Reviewerly/exHarmony huggingface dataset:
Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.
Feature Decoupling and Uncertainty Estimation for 3D Object Detection

In the real scene of 3D object detection, the point cloud collected for a single object is incomplete, resulting in misalignment of classification and regression features and uncertainty of object boundaries. Existing works pay little attention to the uncertainty caused by the incompleteness of point clouds. To address this issue, we propose a Feature Decoupling and Uncertainty Estimation single-stage 3D object detector named FDUE-Net. First, we design a Classification-Regression Attention Decoupling module to extract shared low-level and high-level features in a decoupling paradigm, generating the feature maps that are more helpful for classification or regression by layer-level and space-level attention modules. Furthermore, we propose an Uncertainty Estimation head (UE-head), which improves the quality of the predicted bounding boxes by modeling the regression values as general distributions, and uses the predicted distributions to correct the classification scores. Experiments on the KITTI dataset show that the proposed method achieves significant improvement, with 3D detection performance improved by 1.46% on the moderate set compared to the baseline, and competitive performance compared to the state-of-the-arts.
Deterministic Multi-sensor Measurement-adaptive Birth using Labeled Random Finite Sets

Measurement-adaptive track initiation remains a critical design requirement of many practical multi-target tracking systems. For labeled random finite sets multi-object filters, prior work has been established to construct a labeled multi-object birth density using measurements from multiple sensors. A truncation procedure has also been provided that leverages a stochastic Gibbs sampler to truncate the birth density for scalability. In this work, we introduce a deterministic herded Gibbs sampling truncation solution for efficient multi-sensor adaptive track initialization. Removing the stochastic behavior of the track initialization procedure without impacting average tracking performance enables a more robust tracking solution more suitable for safety-critical applications. Simulation results for linear sensing scenarios are provided to verify performance.
Cloud Computing From an Architectural View

software design patterns are well-liked and regarded as a reusable response to a frequently occurring issue in software design. A collection of common methods, procedures, and/or tasks for creating object-oriented software are referred to as design patterns. Each pattern outlines a persistent issue in our environment before outlining the key components of a workaround that may be applied a million times over, yet never being applied in precisely the same way twice. This paper will reaffirm and reiterate some of these classic principles, as well as analyze how they may evolve in the context of cloud computing. The target audience for this article are cloud architects who are preparing to migrate an enterprise-class application from a fixed physical environment to a virtualized cloud environment. This paper's main objective is to introduce the several patterns that should be taken into account when developing new cloud applications.
Steganalysis on Covert Channel in IPv6 Flow Label

Modern communication networks provide rich channels for steganographic tools to hide secret data. In addition to payload, an emerging trend is to utilize protocol headers to hide data as well. This paper studies the IPv6 packets with data hidden in their Flow Label field, and proposes an algorithm which utilizes chi-square test to detect the existence of hidden data by the conformance of uniform distribution.
Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression

Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the magnetometer. The position of the magnetometer, however, is frequently only approximately known. This negatively affects the quality of the magnetic field map. In this paper, we investigate how an array of magnetometers can be used to improve the quality of the magnetic field map. The position of the array is approximately known, but the relative locations of the magnetometers on the array are known. We include this information in a novel method to make a map of the ambient magnetic field. We study the properties of our method in simulation and show that our method improves the map quality. We also demonstrate the efficacy of our method with experimental data for the mapping of the magnetic field using an array of 30 magnetometers.
Deterministic Sampling of Arbitrary Densities Using Equal Sphere Packing of Volume under the Density (PoVuD)

We present a new deterministic sampling method for arbitrary densities, unnormalized densities, and likelihoods. Our rejection-free and kernel-free method uses dense equal sphere packing of the volume under the density function (PoVuD). In order to obtain an ensemble that is better than independent random particles, we enforce some local homogeneity.
Ensemble Kalman Filter with Bayesian Recursive Update

Nonlinear measurement models pose a challenge to linear filters. The ensemble Kalman filter (EnKF) is a popular choice despite its tendency to diverge in systems with highly accurate, highly nonlinear measurements. In this work, we present the Bayesian Recursive Update EnKF (BRUEnKF): a novel EnKF that employs the Bayesian Recursive Update Filter (BRUF) measurement update. The BRUF divides the the extended Kalman filter (EKF) update into an integer number of steps, allowing for the recomputation of the measurement Jacobian at regular intervals. We adapt the BRUF update for an ensemble filter, taking advantage of the EnKF’s numerical covariance computation at each update step. The BRUEnKF is shown to outperform the EnKF for systems with range measurements.
On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking

Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently truncate the single-sensor and multi-sensor $\delta$-generalized labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive labeled multi-Bernoulli birth distribution. However, only a limited discussion has been provided regarding key Gibbs sampler architecture details including the Markov chain Monte Carlo sample generation technique and early termination criteria. This paper begins with a brief background on Markov chain Monte Carlo methods and a review of the Gibbs sampler implementations proposed for labeled random finite sets filters. Next, we propose a short chain, multi-simulation sample generation technique that is well suited for these applications and enables a parallel processing implementation. Additionally, we present two heuristic early termination criteria that achieve similar sampling performance with substantially fewer Markov chain observations. Finally, the benefits of the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.
Avoiding quantization effect in the vertical trajectory reconstruction filtering system

Within the EUROCONTROL Air Traffic Management (ATM) architecture, the Surveillance Analysis Support System for ATC Centres (SASS-C) is dedicated to the validation of Air Navigation Service Providers. One of its applications is the opportunity Traffic Reconstruction (OTR), which seeks to reconstruct all trajectories by combining noisy measurements from different sensors. It relies on association, tracking and fusion algorithms to determine the true motion of the aircraft on both the vertical and horizontal axes, alongside contextual information useful for identifying the aircraft’s flight mode at any given time. This paper focuses on the filtering of the vertical dimension and outlines certain problems present in the system: the effect of quantized measurements, the appearance of mode of flight transition overshoots and the low reactivity of the filter to abrupt transitions. These problems are analyzed and preliminary improvements according to the ATC context are implemented to overcome them. To demonstrate the improvement, a comparison between the proposed system and the original one is carried out through synthetic trajectory analysis.
Evaluation Scores for Elliptic Extended Object Tracking Considering Diverse Object Sizes

Successful tracking of an extended object requires accurate estimation of the target’s shape. In order to evaluate the estimation performance of such tracking algorithms, the target shape must be incorporated by a suitable metric or score. In this context, a common task is to determine a consolidated scalar score for the estimation accuracy across a set of different target types. We highlight problems exhibited by existing scores in this case, particularly if the data set consists of objects of diverse sizes. To this end, we focus on elliptical targets. Furthermore, we present a scale-invariant adaption of the commonly used Gaussian Wasserstein Distance, which does not suffer from the highlighted problem.
MedFCT: A Frequency Domain Joint CNN-Transformer Network for Semi-supervised Medical Image Segmentation

Semi-supervised learning(SSL) is a data-efficient way in leveraging large-scale data without annotations and alleviating the dependence on labeled data. Mean-Teacher (MT) scheme with teacher-student model architecture has shown its effectiveness in semi-supervised medical image segmentation, where the student network learns from the teacher by minimizing pixel-wise consistency loss. However, existing MT-based SSLs still give rise to two main concerns: (1) limited learning ability of student network that neglects the union of local feature and global cues extraction which may impact the representation learning of variable objects. (2) limited knowledge-transferring ability of teacher network with only pixel-level consistency regularization that may result in inadequate and unstable guidance. To address these limitations, we propose a novel semi-supervised learning scheme, namely MedFCT, with two appealing designs: (1) A dual student architecture with parallel CNN and Transformer branches is designed for local-global feature extraction, where the full-frequency interaction between CNN and Transformer can be explored by a frequency domain cross-fusion (FDCF) module to learn complementarity of the two-paradigm features. (2) A comprehensive multi-level consistency regularization considering pixel-wise, feature-wise and class-wise information is presented to realize more effective guidance and knowledge transfer from teacher network. Experiments show that MedFCT outperforms previous state-of-the-art methods on two public medical image segmentation benchmarks.
Sweeping UV-C Disinfection: a Mathematical Approach

Mobile robots are used to disinfect surfaces with C-band ultraviolet light (UV-C). However, the coverage path planning strategy that delivers the right dose of energy, avoiding waste, is still a challenge. This article proposes a lighting sweep strategy for surfaces, based on robot speed, UV-C output power and distance from the surface, to ensure an adequate dose of UV-C light on the surface. In this work, the distance between the scan paths is mathematically defined and, through simulations, its effectiveness is demonstrated. The experimental results show that when using the ideal speed of sweeping, it is possible to disinfect a surface 2.9 times greater than the best case of the static lamp, in the same period of time. The proposed method in this paper is capable of performing surface coverage, delivering at least the target dose of UV-C while reducing energy and time waste.
Deep Normalizing Flows for State Estimation

Safe and reliable state estimation techniques are a critical component of next-generation robotic systems. Agents in such systems must be able to reason about the intentions and trajectories of other agents for safe and efficient motion planning. However, classical state estimation techniques such as Gaussian filters often lack the expressive power to represent complex underlying distributions, especially if the system dynamics are highly nonlinear or if the interaction outcomes are multi-modal. In this work, we use normalizing flows to learn an expressive representation of the belief over an agent’s true state. Furthermore, we improve upon existing architectures for normalizing flows by using more expressive deep neural network architectures to parameterize the flow. We evaluate our method on two robotic state estimation tasks and show that our approach outperforms both classical and modern deep learning-based state estimation baselines.
Statistical Detection of Coordination in a Cognitive Radar Network through Inverse Multi-Objective Optimization

Consider a target being tracked by a cognitive radar network. If the target can intercept noisy radar emissions, how can it detect coordination in the network? By ‘coordination’ we mean that the radar emissions satisfy Pareto optimality with respect to multi-objective optimization over the objective functions of each radar and a constraint on total network power output. This paper provides a novel inverse multi-objective optimization approach for statistically detecting Pareto optimal (’coordinating’) behavior, from a finite dataset of noisy radar emissions. Specifically, we develop necessary and sufficient conditions for radar network emissions to be consistent with multi-objective optimization (coordination), and we provide a statistical detector with theoretical guarantees for determining this consistency when radar emissions are observed in noise. We also provide numerical simulations which validate our approach. Note that while we make use of the specific framework of a radar network coordination problem, our results apply more generally to the field of inverse multi-objective optimization.
Improving CTC-based Handwritten Chinese Text Recognition with Cross-Modality Knowledge Distillation and Feature Aggregation

Offline handwritten Chinese text recognition (HCTR) models based on connectionist temporal classification (CTC) have recently achieved impressive results. Due to the conditional independence assumption and per-frame prediction characteristics, CTC-based models cannot capture semantic relationships between output tokens and leverage global visual features of characters. To solve these issues, we propose a Cross-Modality knowledge distillation approach that leverages pre-trained LM (BERT) to transfer contextual semantic information, and then design a feature aggregation module to dynamically aggregate local and global features. Experimental results on the HCTR datasets (CASIA-HWDB, ICDAR2013, HCCDOC) show that our proposed method can significantly improve the model’s performance.
WakeIPDA: Target Tracking With Existence Modeling in the Presence of Wakes

We present a novel target tracking algorithm, which is designed to track a target in the presence of wake clutter. What distinguishes the method from previous wake-compensating trackers is that it also models the existence of the target and exploits the information provided by the wake measurements for this purpose. We present two ways of modeling the wake, and we evaluate the algorithm’s performance on simulated data. Results show that the method improves upon comparable target tracking methods when wake measurements are present.
Approximate fusion of probability density functions using Gaussian copulas

Subjective Bayesian estimation perceives probability density functions as expert opinions. Among various rules for combining the opinions, the product and the weighted geometric mean of densities are prominent. Nevertheless, closed-form representations are scarce and non-parametric approaches often suffer from the curse of dimensionality. This paper prospects the fusion of densities represented by non-parametric marginal densities and a parametric Gaussian copula. The explicit reconstruction of the joint densities followed by an optimisation step is avoided. A cheap approximate combination is proposed instead. The combination of marginal densities is tuned by a Gaussian term, while the proposed copula parameter uses moments of the marginal densities. The presented examples illustrate the approximative nature of the approach for non-Gaussian densities and highlight some numerical issues.
Comparison of Classification Techniques for Extremism Detection in French Social Media

With social platforms being used by an increasing number of users, the Internet became a perfect place for extremist ideas and opinions to be created and propagated. Detection of extremist content can be an asset for security agencies but it comes with technical challenges and requires semi-automatic approaches, as reading the sheer amount of data released online is an impossible task for analysts. This paper investigates the use of learning models to detect extremist content in French corpora and focuses on right-wing extremist detection. Several learning models have been developed including unsupervised approaches and neural ones. The models were applied to a data set gleaned online. Experiments show that data representation and parameters of models may affect the overall performance and extremist content can be accurately detected when parameters and thresholds are tuned correctly. These results are novel as they contribute to the analysis of social data conveying extremist ideas in French.
Image Enhancement for Low-Light and Hazy Conditions Using Retinex Theory and Wavelet Transform Fusion

Image enhancement has played a significant role in analyzing and synthesizing images in the modern era. This study proposed a method to improve the visual information, discrete entropy, peak signal-to-noise ratio, and the visual quality of low-light and hazy image data. In this paper a technique for improving low-light and hazy images using illumination estimates, improved haze removal filter coefficients, and discrete wavelet transform fusion has been presented. The presented technique eliminates the low-light effect from the image to help improve the observation quality of low-light and hazy images. In order to eliminate the haze from the image, the optimal de-haze to the inverted image is applied. A better-quality image has been obtained by fusing both of the obtained images through discrete wavelet transform fusion. The simulation study showed a significant improvement in visible and quantitative aspects over previously proposed methods.
Ghost Track Detection in Multitarget Tracking using LSTM Network

This paper analyses the track-level detection of ghost tracks in multitarget tracking with a known reflection surface. In a real-world target tracking problem, the number of targets in surveillance is unknown to the platform. Thus, the tracker will be inadequate to distinguish the direct target return from the multipath return during the track initialization. Therefore, ghost tracks can be created with multipath measurements when they are considered direct path measurements. Even though the possible multipath measurement could be predicted for the existing tracks at a given instance, it is hard to decide whether the detected track is a multipath or a new target. Thus, a sequence of time instances needs to be considered to determine the track status. In this work, we propose a classification model to classify a track as either a multipath or direct path using an LSTM network with sequential data. Additionally, the performance of the proposed approach is compared with four other algorithms using a simulation-based dataset.
Improving robustness of learning-based adaptive video streaming in wildly fluctuating networks

With the development of wireless technology, the fifth-generation mobile communication network (5G) can offer ultra-high bandwidth to support high-quality video streaming services. However, such a network shows much wild fluctuation especially in the case of mmWave 5G, making it challenging to ensure a robust video streaming service. In this paper, we propose a robust adaptive bitrate algorithm based on deep reinforcement learning, named MAP-DRL. In "robust", we mean that MAP-DRL provides high quality of experience (QoE) streaming services under various network conditions and different QoE preferences. In particular, to avoid the gradient explosion caused by network fluctuation, we introduce several reinforcement learning designs into MAP-DRL, such as advantage function normalization and reward scaling. To fully utilize the limited environment feedback, we process environmental information via a multi-type data manner and design the corresponding network structures for each type of them. The experimental results on real-world network trace datasets demonstrate that MAP-DRL can succeed in improving the average QoE by 29% versus heterogeneous networks and user preferences as compared with the representative benchmarks.
Measurement and Track Fusion at the System Level

One line of thinking is that measurement fusion at the system level provides enough value to warrant the cost of communicating the measurement level information to the system level. Another line of thinking is that in general the tracks generated at the source are sufficient to convey the information needed at the system level. There is no argument that measurements provide theoretically more information, but is the cost worth the possible extra value? This paper builds on our previous work that began to explore this issue with a straightforward simulation setup. In the previous paper it was shown that measurement fusion at the system level is better than just source track fusion at the system level. Those results were obtained when the update rate of measurements at the source were the same rate as the source tracks sent. In reality, the source tracks are produced at the full measurement rate, and the full measurement rate is not likely to be sent to the system level. Thus, this paper looks at disparate rates at the source level versus the system level. That is, the tracks produced at the source level will have a higher update rate than the rate at which measurements are sent to the system level to fuse.