Reviewerly/exHarmony huggingface dataset:
Light-SpineNet Variational Autoencoder for Logical Access Spoof Utterances Detection in Speaker Verification Systems

Since the release of the bi-annual ASVspoof challenge series, there has been a rise in the development of spoofing countermeasures for speaker verification systems by the research community. Each edition provides a more challenging evaluation dataset for anti-spoofing tasks involving more robust countermeasures. This paper presents a new logical access anti-spoofing system using various versions of the light SpineNet backbone architecture. Besides, we used two data augmentation strategies to detect spoofed speech utterances: first, by increasing the training data with different noise types, and second, by augmenting the training dataset with only babble noise. Our experiments showed the positive impact of babble-based augmentation with all SpineNet variants. The augmented LSpineNet was then enhanced by integrating a variational autoencoder (VAE) as an embedding regularizer and a probabilistic linear discriminant analysis (PLDA) backend. The proposed LSpineNet-VAE anti-spoofing system achieved a relative decrease of more than 7% and 90% in the tandem detection cost function (t-DCF) and error rate, respectively, when compared to state-of-the-art systems.
Experimental Investigation of an Interactive Animatronic Robotic Head Connected to ChatGPT

With the rise in the emerging technology of Artificial intelligence applications, the animatronics robotics heads are getting more popular and spreading widely. This paper demonstrates the process of developing an animatronics robot head called Adam that integrates a ChatGPT server in a Raspberry Pi. The head is capable of replying to responses to questions asked through a microphone, providing a more interactive and realistic experience. In addition, the head features a camera-based tracking system that enables it to follow the person speaking, utilizing servo motors for smooth and lifelike head movements. The technology used in Adam has the potential to be used in various applications, including customer service and entertainment. The successful implementation of this project highlights the potential for further advancements in robotics and artificial intelligence. This study evaluates the effectiveness of an animatronics robot head in various environments, the robot was tested on its ability to answer five questions, achieving a high success rate of between 4 and 5. The results indicate that the robot’s hardware and software components, including servo motors, motor controller, Raspberry Pi, and ChatGPT server, are reliable and effective with a successful recognition rate of 96%.
Feature Extraction And Machine Learning Classifiers For Elderly Speech Recognition In Comprehensive Geriatric Assessment Cga Questionnaires

Functional decline is a serious syndrome that attacks older adults and can be delayed or prevented by its early risk detection. In this study, speech recognition is used to help in the self-assessment of the geriatric tests used for this purpose. We investigate the effect of the speaker mode, the frame length, the speaker gender, and the vocabulary size in terms of the word recognition rate (WRR) for three different classifiers: RF, KNN, and SVM. The results of our data show that all three classifiers are proper for speech recognition. However, for speaker-dependent mode, the RF gives the maximal performance when separating genders (for females 98.6% and for males 98.1%) and using a 25 ms frame-length with a 10 ms overlap. On the other hand, for the speaker-independent mode, the RF gave also the best performance for 20ms with 10ms overlap, for digits alone WRR=98.1% and binary classification alone WRR=98%.
XAI for Self-supervised Clustering of Wireless Spectrum Activity

The so-called black-box deep learning (DL) models are increasingly used in classification tasks across many scientific disciplines, including wireless communications domain. In this trend, supervised DL models appear as most commonly proposed solutions to domain-related classification problems. Although they are proven to have unmatched performance, the necessity for large labeled training data and their intractable reasoning, as two major drawbacks, are constraining their usage. The self-supervised architectures emerged as a promising solution that reduces the size of the needed labeled data, but the explainability problem remains. In this paper, we propose a methodology for explaining deep clustering, self-supervised learning architectures comprised of a representation learning part based on a Convolutional Neural Network (CNN) and a clustering part. For the state of the art representation learning part, our methodology employs Guided Backpropagation to interpret the regions of interest of the input data. For the clustering part, the methodology relies on Shallow Trees to explain the clustering result using optimized depth decision tree. Finally, a data-specific visualizations part enables connection for each of the clusters to the input data trough the relevant features. We explain on a use case of wireless spectrum activity clustering how the CNN-based, deep clustering architecture reasons.
Evaluation of Change Detection Algorithms using Difficulty Maps

The evaluation of a change detection algorithm should show its superiority over state-of-the-art algorithms' performances. Evaluating an algorithm involves executing it to segment a set of videos and comparing the results with the ground truth. Here, we used the difficulty level to classify each pixel of each frame of the videos of a dataset as an algorithm performance measure. A structure called "difficulty map" stores information about the difficulty of classifying each pixel in a frame. Based on these maps, we developed a metric that aims to evaluate the performance of algorithms on the difficulty map. The results showed that there are algorithms with the characteristic of classifying pixels that most state-of-the-art algorithms cannot classify (promising algorithms). Identifying such algorithms is essential since improving their performance means facing challenges already overcome by existing approaches.
On the Stability Analysis of Open Federated Learning Systems

We consider the open federated learning (FL) systems, where clients may join and/or leave the system during the FL process. Given the variability of the number of present clients, convergence to a fixed model cannot be guaranteed in open systems. Instead, we resort to a new performance metric that we term the stability of open FL systems, which quantifies the magnitude of the learned model in open systems. Under the assumption that local clients’ functions are strongly convex and smooth, we theoretically quantify the radius of stability for two FL algorithms, namely local SGD and local Adam. We observe that this radius relies on several key parameters, including the function condition number as well as the variance of the stochastic gradient. Our theoretical results are further verified by numerical simulations on synthetic data.
Quadrotor Motion Planning in Stochastic Wind Fields

In this paper, we propose a motion planner for quadrotors in windy environments. We extend a well-known convex polynomial optimization (CPO) method to incorporate known stochastic input uncertainties. In particular, we focus on a quadrotor unmanned aerial vehicle (UAV), and propose a new objective for direct minimization of the squared ${{{\mathcal{L}}}_2}$-norm of the UAV thrust, $\left\| f \right\|_{{{{\mathcal{L}}}_2}}^2$. We show that the first two moments of $\left\| f \right\|_{{{{\mathcal{L}}}_2}}^2$ are convex in the optimization variables of the CPO problem, and can be minimized directly. Furthermore, we demonstrate that a constrained CPO approach can be used in this setting, contrary to the more popular unconstrained approaches. We provide examples demonstrating: (i) that inclusion of wind can yield significant improvements in the considered cost; (ii) that re-planning of complex paths at can be done at rates exceeding 100 Hz; and (iii) that the proposed method facilitates online re-planning leveraging wind in free-space defined as the union of convex sets.
Closed-Form Active Learning using Expected Variance Reduction of Gaussian Process Surrogates for Adaptive Sampling

Adaptive sampling of latent fields remains a challenging task, especially in high-dimensional input spaces. In this paper, we propose an active learning method of expected variance reduction with Gaussian process (GP) surrogates using a closed-form gradient. The use of closed-form gradient leads the optimization to find better solutions with reduced computations. We derive the closed-form gradient for active learning Cohn (ALC) using GP surrogates that are formed with the separable squared exponential covariance function. Moreover, we provide algorithmic details for the execution of the closedform ALC (cALC). Numerical experiments with multiple input space dimensions illustrate the efficacy of our method.
Synthesis of Proactive Sensor Placement In Probabilistic Attack Graphs

This paper studies the deployment of joint moving target defense (MTD) and deception against multi-stage cyber-attacks. Given the system equipped with MTD that randomizes between different configurations, we investigate how to allocate a bounded number of sensors in each configuration to optimize the probability of detecting the attack before the attacker achieves its objective. Specifically, two types of sensors are considered: intrusion detectors that are observable by the attacker and stealthy sensors that are not observable to the attacker. We propose a two-step optimization-based approach: Firstly, the defender allocates intrusion detectors assuming the attacker will best respond to evade detection. Secondly, the defender will allocate stealthy sensors, given the best response attack strategy computed in the first step, to further reduce the attacker’s chance of success. We illustrate the effectiveness of the proposed methods using a cyber defense example.
Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents

This paper proposes an algorithm for Federated Learning (FL) with a two-layer structure that achieves both variance reduction and a faster convergence rate to an optimal solution in the setting where each agent has an arbitrary probability of selection in each iteration. The first layer of our algorithm corresponds to the model parameter propagation across agents done by the server. In the second layer, each agent does its local update with a stochastic and variance-reduced technique called Stochastic Variance Reduced Gradient (SVRG). We leverage the concept of variance reduction from stochastic optimization when the agents want to do their local update step to reduce the variance caused by stochastic gradient descent (SGD). The special attention in this paper is on FL operation where the agents’ participation in the update process in each round is probabilistic and non-uniform. We provide a convergence bound for our algorithm which improves the rate from $O\left( {\frac{1}{{\sqrt K }}} \right)$ to $O\left( {\frac{1}{K}} \right)$ by using a constant step-size when the cost is strongly convex. For non-convex costs, we establish a $O\left( {\frac{1}{{\sqrt[3]{{{K^2}}}}}} \right)$ to a stationary point using a vanishing stepsize. We demonstrate the performance of our algorithm using numerical simulations.
GPU Accelerated Batch Trajectory Optimization for Autonomous Navigation

Trajectory optimizations encountered in mobile robot navigation are non-convex, and thus the solution process is prone to get stuck at poor local optima, resulting in collisions with the environment. A conceptually simple workaround is to simply run the optimizer from several initializations in parallel and choose the best solution. But realizing this simple trick with off-the-shelf optimizers is challenging since they are not customized for parallel/batch operation. We fill this gap by proposing a novel batchable and GPU accelerated trajectory optimizer for autonomous navigation. Our batch optimizer can run several hundred instances of the problem in parallel in real time. We improve the state-of-the-art in the following respects. First, we show that parallel initialization naturally discovers a distribution of locally optimal trajectories residing in different homotopies. Second, we improve the navigation quality (success rate, tracking) compared to the baseline approach that relies on computing a single locally optimal trajectory at each control loop. Finally, we show that when initialized with trajectory samples from a Gaussian distribution, our batch optimizer out-performs the state-of-the-art cross-entropy method in solution quality. Codes: https://tinyurl.com/a3b99m8, Video: https://www.youtube.com/watch?v=ZlWJk-w03d8
Multi-Objective Trajectory Planning for Unmanned Aerial Vehicles Using CLF-CBF-Based Quadratic Programs

Control barrier function-based quadratic programs (CBF-based QP) provide an avenue for agile and numerically efficient obstacle avoidance algorithms. However, the CBF-based QP methods may lead to lengthy detours and undesirable transient tracking performance without proper trajectory planning. This paper expands the CBF-based QP concept to create a modified safe reference trajectory with a prescribed avoidance radius and direction, where the modified reference shadows the actual reference during the avoidance maneuver. We use a control Lyapunov function (CLF) to match the modified reference with the actual reference and three CBFs to formulate safety and performance objectives to maintain distance, adjust velocity, and determine the direction of the avoidance maneuver. These formulations produce constraints that are synthesized by means of a quadratic program. The QP generates a desirable velocity profile for the safe reference trajectory. Numerical simulations verify the effectiveness of the proposed trajectory planning method.
Scalable Multi-Agent Reinforcement Learning with General Utilities

We study the scalable multi-agent reinforcement learning (MARL) with general utilities, defined as nonlinear functions of the team’s long-term state-action occupancy measure. The objective is to find a localized policy that maximizes the average of the team’s local utility functions without the full observability of each agent in the team. By exploiting the spatial correlation decay property of the network structure, we propose a scalable distributed policy gradient algorithm with shadow reward and localized policy that consists of three steps: (1) shadow reward estimation, (2) truncated shadow Q-function estimation, and (3) truncated policy gradient estimation and policy update. Our algorithm converges, with high probability, to ϵ-stationarity with $\mathcal{O}\left( {{\varepsilon ^{ - 2}}} \right)$ samples up to some approximation error that decreases exponentially in the communication radius. This is the first result in the literature on multi-agent RL with general utilities that does not require the full observability.
On the Benefits of Leveraging Structural Information in Planning Over the Learned Model

Model-based Reinforcement Learning (RL) integrates learning and planning and has received increasing attention in recent years. However, learning the model can incur a significant cost (in terms of sample complexity), due to the need to obtain a sufficient number of samples for each state-action pair. In this paper, we investigate the benefits of leveraging structural information about the system in terms of reducing sample complexity. Specifically, we consider the setting where the transition probability matrix is a known function of a number of structural parameters, whose values are initially unknown. We then consider the problem of estimating those parameters based on the interactions with the environment. We characterize the difference between the Q estimates and the optimal Q value as a function of the number of samples. Our analysis shows that there can be a significant saving in sample complexity by leveraging structural information about the model. We illustrate the findings by considering how to control a queuing system with heterogeneous servers.
A Transient Response Adjustable MPC for Following A Dynamic Object

As mobile robots increasingly interact with humans, vehicles, and other robots, the ability to follow these dynamic objects becomes a crucial capability. In this paper, a mobile robot motion controller based on a down`scaled Model Predictive Control (MPC) is proposed for performing dynamic object following. The controller can reflect the dynamics of an object for an appropriate response speed while ensuring safety of the robot against predicted risks. In addition, an adaptive prediction time horizon is proposed to improve following robustness. The proposed motion controller is demonstrated through extensive simulations and real-time experiments on mobile robot hardware.
Generalized Moving Horizon Estimation for Nonlinear Systems with Robustness to Measurement Outliers

The accuracy of moving horizon estimation (MHE) significantly degrades under measurement outliers. Existing methods usually formulate combinatorial optimization problems to address this issue and are restricted to linear systems to ensure computational tractability. To overcome those limitations, this paper proposes a generalized MHE (GMHE) approach that formulates MHE as a maximum a posteriori estimation problem and extends the standard MHE with a generalized loss function. The proposed approach avoids the high computational complexity of existing methods and has no restriction on the system models. We demonstrate that the standard MHE is a special case of GMHE, where the loss function uses the Kullback-Leibler (KL) divergence between the empirical distribution of the observations and the assumed likelihood. Because KL divergence is sensitive to outliers, we replace it with a robust β-divergence and name the corresponding GMHE as β-MHE. We prove that for the case of linear Gaussian systems, the gross error sensitivity of the β-MHE remains bounded, which demonstrates its robustness against outliers. The effectiveness of β-MHE is further demonstrated on systems subject to outliers with Gaussian distribution and the student distribution, respectively.
The Kernel-SME Filter with Adaptive Kernel Widths for Association-free Multi-target Tracking

Different objectives and paradigms exist for tracking multiple targets when measurements do not contain information about the target identities (IDs). The Symmetric Measurement Equation (SME) filter can be used when one is agnostic to the labels and does not attempt to assign different IDs to the different targets. We present an extension of the Kernel-SME filter that, unlike the original variant, uses adaptive kernel widths that depend on the respective uncertainty. In our evaluation, it outperformed existing SMEbased approaches, while it is only second to a more complex global nearest neighbor tracker.
Domestic Garbage Target Detection Based on Improved YOLOv5 Algorithm

The output of household garbage has increased rapidly in the world, due to the development of the economy, the improvement of the living standards of residents, and the acceleration of urbanization. The process of manual garbage classification is time-consuming and laborious, and the effect is still not satisfactory. In order to reduce the intensity of manual garbage classification and improve the efficiency and accuracy of garbage classification, a new type of household garbage classification based on improved YOLOv5 algorithm visual recognition is designed. Make a data set for garbage detection, and after training on the improved YOLOv5 network framework, detect the status of garbage in real time. Experiments have proved that the accuracy of intelligent classification reached 98.27%, which is 3.85% higher than the original algorithm. It is verified that the improved YOLOv5 algorithm is very effective when applied to garbage classification, and it has social promotion significance and value.
A Systematic Literature Review on Batik Image Retrieval

Batik is a traditional textile art form originating in Indonesia. The diversity of batik motifs results from the acculturation process, leading to different styles and variations in other regions. However, this diversity also poses a problem for society regarding identifying, classifying, and locating batik based on the motifs and areas. To address this issue, researchers have been using the Content-Based Image Retrieval (CBIR) approach, a study field that has been around for a while and is known for its real-time retrieval capabilities. CBIR is a method that aims to minimize the gap between the image feature set and human visual understanding to recognize the batik pattern. This survey reviews recent instance retrieval works developed based on handcrafted featured extraction and deep learning algorithms. The survey is organized by the most dataset, the most used method, and the survey method with the best performance. This survey highlights recent works, the potential of using handcrafted feature extraction and deep learning algorithms in CBIR, and the promising future prospects in this field.
Study on Continuity of Defuzzification Using Density Moment Method

In this study, general structure of optimal fuzzy feedback control and its mathematical consideration are proposed. Compactness of the set of membership functions which characterizes fuzzy sets in IF-THEN rules is proved for optimization. Moreover two kind of continuity of the density moment method are obtained for that purpose. Since continuity of fuzzy inference calculation on the compact set, existence of IF-THEN rules that minimize the evaluation function of fuzzy control is proved.
An Evaluation Of Ant Colony Optimization Based Techniques for Network Load Balancing

This study uses the Ant Colony Optimization (ACO) algorithm to evaluate the network load balance. The main objectives of the ACO are to decrease execution time and achieve a balanced overall distribution of workloads among the network‘s nodes. There are two priorities of the ACO load balancing algorithm which is first is to ensure the number of tasks assigned to each node with the networking environment are as uniform. Next priority is to select a node with the best capabilities to execute a certain task according to the node‘s current pheromone value. Furthermore, the strategy used by the ACO algorithm is to select the most capable node to execute each of the tasks submitted to the network. The simulations and output for the performance of the ACO algorithm were done in the Cloudsim Plus Toolkit. As a result, it indicates that the ACO algorithm is effective to achieve network load balancing and it is able to outperform the Randomized and Round Robin algorithm in all situations and settings.