Reviewerly/exHarmony huggingface dataset:
Ensembles of Predictive Radar Models for Electronic Intelligence

Predictive radar models allow for the anticipation of the next emissions, which facilitates many tasks in an electronic intelligence context. If various radars have to be considered during a mission, several models need to be combined into a multi-model system or an ensemble. This paper examines three ensemble architectures in different configurations, which either contain radar models composed of Markov chains or Long Short-Term Memory networks. All approaches are based on a hierarchical emission model that understands radars as systems that speak a language. The ensembles&#x0027; prediction accuracies are evaluated with a simulated airborne multifunction radar using three resource management methods of varying complexity. The evaluations consider several conditions, which include ideal and corrupted data, as well as different signal lengths.
Learn to Understand Negation in Video Retrieval

Negation is a common linguistic skill that allows human to express what we do NOT want. Naturally, one might expect video retrieval to support natural-language queries with negation, e.g., finding shots of kids sitting on the floor and not playing with a dog. However, the state-of-the-art deep learning based video retrieval models lack such ability, as they are typically trained on video description datasets such as MSR-VTT and VATEX that lack negated descriptions. Their retrieved results basically ignore the negator in the sample query, incorrectly returning videos showing kids playing with dog. This paper presents the first study on learning to understand negation in video retrieval and make contributions as follows. By re-purposing two existing datasets (MSR-VTT and VATEX), we propose a new evaluation protocol for video retrieval with negation. We propose a learning based method for training a negation-aware video retrieval model. The key idea is to first construct a soft negative caption for a specific training video by partially negating its original caption, and then compute a bidirectionally constrained loss on the triplet. This auxiliary loss is weightedly added to a standard retrieval loss. Experiments on the re-purposed benchmarks show that re-training the CLIP (Contrastive Language-Image Pre-Training) model by the proposed method clearly improves its ability to handle queries with negation. In addition, the model performance on the original benchmarks is also improved.
Execution trace‐based model verification to analyze multicore and real‐time systems

Abstract As a key part of model‐driven development, modeling allows users to represent the application workflow or to automatically generate source code. This is convenient for developers, particularly to create or improve real‐time applications embedded in complex systems. Multicore systems are difficult to debug because the concurrently running processes can interfere with each other. In real‐time systems, timing constraints add to the complexity, invalidating results when a deadline is missed. Tracing is usually the most accurate and reliable tool to study the runtime behaviour of those applications. However, the interpretation of voluminous detailed execution traces requires a deep understanding of the operating system and application behaviour, and time to dig through the millions of trace events.In this paper, we present the use of model‐based constraints on top of user‐space and kernel traces to provide weighted analysis results. Our algorithms have been applied to multiple traces showing common problems for multi‐core real‐time systems. The experimental results show that our algorithms can quickly identify many different types of problems with a low runtime, even for traces with millions of events, thus helping to save time when analyzing thousands of trace events for complex systems.
Radar Emitter Identification using Signal Noise and Power Spectrum Analysis in Deep Learning

Specific emitter identification (SEI) is a vital function of the electronic radar warfare support system. The challenge emphasizes recognizing and locating unique transmitters, avoiding potential threats, and preparing a countermeasure. Unlike the analog transient parameters, radar fingerprinting is effectively possible with digitized feature extraction and image processing methods. Our novel approach utilizes the power spectrum and signals noise to efficiently work on a large image dataset using deep learning techniques. The convolution neural network is tested on various time-frequency estimators to yield the most accurate results. Based on signal-to-noise ratios, radar emitters get distinguished using the ablest estimator.
A Bayesian nonparametric spatial model with covariate-dependent joint weights

This paper presents a spatial process with covariate-dependent random joint distributions. Our construction is based on an extension of the Gaussian copula model using the Beta-regression process. As a generalized form of stick-breaking processes, the proposed model allows the covariance function to be covariate-driven nonstationary. Also, the resulting labeling process provides a covariate-dependent random partitioning. Markov chain Monte Carlo methods are used to make Bayesian inferences. The effectiveness of this model in segmentation of the domain and prediction performance are assessed through a simulation study. Additionally, results from a real dataset demonstrate that the proposed model possesses better spatial prediction performance over other competing models.
Effectiveness of Ehteraz digital contact tracing app versus conventional contact tracing in managing the outbreak of COVID-19 in the State of Qatar

Introduction Contact tracing is an integral component of the overall public health strategy for controlling the outbreak of infectious diseases such as COVID-19. In addition to the conventional contact tracing, Qatar has implemented Ehteraz, a proximity-tracing mobile phone App using global positioning system and Bluetooth technologies. In this paper, we aim to evaluate the accuracy of Ehteraz as a contact tracing App based on the WHO contact tracing criteria and compare its effectiveness to the traditional human led case investigation. Methods A cross-sectional study was conducted using two databases, including Ehteraz proximity data and swab collection and case investigations reports for the same period. Results 46.9% of contacts identified by the Ehteraz app did not meet WHO duration and distance criteria. Among the 5650 contacts flagged by Ehteraz during the period, 4973 (88.0%) of contacts did not share any known relation with the index case they are linked to. Conclusion The study highlighted the inaccuracy of the Ehteraz tool as its technology enables signals passing through walls, so people in different rooms could be unnecessarily flagged as having had contact. Big segments of the population (eg, Children) do not necessarily use smartphones and are not, therefore, flagged by Ehteraz digital contact tracing App. Similarly, Ehteraz rarely flags close contacts identified by human investigation as Bluetooth is usually turned off in a household or at a workplace. These findings highlight that digital contact tracing would be an efficient public health strategy when integrated to and complementing human investigation.
Smart Meter Data Masking Using Conditional Generative Adversarial Networks

We present a novel two-stage smart meter (SM) data masking technique. In the first stage, data masking is carried out at an individual SM using a light-weight approach. Subsequently, the data are transmitted to a third party (TP), where the second-stage data masking is carried out using a conditional generative adversarial networks (CGAN)-based module. The second-stage masked data have the same statistics as those of the actual data; however, their individual values are different. The data are then transmitted to the energy supplier (ES) which uses them to calculate their statistics or aggregate them. The actual SM data are protected from eavesdropper, as well as the TP and ES. We compare the performance of the proposed method with existing GAN-based and Gaussian mixture model (GMM)-based techniques by calculating the mean absolute error (MAE) between the actual mean and standard deviation of the consumer data and those calculated from the second-stage masked data. The results show the lowest MAE obtained with the proposed technique for the estimated mean and standard deviation are at least three times less compared to the MAE values for the GAN-based approach, and between two to three times less than those obtained for the GMM-based method.
Integration of Multi-Band Passive and Multi-Functional Active Radar Data

Active and passive radar sensors come with partially complementary properties, thus a suitable mix of those sensor technologies provides improved performance in a couple of situations of operational interest. Therein, data fusion should be performed in a way that honors the general strengths of both technologies as well as the individual capabilities of the specific sensors as much as possible. In this paper, two Hensoldt sensors will be considered, namely the passive radar system Twinvis and the multi-functional active radar system TRML-4D. A suitable data fusion architecture will be presented that allows for mutual support between the sensors. Fusion results obtained processing live data recordings will be given.
Slovak dialogue corpus with backchannel annotation

The proposed paper introduces the multimodal corpus of Slovak dyadic conversations, where backchannels from listener to speaker are labeled. The corpus consists of a series of podcasts, where two participants discuss various topics. In our research, we have focused on providing backchannels from a listener to the speaker during his/her turns. Backchannel signals play an important role in human-human spoken communication as well as in human-machine spoken dialog. They convey information about listener attention, acknowledgment, acceptance etc. The motivation for preparing the backchannel corpus in Slovak is to bring data, which can be used to train models, which will enable dialogue systems to produce backchannel signals to their human interlocutors.
A Shape Reconstruction and Measurement Method for Spherical Hedges Using Binocular Vision

The center coordinate and radius of the spherical hedges are the basic phenotypic features for automatic pruning. A binocular vision-based shape reconstruction and measurement system for front-end vision information gaining are built in this paper. Parallel binocular cameras are used as the detectors. The 2D coordinate sequence of target spherical hedges is obtained by region segmentation and object extraction process. Then, a stereo correcting algorithm is conducted to keep two cameras to be parallel. Also, an improved semi-global block matching (SGBM) algorithm is studied to get a disparity map. According to the disparity map and parallel structure of the binocular vision system, the 3D point cloud of the target is obtained. Based on this, the center coordinate and radius of the spherical hedges can be measured. Laboratory and outdoor tests on shape reconstruction and measurement are conducted. In the detection range of 2,000-2,600 mm, laboratory test shows that the average error and average relative error of standard spherical hedges radius are 1.58 mm and 0.53%, respectively; the average location deviation of the center coordinate of spherical hedges is 15.92 mm. The outdoor test shows that the average error and average relative error of spherical hedges radius by the proposed system are 4.02 mm and 0.44%, respectively; the average location deviation of the center coordinate of spherical hedges is 18.29 mm. This study provides important technical support for phenotypic feature detection in the study of automatic trimming.
Single image deraining using contrastive perceptual regularization

Rain streaks pollute the image captured from outdoor vision system, and single image deraining approach based on data-driven has witnessed the continuously growing and achieved great success. Here, an end-to-end network for single image deraining is proposed. Firstly, to address the limit of convolution neural network (CNN) which can only extract local feature, a graph based basic block is proposed to extract global feature. The basic block consists of graph convolutional network (GCN) and CNN. The GCN module which combines spatial coherence computing and channel correlation computing is introduced to extract non-local information. While the CNN module, which combines the channel attention and pixel attention, is used to earn more weight from important local features. Secondly, a contrastive perceptual regularization is adopted to enhance the loss function, and a more natural image is restored by utilizing the information from both positive and negative samples with the regularization. The restored image is pulled closer to the positive clear image and pushed farther away to the negative rainy image. The experiment results on several datasets demonstrate that these methods achieve better results than the previous state-of-art methods.
Star topology convolution for graph representation learning

Abstract We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and can share weights across graphs. To test the method, STC was compared with the state-of-the-art graph convolutional methods in a supervised learning setting on nine node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Arxiv, MAG, ACM, DBLP, and IMDB. The experimental results showed that STC achieved the state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed the state-of-the-art essential protein identification methods. An application of using pretrained STC as the embedding for feature extraction of some downstream classification tasks was introduced. The experimental results showed that STC can share weights across different graphs and be used as the embedding to improve the performance of downstream tasks.
Critical review of bio‐inspired data optimization techniques: An image steganalysis perspective

Abstract Image steganalysis involves the discovery of secret information embedded in an image. The common method is blind image steganalysis, which is a two‐class classification problem. Blind steganalysis extracts all possible feature variations in an image due to embedding, select the most appropriate feature data, and then classifies the image. The dimensionality of the extracted image features are high and demand data reduction to identify the most relevant features and to aid accurate classification of an image. The classification is under two classes namely, clean (cover) image and stego (image with embedded secret data) image. Since the classification accuracy depends on selection of most appropriate features, opting for the best data reduction or data optimization algorithms becomes a prime requisite. Research shows that most of the statistical optimization techniques converge to local minima and lead to less classification accuracy as compared to bio‐inspired methods. Bio‐inspired optimization methods obtain improved classification accuracy by reducing the high‐dimensional image features. These methods start with an initial population and then optimize them in steps till a global optimal point is reached. Examples of such methods include Ant Lion Optimization (ALO), Fire Fly Algorithm (FFA), and literature shows around 54 such algorithms. Bio‐inspired optimization has been applied in various fields of design optimization and is novel to image steganalysis. This article analyses the various bio‐inspired optimization techniques and their accuracy in image steganalysis pertaining to the discovery of embedded information in both JPEG and spatial domain steganalysis. This article is categorized under: Technologies &gt; Classification Technologies &gt; Computational Intelligence Technologies &gt; Artificial Intelligence
A Neuromorphic Normalization Algorithm for Stabilizing Synaptic Weights with Application to Dictionary Learning in LCA

Instabilities in neuromorphic machine learning can occur when synaptic updates meant to encode matrix transforms are not normalized. This phenomenon is encountered in Hebbian learning [5], where, as a synapse’s strength grows, post-synaptic activity increases, further enhancing synaptic strength, leading to a runaway condition, where synaptic strength becomes saturated [3, 7]. A number of mechanisms have been suggested for regulating and stabilizing this phenomenon [1, 2, 4, 6]. Here, we present a new neuromorphic algorithm to directly normalize a synaptic connectivity. The algorithm is based on a synfire-gated synfire chain-based information control network in concert with Hebbian synapses [8, 10]. The algorithm is designed to directly normalize synaptic weights via the minimization of a cost function. We demonstrate its effectiveness as a component of a Locally Competitive Algorithm (LCA) [9] with dictionary learning, which exhibits runaway in the absence of an effective normalization procedure [15]. LA-UR-21-31884
Doppler Signature Analysis of Over-The-Horizon Radar Signals with Target Altitude Perturbation

In this paper, we examine the Doppler signatures of local-multipath signals in over-the-horizon radar for a target that moves with a constant horizontal velocity but experiences altitude perturbation. In practice, the target altitude may deviate from its scheduled altitude due to, e.g., flight dynamics. After describing the Doppler signatures for a target that maintains a constant altitude without perturbation, we develop the mathematical framework for target Doppler signature analysis that includes target altitude perturbations. It is observed that altitude variations result in spreading of Doppler frequencies, and the bias is associated with the average target altitude. It is observed that, for mild target altitude perturbations, existing analysis methods remain effective for local-multipath Doppler frequency estimation. The Doppler frequency estimation capability and performance are verified using simulation results.
WhatsApp: Cyber Security Risk Management, Governance and Control

This document takes an in-depth approach to identify WhatsApp's Security risk management, governance and controls. WhatsApp is a communication mobile application that is available on both android and IOS, recently acquired by Facebook and allows us to stay connected. This document identifies all necessary assets, threats, vulnerabilities, and risks to WhatsApp and further provides mitigations and security controls to possibly utilize and secure the application.
Vision-Based American Sign Language Classification Approach via Deep Learning

Hearing-impaired is the disability of partial or total hearing loss that causes a significant problem for communication with other people in society. American Sign Language (ASL) is one of the sign languages that most commonly used language used by Hearing impaired communities to communicate with each other. In this paper, we proposed a simple deep learning model thataims to classify the American Sign Language letters as a step in a path for removing communication barriers that are related to disabilities.
Efficient descriptors selection in automatic image retrieval using DENOL

A well-structured and indexed database alleviates the computing burden on large data. This paper describes groundwork for presenting the data in a compact, distinctive form, improving the procedures of applying keypoint detection algorithms to preprocess and reduce the relevant features of the images. Our method computes for an image a number of SURF keypoints in a given interval, by adapting the threshold related to the Hessian matrix blob detector. This type of approach allows selecting the level of detail to use in image description and gives us control over the computing time. We named this method DENOL (Descriptor Number On Limits) and tested it on images from two datasets, UCID and an original image database which we propose, IIT_DB. Very good retrieval results and a significantly reduced computing time are achieved.
A local-nonlocal mathematical morphology

Mathematical morphology (MM) is traditional and yet applied in many areas. Among the relevant researches, nonlocal extensions have been studied due to their advantages of adaptivity and nonlocal self-similarity. However, these extensions are fragile to noises and easily result in gray value deviation (the maximum grayscale value is changed significantly). In this paper, a local-nonlocal mathematical morphology (LNLMM) is proposed: we use flat structuring element (SE) to avoid gray value deviation and introduce local information to suppress noises. Moreover, to speed up the nonlocal computation involved, we construct the SE in low-dimensional space. Benefiting from the constraint of k-reciprocal nearest neighbors (KRNN) on the SE, the operators of LNLMM theoretically inherit the important mathematical properties from traditional MM, that gives solid supports in applications. With denoising experiments, the powerful performance of LNLMM is preliminarily verified.
A general real-time optimization framework for polynomial-based trajectory planning of autonomous flying robots

This paper presents a general real-time, numerically stable optimization framework for time polynomial-based trajectory generation of autonomous aerial robots. The proposed general optimization framework (GOF) allows various optimization criteria for trajectory generation cost-function, such as minimizing the trajectory total length, time, and position derivatives. Minimizing position derivatives includes velocity, acceleration, jerk, and snap, or any combination of them. This study considers the quadrotor as the test platform. By exploiting tools from the calculus of variations, differential flatness property, and polynomial-based trajectories, the developed algorithm finds feasible trajectories without extensive computational sampling and iterative searching in the high-dimensional state space of quadrotor dynamics. The GOF includes a segment-wise gradient descent-like algorithm to iteratively decrease the allowed time of each segment individually so as to avoid getting stuck at a local minimum. The comparison analysis with existing methods validated the numerical stability and computational speed advantages of the proposed approach. It also shows that the algorithm is suitable for the real-time generation of high-performance long-range trajectories consisting of a large number of waypoints and high-order piecewise polynomials. An animated simulation of this work is available at https://youtu.be/E1AC1vyPqOE