Reviewerly/exHarmony huggingface dataset:
Monocular Depth Estimation Using a Deep Learning Model with Pre-Depth Estimation based on Size Perspective

In this paper, For the task of the depth map of a scene given a single RGB image. We present an estimation method using a deep learning model that incorporates size perspective (size constancy cues). By utilizing a size perspective, the proposed method aims to address the difficulty of depth estimation tasks which stems from the limited correlation between the information inherent to objects in RGB images (such as shape and color) and their corresponding depths. The proposed method consists of two deep learning models, a size perspective model and a depth estimation model, The size-perspective model plays a role like that of the size perspective and estimates approximate depths for each object in the image based on the size of the object's bounding box and its actual size. Based on these rough depth estimation (pre-depth estimation) results, A depth image representing through depths of each object (pre-depth image) is generated and this image is input with the RGB image into the depth estimation model. The pre-depth image is used as a hint for depth estimation and improves the performance of the depth estimation model. With the proposed method, it becomes possible to obtain depth inputs for the depth estimation model without using any devices other than a monocular camera be forehand. The proposed method contributes to the improvement in accuracy when there are objects present in the image that can be detected by the object detection model. In the experiments using an original indoor scene dataset, the proposed method demonstrated improvement in accuracy compared to the method without pre-depth images.
Exploration of Technological Trends in the Aerospace Field in Korea

The purpose of this study is to explore technology trends in the aerospace field in Korea. To this end, domestic patents applied by the Korea Aerospace Research Institute were collected and topic modeling analysis was performed. As a result of the study, research topics in the aerospace field in Korea were largely identified as 15 technologies. Of the 15 technologies derived, 9 were common to the space sector, 2 to the aviation sector, and 4 to the aerospace sector. From the research results, it can be seen that Korea
A Novel Composite DTERM-MDI Model for the Recovery of Missing Data in Internet of Medical Things

Background/Objectives: The Internet of Things (IoT) relies on consistent data delivery, crucial for maintaining service quality. However, challenges like connection issues, external threats, and sensor malfunctions can lead to data insufficiency, affecting IoT applications. Addressing the problem of missing data in the vast streams of IoT-generated data is essential. This paper introduces a novel Composite DTERM Missing Data Imputation (MDI) model for the Internet of Medical Things (IoMT) aimed at robustly recovering missing data. Methods: The Composite DTERM-MDI model comprises three phases: Dual Strategy based Missing Data Imputation (DS-MDI), Two Tier Missing Data Imputation (TT-MDI), and Ensemble Regression Model (ERM) for progressive imputation of Missing Not at Random (MNAR) type data. The effectiveness of the Composite DTERM-MDI model is demonstrated using the cStick dataset, achieving significantly improved accuracy, precision, recall, and F-measure compared to the original dataset. Additionally, a comparison of the Composite DTERM-MDI model with two standard imputation techniques, MICE and SICE, is performed using the UCI car dataset. Findings: Experimental results showcase the superiority of the Composite DTERM-MDI model-based imputed cStick dataset, with accuracy at 99.12%, precision at 99.98%, recall at 98.53%, and F-measure at 98.89%, outperforming the original cStick dataset. The study highlights the Composite DTERM-MDI model\'s efficiency and accuracy in addressing missing data challenges in IoMT, which is vital for informed medical decision-making. Novelty: Furthermore, a comparison of the Composite DTERM-MDI model with MICE and SICE using the UCI car dataset evaluates accuracy and F-measure across four classification algorithms (PMM, POLYREG, CART, and LDA). The Composite DTERM-MDI model achieves accuracy rates of 91.08%, 81.58%, 97.74%, and 97.74%, along with F-measures of 84.97%, 89.58%, 98.71%, and 99.17% for PMM, POLYREG, CART, and LDA, respectively. This comparison demonstrates the model\'s performance against established imputation techniques in a different context. Keywords: Internet of Medical Things (IoMT), Missing data imputation, Missing completely at random (MCAR), Missing at random (MAR), Missing not at random (MNAR), DTERM model
The Data Value based Asynchronous Federated Learning for UAV Swarm under Unstable Communication Scenarios

Federated learning has provided a new approach to coordinating a group of clients to train a machine learning model collaboratively, which can be easily embedded into Unmanned Aerial Vehicle (UAV) swarms. Compared with the terrestrial wireless networks, the UAV swarm faces more precarious communication conditions, rendering synchronous aggregation no longer tenable. Additionally, the data collected from UAVs tend to be heterogeneous due to different deployment regions or requirements. To overcome these restrictions, this paper has proposed a novel two-stage Asynchronous Federated Learning scheme for the UAV swarm. Initially, the convergence property of both convex and non-convex models trained by the proposed scheme is analyzed. In the pre-training stage, we modeled the learning process as a cooperative game with demonstrated monotonicity and submodularity. Furthermore, the Shapley Value is imported to quantify data values of UAVs, and the upper bound of its estimation error rate is derived. In the training stage, a new concept named Network Age of Updates (AoU) is proposed to address the fairness issue, quantifying the model’s generalization capability with data value consideration, and a sequential UAV selection scheduling is performed through the AoU minimization by Whittle Index method. Finally, the system performance is validated through both theoretical analysis and simulations.
Federated Split Learning With Joint Personalization-Generalization for Inference- Stage Optimization in Wireless Edge Networks

The demand for intelligent services at the network edge has introduced several research challenges. One is the need for a machine learning architecture that achieves personalization (to individual clients) and generalization (to unseen data) properties concurrently across different applications. Another is the need for an inference strategy that can satisfy network resource and latency constraints during testing-time. Existing techniques in federated learning have encountered a steep trade-off between personalization and generalization, and have not explicitly considered the resource requirements during the inference-stage. In this paper, we propose SplitGP, a joint edge-AI training and inference strategy that simultaneously captures generalization/personalization for efficient inference across resource-constrained clients. The training process of SplitGP is based on federated split learning, with the key idea of optimizing the client-side model to have personalization capability tailored to its main task, while training the server-side model to have generalization capability for handling out-of-distribution tasks. During testing-time, each client selectively offloads inference tasks to the server based on the uncertainty threshold tunable based on network resource availability. Through formal convergence analysis and inference time analysis, we provide guidelines on the selection of key meta-parameters in SplitGP. Experimental results confirm the advantage of SplitGP over existing baselines.
Exploring Benchmarks for Self-Driving Labs using Color Matching

Self Driving Labs (SDLs) that combine automation of experimental procedures with autonomous decision making are gaining popularity as a means of increasing the throughput of scientific workflows. The task of identifying quantities of supplied colored pigments that match a target color, the color matching problem, provides a simple and flexible SDL test case, as it requires experiment proposal, sample creation, and sample analysis, three common components in autonomous discovery applications. We present a robotic solution to the color matching problem that allows for fully autonomous execution of a color matching protocol. Our solution leverages the WEI science factory platform to enable portability across different robotic hardware, the use of alternative optimization methods for continuous refinement, and automated publication of results for experiment tracking and post-hoc analysis.
Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion

Concerned with user data privacy, this paper presents a new federated learning (FL) method that trains machine learning models on edge devices without accessing sensitive data. Traditional FL methods, although privacy-protective, fail to manage model heterogeneity and incur high communication costs due to their reliance on aggregation methods. To address this limitation, we propose a resource-aware FL method that aggregates local knowledge from edge models and distills it into robust global knowledge through knowledge distillation. This method allows efficient multi-model knowledge fusion and the deployment of resource-aware models while preserving model heterogeneity. Our method improves communication cost and performance in heterogeneous data and models compared to existing FL algorithms. Notably, it reduces the communication cost of ResNet-32 by up to 50% and VGG-11 by up to 10 × while delivering superior performance.
Characterizing the Performance of Triangle Counting on Graphcore's IPU Architecture

In recent years, we have seen an emergence of novel spatial architectures to accelerate domain-specific workloads like Machine Learning. There is a need to investigate their performance characteristics for traditional HPC workloads for their tighter integration with current and future heterogeneous compute resources. In this work, we implement, optimize and evaluate a parallel algorithm for Triangle Counting for graphs in Bulk Synchronous Parallel (BSP) model for Graphcore’s IPU architecture as well as discuss lessons learned. This study demonstrates IPU’s competency in handling such irregular workloads by providing an average speedup of up to 5.3x over NVIDIA A100 GPU on real-world datasets.
The framing of time-dependent machine learning models improves risk estimation among young individuals with acute coronary syndromes

Abstract Introduction Acute coronary syndrome (ACS) is a common cause of death in individuals older than 55 years. Although younger individuals are less frequently seen with ACS, this clinical event shows high recurrence rates and triggers considerable economic burden. Young individuals with ACS (yACS) are usually underrepresented and show idiosyncratic epidemiologic features compared to older subjects. These differences may justify why available risk prediction models usually penalize yACS with higher false positive rates compared to older subjects. We hypothesized that exploring temporal framing structures such as prediction time, observation windows and subgroup-specific prediction, could improve time-dependent prediction metrics. More specifically, we evaluated potential improvements in risk prediction quality among yACS by (i) developing models specifically in yACS versus a global cohort; (ii) splitting predictive rules into short- and long-term prediction windows [STWm and LTWm] versus a global follow-up model (GFm). Methods We included 6341 consecutive subjects with ACS (2242 with yACS) admitted into public hospitals in a large city in Brazil who undergone coronarography up to 48h after hospital admission from 2011 to 2020. The observation window in STWm and GFm included the first 48h upon hospital admission, and LTWm included all in-hospital information. yACS cohort was divided into train/validation-set (70%, n=1569) and test-set (30%, n=673); global cohort was divided in training/validation-set (70%, n=4439) and test-set including the 673 yACS subjects. Models were repeated over five cross-validation folds and then assessed in the test-set. C-statistics was the evaluation metric for STWm and time-dependent concordance (Ctd-index) for LTWm. STWm evaluated the occurrence of in-hospital cardiovascular deaths and recurrent ACS (MACE) and LTWm estimated events occurring post-discharge from index ACS hospitalization considering time-to-event with competing risks (MACE versus non-cardiovascular deaths). Results Among yACS and older subjects, respectively, in-hospital MACE occurred in 180 and 493 individuals, post-discharge MACE in 454 and 881; and post-discharge non-cardiovascular death in 47 and 285 after 6.67 years (95%CI 5.59-7.24) of follow-up. The best strategy was to design models specifically in yACS individuals combining STWm and LTWm. Among yACS subjects, STWm and LTWm yielded a C-statistics [0.921 (95%CI 0.889-0.953)] and Ctd-index [0.722 (95%CI 0.678-0.760)], respectively, while the best Ctd-index in GFm was 0.681 (95%CI 0.654-0.703). There was very low concordance among top predictors of MACE for prACS versus global cohort, as well as for STWm versus LTWm. Conclusions Predictive accuracy for adverse clinical events was optimized by using specific rules for yACS and splitting short-term and long-term prediction windows, leading to the detection of 80% of events, compared to 69% by using a rule designed for the global cohort.
SOUTH SUDAN: WFP Warning

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 60, Issue 9 p. 24448B-24449B Policy and Practice SOUTH SUDAN: WFP Warning First published: 10 November 2023 https://doi.org/10.1111/j.1467-6346.2023.11313.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume60, Issue9November 2023Pages 24448B-24449B RelatedInformation
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 60, Issue 9 p. I-I Full Issue Full Issue First published: 10 November 2023 https://doi.org/10.1111/j.1467-6346.2023.11294.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume60, Issue9November 2023Pages I-I RelatedInformation
Anomaly data synthesis and detection via domain randomization

The demand for a large amount of data necessary for learning is increasing with the great development of artificial intelligence. The synthesis of engineering data is challenging in that it is not only to combine data, but also to proceed with data synthesis while keeping the engineering characteristics intact. To address this problem, this work proposes a synthesis and detection model of anomalous data utilizing domain randomization. This model learns data from existing systems to identify patterns and synthesizes new data by itself with domain randomization. The learned model can accurately detect anomaly data in the system in various environments.
Image restoration with group sparse representation and low‐rank group residual learning

Abstract Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practice. In order to improve the quality of image restoration based on GSR, the sparsity residual model expects the representation learned from degraded images to be as close as possible to the true representation. In this article, a group residual learning based on low‐rank self‐representation is proposed to automatically estimate the true group sparse representation. It makes full use of the relation among patches and explores the subgroup structures within the same group, which makes the sparse residual model have better interpretation furthermore, results in high‐quality restored images. Extensive experimental results on two typical image restoration tasks (image denoising and deblocking) demonstrate that the proposed algorithm outperforms many other popular or state‐of‐the‐art image restoration methods.
Statistical Analysis and Runtime Monitoring for an AI-based Autonomous Centerline Tracking System

Autonomous Centerline Tracking (ACT) enables an unmanned aircraft to be guided down the center of the runway, using a camera-based Deep Neural Network (DNN). ACT is safety-critical. The EASA Guidelines for machine-learning based systems list numerous assurance objectives that must be met toward certification and V&amp;V. We extend our analysis framework SYSAI to provide feedback on performance of system and AI component to the designer and describe a combination with a runtime monitoring architecture that also supports advanced risk mitigation to support safety assurance of a complex AI-based aerospace system.
Penerapan Model Pembelajaran Cooperative Integrated Reading and Compocition (CIRC) untuk Meningkatkan Hasil Belajar Biologi

Understanding and student learning outcomes have become a major problem in education today. There are various learning models that can be used by teachers, including the Cooperative Integrated Reading and Compocition (CIRC) learning model. This study aims to determine the application of the CIRC learning model in improving Biology learning outcomes. The type of research used in this study was descriptive quantitative with a sample of 29 students. The sampling technique uses cluster random sampling. The instruments of this study are observations and tests. Observation guidelines are carried out to determine the objective learning conditions carried out by teachers and students. The test used in this study is in the form of student learning results obtained in the implementation of the test. The results showed that the application of the CIRC learning model can improve student learning outcomes.
Movement in Video Classification Using Structured Data: Workout Videos Application

Nowadays, several video movement classification methodologies are based on reading and processing each frame using image classification algorithms. However, it is rare to find approaches using angle distribution over time. This paper proposes video movement classification based on the exercise states calculated from each frame’s angles. Different video classification approaches and their respective variables and models were analyzed to achieve this, using unstructured data: images. Besides, structure data as angles from critical joints Armpits, legs, elbows, hips, and torso inclination were calculated directly from workout videos, allowing the implementation of classification models such as the KNN and Decision Trees. The result shows these techniques can achieve similar accuracy, close to 95%, concerning Neural Networks algorithms, the primary model used in the previously mentioned approaches. Finally, it was possible to conclude that using structured data for movement classification models allows for lower performance costs and computing resources than using unstructured data without compromising the quality of the model.
Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method

Breast cancer’s high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks—EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50—integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system’s detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system’s superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes.
Omni-Modeler: Rapid Adaptive Visual Recognition with Dynamic Learning

Deep neural network (DNN) image classification has grown rapidly as a general pattern detection tool for an extremely diverse set of applications; yet dataset accessibility remains a major limiting factor for many applications. This paper presents a novel dynamic learning approach to leverage pretrained knowledge to novel image spaces in the effort to extend the algorithm knowledge domain and reduce dataset collection requirements. The proposed Omni-Modeler generates a dynamic knowledge set by reshaping known concepts to create dynamic representation models of unknown concepts. The Omni-Modeler embeds images with a pretrained DNN and formulates compressed language encoder. The language encoded feature space is then used to rapidly generate a dynamic dictionary of concept appearance models. The results of this study demonstrate the Omni-Modeler capability to rapidly adapt across a range of image types enabling the usage of dynamically learning image classification with limited data availability.