Reviewerly/exHarmony huggingface dataset:
Model-Based 3D Gaze Estimation Using a TOF Camera

Authors:
raw_author_name: Kuanxin Shen
raw_affiliation_string: School of Chemical Process Automation, Shenyang University of Technology, Liaoyang 111003, China
raw_author_name: Yingshun Li
raw_affiliation_string: School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
raw_author_name: Zhannan Guo
raw_affiliation_string: School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
raw_author_name: Jintao Gao
raw_affiliation_string: School of Chemical Process Automation, Shenyang University of Technology, Liaoyang 111003, China

Abstract: Among the numerous gaze-estimation methods currently available, appearance-based methods predominantly use RGB images as input and employ convolutional neural networks (CNNs) to detect facial images to regressively obtain gaze angles or gaze points. Model-based methods require high-resolution images to obtain a clear eyeball geometric model. These methods face significant challenges in outdoor environments and practical application scenarios. This paper proposes a model-based gaze-estimation algorithm using a low-resolution 3D TOF camera. This study uses infrared images instead of RGB images as input to overcome the impact of varying illumination intensity in the environment on gaze estimation. We utilized a trained YOLOv8 neural network model to detect eye landmarks in captured facial images. Combined with the depth map from a time-of-flight (TOF) camera, we calculated the 3D coordinates of the canthus points of a single eye of the subject. Based on this, we fitted a 3D geometric model of the eyeball to determine the subject’s gaze angle. Experimental validation showed that our method achieved a root mean square error of 6.03° and 4.83° in the horizontal and vertical directions, respectively, for the detection of the subject’s gaze angle. We also tested the proposed method in a real car driving environment, achieving stable driver gaze detection at various locations inside the car, such as the dashboard, driver mirror, and the in-vehicle screen.
Do Engineering Students Learn Ethics From an Ethics Course?

Authors:
raw_author_name: Roman Taraban
raw_affiliation_string: Texas Tech University Lubbock, TX 79409 U.S.A.
raw_author_name: William M. Marcy
raw_affiliation_string: Texas Tech University Lubbock, TX 79409 U.S.A.

Abstract: The goal of the present research is to develop machineassisted methods that can assist in the analysis of students' written compositions in ethics courses.As part of this research, we analyzed Social Impact Assessment (SIA) papers submitted by engineering undergraduates in a course on engineering ethics.The SIA papers required students to identify and discuss a contemporary engineering technology (e.g., autonomous tractor trailers) and to explicitly discuss the ethical issues involved in that technology.Here we describe the ability of three machine tools to discriminate differences in the technical compared to ethical portions of the SIA papers.First, using LIWC (Language Inquiry and Word Count) we quantified differences in analytical thinking, expertise and selfconfidence, disclosure, and affect, in the technical and ethical portions of the papers.Next, we applied MEH (Meaning Extraction Helper) to examine differences in critical concepts in the technical and ethical portions of the papers.Finally, we used LDA (Latent Dirichlet Allocation) to examine differences in the topics in the technical and ethical portions of the papers.The results of these three tests demonstrate the ability of machine-based tools to discriminate conceptual, affective, and motivational differences in the texts that students compose that relate to engineering technology and to engineering ethics.We discuss the utility and future directions for this research.
Applying Agile Business Solutions as a Graduate Capstone Project

Authors:
raw_author_name: David Greenburg
raw_affiliation_string: The Citadel, School of Engineering, Charleston, SC
raw_author_name: Dimitra Michalaka
raw_affiliation_string: The Citadel, School of Engineering, Charleston, SC
raw_author_name: Tyler Miner
raw_affiliation_string: The Citadel, School of Engineering, Charleston, SC; ©American Society for Engineering Education, 2023
raw_author_name: Susan Huntington
raw_affiliation_string: The Citadel, School of Engineering, Charleston, SC; ©American Society for Engineering Education, 2023

Abstract: The Department of Engineering Leadership and Program Management at the Citadel designed and implemented a graduate course to teach the fundamentals of Agile project management as part of the MS Technical Project Management curriculum.The course was designed to provide a solid foundation in Agile methodologies for project management practice to enable graduates to execute appropriate Agile techniques to plan, and deliver quality results that frequently deliver value to customers.During the course students are introduced to Agile processes which include developing user stories and story maps, estimate and plan for sprints, track project execution and releases using burn-down charts and conduct retrospective sessions.This paper discusses how a graduate student was able to successfully apply the Agile project management principles and processes learned in the graduate program to an Independent Study project designed to provide students with the opportunity to apply their knowledge and experience gained during the curriculum to addressing and solving a real business problem.In this case the student chose to develop and implement an Agile framework solution to a software implementation and performance problem his business organization was facing.
ReQUSA: a novel ReRAM-based hardware accelerator architecture for high-speed quantum computer simulation

Authors:
raw_author_name: Leanghok Hour
raw_affiliation_string: Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea
raw_author_name: Yongtae Kim
raw_affiliation_string: Department of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea

Abstract: Abstract Quantum computers are currently regarded as an emerging computing technology that can solve problems more quickly than classical computers. However, since constructing a general quantum computer is technically difficult, quantum computer simulation has been used instead of real quantum computers. Simulating quantum computers on classical computers is challenging because the time and resources required for the vectormatrix multiplication (VMM) increase exponentially with the number of qubits. This paper proposes a new accelerator architecture called ReQUSA that leverages resistive random access memory (ReRAM) to accelerate the quantum computer simulation. The ReQUSA employs a ReRAM crossbar array structure, which is specialized for implementing the VMM, and a realized state method for reduced VMM operation. To the best of our knowledge, ReRAM-based accelerators for quantum computer simulators has not been previously reported. Here, we describe the hardware design of the architecture and compare the performances (hardware resource, simulation time, and accuracy) of our accelerator with those of current quantum computer simulators (QuEST, QPlayer, and Qiskit). On average, our proposed architecture reduced the simulation times by factors of ×10 4 and ×10 3 (×10 2 ) on average from those of QuEST and QPlayer (also Qiskit), respectively. In addition, our architecture achieved 99% accuracy in 16-bit fixed-point data representation.
Polarization-based all-optical logic gates using diffractive neural networks

Authors:
raw_author_name: Kuo Zhang
raw_affiliation_string: School of Science, Minzu University of China, Beijing 100081, People's Republic of China
raw_author_name: Kun Liao
raw_affiliation_string: State Key Laboratory for Mesoscopic Physics and Department of Physics, Collaborative Innovation Center of Quantum Matter, Beijing Academy of Quantum Information Sciences, Nano-optoelectronics Frontier Center of Ministry of Education, Peking University, Beijing 100871, People's Republic of China
raw_author_name: Xinping Zhang
raw_affiliation_string: School of Physics and Optoelectronic Engineering, Beijing University of Technology, Beijing 100124, People's Republic of China
raw_author_name: Shuai Feng
raw_affiliation_string: School of Science, Minzu University of China, Beijing 100081, People's Republic of China
raw_author_name: Xiaoyong Hu
raw_affiliation_string: Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, People's Republic of China; Peking University Yangtze Delta Institute of Optoelectronics, Nantong, Jiangsu 226010, People's Republic of China; State Key Laboratory for Mesoscopic Physics and Department of Physics, Collaborative Innovation Center of Quantum Matter, Beijing Academy of Quantum Information Sciences, Nano-optoelectronics Frontier Center of Ministry of Education, Peking University, Beijing 100871, People's Republic of China

Abstract: Abstract Optical logic operations are an essential part of optical computing. The inherent stability and low susceptibility of polarization to the external environment make it a suitable choice for acting as the logical state in computational tasks. Traditional polarization-based optical logic devices often rely on complex cascading structures to implement multiple logic gates. In this work, by leveraging the framework of deep diffractive neural networks (D 2 NN), we proposed a uniform approach to designing polarization-encoded all-optical logic devices with simpler and more flexible structures. We have implemented AND, OR, NOT, NAND, and NOR gates, as well as High-order Selector and Low-order Selector. These polarization-based all-optical logic devices using D 2 NN offer passive nature, stability, and high extinction ratio features, paving the way for a broader exploration of optical logic computing in the future.
Correction: Developing a patient flow visualization and prediction model using aggregated data for a healthcare network cluster in Southwest Ethiopia

Authors:
raw_author_name: Balew Ayalew Kassie
raw_author_name: Geletaw Sahle Tegenaw

Abstract: [This corrects the article DOI: 10.1371/journal.pdig.0000376.].
Quantum filter routing of single photons

Authors:
raw_author_name: Jun Xin
raw_affiliation_string: School of Sciences, Hangzhou Dianzi Universityhttps://ror.org/0576gt767, Hangzhou 310018, China
raw_author_name: Guolong Li
raw_affiliation_string: School of Sciences, Hangzhou Dianzi Universityhttps://ror.org/0576gt767, Hangzhou 310018, China
raw_author_name: Xiao-Ming Lu
raw_affiliation_string: School of Sciences, Hangzhou Dianzi Universityhttps://ror.org/0576gt767, Hangzhou 310018, China
raw_author_name: Lianfu Wei
raw_affiliation_string: Information Quantum Technology Laboratory, International Cooperation Research Center of China Communication and Sensor Networks for Modern Transportation, School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

Abstract: A quantum router takes the central role in an optical quantum network. However, how to route the expected photons with different frequencies to the targeted output ports of the quantum network is still a basic challenge. Here, we propose an effective approach, by setting the proper cavity-atom and photon-cavity chiral interactions, to realize quantum filter routing of single photons with different frequencies in a multi-channel quantum network. With the frequency serving as the signpost, the photons can be effectively routed to the targeted output ports by modulating the detunings between the cavities and the auxiliary atoms. Hopefully, this technique can play an important role in the construction of a highly efficient optical quantum network.
Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

Authors:
raw_author_name: Xiao Han
raw_affiliation_string: Collaborative Innovation Center, Communication University of China, Beijing 100024, China; School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
raw_author_name: Qi Wang
raw_affiliation_string: Collaborative Innovation Center, Communication University of China, Beijing 100024, China; School of Computer and Cyber Sciences, Communication University of China, Beijing 100024, China
raw_author_name: Yongbin Wang
raw_affiliation_string: Collaborative Innovation Center, Communication University of China, Beijing 100024, China

Abstract: Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection omissions due to small object sizes and trajectory loss due to occlusion. To address these challenges, we propose a ball detection and tracking model called HMMATrack based on multiscale feature enhancement and multilevel collaborative matching to improve ball-tracking results from the entire process of sampling, feature extraction, detection, and tracking. It includes a Heuristic Compound Sampling Strategy to deal with tiny sizes and imbalanced data samples; an MNet-based detection module to improve the ball detection accuracy; and a multilevel cooperative matching and automatic trajectory correction tracking algorithm that can quickly and accurately correct the ball’s trajectory. We also hand-annotated SportsTrack, a ball-tracking dataset containing soccer, basketball, and volleyball scenes. Extensive experiments are conducted on the SportsTrack, demonstrating that our proposed HMMATrack model outperforms other representative state-of-the-art models in ball detection and tracking.
Board 336: Math to Makerspace: Evolution of a Bridge Program to Support Cohort Development

Authors:
raw_author_name: Andrew Klein
raw_affiliation_string:  BS in Biological Sciences, MA; Old Dominion University; University of Washington; de-partment at Western Washington University.

Abstract: Jill's teaching, service, and research activities focus on enhancing the first year student experience by providing the foundational technical skills, student engagement opportunities, and professional skill development necessary to improve success in the major, with emphasis on
Response Process Validity of the CBE Adaptability Instrument When Used with Engineering Instructors

Authors:
raw_author_name: Heidi Diefes-Dux
raw_affiliation_string: Department of Agricultural and Biological Engineering at Purdue Univer-sity.; University of Nebraska - Lincoln

Abstract: Her role in the College of Engineering at UNL is to lead the disciplinary-based education research initiative, establishing a cadre
Bayesian approximation for parameterized KALMAN filter for investigation and simulation of unknown noise variance trajectory following in state space models with different noise distributions

Authors:
raw_author_name: None S. Hossein Sadati

Abstract: Bayesian approach can be used for parameter identification and extraction in state space models and its ability for analyzing sequence of data in dynamical system is proved in different literatures. In this paper, Bayesian approach for approximation of variances in measurement noise with KALMAN filter is applied for estimation of the dynamical state and measurement data in discrete dynamical system. Detection of uncertainty and estimation of those can be done simultaneously with adaptive KALMAN filter. This algorithm at each step time estimates noise variance and state of system with KALMAN filter. Then, approximation is formed at each step separately and at each step sufficient statistics of the state and noise variances are computed with a fixed-point iteration of a KALMAN filter. For showing influence of variance in measurement data on algorithm different simulations is applied. First, effect of variance and its distribution on detection performance is simulated in KALMAN filter without Bayesian formulation. Then simulation is applied to KALMAN filter with ability of variance tracking of measurement data.in these simulations, influence of distribution of measurement data in each step is estimated and true variance of data is obtained by algorithm and is compared in different scenarios. Afterwards, one typical modeling of nonlinear state space model with inducing noise measurement is simulated by this approach. Finally, the performance and the important limitations of this algorithm in these simulations are explained.
A Multi-strategy Enhanced Arithmetic Optimization Algorithm and Its Application in Path Planning of Mobile Robots

Authors:
raw_author_name: Xuzhen Deng
raw_author_name: Dengxu He
raw_author_name: Liangdong Qu
raw_affiliation_string: School of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, Guangxi, China

Abstract: Abstract A multi-strategy enhanced arithmetic optimization algorithm called MSEAOA is proposed to address the issues of low population diversity, imbalanced exploration and exploitation capabilities, and low accuracy of optimal solution in the Arithmetic Optimization Algorithm. Firstly, using the good point set strategy for population initialization to improve population diversity and thus accelerate convergence speed. Secondly, we integrate the exploration and exploition capabilities of differential self-learning strategy, best example learning strategy, and second-order differential perturbation strategy balancing algorithm. Finally, the introduction of somersault foraging strategy improves the accuracy of the optimal solution. We select 14 classical benchmark test functions and the CEC2019 function test set to test the optimization ability of MSEAOA, and apply MSEAOA to the path planning problem of mobile robots. MSEAOA is compared with other meta-heuristic optimization algorithms, and the experimental results are statistically analyzed by the Wilcoxon rank-sum test. The simulation experimental results show that MSEAOA performs the best among 14 benchmark functions, but for 10 CEC2019 functions, MSEAOA has the best optimization performance among 5 of them (50%). In the path optimization problem of mobile robots, the path obtained by MSEAOA is also the best among all algorithms, its path shortening rate exceeds 8.8% in 83% of environments. The results indicate that MSEAOA is a reliable algorithm suitable for function optimization and practical optimization problems.
Detection of centroblast cells in H&amp;E stained whole slide image based on object detection

Authors:
raw_author_name: Sumeth Yuenyong
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
raw_author_name: Narit Hnoohom
raw_affiliation_string: Image Information and Intelligence Laboratory, Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand

Abstract: Introduction Detection and counting of Centroblast cells (CB) in hematoxylin &amp;amp; eosin (H&amp;amp;E) stained whole slide image (WSI) is an important workflow in grading Lymphoma. Each high power field (HPF) patch of a WSI is inspected for the number of CB cells and compared with the World Health Organization (WHO) guideline that organizes lymphoma into 3 grades. Spotting and counting CBs is time-consuming and labor intensive. Moreover, there is often disagreement between different readers, and even a single reader may not be able to perform consistently due to many factors. Method We propose an artificial intelligence system that can scan patches from a WSI and detect CBs automatically. The AI system works on the principle of object detection, where the CB is the single class of object of interest. We trained the AI model on 1,669 example instances of CBs that originate from WSI of 5 different patients. The data was split 80%/20% for training and validation respectively. Result The best performance was from YOLOv5x6 model that used the preprocessed CB dataset achieved precision of 0.808, recall of 0.776, mAP at 0.5 IoU of 0.800 and overall mAP of 0.647. Discussion The results show that centroblast cells can be detected in WSI with relatively high precision and recall.
Optimal Realization of Yang–Baxter Gate on Quantum Computers

Authors:
raw_author_name: Kun Zhang
raw_affiliation_string: Peng Huanwu Center for Fundamental Theory Xi'an 710127 China; School of Physics Northwest University Xi'an 710127 China; Shaanxi Key Laboratory for Theoretical Physics Frontiers Xi'an 710127 China
raw_author_name: Kwangmin Yu
raw_affiliation_string: Computational Science Initiative Brookhaven National Laboratory Upton NY 11973 USA
raw_author_name: Vladimir Korepin
raw_affiliation_string: C. N. Yang Institute for Theoretical Physics Stony Brook University NY 11794 USA

Abstract: Abstract Quantum computers provide a promising method to study the dynamics of many‐body systems beyond classical simulation. On the other hand, the analytical methods developed and results obtained from the integrable systems provide deep insights on the many‐body system. Quantum simulation of the integrable system not only provides a valid benchmark for quantum computers but is also the first step in studying integrable‐breaking systems. The building block for the simulation of an integrable system is the Yang–Baxter gate. It is vital to know how to optimally realize the Yang–Baxter gates on quantum computers. Based on the geometric picture of the Yang–Baxter gates, the optimal realizations of two types of Yang–Baxter gates with a minimal number of controlled NOT (CNOT) or gates are presented. It is also shown how to systematically realize the Yang–Baxter gates via the pulse control. The different realizations on IBM quantum computers are tested and compared. It is found that the pulse realizations of the Yang–Baxter gates always have a higher gate fidelity compared to the optimal CNOT or realizations. On the basis of the above optimal realizations, the simulation of the Yang–Baxter equation on quantum computers is demonstrated. These results provide a guideline and standard for further experimental studies based on the Yang–Baxter gate.
Augmented Botswanan Learning Experience

Authors:
raw_author_name: Niloufar Bayati
raw_affiliation_string: Dept. of Science, Technology, Engineering and Mathematics (STEM) Education at N.C. State University.

Abstract: The Augmented Botswana Learning Experience (ABLE) is an international collaboration between two STEM institutions that engages engineering students in solving real-world problems using a problem-based learning approach and service-based learning pedagogy.The ABLE project aims to create a collaborative learning environment for engineering students in Botswana and the US by utilizing augmented reality and Onshape technologies to develop their visualization and 3D modeling skills.The project provides an eight-week co-curricular program focusing on problem-based learning approaches to address authentic real-world problems, particularly in Botswana.To accomplish the ABLE project's goals, six high-performing students from an introductory engineering graphics course in the US will serve as consultants for design teams in Botswana.The emphasis is on creating an immersive experience that utilizes mixed reality for the students using AR technology, enabling them to participate in activities that involve decision-making, object design, basic assembly and the mentality of "modify and redefine".The ABLE project represents an innovative approach to engineering education that leverages technology and cross-cultural collaboration to prepare students for real-world problem-solving, using 3D Modeling along with AR technology.Deliverables for the project include a 3-D model of the original device, 3-D model of modified model utilizing AR technology, 3-D printed examples of final products and a 2-page write up detailing the modifications and describing its presumed increased functionality.