Reducing Item Exposure in Computerized Adaptive Testing Systems Using Automatic Item Generation

This paper studies the technics of reducing item exposure by utilizing automatic item generation methods. Known test item calibration method uses item parameter estimation with the statistical data, collected during examinees prior testing. Disadvantage of the mentioned item calibration method is the item exposure; when test items become familiar to the examinees. To reduce the item exposure, automatic item generation method is used, where item models are being constructed based on already calibrated test items without losing already estimated item parameters. A technic of item model extraction method from the already calibrated and therefore exposed test items described, which can be used by the test item development specialists to integrate automatic item generation principles with the existing testing applications.

Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order Control Systems:A Case Study in Level Control Process

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this work.Interval uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world plants.Also,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)controller.To the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time delay.The three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise reduction.Finally,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

Investigating the Use of Email Application in Illiterate and Semi-Illiterate Population

The use of electronic communication has been significantly increased over the last few decades.Email is one of the most well-known means of electronic communication.Traditional email applications are widely used by a large population;however,illiterate and semi-illiterate people face challenges in using them.A major population of Pakistan is illiterate that has little or no practice of computer usage.In this paper,we investigate the challenges of using email applications by illiterate and semi-illiterate people.In addition,we also propose a solution by developing an application tailored to the needs of illiterate/semi-illiterate people.Research shows that illiterate people are good at learning the designs that convey information with pictures instead of text-only,and focus more on one object/action at a time.Our proposed solution is based on designing user interfaces that consist of icons and vocal/audio instructions instead of text.Further,we use background voice/audio which is more helpful than flooding a picture with a lot of information.We tested our application using a large number of users with various skill levels(from no computer knowledge to experts).Our results of the usability tests indicate that the application can be used by illiterate people without any training or third-party’s help.

Revisiting the LQR Problem of Singular Systems

In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman's criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.

Contribution of the Earth's gravitational potential to variations in orbital motion of short-period comets

We present simulation results on evolution development of orbital motion of short-period comets with the revolution period not exceeding 6-7 years, namely comets 21P/Giacobini-Zinner, 26P/Grigg- Skjellerup and 7P/Pons-Winnecke. The calculations cover the range from the date of the object＇s discovery to 2100. Variations in the objects＇ orbital elements under the action of gravity disturbances, taking Earth＇s gravitational potential into account when the small body approaches, are analyzed. Corrected dates of peri- helion passages can be used for scheduling observations.

Analysis of Factors that Affect Government Digitization:A Pilot Case Study of Pakistan

One of the greatest factors that affects the economic condition of a country is its institutions.In the model of good governance,the primary elements for stronger institution include efficiency,transparency,and accountability;and technology plays a major role in improving these elements.However,there are myriad of challenges when it comes to practical integration of technology in these institutions for efficiency.It is more challenging when a country is developing and one that is already weak economically.It is also important to mention that the challenges of digitization in public sector is not limited to developing countries only.It is equally challenging,even today,in already developed countries to digitally transform their public institutions for improved policymaking and for responsive service delivery.Many factors contribute to the failure of such digitization initiatives,more so within developing countries.And the purpose of this paper is to identify those factors,to measure the significance of each of those factors,and to realize and overcome them.This research considered the case study of Pakistan;however,the results are very likely to match the conditions of other developing regions around the world.Through questionnaires and interviews,valuable feedback was gathered from up to 25 senior government officers that are closely associated with digitization initiatives in public sector.The feedback to the questions were overall unanimous.The results indicate the most significant of factors that affect government digitization in this developing region,including some factors that were not expected.

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

Developing a Data Modelling Tool to Visualize the Transformation of an ER Diagram into a Relational Schema

In this paper, the authors present the development of a data modelling tool that visualizes the transformation process of an ＂Entity-Relationship＂ Diagram （ERD） into a relational database schema. The authors＇ focus is the design of a tool for educational purposes and its implementation on e-learning database course. The tool presents two stages of database design. The first stage is to draw ERD graphically and validate it. The drawing is done by a learner. Then at second stage, the system enables automatically transformation of ERD to relational database schema by using common rules. Thus, the learner could understand more easily how to apply the theoretical material. A detailed description of system functionalities and algorithm for the conversion are proposed. Finally, a user interface and usage aspects are exposed.

The Methodology of Application Development for Hybrid Architectures

This paper provides an overview of the main recommendations and approaches of the methodology on parallel computation application development for hybrid structures. This methodology was developed within the master＇s thesis project ＂Optimization of complex tasks＇ computation on hybrid distributed computational structures＂ accomplished by Orekhov during which the main research objective was the determination of＂ patterns of the behavior of scaling efficiency and other parameters which define performance of different algorithms＇ implementations executed on hybrid distributed computational structures. Major outcomes and dependencies obtained within the master＇s thesis project were formed into a methodology which covers the problems of applications based on parallel computations and describes the process of its development in details, offering easy ways of avoiding potentially crucial problems. The paper is backed by the real-life examples such as clustering algorithms instead of artificial benchmarks.

Evaluation of Hypervisor Stability towards Insider Attacks

Virtualization technology plays a key role in cloud computing.Thus,the security issues of virtualization tools（hypervisors,emulators,etc.） should be under precise consideration.However,threats of insider attacks are underestimated.The virtualization tools and hypervisors have been poorly protected from this type of attacks.Furthermore,hypervisor is one of the most critical elements in cloud computing infrastructure.Firstly,hypervisor vulnerabilities analysis is provided.Secondly,a formal model of insider attack on hypervisor is developed.Consequently,on the basis of the formal attack model,we propose a new methodology of hypervisor stability evaluation.In this paper,certain security countermeasures are considered that should be integrated in hypervisor software architecture.

A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications.These will range from networked Cyber-Physical Systems to edge computing devices,establishing real-time feedback control loops critical for managing Industry 5.0 deployments,digital agriculture systems,and essential infrastructures.The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised.While full automation will enhance industrial efficiency significantly,it concurrently introduces new cyber risks and vulnerabilities.In particular,unattended systems are highly susceptible to trust issues:malicious nodes and false information can be easily introduced into control loops.Additionally,Denialof-Service attacks can be executed by inundating the network with valueless noise.Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models.Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow.Standard models,with their limited understanding of mobile networks,can achieve precision rates no higher than 75%.Therefore,more general and transversal protection mechanisms are needed to detect malicious behaviors transparently.This paper introduces a probabilistic trust model and control algorithm designed to address this gap.The model determines the probability of any node to be trustworthy.Communication channels are pruned for those nodes whose probability is below a given threshold.The trust control algorithmcomprises three primary phases,which feed themodel with three different probabilities,which are weighted and combined.Initially,anomalous nodes are identified using Gaussian mixture models and clustering technologies.Next,traffic patterns are studied using digital Bessel functions and the functional scalar product.Finally,the information coherence and content are analyzed.The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters.An experimental validation based on simulation tools and environments was carried out.Results show the proposed solution can successfully detect up to 92%of malicious data injection attacks.

一种新的视频图像分块匹配压缩算法

视频图像压缩的分形压缩方法是当今图像压缩编码中的热门话题,通过研究双变量分形函数空间,得出了一些关于双变量分形函数的有用的结果;提出了一种基于双变量分形函数空间理论的帧内分块匹配视频图像压缩算法;给出了算法框图和帧间编码的有效花费函数,形成了一种新的视频图像分块匹配压缩算法,在计算机上实现效果良好。

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.

Reconfigurable Compact Wideband Circularly Polarised Dielectric Resonator Antenna for Wireless Applications

In this work,a novel compact wideband reconfigurable circularly polarised(CP)dielectric resonator antenna(DRA)is presented.The L-shaped Dielectric resonator antenna is excited by an inverted question mark shaped feed.This arrangement of feed-line helps to generate two orthogonal modes inside the DR,which makes the design circularly polarised.A thin micro-strip line placed on the defected ground plane not only helps to generate a wideband response but also assist in the positioning of the two diode switches.These switches located at the left and right of the micro-strip line helps in performing two switching operations.The novel compact design offers the reconfigurability between 2.9–3.8 GHz which can be used for different important wireless applications.For the switching operation I,the achieved impedance bandwidth is 24%while axial ratio bandwidth(ARBW)is 42%.For this switching state,the design has 100%CP performance.Similarly,the switching operation II achieves 60%impedance bandwidth and 58.88%ARBW with 76.36%CP performance.The proposed design has a maximum measured gain of 3.4 dBi and 93%radiation efficiency.The proposed design is novel in terms of compactness and performance parameters.The prototype is fabricated for the performance analysis which shows that the simulated and measured results are in close agreement.

Local Features-Based Watermarking for Image Security in Social Media

The last decade shows an explosion of using social media,which raises several challenges related to the security of personal files including images.These challenges include modifying,illegal copying,identity fraud,copyright protection and ownership of images.Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes.In this paper,we propose a hybrid digital watermarking and image processing approach to improve the image security level.Specifically,variants of the widely used Least-Significant Bit(LSB)watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels of the largest blob of a digital image.The proposed algorithms are tested using several experiments and techniques,which are followed by uploading the watermarked images into a social media site to evaluate the probability of extracting the embedding watermarks.The results show that the proposed approaches outperform the traditional LSB algorithm in terms of time,evaluation criteria and the percentage of pixels that have changed.

A Cache Replacement Policy Based on Multi-Factors for Named Data Networking

Named Data Networking(NDN)is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by.It greatly reduces network traffic and improves the speed of content distribution and retrieval.In order to make full use of the limited caching space in routers,it is an urgent challenge to make an efficient cache replacement policy.However,the existing cache replacement policies only consider very few factors that affect the cache performance.In this paper,we present a cache replacement policy based on multi-factors for NDN(CRPM),in which the content with the least cache value is evicted from the caching space.CRPM fully analyzes multi-factors that affect the caching performance,puts forward the corresponding calculation methods,and utilize the multi-factors to measure the cache value of contents.Furthermore,a new cache value function is constructed,which makes the content with high value be stored in the router as long as possible,so as to ensure the efficient use of cache resources.The simulation results show that CPRM can effectively improve cache hit ratio,enhance cache resource utilization,reduce energy consumption and decrease hit distance of content acquisition.

IRMIRS:Inception-ResNet-Based Network for MRI Image Super-Resolution

Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues.These challenges are increasing the interest in the quality of medical images.Recent research has proven that the rapid progress in convolutional neural networks(CNNs)has achieved superior performance in the area of medical image super-resolution.However,the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance(MR)images,adding extra noise in the models and more memory consumption.Furthermore,conventional deep CNN approaches used layers in series-wise connection to create the deeper mode,because this later end layer cannot receive complete information and work as a dead layer.In this paper,we propose Inception-ResNet-based Network for MRI Image Super-Resolution known as IRMRIS.In our proposed approach,a bicubic interpolation is replaced with a deconvolution layer to learn the upsampling filters.Furthermore,a residual skip connection with the Inception block is used to reconstruct a high-resolution output image from a low-quality input image.Quantitative and qualitative evaluations of the proposed method are supported through extensive experiments in reconstructing sharper and clean texture details as compared to the state-of-the-art methods.