Reviewerly/exHarmony huggingface dataset:
Visual Detection Method for Missing Infusion Bag Pipeline

As an essential medical device, a bag infusion set is often used for intravenous infusion, and an infusion bag is an essential part of the bag infusion set. Due to the unavoidable defects in the production process, quality detection of infusion bags is critical to ensure the use quality of the infusion set. In this paper, we adopt a machine vision system to inspect the assembly quality of the lanyard and dosing interface of liquid bag assembly and conduct in-depth discussion and research from image acquisition, a defect detection strategy, and a defect detection algorithm of a vision system for two defects of lanyard missing and dosing interface missing. The design of the image acquisition auxiliary mechanism is realized to solve the complex problem of image acquisition due to the irregular shape of the liquid bag assembly; based on determining the defect detection strategy, the algorithm study of contour extraction is finally completed through comparison experiments to extract a precise contour of the liquid bag piping area; finally, the virtual straight line method is proposed and combined with the ROI selected according to the position feature of the outer rectangle of the contour in this paper, the count of the number of contours is completed, and the defect detection goal is finally achieved. The pipeline defect detection rate of the method proposed in this paper reaches 100%, which can perfectly replace the existing manual visual inspection and reduce the employment cost of enterprises.
Recoloring Grayscale Images using GAN

In this paper, we intend to develop an efficient method for Colorizing Grayscale Images such that the output images are close to their real colors. The main objective is to produce a precise image should be produced, which means that the output image should be as close to its natural color as possible. In colorization, there is no aim to recover the ground truth color; rather, the aim is to create a plausible colorization that is useful to the user, even if it differs from the ground truth color. In order to solve this problem, we use Convolutional Neural Networks (CNN) and a variety of deep learning techniques. Instead of using vectors drawn at random from the probability distribution, we employ Conditional Generative Adversarial Networks (GAN). This will enable it to extract different features and understand the correlations between them in order to predict colored images.
Semantic segmentation via pixel‐to‐center similarity calculation

Abstract Since the fully convolutional network has achieved great success in semantic segmentation, lots of works have been proposed to extract discriminative pixel representations. However, the authors observe that existing methods still suffer from two typical challenges: (i) The intra‐class feature variation between different scenes may be large, leading to the difficulty in maintaining the consistency between same‐class pixels from different scenes; (ii) The inter‐class feature distinction in the same scene could be small, resulting in the limited performance to distinguish different classes in each scene. The authors first rethink semantic segmentation from a perspective of similarity between pixels and class centers. Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset, which can be regarded as the embedding of the class center. Thus, the pixel‐wise classification amounts to computing similarity in the final feature space between pixels and the class centers. Under this novel view, the authors propose a Class Center Similarity (CCS) layer to address the above‐mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers. The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene, which adapt the large intra‐class variation between different scenes. Specially designed Class Distance Loss (CD Loss) is introduced to control both inter‐class and intra‐class distances based on the predicted center‐to‐center and pixel‐to‐center similarity. Finally, the CCS layer outputs the processed pixel‐to‐center similarity as the segmentation prediction. Extensive experiments demonstrate that our model performs favourably against the state‐of‐the‐art methods.
Addressing Interpersonal Harm in Online Gaming Communities: The Opportunities and Challenges for a Restorative Justice Approach

Most social media platforms implement content moderation to address interpersonal harms such as harassment. Content moderation relies on offender-centered, punitive approaches, e.g., bans and content removal. We consider an alternative justice framework, restorative justice, which aids victims in healing, supports offenders in repairing the harm, and engages community members in addressing the harm collectively. To assess the utility of restorative justice in addressing online harm, we interviewed 23 users from Overwatch gaming communities, including moderators, victims, and offenders; such communities are particularly susceptible to harm, with nearly three quarters of all online game players suffering from some form of online abuse. We study how the communities currently handle harm cases through the lens of restorative justice and examine their attitudes toward implementing restorative justice processes. Our analysis reveals that cultural, technical, and resource-related obstacles hinder implementation of restorative justice within the existing punitive framework despite online community needs and existing structures to support it. We discuss how current content moderation systems can embed restorative justice goals and practices and overcome these challenges.
DDL-MVS: Depth Discontinuity Learning for Multi-View Stereo Networks

We propose an enhancement module called depth discontinuity learning (DDL) for learning-based multi-view stereo (MVS) methods. Traditional methods are known for their accuracy but struggle with completeness. While recent learning-based methods have improved completeness at the cost of accuracy, our DDL approach aims to improve accuracy while retaining completeness in the reconstruction process. To achieve this, we introduce the joint estimation of depth and boundary maps, where the boundary maps are explicitly utilized for further refinement of the depth maps. We validate our idea by integrating it into an existing learning-based MVS pipeline where the reconstruction depends on high-quality depth map estimation. Extensive experiments on various datasets, namely DTU, ETH3D, “Tanks and Temples”, and BlendedMVS, show that our method improves reconstruction quality compared to our baseline, Patchmatchnet. Our ablation study demonstrates that incorporating the proposed DDL significantly reduces the depth map error, for instance, by more than 30% on the DTU dataset, and leads to improved depth map quality in both smooth and boundary regions. Additionally, our qualitative analysis has shown that the reconstructed point cloud exhibits enhanced quality without any significant compromise on completeness. Finally, the experiments reveal that our proposed model and strategies exhibit strong generalization capabilities across the various datasets.
Semantic Communication System for 3D Video

Semantic communication systems are targeted to be the new frontier of 6G communications. They will solve the issue that the current 5G systems are getting close to the Shannon limit while the amount of data is constantly growing. The semantic representation of the world will allow understanding the data by the end nodes as well as the network itself. Using common knowledge of the world by all network nodes will result in data compression beyond the existing compression types. This will be achieved by filtering redundant and irrelevant information and extracting the meaning. Undoubtedly, the main force behind the semantic communications is the development of AI. It can understand the world and because of this, it is in the heart of this new paradigm, semantic communications. Considering the emerging communication applications, such as the metaverse, compression and transmission of 3D data is expected to be one of the primary types of data in 6G. This type of data has properties that are not inherent to traditional 2D images and video, and therefore necessitates the use of new compression types. In this paper, we propose a semantic representation-based system for semantic communication of 3D video data. For encoding and decoding 3D data in the system, graph-convolutional neural networks are used. A dataset of 3D models of moving human bodies that were artificially created is utilized to test it.
Understanding Spatial Context in Convolutional Neural Networks Using Explainable Methods: Application to Interpretable GREMLIN

Abstract Convolutional neural networks (CNNs) are opening new possibilities in the realm of satellite remote sensing. CNNs are especially useful for capturing the information in spatial patterns that is evident to the human eye but has eluded classical pixelwise retrieval algorithms. However, the black-box nature of CNN predictions makes them difficult to interpret, hindering their trustworthiness. This paper explores a new way to simplify CNNs that allows them to be implemented in a fully transparent and interpretable framework. This clarity is accomplished by moving the inner workings of the CNN out into a feature engineering step and replacing the CNN with a regression model. The specific example of the GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is used to demonstrate that such simplifications are possible and to show the benefits of the interpretable approach. GREMLIN translates images of GOES radiances and lightning into images of radar reflectivity, and previous research used explainable artificial intelligence (XAI) approaches to explain some aspects of how GREMLIN makes predictions. However, the Interpretable GREMLIN model shows that XAI missed several strategies, and XAI does not provide guarantees on how the model will respond when confronted with new scenarios. In contrast, the interpretable model establishes well-defined relationships between inputs and outputs, offering a clear mapping of the spatial context utilized by the CNN to make accurate predictions, and providing guarantees on how the model will respond to new inputs. The significance of this work is that it provides a new approach for developing trustworthy artificial intelligence models. Significance Statement Convolutional neural networks (CNNs) are very powerful tools for interpreting and processing satellite imagery. However, the black-box nature of their predictions makes them difficult to interpret, compromising their trustworthiness when applied in the context of high-stakes decision-making. This paper develops an interpretable version of a CNN model, showing that it has similar performance as the original CNN. The interpretable model is analyzed to obtain clear relationships between inputs and outputs, which elucidates the nature of spatial context utilized by CNNs to make accurate predictions. The interpretable model has a well-defined response to inputs, providing guarantees for how it will respond to novel inputs. The significance of this work is that it provides an approach to developing trustworthy artificial intelligence models.
Contingent connectivity: Internet shutdowns and the infrastructural precarity of digital citizenship

The Indian state has invested simultaneously in connectivity by pursuing universal Internet access and in disconnectivity by leading the world in state-ordered Internet shutdowns. How can we make sense of these contradictory approaches to technology policy? This article argues that this paradox illustrates a bifurcated experience of digital citizenship moderated infrastructurally through differential access to mobile connectivity. While previous research has largely interpreted Internet shutdowns as curtailments of freedom of expression, this article evaluates the implications for citizenship itself by bringing together scholarship on digital governance, science and technology studies (STS) approaches to Internet governance, and postcolonial and decolonial theory. More broadly, this article raises the stakes for critical analysis of how authoritarian states approach Internet policy to bridge digital divides—and for evaluating quality and contingency of connectivity experienced by marginalized and peripheral communities.
Towards Gesture-Based Cooperation with Cargo Handling Unmanned Aerial Vehicles

This work provides the fundament for a gesture-based interaction system between cargo-handling unmanned aerial vehicles (UAVs) and ground personnel. It enables novice operators to visually communicate commands with higher abstractions through a minimum number of necessary gestures. The interaction concept intends to transfer two goal-directed control techniques to a cargo-handling use case: Selecting objects via deictic pointing communicates intention and a single proxy manipulation gesture controls the UAV’s flight. A visual processing pipeline built around an RGB-D sensor is presented and its subordinate components like lightweight object detectors and human pose estimation methods are benchmarked on the UAV-Human dataset. The results provide an overview of suitable methods for 3D gesture-based human drone interaction. A first unoptimized model ensemble runs with 7 Hz on a Jetson Orin AGX Developer Kit.
Fast template matching and object detection techniques using φ-correlation and binary circuits

Abstract In this paper, two new accelerated and robust template matching and object detection algorithms are established. The algorithms retain the accuracy measures compared to relevant well-known standard and recent schemes, and remarkably accelerate the execution time simultaneously. Instead of dealing with images in the grayscale or color representations, the proposed methods are established by measuring resemblance on extracted binary images. The two different techniques are based on the φ -correlation coefficient and logical circuits. Both techniques enhance the accuracy, compared to two classical and two recent methods, while giving very efficient running time. The algorithms are carried out on two different datasets used in state-of-the-art methods with performance and comparison analysis. Robustness of the proposed algorithms against normal and artificial noises is examined and assured as well.
Hierarchical Attention-Based Sensor Fusion Strategy for Depth Estimation in Diverse Weather

In this paper, we propose a hierarchical attention-based sensor fusion strategy for depth estimation under various weather conditions. Multiple-sensor fusion is proven as a promising solution for predicting accurate depth maps in diverse weather conditions, especially for extreme weather conditions. However, most of the current studies simply fuse the information from different sensors without jointly considering the difference in their performance at the sensor level and feature level. To fill this gap, our hierarchical attention-based fusion strategy uses two attention mask-generation modules that weigh sensor data from branches (i.e. different sensors) and features. With the cooperation of these two masks, our system is able to determine the adaptive contribution of each sensor as well as the individual contribution of each feature in the sensor regarding their performance in different weather. We compare the proposed methods with the baseline, i.e. the late fusion Sparse-to-Dense model, and two extended models individually with the branch-wise-only and feature-wise-only masks. The results show a robust and superior performance of our methods even in clear environments where the baseline already performs well enough. Moreover, we investigate the performance of RGB camera, radar, and LiDAR in foggy environments comprehensively by visualizing the generated mask. Our results show a significantly increased importance of radar sensors in extreme weather conditions, e.g. dense fog.
Deep Time-Aware Attention Neural Network for Sequential Recommendation

For click-through rate (CTR) prediction tasks, a good prediction performance can be obtained by full explorations of both user behavior and item behavior. Since user’s interests have a great influence on user’s behaviors, it is very important to learn users’ intrinsic interests according to their behaviors. User interests are not only diverse but also in dynamic change. However, the dynamics of user interests’ change are not fully taken into account by the majority of current CTR models. The latest sequential recommendation algorithm ignores the subjectivity of users when it uses a two-layer recurrent neural network to model the item behavior from the perspective of the evolution of items. In this work, we propose a recurrent neural network model called DTIAN (Deep Time-Aware Interest Attention Network) to address these issues. By leveraging the user behaviors and the corresponding temporal information, DTIAN captures user interests and intent changes to the target item. Therefore, the users’ recent interests are enhanced compared to early interests with the attention mechanism. In addition, each module of the proposed model can be plugged into other mainstream models to improve the performance of current models. The experimental results show that the proposed DTIAN can outperform the current popular CTR prediction models slightly and significantly reduce the training time, which makes it possible to implement lightweight models.
Effective 3C Resource Utilization and Fair Allocation Strategy for Multi-Task Federated Learning

Nowadays, one of the main challenges in expanding AI applications is the effective use of Computation, Communication, and Caching (3C) resources. The complexity of the cloud environment and the diversity of resource usage make it challenging to complete federated learning tasks in a cost-effective, timely, and seamless manner. To address these issues, this paper proposes a comprehensive approach to optimize the overall service efficiency of federated learning in time-varying and 3C-constrained environments. Firstly, a utility function based on convergence efficiency is proposed to reflect the physical benefits of processing AI tasks. Then, a fair allocation strategy consistent with the optimization goal is designed by modeling the task allocation process through virtual queue Lyapunov drift. Next, a Federated Learning Long Short-Term Memory (LSTM) based Queuing Optimization and Allocation Policy Calculation Algorithm (FL-QAPC) is proposed for resource allocation policy calculation using multi-dimensional network state inputs with time series. This algorithm implements predictive control based on historical records. Finally, a feasible experimental test platform is conducted, which is extended to an actual wireless mobile scenario based on 5G. The superiority of the proposed solution is verified through comparison with other benchmarks.
LEPF-Net: Light Enhancement Pixel Fusion Network for Underwater Image Enhancement

Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.
A parallel and balanced SVM algorithm on spark for data-intensive computing

Support Vector Machine (SVM) is a machine learning with excellent classification performance, which has been widely used in various fields such as data mining, text classification, face recognition and etc. However, when data volume scales to a certain level, the computational time becomes too long and the efficiency becomes low. To address this issue, we propose a parallel balanced SVM algorithm based on Spark, named PB-SVM, which is optimized on the basis of the traditional Cascade SVM algorithm. PB-SVM contains three parts, i.e., Clustering Equal Division, Balancing Shuffle and Iteration Termination, which solves the problems of data skew of Cascade SVM and the large difference between local support vector and global support vector. We implement PB-SVM in AliCloud Spark distributed cluster with five kinds of public datasets. Our experimental results show that in the two-classification test on the dataset covtype, compared with MLlib-SVM and Cascade SVM on Spark, PB-SVM improves efficiency by 38.9% and 75.4%, and the accuracy is improved by 7.16% and 8.38%. Moreover, in the multi-classification test, compared with Cascade SVM on Spark on the dataset covtype, PB-SVM improves efficiency and accuracy by 94.8% and 18.26% respectively.
Decentralized Machine Learning Training: A Survey on Synchronization, Consolidation, and Topologies

Federated Learning (FL) has emerged as a promising methodology for collaboratively training machine learning models on decentralized devices.Notwithstanding, the effective synchronization and consolidation of model updates originating from diverse devices, in conjunction with the appropriate configuration of network topologies, persist as crucial obstacles.This paper provides a comprehensive analysis of the current techniques and methodologies utilized in the synchronization, consolidation, and network topologies of Federated Learning.The present study explores diverse synchronization strategies utilized for the purpose of coordinating model updates from geographically distributed cross-silo edge nodes.The study takes into account several factors, including communication efficiency and privacy preservation.This study delves into the intricacies of model consolidation techniques, such as weighted and personalized aggregation methods, to evaluate their efficacy in consolidation of local model updates into a global model, while taking into consideration statistical heterogeneity and resource constraints.In addition, an examination is conducted on the importance of network topologies in Federated Learning (FL), taking into account their influence on communication efficacy, confidentiality, expandability, resilience, and resource allocation.The survey assesses and contrasts the efficacies and constraints of extant methodologies, discerns deficiencies in present investigations, and provides insights for future progressions.The objective of this survey is to provide a thorough examination of FL synchronization, consolidation, and network topologies, with the intention of offering a valuable reference for individuals engaged in Federated Learning, including researchers, practitioners, and stakeholders.This survey aims to support the advancement of more effective and resilient FL systems.