Reviewerly/exHarmony huggingface dataset:
A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing

Authors:
raw_author_name: Tasiu Muazu
raw_affiliation_string: College of Computer and Information, Hohai University, Nanjing, 210098, PR China
raw_author_name: Yingchi Mao
raw_affiliation_string: Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, 210098, PR China
raw_author_name: Abdullahi Uwaisu Muhammad
raw_affiliation_string: College of Computer and Information, Hohai University, Nanjing, 210098, PR China
raw_author_name: Muhammad Ibrahim
raw_affiliation_string: College of Environment, Hohai University, Nanjing, 210098, PR China
raw_author_name: Omaji Samuel
raw_affiliation_string: Department of Computer Science, Edo State University, Uzairue, Iyamho 312101, Nigeria; Department of Computer Science, INTI International University, Nilai 71800, Malaysia

Abstract: In the Internet of medical things, data from a single source can be easily analyzed. Besides, it is paramount to collect data from multiple sources to provide consistent, accurate, and vital information as compared to that collected from a single source. Data fusion enables the combination of data from multiple sensors. However, privacy concerns need to be addressed during data aggregation. Furthermore, since medical data is becoming increasingly sophisticated via multimodal means, there is a high likelihood that a complex relationship among biological processes exists. This challenge motivates the study. This study is proposing a secure federated learning system based on data fusion using multi-party computation and additive secret-sharing method. The gradient parameters of the federated training model are protected rather than the actual data. The purpose of protecting the gradient parameters is to address the tradeoff between prediction accuracy and data privacy. In the proposed model, a convolutional neural network, which has an efficient weight-sharing approach, is employed for the prediction. The weight of the model is encrypted using a multi-computation and additive secret sharing. Extensive simulations are carried out to ascertain the efficiency of the proposed model. Besides, the proposed system model is compared with existing works in the literature in terms of accuracy, precision, recall and f1-score. Simulation results show that the proposed model has 97% accuracy as compared to 93% for random forest, 91% for Naive Bayes, 92% for logistic regression and 95% for decision tree. Furthermore, F1-score of the proposed model is 89% as compared to 85% for random forest, 80% for Naive Bayes, 82% for logistic regression and 79% for decision tree. Security analysis of the proposed system model is performed, which shows that the system is robust against weighted majority vote attack, honest-but-curious party and other security related attacks.
Post-training quantization for re-parameterization via coarse &amp; fine weight splitting

Authors:
raw_author_name: Dawei Yang
raw_affiliation_string: School of Computer Engineering, Nanjing Institute of Technology, China
raw_author_name: Ning He
raw_affiliation_string: Houmo AI, China; Southeast University, China
raw_author_name: Zhihang Yuan
raw_affiliation_string: Houmo AI, China
raw_author_name: Jiangyong Yu
raw_affiliation_string: Houmo AI, China
raw_author_name: Chen Xu
raw_affiliation_string: Houmo AI, China
raw_author_name: Zhe Jiang
raw_affiliation_string: Southeast University, China; University of Cambridge, United Kingdom

Abstract: Although neural networks have made remarkable advancements in various applications, they require substantial computational and memory resources. Network quantization is a powerful technique to compress neural networks, allowing for more efficient and scalable AI deployments. Recently, Re-parameterization has emerged as a promising technique to enhance model performance while simultaneously alleviating the computational burden in various computer vision tasks. However, the accuracy drops significantly when applying quantization on the re-parameterized networks. We identify that the primary challenge arises from the large variation in weight distribution across the original branches. To address this issue, we propose a coarse & fine weight splitting (CFWS) method to reduce quantization error of weight, and develop an improved KL metric to determine optimal quantization scales for activation. To the best of our knowledge, our approach is the first work that enables post-training quantization applicable on re-parameterized networks. For example, the quantized RepVGG-A1 model exhibits a mere 0.3% accuracy loss. The code is in https://github.com/NeonHo/Coarse-Fine-Weight-Split.git
Three dimensional tracking of rigid objects in motion using 2D optical flows

Authors:
raw_author_name: Matthew N. Dailey
raw_affiliation_string: School of Engineering and Technology, Asian Institute of Technology, Klong Luang, 12120 Pathum Thani, Thailand
raw_author_name: Mongkol Ekpanyapong
raw_affiliation_string: School of Engineering and Technology, Asian Institute of Technology, Klong Luang, 12120 Pathum Thani, Thailand

Abstract: Detecting and tracking objects in image sequences is paramount for any video analytic. While object detectors have become increasingly robust, motion estimates based on the straightforward approach of running a detector and linking detections are prone to bounding box noise. An algorithm for the monocular estimation of the 3D motion of rigid objects is presented, combining an object detector, minimal camera calibration, and 2D optical flows observed on the image plane. The algorithm utilizes the 2D Delaunay triangulation over geometrically consistent optical flow tracks to identify regions common to the same object. The algorithm is evaluated on the special case of image sequences of vehicles on roads. Experiments on BrnoCompSpeed dataset show that speed estimates from both detector-based tracking (mean error of 1.62 km/h) and optical flow based tracking (mean error of 2.19 km/h) perform competitively on straight roads. An extensive empirical evaluation is also conducted on a new dataset containing synthetic and real world scenes that also include vehicle trajectories involving rotation. A naive baseline bounding box track based method obtained a mean error of 5.29 km/h for vehicle speed, but optical flow tracks performed significantly better with mean error of 1.65 km/h on the new dataset including rotation. With the new method, video analytics such as vehicle speed estimation and lane change detection can obtain precise information about the 3D trajectory of rigid objects in motion.
Speech driven video editing via an audio-conditioned diffusion model

Authors:
raw_author_name: Dan Bigioi
raw_affiliation_string: University of Galway, Ireland
raw_author_name: Shubhajit Basak
raw_affiliation_string: University of Galway, Ireland
raw_author_name: Michał Stypułkowski
raw_affiliation_string: University of Wrocław, Poland
raw_author_name: Maciej Zieba
raw_affiliation_string: Tooploox, Poland; Wrocław University of Science and Technology, Poland
raw_author_name: Hugh Jordan
raw_affiliation_string: Trinity College Dublin, Ireland
raw_author_name: Rachel McDonnell
raw_affiliation_string: Trinity College Dublin, Ireland
raw_author_name: Peter Corcoran
raw_affiliation_string: University of Galway, Ireland

Abstract: Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditory speech recording, the lip and jaw motions are re-synchronised without relying on intermediate structural representations such as facial landmarks or a 3D face model. We show this is possible by conditioning a denoising diffusion model on audio mel spectral features to generate synchronised facial motion. Proof of concept results are demonstrated on both single-speaker and multi-speaker video editing, providing a baseline model on the CREMA-D audiovisual data set. To the best of our knowledge, this is the first work to demonstrate and validate the feasibility of applying end-to-end denoising diffusion models to the task of audio-driven video editing. All code, datasets, and models used as part of this work are made publicly available here: https://danbigioi.github.io/DiffusionVideoEditing/.
A multi-in and multi-out dendritic neuron model and its optimization

Authors:
raw_author_name: Jun Yu
raw_affiliation_string: Niigata University, Niigata-shi, 9502181, Niigata, Japan
raw_author_name: Chunzhi Gu
raw_affiliation_string: Toyohashi University of Technology, Toyohashi-shi, 4418580, Aichi, Japan
raw_author_name: Shangce Gao
raw_affiliation_string: University of Toyama, Toyama-shi, 9308555, Toyama, Japan
raw_author_name: Chao Zhang
raw_affiliation_string: University of Fukui, Fukui-shi, 9108507, Fukui, Japan

Abstract: Artificial neural networks (ANNs), inspired by the interconnection of real neurons, have achieved unprecedented success in various fields such as computer vision and natural language processing. Recently, a novel mathematical ANN model, known as the dendritic neuron model (DNM), has been proposed to address nonlinear problems by more accurately reflecting the structure of real neurons. However, the single-output design limits its capability to handle multi-output tasks, significantly lowering its applications. In this paper, we propose a novel multi-in and multi-out dendritic neuron model (MODN) to tackle multi-output tasks. Our core idea is to introduce a filtering matrix to the soma layer to adaptively select the desired dendrites to regress each output. Because such a matrix is designed to be learnable, MODN can explore the relationship between each dendrite and output to provide a better solution to downstream tasks. We also model a telodendron layer into MODN to simulate better the real neuron behavior. Importantly, MODN is a more general and unified framework that can be naturally specialized as the DNM by customizing the filtering matrix. To explore the optimization of MODN, we investigate both heuristic and gradient-based optimizers and introduce a two-step training method for MODN. Extensive experimental results performed on 11 datasets on both binary and multi-class classification tasks demonstrate the effectiveness of MODN, with respect to accuracy, convergence, and generality.
Variations on a Hexagon: Iterative Design of Interactive Cyberphysical Tokens and Constraints

Authors:
raw_author_name: Brygg Ullmer
raw_affiliation_string: Human-Centered Computing Division, School of Computing, Tangible Visualization Group, Clemson University, United States
raw_author_name: Alexandre Gomes De Siqueira
raw_affiliation_string: Virtual Environments Research Group, Florida University, United States and Tangible Visualization Group, Clemson University, United States

Abstract: We describe iterations on the design of a hexagonal token and constraint tangible and cyberphysical interface. Our system has been co-designed both for use in tangible, embedded, embodied interaction and extended reality classroom contexts, and within broader applied generalizations. We survey some of the related literature in hexagonal, rectangular, and triangular tangible interfaces. We express a design space characterizing several aspects of these prior systems; and apply these to a number of additional virtual and physical iterations. We accompany our text with 3D printable, circuit board, and virtual models for a number of these interactors, usable in both tangible and purely virtual forms, including versions engaging four different embedded computing platforms (Raspberry Pi Zero, Raspberry Pi Pico, Adafruit Circuit Playground, and Blinks). We discuss experiences from use of these in classroom settings, and steps toward broader applications.
MorphMatrix: A Toolkit Facilitating Shape-Changing Interface Design

Authors:
raw_author_name: Brygg Ullmer
raw_affiliation_string: Human Centered Computing division, School of Computing, Tangible Visualization Lab, Clemson University, United States

Abstract: Shape-changing interfaces offer transformative potential for user interaction across numerous sectors, facilitating more intuitive and immersive experiences. In parallel, their significant design complexity and cost constrain the widespread adoption of these interfaces. To tackle these challenges, MorphMatrix is a toolkit that lowers the entry barrier for designing and implementing shape-changing interfaces. MorphMatrix includes a software system with customized GUI and simulation capabilities; a flexible physical framework adaptable for various application scenarios; and a kinetic system that governs interface transformations. We illustrate the potential of MorphMatrix through five diverse application scenarios, complemented by a user study validating its effectiveness and usability. MorphMatrix streamlines the design and construction of shape-changing interfaces, thus facilitating broader applicability and potential impacts across interactive computational systems.
UFPS: A unified framework for partially annotated federated segmentation in heterogeneous data distribution

Authors:
raw_author_name: Li Yan Ma
raw_affiliation_string: School of Computer Engineering and Science, Shanghai University, Shanghai, China
raw_author_name: Tie Yong Zeng
raw_affiliation_string: Department of Mathematics, Chinese University of Hong Kong, Hongkong, China

Abstract: Partially supervised segmentation is a label-saving method based on datasets with fractional classes labeled and intersectant. Its practical application in real-world medical scenarios is, however, hindered by privacy concerns and data heterogeneity. To address these issues without compromising privacy, federated partially supervised segmentation (FPSS) is formulated in this work. The primary challenges for FPSS are class heterogeneity and client drift. We propose a unified federated partially labeled segmentation (UFPS) framework to segment pixels within all classes for partially annotated datasets by training a comprehensive global model that avoids class collision. Our framework includes unified label learning (ULL) and sparse unified sharpness aware minimization (sUSAM) for class and feature space unification, respectively. Through empirical studies, we find that traditional methods in partially supervised segmentation and federated learning often struggle with class collision when combined. Our extensive experiments on real medical datasets demonstrate better deconflicting and generalization capabilities of UFPS.
Parallel Light Fields: A Perspective and A Framework

Authors:
raw_author_name: Fei-Yue Wang
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
raw_author_name: Yu Shen
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Abstract: Light fields give relatively complete description of scenes from perspective of angles and positions of rays. At present time, most of the computer vision algorithms take 2D images as input which are simplified expression of light fields with depth information discarded. In theory, computer vision tasks may achieve better performance as long as complete light fields are acquired. Light field data enjoy a natural advantage over images or videos in 3D reconstruction, and are in great demand in applications such as virtual reality (VR) and augmented reality (AR). However, the high cost hardware and complicated synchronization issues in their array deployment severely hindered development of light field cameras. As a consequence, available light field data are much smaller than traditional images and videos in volume, and a lot of deep learning based computer vision algorithms that take light field images as input are difficult to be deployed.
The Relative Motion When Accelerating the Container of an Object Submerged in a Fluid

Authors:
raw_author_name: Yu Chen
raw_affiliation_string: Korea National University of Education , Cheongju, South Korea
raw_author_name: Yu Jin Ahn
raw_affiliation_string: Korea National University of Education , Cheongju, South Korea
raw_author_name: Jung Bog Kim
raw_affiliation_string: Korea National University of Education , Cheongju, South Korea

Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ah Reum Yang, Yu Chen, Hee Ra Kim, Yu Jin Ahn, Jung Bog Kim; The Relative Motion When Accelerating the Container of an Object Submerged in a Fluid. Phys. Teach. 1 February 2024; 62 (2): 110–112. https://doi.org/10.1119/5.0109132 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Revolutionizing Education: Navigating the New Landscape Post-COVID-19: A Scoping Review

Authors:
raw_author_name: Siti Mistima Maat
raw_author_name: Muhammad Sofwan Mahmud

Abstract: &lt;p style="text-align:justify"&gt;Education systems worldwide have been significantly disrupted due to the COVID-19 pandemic, creating an immediate need for a revamp of conventional teaching and learning techniques. To explore how this has affected the educational landscape, a scoping review was conducted. This scoping review aimed to examine the changes that occurred in the education field and to explore how it has transformed the educational landscape review. Using Arksey and O'Malley's methodology, 51 articles were selected for analysis from two leading databases: Scopus and Web of Science. All chosen articles were then subjected to thematic analysis. Three main aspects impacted by this global event were uncovered, which are technological advancements and digital transformation, changes in pedagogy and teaching methods, and mental health and well-being issues. This scoping review provides valuable insights into one of the most critical sectors affected by COVID-19, which can assist with planning future strategies for similar crises.&lt;/p&gt;
Extract, detect, eliminate: Enhancing reliability and performance of computational pathology through artifact processing pipelines

Authors:
raw_author_name: Neel Kanwal
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway
raw_author_name: Kjersti Engan
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway

Abstract: Cancer diagnosis involves obtaining tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing results in the introduction of artifacts, which are ultimately transferred to the digitized version of the glass slide, known as the whole slide image (WSI). Histological artifacts are diagnostically irrelevant areas and may result in wrong predictions by a deep learning (DL) algorithm. These WSIs are also typically large giga-pixel images, raising computational concerns for processing all WSIs simultaneously. Therefore, an artifact processing pipeline that can extract, detect, and exclude artifacts from WSI before running a diagnostic algorithm is much needed for a reliable computational pathology (CPATH) system. Artifact detection will not only ensure accurate CPATH predictions but also provide quality control. In this video, we demonstrate an artifact processing pipeline that can detect five notable artifacts: blood, folded tissue, blurry areas, damaged tissue, and air bubbles, and provide various post-processing outputs such as a segmentation mask for highlighting regions of interest, artifact report and artifact-refined WSI for further computational analysis. Equipping modern CPATH systems with the proposed artifact detection pipeline can ensure histologically relevant areas for diagnostic algorithms, stepping towards bringing automated diagnostics into clinical practice.
A High-Capacity and High-Security Image Steganography Network Based on Chaotic Mapping and Generative Adversarial Networks

Authors:
raw_author_name: Lin Huo
raw_affiliation_string: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
raw_author_name: Jie Wei
raw_affiliation_string: Guangxi Key Laboratory of Digital Infrastructure, Guangxi Information Center, Nanning 530000, China
raw_author_name: Lang Huang
raw_affiliation_string: School of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Abstract: With the enhancement of information volume, people are not satisfied with transmitting only a single secret image at a time but chase to hide multiple secret images in a single picture; however, the large-capacity steganographic scale can easily lead to the degradation of the quality of the image, which attracts the attention of eavesdroppers. In this paper, we propose a Chaotic mapping-enHanced imAge Steganography nEtwork (CHASE), which pioneers to hide colour images in grey images and reduces the difference between the container image and the cover image through the image permutation method, so as to enhance the security of the steganography. The method demonstrates excellent steganalysis resistance in experiments and introduces Generative Adversarial Networks (GANs) to improve the image fidelity in large-capacity steganographic scales. The fusion of chaotic mapping and GAN optimisation enables the steganographic network to simultaneously balance security and image quality. The experimental results show that CHASE can keep the secret image with good invisibility under large-capacity steganographic scales, and at the same time, it can reveal the secret image with high fidelity, and its steganalysis-resistant capability is much better than other state-of-the-art methods.
On clustering levels of a hierarchical categorical risk factor

Authors:
raw_author_name: Katrien Antonio
raw_affiliation_string: Faculty of Economics and Business, KU Leuven, Belgium; Faculty of Economics and Business, University of Amsterdam, Amsterdam, The Netherlands; LRisk, Leuven Research Center on Insurance and Financial Risk Analysis, KU Leuven, Belgium; LStat, Leuven Statistics Research Center, KU Leuven, Belgium

Abstract: Abstract Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. We commonly rely on methods such as the random effects approach to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers’ compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. Our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, we obtain a better differentiation between high-risk and low-risk companies.