Reviewerly/exHarmony huggingface dataset:
Learning channel-selective and aberrance repressed correlation filter with memory model for unmanned aerial vehicle object tracking

To ensure that computers can accomplish specific tasks intelligently and autonomously, it is common to introduce more knowledge into artificial intelligence (AI) technology as prior information, by imitating the structure and mindset of the human brain. Currently, unmanned aerial vehicle (UAV) tracking plays an important role in military and civilian fields. However, robust and accurate UAV tracking remains a demanding task, due to limited computing capability, unanticipated object appearance variations, and a volatile environment. In this paper, inspired by the memory mechanism and cognitive process in the human brain, and considering the computing resources of the platform, a novel tracking method based on Discriminative Correlation Filter (DCF) based trackers and memory model is proposed, by introducing dynamic feature-channel weight and aberrance repressed regularization into the loss function, and by adding an additional historical model retrieval module. Specifically, the feature-channel weight integrated into the spatial regularization (SR) enables the filter to select features. The aberrance repressed regularization provides potential interference information to the tracker and is advantageous in suppressing the aberrances caused by both background clutter and appearance changes of the target. By optimizing the aforementioned two jointly, the proposed tracker could restrain the potential distractors, and train a robust filter simultaneously by focusing on more reliable features. Furthermore, the overall loss function could be optimized with the Alternative Direction Method of Multipliers (ADMM) method, thereby improving the calculation efficiency of the algorithm. Meanwhile, with the historical model retrieval module, the tracker is encouraged to adopt some historical models of past video frames to update the tracker, and it is also incentivized to make full use of the historical information to construct a more reliable target appearance representation. By evaluating the method on two challenging UAV benchmarks, the results prove that this tracker shows superior performance compared with most other advanced tracking algorithms.
APVAS: Reducing the Memory Requirement of AS_PATH Validation by Introducing Aggregate Signatures into BGPsec

The BGPsec protocol, which is an extension of the border gateway protocol (BGP) for Internet routing known as BGPsec, uses digital signatures to guarantee the validity of routing information. However, the use of digital signatures in routing information on BGPsec causes a lack of memory in BGP routers, creating a gaping security hole in today's Internet. This problem hinders the practical realization and implementation of BGPsec. In this paper, we present APVAS (AS path validation based on aggregate signatures), a new protocol that reduces the memory consumption of routers running BGPsec when validating paths in routing information. APVAS relies on a novel aggregate signature scheme that compresses individually generated signatures into a single signature. Furthermore, we implement a prototype of APVAS on BIRD Internet Routing Daemon and demonstrate its efficiency on actual BGP connections. Our results show that the routing tables of the routers running BGPsec with APVAS have 20% lower memory consumption than those running the conventional BGPsec. We also confirm the effectiveness of APVAS in the real world by using 800,000 routes, which are equivalent to the full route information on a global scale.
ConvMS: Improving Convolutional Knowledge Graph Embeddings via Integrating Information of Multiple Subspaces

Knowledge graphs are involved in more and more applications to further improve intelligence. Owing to the inherent incompleteness of knowledge graphs resulted from data updating and missing, a number of knowledge graph completion models are proposed in succession. To obtain better performance, many methods are of high complexity, making it time-consuming for training and inference. This paper proposes a simple but effective model using only shallow neural networks, which combines enhanced feature interaction and multi-subspace information integration. In the enhanced feature interaction module, entity and relation embeddings are almost peer-to-peer interacted via multi-channel 2D convolution. In the multi-subspace information integration module, entity and relation embeddings are projected to multiple subspaces to extract multi-view information to further boost performance. Extensive experiments on widely used datasets show that the proposed model outperforms a series of strong baselines. And ablation studies demonstrate the effectiveness of each submodule in the model.
Learned image compression via multiscale prior for machine recognition

The conventional image compression framework is pixel fidelity-driven, which can generate compressed images with considerable visual quality even at low bit rates. However, these methods emphasize the human visual experience and ignore the need for machine recognition-driven tasks. To this end, we propose an image compression framework that utilizes multiscale prior information extracted from the machine perceptual model to improve the machine recognition accuracy of compressed images. Specifically, the interaction refinement module (IRM) is designed to interact multiscale prior information with each other, adaptively retaining machine recognition–relevant features to enhance its expression on compact features. To further improve the accuracy of machine recognition, machine vision perceptual loss is designed on semantic variation weight, which is the weight of semantic variation degree of deep adjacent layers in multiscale priors. Machine vision perceptual loss is used to optimize the semantic distortion of compressed images for retaining important semantic information. Experimental results show that compared with compression methods including BPG, WebP, Mentzer, NIC, IUWD, and RCIS, the Top-1 recognition accuracy of the proposed method is improved by 10.9%, 19%, 11.6%, 12.9%, 6%, and 2.7% at a lower bit rate (0.2 bpp). In addition, the performance improvement on other machine recognition networks and machine vision tasks shows the versatility of the proposed method.
A mechanics model in design of flexible nozzle with multiple movable hinge boundaries

In this paper, we propose a modified variational approach to predict the morphology of the flexible nozzle used in wind tunnel. Different from previous studies, the movements of the multiple hinges are considered as movable displacement boundary conditions during establishing the potential energy functional. The cubic spline interpolation method is employed to supply the supplementary boundary conditions in calculation of the functional minimization problem. Current analytical model is verified by experiments carried out on a fixed-flexible nozzle structure whose geometries and materials are the same as those from a commissioned supersonic nozzle. The maximum deviation between the predictions from theoretical method and laser displacement testing does not exceed 0.5 mm. This method can also deal with the large deflection beam problem with multiple movable boundaries.
Secure collaborative control on lean embedded hardware

We use the homomorphic properties of the Paillier cryptosystem to implement decoupling control over a wireless network without disclosing any information of a control agents state to neighboring agents. The results from a hardware in the loop implementation reveal the primary bottlenecks regarding the computational load on the local agents. Nevertheless they demonstrate a basic usability of Micropython for encrypted control with viable key sizes.
Analysis and Synthesis of Adaptive Gradient Algorithms in Machine Learning: The Case of AdaBound and MAdamSSM

Adaptive gradient algorithms have become the prevalent tool in training complex neural networks; recent examples include AdaBound and MAdam. For a better understanding of such existing optimization algorithms and design ideas for new algorithms, well-known tools from classical control appear to be promising. This area of research is built upon modeling optimization algorithms as closed-loop dynamical systems. Consequently, this paper exploits a control-theoretic methodology in analyzing AdaBound, a recent adaptive gradient algorithm, and proposing a novel optimizer for machine learning. Specifically, inspired by the recently developed state-space perspective in the G-AdaGrad and the Adam algorithm, we present a simple convergence analysis of the AdaBound algorithm for non-convex optimization problems. Next, we propose a new variant of the MAdam algorithm upon applying the concept of transfer functions. Our experimental results demonstrate the efficiency of the proposed algorithm in training CNN models for image classification problems. The findings in this paper suggest further exploration of the existing tools from control theory in complex machine learning problems.
Dynamic Image-Based Visual Servoing for Quadrotor to Track a Planar Target with Unknown Motion

This paper presents an image-based visual servoing (IBVS) technique for a quadrotor to track a moving planar target having unknown linear and angular velocities. Image moment-based features are derived after transforming the optical measurements to a virtual image plane parallel to the planar target. The kinematic controller is designed incorporating the linear and angular target velocity estimator blocks. The theoretical analysis demonstrates that the errors converge exponentially to an ultimate bound for time-varying target motion and exponentially to zero if the target motion is purely translational with constant velocity. Unlike past literature, the proposed IBVS scheme can handle unknown arbitrary translational and angular (along the z-axis) motions of the target in a unified and model-free manner. Simulation results using circular and Liassajous target motion are provided to validate the efficacy of the proposed scheme.
FairMOT-X: Real-time One-shot Methods For Multi-class Multi-object Tracking

Multi-Object Tracking (MOT) is an important task in computer vision. Most research works on MOT focus on tracking multiple objects of a single category, such as humans. However, many applications, such as wildlife surveillance and autonomous driving, require multi-object trackers to track multiple categories of objects. In this work, the extension of state-of-the-art one-shot MOT methods for multi-class tracking is shown to be non-trivial, yielding deteriorated tracking performance due to the complexity of balancing the jointly learned detection and association tasks. To tackle this limitation, a simple improvement to FairMOT is proposed, which replaces the detection head with the decoupled head from YOLOX. The simplified multi-class detection framework yields improved detection and tracking results on the BDD100K MOT validation dataset, yielding an mHOTA of 25.3% compared to 19.7%.
Right-of-Way-based Probabilistic Acceleration Velocity Obstacle

A novel second order online local reactive motion planner, named ‘Right-of-Way-based Probabilistic Acceleration Velocity Obstacle’ (R-PAVO), is developed in this paper in a probabilistic set-up, in which right-of-way rules posed by regulatory authorities are also embedded. The developed motion planning algorithm is capable of generating a collision-free, dynamically feasible and goal-oriented trajectory for each unmanned vehicle (a.k.a. agent) in a multi-agent environment in the presence of uncertainties associated with information on other agents’ motion and occluded regions. The considered right-of-way rules facilitate an implicit coordination among the agents that also help in avoiding any reciprocal oscillation in the agents’ planned trajectories. Extensive simulation studies are carried out to demonstrate the effectiveness of the developed algorithm in terms of relevant measures of effectiveness indicating real-time implementability of the developed algorithm.
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models

This paper proposes a probabilistic Bayesian formulation for system identification (ID) and estimation of nonseparable Hamiltonian systems using stochastic dynamic models. Nonseparable Hamiltonian systems arise in models from diverse science and engineering applications such as astrophysics, robotics, vortex dynamics, charged particle dynamics, and quantum mechanics. The numerical experiments demonstrate that the proposed method recovers dynamical systems with higher accuracy and reduced predictive uncertainty compared to state-of-the-art approaches. The results further show that accurate predictions far outside the training time interval in the presence of sparse and noisy measurements are possible, which lends robustness and generalizability to the proposed approach. A quantitative benefit is prediction accuracy with less than 10% relative error for more than 12 times longer than a comparable least-squares-based method on a benchmark problem.
Controlling Robots using Image Analysis and a Consortium Blockchain

Blockchain is a disruptive technology, normally used within financial applications, however, it can be very beneficial in certain robotic contexts, such as when an immutable register of events is required. Among the several properties of Blockchain that can be useful within robotic environments, we find not just immutability but also data decentralization, irreversibility, accessibility and non-repudiation. In this paper, we propose an architecture that uses blockchain as a ledger, and smart-contracts for robotic control by using oracles to process data. We show how to register events in a secure way, how it is possible to use smart-contracts to control robots and how to interface with external algorithms for image analysis. The proposed architecture is modular and can be used in multiple contexts such as in manufacturing, network control, robot control, and others, since it is easy to integrate, adapt, maintain and extend to new domains, only requiring new tailored smart-contracts.
A Dueling Twin Delayed DDPG Architecture for mobile robot navigation

Collision-free path planning is challenging for mobile robot navigation tasks. Recently, the deep reinforcement learning method provided a more effective way to derive safe and efficient velocity commands directly from raw sensor information. Twin Delayed Deep Deterministic policy gradient (TD3) is an efficient approach for DRL navigation. However, original TD3 exists issues such as inefficiency learning and slow convergence speed which may influence the model to derive an ideal action for mobile robot navigation. To address these issues, we proposed a novel dueling architecture model, dueling deep deterministic policy gradient (Dueling-TD3), we compose the dueling network architecture into the critic network to increase the Q-value estimate precision. The results demonstrate that our proposed model outperforms the original model in terms of route planning capabilities.
Multi-objective Model Predictive Control for Trajectory Tracking of Intelligent Electric Vehicles

This paper presents a multi-objective strategy for trajectory tracking of intelligent electric vehicles incorporating tracking performance with the energy economy. A model predictive controller is built using the single-track vehicle dynamics model to predict planar motions. The optimization problem is formulated with a cost function involving tracking errors and motor efficiency. The proposed system is verified on a typical road in the co-simulation platform - CarSim/Simulink. Experimental results from the simulation demonstrate the expected performance of the developed system. Intelligent electric vehicles, trajectory tracking, multi-objective optimization, model predictive control
Classification of White Blood Cells Subtype Using MCNN

White Blood cells are building blocks of the immune system of humans as they fight different types of infections, which is vital for healthy recovery. Changes in the number of White Blood Cell subtypes (WBCs) rule out certain diseases such as infection, heart disease and diabetes in medical practices. Conventional methods of counting the number of WBCs are dependent on manual testing and have chances of human error and the automated method apparatus is very costly. Thus the classification of White Blood Cell subtypes is of vital importance. In this study, CV based solution is proposed for White Blood Cell subtype identification. Different MCNN-based models along with transfer learning-based models (VGG16 & Resnet50) are trained and implemented for performance comparison and the effect of different training parameters on the performance of the models is also explored in the study. It was observed that changing the training parameters also affects the accuracy of the model. The highest accuracy of 96.6% was achieved using the MCNN-based model for the classification of White Blood Cells.
A Comparative Study on Machine Learning algorithms for Knowledge Discovery

For centuries, the process of formulating new knowledge from observations has driven scientific discoveries. With rapid advancements in machine learning, it is natural to question the possibility of automating knowledge discovery in the scientific field. A benchmark task for automated knowledge discovery is called symbolic regression. The task aims to predict a mathematical equation that best describes the observational data. The advancements in symbolic regression have significant potential to aid research in understanding unexplored systems' dynamics and governing properties. However, the combinatorial nature of the problem makes it an expensive and challenging problem to solve efficiently. Several types of symbolic regression algorithms exist, from genetic programming and sparse regression to deep generative models. However, no survey collates these prominent algorithms. Therefore, this paper aims to summarize key research works in symbolic regression and perform a comparative study to understand the strength and limitations of each method. Finally, we highlight the challenges in the current methods and future research directions in the application of machine learning in knowledge discovery.
Autonomous Navigation in Environments with Arbitrary Non-convex Obstacles

We develop an autonomous navigation algorithm for a robot operating in two-dimensional environments cluttered with obstacles which can have arbitrary non-convex shapes and can be in close proximity with each other, under the assumption that there exists a safe path connecting the initial and the target locations. We propose a hybrid feedback controller, with Zeno-free switching between the move-to-target mode and the obstacle-avoidance mode, guaranteeing global asymptotic stability of the target equilibrium. To handle the obstacle non-convexity, we introduce a transformation that modifies (virtually) the obstacles’ shapes, in a non-conservative manner, to generate a modified free-space suitable for the design of a reliable obstacle avoidance strategy. Finally, we validate the efficacy of the proposed hybrid feedback controller through simulations.
Applying classical control techniques to quantum systems: entanglement versus stability margin and other limitations

Development of robust quantum control has been challenging and there are numerous obstacles to applying classical robust control to quantum system including bilinearity, marginal stability, state preparation errors, nonlinear figures of merit. The requirement of marginal stability, while not satisfied for closed quantum systems, can be satisfied for open quantum systems where Lindbladian behavior leads to non-unitary evolution, and allows for nonzero classical stability margins, but it remains difficult to extract physical insight when classical robust control tools are applied to these systems. We consider a straightforward example of the entanglement between two qubits dissipatively coupled to a lossy cavity and analyze it using the classical stability margin and structured perturbations. We attempt, where possible, to extract physical insight from these analyses. Our aim is to highlight where classical robust control can assist in the analysis of quantum systems and identify areas where more work needs to be done to develop specific methods for quantum robust control.
MathCLM: Mathematical Cognitive Learning Model Based on the Evolution of Knowledge Graph

In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a challenging mission. However, such integration may lead to a better understanding, practice, and construction of more realistic models, especially for mathematical learning. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable for online learning and education. Therefore, we propose a novel cognitive learning model, called Mathematical Cognitive Learning Model (MathCLM) based on the evolution of knowledge graph, for online mathematical learning that seeks to effectively represent, learn, and reason in online learning environments. The model's architecture combines cognitive learning with symbolic knowledge representation based on natural language processing (NLP). We introduce the mathematical instance concept to build the strategies by mathematical knowledge, such as theorems, axioms, etc., and infer new custom instances based on the learning knowledge. Furthermore, it can deal with uncertainty and errors from instances recommendation using a graph matching model and displays the inference progressing with different combinations of instances. We build a platform to promote and validate our model. The validation of the model on the real-world platform and the results presented here indicate the promise of the approach when performing online learning and reasoning in real-world scenarios, with possible applications in various areas.
Online Planning of Uncertain MDPs under Temporal Tasks and Safe-Return Constraints

This paper addresses the online motion planning problem of mobile robots under complex high-level tasks. The robot motion is modeled as an uncertain Markov Decision Process (MDP) due to limited initial knowledge, while the task is specified as Linear Temporal Logic (LTL) formulas. The proposed framework enables the robot to explore and update the system model in a Bayesian way, while simultaneously optimizing the asymptotic costs of satisfying the complex temporal task. Theoretical guarantees are provided for the synthesized outgoing policy and safety policy. More importantly, instead of greedy exploration under the classic ergodicity assumption, a safe-return requirement is enforced such that the robot can always return to home states with a high probability. The overall methods are validated by numerical simulations.
TSN-GReID: Transformer-based Siamese Network for Group Re-Identification

Group re-identification (GReID) is an important yet less-studied task. GReID focuses on associating the group images across non-overlapping cameras. The key challenges of GReID include layout variation, membership changes, and occlusion. Most existing methods focus on the group variation but ignore the occlusion that frequently occurs in the group. In practical scenarios, so many activities like conversing, queuing, or fighting consist of groups. To this end, we design a novel Transformer-based Siamese Network for GReID (TSN-GReID) for joint learning of classification and correspondence tasks to learn more robust group features for group layout and membership changes. Furthermore, we put forward an original regrouping random patch module(RRPM) which respectively regroups the member patch embedding and member-level local features to generate group features with improved discrimination ability and more diversified coverage to deal with occlusion. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms state-of-the-art methods by 4.6 % Rank-1 on the CUHK-SYSU Group (CSG) dataset and by 7.1% Rank-1 on the DukeMTMC Group dataset.