Reviewerly/exHarmony huggingface dataset:
Decentralized Federated Learning Via Mutual Knowledge Distillation

Federated learning (FL), an emerging decentralized machine learning paradigm, supports the implementation of common modeling without compromising data privacy. In practical applications, FL participants heterogeneity poses a significant challenge for FL. Firstly, clients sometimes need to design custom models for various scenarios and tasks. Secondly, client drift leads to slow convergence of the global model.Recently, knowledge distillation has emerged to address this problem by using knowledge from heterogeneous clients to improve the model’s performance. However, this approach requires the construction of a proxy dataset. And FL is usually performed with the assistance of a center, which can easily lead to trust issues and communication bottlenecks. To address these issues, this paper proposes a knowledge distillation-based FL scheme called FedDCM. Specifically, in this work, each participant maintains two models, a private model and a public model. The two models are mutual distillations, so there is no need to build proxy datasets to train teacher models. The approach allows for model heterogeneity, and each participant can have a private model of any architecture. The direct and efficient exchange of information between participants through the public model is more conducive to improving the participants’ private models than a centralized server. Experimental results demonstrate the effectiveness of FedDCM, which offers better performance compared to s the most advanced methods.
An Educational Perspective on Online Learning Platforms

The World Health Organization has identified the COVID-19 pandemic as a current hazard to humanity. As a result of the pandemic's successful global shutdown of several activities, including educational activities, colleges have migrated dramatically as a crisis response. Due to the cancellation of in-person classes, online learning has become more popular and has enabled students to continue their education. However, the abrupt shift from in-person to online learning has presented many difficulties for students, instructors, administrators and educational leaders. Zoom, Google Meet, Google Classroom and LMS platforms are the most frequently used media. Perceptions of the learning media have an impact on how students use them. This study intends to describe how students perceive the convenience and usefulness of the various learning tools accessibility. Overall, the research shows that the advantages of taking classes online exceed the difficulties that students may encounter. After showing the numerous advantages and drawbacks of using virtual learning in higher education, it is clear that there is still much opportunity for the future.
Shape Tracking Using Fourier-Chebyshev Double Series for 3D Distance Measurements

In the past years, algorithms for 3D shape tracking using radial functions in spherical coordinates represented with different methods have been proposed. However, we have seen that mainly measurements from the lateral surface of the target can be expected in a lot of dynamic scenarios and only few measurements from the top and bottom parts leading to an error-prone shape estimate in the top and bottom regions when using a representation in spherical coordinates. We, therefore, propose to represent the shape of the target using a radial function in cylindrical coordinates, as these only represent regions of the lateral surface, and no information from the top or bottom parts is needed. In this paper, we use a Fourier-Chebyshev double series for 3D shape representation since a mixture of Fourier and Chebyshev series is a suitable basis for expanding a radial function in cylindrical coordinates. We investigate the method in a simulated and real-world maritime scenario with a CAD model of the target boat as a reference. We have found that shape representation in cylindrical coordinates has decisive advantages compared to a shape representation in spherical coordinates and should preferably be used if no prior knowledge of the measurement distribution on the surface of the target is available.
Generic Multimodal Gradient-based Meta Learner Framework

Research in Natural Language Processing, bio-medicine, and computer vision achieved excellent results in machine learning due to the success of the Transformer-based models. However, these excellent results depend on the labeled high-quality and large-scale datasets. If one of these requirements is not met, the model may lack generalization ability, and its performance will be unsatisfactory. To address these issues, this research proposes a Generic Multimodal Gradient-Based Meta Framework (GeMGF) trained from scratch to avoid language bias, learns from a few data, and reduces the model degradation trained on a finite dataset. GeMGF was evaluated using the benchmark dataset CUB-200-2011 for the text and image classification tasks. The results show that GeMGF outperforms the state-of-the-art models with 93.2% accuracy. GeMGF is simple, efficient, and adaptable to other data modalities and fields.
Evaluation of Counter Unmanned Aerial Systems through the levels

Countering unmanned aerial threats is critical for both military and civilian surveillance and protection systems. The complementarity and redundancy of sensors enables detecting, tracking and classifying Unmanned Aerial Vehicles (UAVs), further assessing their possible threat and planning the proper counter-measure. Despite efforts to develop dedicated systems (sensors, processing, hard and soft kill systems), Counter Unmanned Aerial Systems (CUAS) are often deployed in complex civilian areas, which requires an adequate understanding of the situation including the UAVs behavior for an appropriate response. The evaluation of a CUAS should thus consider not only its ability to provide a tactical picture of sufficient quality, but also its ability to provide semantic information, to establish possible links between objects and to contextualize their behavior. In this paper, we set up the basics for an evaluation platform covering the lower levels but also higher levels of the JDL (Joint Directors of Laboratories) functional model of information fusion. We consider the six classical criteria of a tactical picture (used in Level 1) and extend them to higher level tasks. Evaluation criteria are aligned with the URREF (Uncertainty Representation and Reasoning Evaluation Framework) ontology evaluation criteria. Furthermore, we propose some metrics to quantity such evaluation criteria. The framework is illustrated in a CUAS scenario, with data provided by the LEXLUTOR platform. Several fusion solutions are compared which differ in the way uncertainty is represented, handled and provided to the user.
Design of Efficient Point-Mass Filter with Terrain Aided Navigation Illustration

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel efficient PMF (ePMF) estimator, unifying continuous and discrete, approaches is proposed, designed, and discussed. By numerical illustrations, it is shown, that the proposed ePMF can lead to a time complexity reduction that exceeds 99.9% without compromising accuracy. The MATLAB® code of the ePMF is released with this paper.
Constrained Gaussian-Process State-Space Models for Online Magnetic-Field Estimation

We address the magnetic-field simulteanous localization and mapping (SLAM) problem for global positioning. We leverage a previously-developed particle filter (PF)-based framework for online Bayesian inference and learning of Gaussian Process state-space models (GP-SSMs). We extend the framework to directly incorporate physical properties of the magnetic field in the GP formulation, by leveraging that magnetic fields under the absence of free currents are curl free. Because of its flexibility, the method can include any motion model that can be expressed by a general nonlinear function, with potential applications to, e.g., mobile robotics and pedestrian localization. Simulation results indicate that our method performs similar to recent batch methods for magnetic-field slam, while the computation times are feasible for online implementations.
Nonlinearity-Aware Partial-Update Schmidt-Kalman Filter

The partial-update filter is a Kalman filter modification that can accommodate higher nonlinearities and uncertainties than a nominal and Schmidt-Kalman filter. This robustness enhancement of the partial-update filter is attributed to its capability to limit the impact of incorrect updates by applying user-selected static percentages of the nominal Kalman update, to user-selected states at any time step.To further extend the partial-update capabilities and applicability, this paper presents two methods for dynamically and automatically selecting the partial-update percentages based on nonlinearity metrics of the process and measurement model. By enabling dynamic update percentages, the filter automatically leverages situations where higher updates can be applied and lower updates are deemed suitable. This leads to higher statistical consistency and accuracy with respect to the nominal Kalman and static partial-update filters. The superior accuracy and consistency of the proposed nonlinearity-aware partial-update methods are shown via a numerical example.
Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.
Learning Empirical Bregman Divergence for Uncertain Distance Representation

Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to suboptimal performance for capturing the complex data distribution. The Bregman divergence generalizes measures of various distance metrics and arises throughout many fields of deep metric learning. In this paper, we first show how deep metric learning loss can arise from the Bregman divergence. We then introduce a novel method for learning empirical Bregman divergence directly from data based on parameterizing the convex function underlying the Bregman divergence with a deep learning setting. We further experimentally show that our approach performs effectively on five popular public datasets compared to other SOTA deep metric learning methods, particularly for pattern recognition problems.
Knowledge: Making a Quality Choice

We discuss in this paper how we teach an intro to data science course. Knowledge is a matter of choice and choosing the best course of action is through computer programming this is the way which we consider how to teach this course. We begin with an introduction to big data, data mining, machine learning and data visualization. We use python as the language to clean big data files and to select the right attributes for classification when mining the data. We discuss the Bayes method for classification, then we move to java, specifically Weka, to show how to program the algorithms, some applications are also discussed specifically using Bayes for text processing, we then move to other supervised classification algorithm such as J4.8 and Random Forest trees. And then move to clustering, where we discuss both KMeans and EM algorithms. We discuss truth and knowledge and it is where we use cross validation as the most reasonable approach to find truth based on historical data. Later we discuss supervised and unsupervised Learning, briefly talking about Neural Networks collective and reinforced learning. Briefly talk about current deep learning using CNNs. We finish the course talking about Genetic Algorithms and soft computing.
MSG-CAM:Multi-scale inputs make a better visual interpretation of CNN networks

The visualization of deep learning models has been widely studied as an effective means of exploring the decision-making processes within these models. However, current visualization methods suffer from several limitations, such as low resolution and poor visualization of multiple occurrences of the same class. In this paper, we propose a novel visualization technique called MSG-CAM, which is an improvement on the existing Group-CAM method. Our method uses the feature maps and gradients of the last layer of the convolutional neural network to create masks through multi-scale enlargement of the original input image and fusion of the resulting feature maps and gradients. Through both qualitative and quantitative analysis, we have demonstrated that the saliency maps generated by our method are more reasonable and accurately reflect the internal decision-making processes of the neural network.
Energy-aware Coverage Path Planning of Multi-UAV Based on Relative Distance Scaling Cluster Method

In recent years, unmanned aerial vehicle (UAV) aerial photography becomes a new method of disaster area exploration. UAVs have been widely used in the fields for aerial photography, monitoring, transportation, communication and so on. They can expand the dimension and scale of search and rescue (SAR) work, reduce the cost of human and material resources, and improve the efficiency of SAR work. Considering the limited of a single UAV, multi-UAV and energy-aware path planning algorithms are proposed here for SAR missions, which improve the cluster algorithm. In the algorithm design, we take the energy consumption of each UAV as an important factor for optimizing the division of search area, and propose the spiral search path generation algorithm. And numerical examples corroborate the effectiveness of the theoretical results.
Ensemble Gaussian Mixture Filtering with Particle-localized Covariances

The ensemble Gaussian mixture filter (EnGMF) is a powerful filter for highly non-Gaussian and non-linear models that has practical utility in the case of a small number of samples, and theoretical convergence to full Bayesian inference in the ensemble limit. We aim to increase the utility of the EnGMF by introducing a particle-local notion of covariance into the Gaussian mixture estimate of the prior distribution. We show on a simple bivariate problem that each particle having its own local estimate of the covariance both has nice qualitative and quantitative properties, and significantly improves the estimate of the prior and posterior distributions for all ensemble sizes. We additionally show the utility of the proposed methodology for sequential filtering for the Lorenz ’63 equations, achieving a significant reduction in error in the low ensemble size regime.
Interframe Association of YOLO Bounding Boxes in the Presence of Camera Panning and Zooming

In this paper, we develop an approach for measurement-to-track association (M2TA) in the presence of (unknown) camera panning and zooming from drone-captured video. Standard M2TA methods assume that the target motion can be used to predict the “measurement association regions” for the bounding boxes. However, if there is a sudden state change due to camera shift (panning) and zooming, it will lead to incorrect associations and poor tracking results. To solve this, the zoom ratio and panning in 2D coordinates are used to describe the camera motion parameters in each frame. The estimated parameters are obtained by a grid search combined with global assignment or directly solved using the linear least squares method, which is also combined iteratively with assignment. The goal is to achieve correct M2TA by adjusting the predicted measurements using the estimated camera parameters. These “improved” predictions can also be used to update the target state with filtering algorithms. Frames with panning or/and zooming from real data are used to illustrate the effectiveness of the proposed methods and compared with the validation gate method based on inflated covariances.
When Does the Marginalized Particle Filter Degenerate?

The Particle filter can in theory estimate the state of any nonlinear system, but in practice it suffers from an exponential complexity in terms of the number of particles as the dimension of the state increases. The marginalized particle filter can potentially reduce this problem by improving the estimates, particularly for lower number of particles. However, it turns out that for certain systems, it does not provide any improvement in the accuracy of the estimate. The core cause of degeneracy is linked to when the uncertainty of the linear state conditioned on the nonlinear state is 0. Conditions for determining when this occurs are presented and applied to common constant velocity, constant acceleration and constant jerk models with various sampling methods. Interestingly, some combinations are useful while others should be avoided. These findings are supported using simulated systems.
Masked Cross-image Encoding for Few-shot Segmentation

Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images. The key challenge in FSS is to classify the labels of query pixels using class prototypes learned from the few labeled support exemplars. Prior approaches to FSS have typically focused on learning class-wise descriptors independently from support images, thereby ignoring the rich contextual information and mutual dependencies among support-query features. To address this limitation, we propose a joint learning method termed Masked Cross-Image Encoding (MCE), which is designed to capture common visual properties that describe object details and to learn bidirectional inter-image dependencies that enhance feature interaction. MCE is more than a visual representation enrichment module; it also considers cross-image mutual dependencies and implicit guidance. Experiments on FSS benchmarks PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> demonstrate the advanced meta-learning ability of the proposed method.
DF-CLIP: Towards Disentangled and Fine-grained Image Editing from Text

Inspired by CLIP’s excellent image/text representation capability and StyleGAN’s disentangled latent space, text-guide image editing techniques make significant progress. However, as CLIP cannot perform local fine-grained image/text alignment, existing methods suffer from entanglement problems. Moreover, there lacks a deep interaction between textual tokens and visual features, which may lead to unfaithful editing results. In this paper, we propose DF-CLIP for Disentangled and Fine-grained text-guide image editing. Specifically, we design a novel dual-branch LatentMask module to generate more accurate editing directions in StyleGAN’s latent space, which can avoid changes in text-unrelated areas. Furthermore, we present a Multi-modal Interaction module to associate the text embedding with the image embedding and perform a deep interaction between them, which greatly enhance the guidance of text in image editing process and accelerate the training convergence. Extensive experiments show that our models perform more disentangled and natural editing results with a shorter training time.
Denser is Better:cost distribution super-resolution network for more accurate sub-pixel disparity

The low-quality cost distribution obtained by simple upsampling leads to disparity maps with many outliers and low sub-pixel accuracy. We propose the Cost Distribution Super-Resolution Network (CDSRNet), which directly extracts high-resolution cost distribution from the low-resolution 4D cost volume.The similarity extraction module of CDSRNet decomposes the task of estimating high-resolution cost distribution into multiple subtasks and completes each subtask by a specific translation block, ensuring high discrimination of the predicted cost distribution. The inter-block aggregation module aggregates information from other subtasks, obtaining a voting volume with global information for correcting errors in the current subtask. Experiment results demonstrate that the proposed method significantly reduces the ratio of outliers and improves the sub-pixel accuracy.
When AI Facilitates Trust Violation: An Ethical Report on Deep Model Inversion Privacy Attack

This article raises concerns about the considerable capability of artificial intelligence in boosting privacy violations and motivates the necessity of AI ethics. Despite all AI advantages like the efficiency and accuracy of recent techniques, and its positive effects on our life quality, when it comes to security and privacy the facilities with AI empowerment have been met with anxiousness and distrust in the public. This article is an ethical view of the AI role in a recent work wherein AI considerably facilitates privacy violation in a gray-box attack on a deep face recognition system. While the user identities' data is fully secured and just the recognition deep model is accessible, AI-boosted model inversion reveals the faces of the identities via high-accuracy generated clones. An analytical and subjective evaluation of the generated face clones with and without AI integration in model inversion illustrates a big gap from non-clear noise face clones to crystal clear face clones which efficiently reveal the identity of a targeted user by their high-level naturalness, similarity, and recognizability amongst many users.