Computer Engineering Commons^™

Apple “Porn” 2.0: Apple’S Vision (Pro), Suzanne E. Ferriss

Class, Race and Corporate Power

This article extends the argument made in “Apple ‘Porn’: Design Videos as Seduction and Exploitation” (Ferriss 2018) to consider the corporation’s filmed representation of its newest device: an augmented reality headset dubbed Vision Pro. It argues that Apple’s latest narratives further relegate human work and community to the margins by presenting human experience as thoroughly mediated by computer-enhanced simulation, its pinnacle achieved through its Apple Vision Pro headset that turns the home and workspace into one immersive audiovisual world. Rather than its devices and software becoming an inseparable part of our personal and shared spaces, they become the spaces. We …

Executive Order On The Safe, Secure, And Trustworthy Development And Use Of Artificial Intelligence, Joseph R. Biden

Copyright, Fair Use, Scholarly Communication, etc.

Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

My Administration places the highest urgency …

A Dynamic Online Dashboard For Tracking The Performance Of Division 1 Basketball Athletic Performance, Erica Juliano, Chelsea Thakkar, Christopher Taber, Mehul S. Raval, Kaya Tolga, Samah Senbel

School of Computer Science & Engineering Undergraduate Publications

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 'Women's basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes …

A Framework For Biomechanical Analysis Of Jump Landings For Injury Risk Assessment, Srishti Sharma, Srikrishnan Divakaran, Kaya Tolga, Christopher Taber, Mehul S. Raval

School of Computer Science & Engineering Faculty Publications

Competitive sports require rapid and intense movements, such as jump landings, making athletes susceptible to injuries due to altered neuromuscular control and joint mechanics. Biomechanical features during landings are associated with injury risk, emphasizing proper movement and postural stability. Computer vision techniques offer a time-efficient, noninvasive, and unbiased method to assess jump-landings and identify injury risks. This study proposes a video analysis framework to evaluate jump landing biomechanics in athletes todetermineirregularmovementsandincorrectpostures.It providesadviceandrecommendationstocoachesforinjury predictionandtrainingimprovements.Theproposedframework istestedusingcountermovementjumpvideosof17NCAA DivisionIfemalebasketballathletes.Theresultsindicateda lowMeanAbsoluteError(0.97),highcorrelation(0.89),high averageaccuracy(98.31%)andF1score(0.98),signifyingthe framework’sreliabilityinidentifyinginjuryrisk.

Leveraging Vr/Ar/Mr/Xr Technologies To Improve Cybersecurity Education, Training, And Operations, Paul Wagner, Dalal Alharthi

Journal of Cybersecurity Education, Research and Practice

The United States faces persistent threats conducting malicious cyber campaigns that threaten critical infrastructure, companies and their intellectual property, and the privacy of its citizens. Additionally, there are millions of unfilled cybersecurity positions, and the cybersecurity skills gap continues to widen. Most companies believe that this problem has not improved and nearly 44% believe it has gotten worse over the past 10 years. Threat actors are continuing to evolve their tactics, techniques, and procedures for conducting attacks on public and private targets. Education institutions and companies must adopt emerging technologies to develop security professionals and to increase cybersecurity awareness holistically. …

The Intersection Of Ai And Employment, Sachit Kamat

River Cities Industrial and Organizational Psychology Conference

Artificial Intelligence (AI) is rapidly transforming every industry. This session will focus on the practical applications of AI within employment hiring and succession strategies, enabling organizations to automate processes, improve efficiency, and uncover data-driven insights for enhanced decision making. We will discuss advancements using AI in the recruitment process, including the use of machine learning algorithms to screen resumes and identify successful candidates. The talk will highlight the regulatory environment and the intersection of the IO and AI fields within this rapidly evolving landscape. Additionally, the presentation will explore where AI is being used to improve organizational retention and performance. …

Workshop On Data For Ai In Network Systems - Event Summary, Kuang-Ching Wang, Ron Hutchins, Anita Nikolich

Workshop on Data for AI in Network Systems

No abstract provided.

Data For Ai In Network Systems Workshop Report, Kuang-Ching Wang, Ron Hutchins, Anita Nikolich

Workshop on Data for AI in Network Systems

No abstract provided.

Integrating Nist And Iso Cybersecurity Audit And Risk Assessment Frameworks Into Cameroonian Law, Bernard Ngalim

Journal of Cybersecurity Education, Research and Practice

This paper reviews cybersecurity laws and regulations in Cameroon, focusing on cybersecurity and information security audits and risk assessments. The importance of cybersecurity risk assessment and the implementation of security controls to cure deficiencies noted during risk assessments or audits is a critical step in developing cybersecurity resilience. Cameroon's cybersecurity legal framework provides for audits but does not explicitly enumerate controls. Consequently, integrating relevant controls from the NIST frameworks and ISO Standards can improve the cybersecurity posture in Cameroon while waiting for a comprehensive revision of the legal framework. NIST and ISO are internationally recognized as best practices in information …

Smart Service Function Chain System For Dynamic Traffic Steering Using Reinforcement Learning (Chrl), Ahmed Nadhum, Ahmed Al-Saadi

Karbala International Journal of Modern Science

The rapid development of the Internet and network services coupled with the growth of communication infrastructure necessitates the employment of intelligent systems. The complexity of the network is heightened by these systems, as they offer diverse services contingent on traffic type, user needs, and security considerations. In this context, a service function chain offers a toolkit to facilitate the management of intricate network systems. However, various traffic types require dynamic adaptation in the sets of function chains. The problem of optimizing the order of service functions in the chain must be solved using the proposed approach, along with balancing the …

Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima

Turkish Journal of Electrical Engineering and Computer Sciences

The conventional approach to creating 3D surfaces from 2D medical images is the marching cube algorithm, but it often results in rough surfaces. On the other hand, B-spline curves and nonuniform rational B-splines (NURBSs) offer a smoother alternative for 3D surface reconstruction. However, NURBSs use control points (CTPs) to define the object shape and corners play an important role in defining the boundary shape as well. Thus, in order to fill the research gap in applying corner detection (CD) methods to generate the most favorable CTPs, in this paper corner points are identified to predict organ shape. However, CTPs must …

Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …

Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy

Turkish Journal of Electrical Engineering and Computer Sciences

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with …

Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi

Turkish Journal of Electrical Engineering and Computer Sciences

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …

A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj

Turkish Journal of Electrical Engineering and Computer Sciences

Early diagnosis of colorectal cancer lengthens human life and is helpful in efforts to cure the illness. Histopathological inspection is a routinely utilized technique to diagnose it. Visual assessment of histopathological images takes more investigation time, and the decision is based on the individual perceptions of clinicians. The existing methods for colorectal cancer classification use only spatial information. However, studies on the spectral domains of information are lacking in the literature. Therefore, the performance of the existing techniques is moderate. To improve the performance of colorectal cancer classification, this work proposes a unique hybrid domain hand-crafted feature formulated using scale-invariant …

Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram

Turkish Journal of Electrical Engineering and Computer Sciences

Hyperspectral imaging has emerged as a prominent area of research in the field of remote sensing science. However, hyperspectral images (HSIs) pose a notable challenge due to the presence of numerous irrelevant and redundant spectral bands exhibiting high correlation. Therefore, it is necessary to enhance the classification performance for HSI processing by selecting the most relevant discriminative spectral bands. To this end, this paper introduces a metaheuristic search method called enhancing exploration-exploitation in harmony search (E3HS). The standard harmony search suffers from many weaknesses, such as premature convergence and falling easily into the local optimum. Consequently, E3HS was proposed to …

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur

Turkish Journal of Electrical Engineering and Computer Sciences

Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants …

Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli

Turkish Journal of Electrical Engineering and Computer Sciences

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, …