial elements). The EEG alpha frequency band power at parietal electrodes may serve as a valid measure of cognitive load, whereas the frontal alpha asymmetry may serve as a measure of affective variations. I will introduce and motivate the measures and the methodology and discuss methodological challenges and potential ways to overcome them. The methodology is frontline for learning research, first, as to date the EEG has been rarely used to study design effects of multimedia learning materials and second, as fixation-related EEG data analysis has rarely been done focussing on the frequency domain (i.e., FRBP). Despite methodological challenges still to be solved, FRBP may provide a more in-depth picture of cognitive processing during multimedia learning compared to eye-tracking data or EEG data in isolation and thus may help clarifying effects of multimedia design decisions
Beam reconfigurable microstrip antennas based on parasitics with linear and circular polarisation capability.

Millimetre wave beam and polarization reconfigurable antennas for future wireless communications are investigated in this thesis. The millimetre wave frequency spectrum has recently attracted large attention from researchers and the industry of wireless communications. Millimetre wave frequencies is considered to be the frequency spectrum between 30 GHz and 300 GHz. However, the industry considers the spectrum above 10 GHz as millimetre wave, due to the fact that it shows similar propagation characteristics with the spectrum above 30 GHz. The aforementioned spectrum of frequencies offers a lot of advantages compared to lower frequency spectrum, due to the fact that it offers large and mostly unexploited bandwidths. The need for very high data rates, in future wireless communications, increases the need for bandwidth. Millimetre wave frequencies can be used to fulfill future bandwidth demands. Although millimeter wave frequencies offer several advantages and good potential for future wireless communications, they also impose several challenges. This thesis discusses the need for highly directive and beam reconfigurable antennas for such high frequencies. It also discusses how an antenna design can benefit from being circularly polarised for several wireless communication applications and how antennas for future wireless communications must be able to reconfigure several parameters, without compromising the performance, cost and size, giving the exibility to a wireless terminal to operate in several different modes. This thesis proposes novel reconfigurable antennas for portable devices, which can be used at millimetre wave frequencies, and which offer high gain, wide steering range, low scan loss and multi-parameter reconfigurability; essential characteristics that antennas designed for future wireless communications should offer.
Design of flexure hinges in an H-drive precision stage with a large span

In order to balance the high translation stiffness and low twist stiffness in an H-drive precision stage with a large span, a design method based on flexure hinges combination is proposed. Firstly, various stiffness requirements in different degrees of freedom (DOF) of an H-drive precision stage with a large span are analyzed. Secondly, half-cartwheel flexure hinges combination is applied to balance these stiffness requirements. Thirdly, based on the Timoshenko beam theory, stiffness matrices of a single flexure hinge and combined hinges are established. Then, various structural parameters of the flexure hinge are determined by analyzing their influence on two performance indicators, including stiffness and motion precision. After that, finite element method is applied to verify these theoretical results. Finally, a stiffness identification experiment and a modal analysis experiment are conducted to test the 2-DOF stiffness of a single half-cartwheel flexure hinge and the natural frequency of the H-drive precision stage. The maximum error of stiffness between theoretical, simulation and experimental results is 13.4%. The natural frequency is up to 176.563 Hz, which could satisfy the control bandwidth requirement of the H-drive precision stage.
Image and WLAN Bimodal Integration for Indoor User Localization

Recently, we experience the increasing prevalence of wearable cameras, some of which feature Wireless Local Area Network (WLAN) connectivity, and the abundance of mobile devices equipped with on-board camera and WLAN modules. Motivated by this fact, this work presents an indoor localization system that leverages both imagery and WLAN data for enabling and supporting a wide variety of envisaged location-aware applications ranging from ambient and assisted living to indoor mobile gaming and retail analytics. The proposed solution integrates two complementary localization approaches, i.e., one based on WLAN and another one based on image location-dependent data, using a fusion engine. Two fusion strategies are developed and investigated to meet different requirements in terms of accuracy, run time, and power consumption. The one is a light-weight threshold-based approach that combines the location outputs of two localization algorithms, namely a WLAN-based algorithm that processes signal strength readings from the surrounding wireless infrastructure using an extended Naive Bayes approach and an image-based algorithm that follows a novel approach based on hierarchical vocabulary tree of SURF (Speeded Up Robust Features) descriptors. The second fusion strategy employs a particle filter algorithm that operates directly on the WLAN and image readings and also includes prior position estimation information in the localization process. Extensive experimental results using real-life data from an indoor office environment indicate that the proposed fusion strategies perform well and are competitive against standalone WLAN and imaged-based algorithms, as well as alternative fusion localization solutions.
Guidelines for defining user requirement specifications (URS) of manufacturing execution system (MES) based on ISA-95 standard

To standardize the authoring of user requirement specifications (URS) for manufacturing execution system (MES) proposed by manufacturing enterprises from users’ perspectives, and facilitate a quick and effective communication between them and MES vendor or integrator, an approach for URS definition was introduced based on the ISA-95 standard, in which a method for requirement elicitation based on goal and scenario was combined. In this approach, a framework for MES problem space was constructed first from three dimensions: generic activity model for manufacturing operations management (MOM), manufacturing operations categories and supporting activities, and then a series of guidelines for standard application, scenario authoring and requirement arrangement were established to guide the users in seeking the needs and writing them in a normalized sentence form step by step. Additionally, a structure of the table of content for URS documentation was recommended to help the users organizing the textual requirement description. Finally, a case study for applying this approach was performed in a pharmaceutical company’s example, which illustrates the application prospect in MES product selection.
A Constraints Driven PSO Based Approach for Text Summarization

In the present scenario we are living in a digital media and virtual world. To conveniently communicate in digital world electronic data have to gradually increase. So it is a serious challenge to manage the huge digital and electronic resources efficiently and accurately. One of the important solutions of the above problem is text summarization i.e. an application of text mining. Representing the gist of a text document is called summary. A rich summary always covers the maximum coverage, high level of diversity and with user defined size. This paper proposes an approach for summarizing the text documents by extractive way using Particle Swarm Optimization (PSO) that is known as population based stochastic optimization technique and it has many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The huge volume and dimensions of terms have managed by the concepts of term document matrix followed by K-Means clustering with PSO for acquiring optimal number of concepts clusters. Then apply constraint-driven concept for selecting the best one. These key concepts were used to identify the significant gist in documents for text summarization.
Vulnerability Profile of Online Social Network Users

— This article presents an approach to the vulnerability profiling for online social network users. The profile includes information about the socio-demographic group of the user, calculated indicators of the user's emotionality by eight scales: "Inspiration", "Uncertainty", "Anger", "Discontent", "Joy", "Fear", "Indifference", "Disorder". The authors propose a method for estimation of emotional indicators based on the users’ message analysis. Associative experiment, the method of thematic classification, semantic differentials, ontology have been used in the analysis of texts. We use the PMI (Pointwise Mutual Information) index as a numerical characteristic of emotionality. The authors analyze the pilot research results and make recommendations concerning the application of the proposed approach.
Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc. By accounting for these problems, this paper proposes to model the RSSI probability density distributions using the Censoring Gaussian Mixture Model (C-GMM) and develop the Expectation-Maximization (EM) algorithm to estimate the parameters of this model in the offline phase of the Wi-Fi fingerprinting based Indoor Positioning Systems (IPS). The simulation results demonstrate the effectiveness of the proposed method.
Sensor Deployment for Optimal Coverage: BFO Approach

The placement of sensors in given physical space is a critical parameter to be considered while studying the effectiveness of sensing network. The sensors may be deployed in a determined or a random order according to the monitoring sense of coverage. The random deployment provides better coverage in case when sensors are to deployed in large physical space or when condition are harsh at target area. Optimal coverage to target area is provided by using efficient deployment algorithms based on optimization techniques. In this paper, two deployment approaches based on Genetic Algorithm and BFO Algorithm is proposed to optimize the coverage provide by sensors. These approaches minimize overlapping (fitness) amongst the sensors in order to increase the coverage. As the fitness value decreases the coverage percentage increases in both the approaches.
Predicting Group Convergence in Egocentric Videos

In this work, our aim is to find the group dynamics in social gathering videos that are captured from the firstperson perspective. The complexity of the task is high as only one sensor (wearable camera) is present to sense all the N agents. An additional complexity arises as the first-person who is part of the group is not visible in the video. In particular, we are interested in identifying the group (with certain number of agents) convergence. We generate a dataset named EgoConvergingGroup to evaluate our proposed method. The proposed method predicts the group convergence in 90 to 250 number of frames, which is much ahead of the actual convergence.
Suggesting Moving Positions in Go-Game with Convolutional Neural Networks Trained Data

Nowadays, machine learning and deep learning has been becoming popular and useful in applying to resolve people’s problem. Specially, in HCI (Human Computer Interaction) field like robots or automatic game programs. Go-Game (the game of Go) is still a challenge in coding to get the wisest moves each turn to achieve the winner at the end of a game. In our work, we suggest the next move based on Convolutional Neural Networks (CNNs) and make evaluations and comparisons to gamers separate in 3 ranks (levels). We train 5-layers CNNs by supervised learning from a database of human games using the board-states. The network suggests the move of the selected player and the others player can be helped or not-depend on playing option. The program can also play the game automatically without human interactions during all the game progress (Machine-Machine game). In the other way, our program can interact with a human-player and accept move commands from player (Human-Machine or Human-Human). This technique allows Go-game program play the game without searching as traditional program but trained by convolutional neural networks. In our tests, we separate in 3 levels and use totally 598,472 board-states for training data. Our main aim is to help people who are the newbie in playing Go-game. With this technique, we expected that we can apply to develop AI programs and devices with more and more effects and higher performance.