Reviewerly/exHarmony huggingface dataset:
A novel approach for detecting deep fake videos using graph neural network

Authors:
raw_author_name: M. M. El-Gayar
raw_affiliation_string: Department of Information Technology, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
raw_author_name: Mohamed Abouhawwash
raw_affiliation_string: Department of Computational Mathematics, Science and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI, 48824, USA
raw_author_name: S. S. Askar
raw_affiliation_string: Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
raw_author_name: Sara Sweidan
raw_affiliation_string: Artificial Intelligence Department, Faculty of Computer and Artificial Intelligence, Benha University, Banha, Egypt

Abstract: Abstract Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs.
Accurate Fourier-space statistics for line intensity mapping: Cartesian grid sampling without aliased power

Authors:
raw_author_name: Steven Cunnington
raw_affiliation_string: Jodrell Bank Centre for Astrophysics, Department of Physics & Astronomy, The University of Manchester , Manchester M13 9PL, UK

Abstract: Abstract Estimators for n-point clustering statistics in Fourier-space demand that modern surveys of large-scale structure be transformed to Cartesian coordinates to perform Fast Fourier Transforms (FFTs). In this work, we explore this transformation in the context of pixelised line intensity maps (LIM), highlighting potential biasing effects on power spectrum measurements. Current analyses often avoid a complete resampling of the data by approximating survey geometry as rectangular in Cartesian space, an increasingly inaccurate assumption for modern wide-sky surveys. Our simulations of a 20 × 20 deg2 21cm LIM survey at 0.34 &amp;lt; z &amp;lt; 0.54 show this assumption biases power spectrum measurements by ${&amp;gt;}\, 20~{{\%}}$ across all scales. We therefore present a more robust framework for regridding the voxel intensities onto a 3D FFT field by coordinate transforming large numbers of Monte-Carlo sampling particles. Whilst this unbiases power spectrum measurements on large scales, smaller-scale discrepancies remain, caused by structure smoothing and aliasing from separations unresolved by the grid. To correct these effects, we introduce modelling techniques, higher-order particle assignments, and interlaced FFT grids to suppress the aliased power. Using a Piecewise Cubic Spline (PCS) particle assignment and an interlaced FFT field, we achieve sub-percent accuracy up to 80% of the Nyquist frequency for our 21cm LIM simulations. We find a more subtle hierarchical improvement in results for higher-order assignment schemes, relative to the gains made for galaxy surveys, which we attribute to the extra complexity in LIM from additional discretising steps. Python code accompanying this paper is available at github.com/stevecunnington/gridimp.
Constructing new high-order polynomial chaotic maps and application in pseudorandom number generator

Authors:
raw_author_name: WanQing Wu
raw_affiliation_string: Hebei University, Hebei University, Baoding, 071002, CHINA

Abstract: Abstract The chaotic map have been widely applied in fields such as pseudorandom number generation(PRNG) and image encryption due to its excellent chaotic performance. In this paper, two classes of high-order polynomial chaotic maps of special form are proposed based on the Li-Yorke theorem. The dynamic behavior of the proposed maps is numerically analyzed, including bifurcation and Lyapunov exponent, and the analysis results prove the validity of the proposed conclusions. The proposed polynomial chaotic maps have a larger parameters and chaotic range, as well as a more stable Lyapunov exponent. Furthermore, based on the coupling chaotic systems, we design a pseudorandom number generator(PRNG), and the number of chaos parameters are expanded through the coupling control parameters in a PRNG. Then the performance of the pseudorandom sequence generated by the PRNG is tested and analysed. The test and analysis results show that the pseudorandom sequence has favorabble security, structural complexity, and randomness. . Especially, the information entropy of 7.9998 and the key space size of 2^ 208 exceed the recently reported pseudorandom number generators(PRNGs). In comparison with other pseudorandom number generators(PRNGs) based on chaotic maps in the recent literature, this paper provides comprehensive performance test and analysis of the proposed PRNG and demonstrates its potential for cryptographic applications.
Hamiltonian Switching Control of Noisy Bipartite Qubit Systems

Authors:
raw_author_name: Zhibo Yang
raw_affiliation_string: Chemistry, University of California Berkeley, University of California, Berkeley, Berkeley, California, 94720, UNITED STATES
raw_author_name: Robert L Kosut
raw_affiliation_string: SC Solutions Inc, 1261 Oakmead Parkway���, Sunnyvale, California, 94085, UNITED STATES
raw_author_name: K Birgitta Whaley
raw_affiliation_string: Chemistry, University of California - Berkeley, B84 Hildebrand Hall, University of California, Berkeley, Berkeley, California, 947200-1460, UNITED STATES

Abstract: Abstract We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate environmental noise on qubits. We demonstrate the control for a central spin coupled to bath spins via isotropic Heisenberg interactions, and then make physical applications to the protection of quantum gates performed on superconducting transmon qubits coupling to environmental two-level-systems (TLSs) through dipole-dipole interactions, as well as on such qubits coupled to both TLSs and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient (PG) to optimize the Hamiltonian switching control protocols, using a fidelity objective for specific target quantum gates. &amp;#xD; We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999999999 (nine nines) upon a subsequent optimization by Gradient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.
The Applications based on Video Motion Magnification Techniques

Authors:
raw_author_name: Asmaa Sadiq
raw_affiliation_string: Al-Mustansiriyah University, Iraq

Abstract: This research study's major goal is to present an important overview of recent work on applications based on Video Motion magnification (VMM) approaches during the course of the last 10 years. Over the past few years, video motion magnification (VMM) technologies have attracted a lot of attention and research, particularly as applications based on video motion have become more and more necessary. With an increase in the number of recommended procedures, surveying and evaluation become necessary. In this study, we will highlight how the survey was focused on several articles that used motion video augmentation techniques in their applications. We contrast these applications as well.
A Method of Applying Virtual Reality Converged Remote Platform Based on Crawfish Optimization Algorithm to Improve ESN Network

Authors:
raw_author_name: Lili Ma
raw_affiliation_string: Tangshan Preschool Teachers College
raw_author_name: Bin Xie
raw_affiliation_string: Qing Gong College North University of Science and Technology, Tangshan 063000, Hebei, China
raw_author_name: Liying Ma
raw_affiliation_string: Tangshan Guanghuadao Primary School, Tangshan 063000, Hebei, China

Abstract: INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch.OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods.METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments.RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms.CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.
The Impact of Rotation Model on Minimizing Inflectional Morphemes Errors in English Writing: A Comparative Study of Error Analysis

Authors:
raw_author_name: Mohammad Usama
raw_affiliation_string: National Institute of Technology
raw_author_name: Sohaib Alam
raw_affiliation_string: Prince Sattam Bin Abdulaziz University
raw_author_name: Sameena Banu
raw_affiliation_string: Prince Sattam Bin Abdulaziz University

Abstract: The study aims to analyze 12th-grade students’ writing errors related to inflectional morphemes. In addition, the research measures efficacy of the Rotation Model (RM) and the Grammar Translation Method (GTM) in minimizing writing errors among learners of English. Comparative studies are rare to find the effectiveness of two teaching models in the context of error analysis. Eighty-two samples were taken into consideration for randomize sampling. The present research is divided into two groups; the experimental group, consisting of 41 participants, provided with instruction via RM, and the control group, also comprising 41 individuals, being taught through GTM. Pre and post-test treatment data were collected and analyzed with the help of SPSS 22. The results revealed common errors with inflectional morphemes among both groups. Additionally, the mean values for each type of error were significantly minimized due to the post-instruction of RM compared to the GTM. The study’s findings largely devoted to pedagogical improvement among ESL learners’ writing. The findings are reviewed concerning future research directions and instructional strategies.
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

Authors:
raw_author_name: Nan Li
raw_affiliation_string: IDLab, Department of Electronics and Information Systems, Ghent University, Gent, Belgium
raw_author_name: Bo Kang
raw_affiliation_string: IDLab, Department of Electronics and Information Systems, Ghent University, Gent, Belgium
raw_author_name: Jefrey Lijffijt
raw_affiliation_string: IDLab, Department of Electronics and Information Systems, Ghent University, Gent, Belgium
raw_author_name: Tijl De Bie
raw_affiliation_string: IDLab, Department of Electronics and Information Systems, Ghent University, Gent, Belgium

Abstract: Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g. by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority : a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy : a previously-proposed fairness notion that quantifies the extent to which a user prefers other users’ recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility : the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state of the art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible.
HA-DQS-Net: dynamic query design based on transformer with hollow attention

Authors:
raw_author_name: Hongyi Wang
raw_affiliation_string: Tiangong University, School of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tianjin, China (China)
raw_author_name: Di Yan
raw_affiliation_string: Tiangong University, School of Control Science and Engineering, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tianjin, China (China)
raw_author_name: Yunpeng Li
raw_affiliation_string: Tiangong University, School of Artificial Intelligence, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tianjin, China (China)
raw_author_name: Limei Song
raw_affiliation_string: Tiangong University, School of Control Science and Engineering, Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tianjin, China (China)

Abstract: A common problem in the field of object detection is that the image features could not be fully expressed. And another issue is that the static query selection in the detection transformer (DETR)-like models cannot adapt well to different datasets due to the fixed number of selected object queries. To solve these problems, hollow attention (HA) and dynamic query selection (DQS) modules were proposed, and a network HA-DQS-Net was further formed. HA integrates specially designed masks into self-attention to better combine channel and spatial directional feature information, thereby learning more complex and comprehensive target features. DQS improves the idea of static query selection in the current DETR-like model by dynamically selecting the number of object queries based on the actual number of targets in the image, which enhances the accuracy of the model. HA-DQS-Net, which combines the advantages of HA and DQS, has a competitive performance in the field of object detection. The excellent detection effectiveness of our viewpoint is validated based on PASVAL VOC and a homemade smoking dataset. It is worth noting that all APs have been improved when HA is applied to different DETR-like models, which improves the universality of the HA module.
Work In Progress: Implementing Team Projects in Online Courses - Balancing Individual Responsibility and Team Collaboration

Authors:
raw_author_name: Haolin Zhu
raw_affiliation_string: Arizona State University; Engineering Mechanics from Shanghai Jiao Tong University; Theoretical and Applied Mechanics from Cornell University,
raw_author_name: David Taylor
raw_affiliation_string: Arizona State University; Engineering Mechanics from Shanghai Jiao Tong University; Theoretical and Applied Mechanics from Cornell University,

Abstract: In this role, she focuses on
Augmented Reality for Sustainable Collaborative Design

Authors:
raw_author_name: Shulong Yan
raw_affiliation_string: Human-Centered Engineering program at Boston College.
raw_author_name: Eunice Kang
raw_affiliation_string: Human-Centered Engineering program at Boston College.
raw_author_name: Andrew Katz
raw_affiliation_string: Human-Centered Engineering program at Boston College.; Virginia Polytechnic Institute and State University
raw_author_name: Avneet Hira
raw_affiliation_string: Human-Centered Engineering program at Boston College.

Abstract: Her scholarship is motivated by the fundamental question of how
A project-based platform for students’ Robot Operation System (ROS) programming experience

Authors:
raw_author_name: Yizhe Chang
raw_author_name: Yifan Wang
raw_author_name: Zhou Zhang
raw_affiliation_string: New York City College of Technology

Abstract: Abstract Robot Operating System (ROS) is an open-source software framework for robot automation. ROS includes a protocol of robot commanding and integrates with a collection of software libraries. ROS aims to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms. Currently, ROS is the major robotics software framework for researchers in graduate schools. One of the obstacles in exposing ROS to students is lacking appropriate projects for starters. ROS is not learner-friendly for its steep learning curve and comprehensive programming background needed. In this article, a robotics platform is designed by the author's institution, based on an iRobot Create robot, is introduced. The robotics platform that includes a mobile base, a webcam, and a Lidar, can fully support ROS-based programming for autonomous navigation. To help students start ROS programming practicing, a ROS-based student autonomous navigation project, including components of path-planning, image recognition, and simultaneous localization and mapping (SLAM) is implemented on this robotics platform. A pilot study shows that the platform is favored by students and inspiring for students to learn ROS. However, the curriculum needs improvements to ensure the students' success in completing the tasks.
Physically Contextualized Machining Instructions through Augmented Reality

Authors:
raw_author_name: John Liu
raw_affiliation_string: Massachusetts Institute of Technology

Abstract: Abstract Hands-on fabrication skills require complementary capacity for spatial thinking, but teaching often relies on 2D drawings to render inherently three-dimensional concepts. Computer-aided Design (CAD) software provides robust engineering visualization, but 3D models are displayed on a 2D screen and separate from the learner's physical context. Augmented Reality (AR) is another 3D technology that has the potential to facilitate embodied learning and an introductory student's transfer of concepts to tasks, because of its ability to integrate three-dimensional information into the physical context of authentic tasks. We present the design, development, and initial course implementation of AR instructions to fabricate a metal flashlight using common fabrication tools and equipment in the machine shop. The app gives written instructions and engineering drawings, accompanied by an AR visualization of how the workpiece changes step-by-step and how all of the parts are eventually assembled. This app has now been released into an undergraduate-level introductory fabrication course ("Mechanical Engineering Tools") at the Massachusetts Institute of Technology (MIT). We developed pre- and post-assessments to measure cognitive and affective outcomes and compare outcomes between a pilot AR-based cohort (N=6) to the traditional cohort (N=20). A psychomotor assessment was also carried out for a subset of the cohorts. This work suggests AR-enhanced instruction may promote learning transfer in hands-on skills training.
A VIRTUAL WALKTHROUGH OF AN ENERGY CONSCIOUS SINGLE-FAMILY HOME

Authors:
raw_author_name: Mohammed E. Haque
raw_affiliation_string: Department of Construction Science Texas A&M University
raw_author_name: Gaurav Agarwal
raw_affiliation_string: Department of Construction Science Texas A&M University

Abstract: With the advances in information technology (IT) over the last decade, the traditional teaching format of having an individual lecture to an audience has been supplemented, and in some cases, replaced by the rapid development and implementation of new distance learning methods.It is becoming increasingly important to develop web based 3-D visualization and animation to explain the various environmental conscious concepts and elements.The objective of this research paper was to showcase the energy saving elements used in an energy conscious singlefamily home design using 3D animation and virtual walkthrough.The model and animation can act as an excellent teaching tool to explain the various concepts integral to an energy conscious design, as the instructor would present the entire scheme in a virtual world so that the students can understand the concepts with more clarity and ease.The model would also help generate awareness among common people about energy conscious design as the web interface is easily accessible and user friendly.
Research with an Undergraduate Student: Using Entropy to Assess the Training of a Neural Network

Authors:
raw_author_name: Alexey L. Sadovski
raw_affiliation_string: Department of Computing and Mathematical Sciences Texas A&M University -Corpus Christi

Abstract: This paper reports on enhancing undergraduate education by doing research with students.The work presented was done with a freshman mathematics
Development of Social Engagement Activities to Increase Student Participation in a Makerspace

Authors:
raw_author_name: Jill Davishahl
raw_affiliation_string: Western; Western Washington University
raw_author_name: Madison Andrews
raw_affiliation_string: University of Texas at Austin

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 supporting non-dominant