Reviewerly/exHarmony huggingface dataset:
A Path Planning Technique to Reduce the Travel Time of a Micromouse

Autonomous navigation has become a very popular topic among researchers due to its numerous real-world applications including self-driving vehicles, autonomous robots in military services, and healthcare applications. Many of the aforementioned applications are time-critical and require a greater level of precision. Micromouse robot developers have been trying to address the above problems for a long time. However, many challenges still exist, especially in optimizing the time that robot takes to complete a given maze by determining the best travel path. This paper proposes a novel approach to reduce the travel time through a maze of a micromouse using the Floodfill algorithm, Bézier curve interpolation, and trajectory following techniques. Through computer simulations conducted in the Gazebo Simulation environment, the performance of the proposed approach was tested against a conventional Floodfill algorithm-based maze-solving technique. Finally, the proposed approach was tested against other micromouse robots with a range of approaches to solve a maze at the “International Micromouse Challenge - Techfest 2021”, in which the micromouse robot with this proposed approach was ahle to win.
Simulated Multiparty Quantum Digital Signature in Cyberspace Operations

Research on new cryptographic mechanisms to protect against the threat of quantum computers has attracted great interest, especially due to the impact that this new technologies will have on the security of cyber-operations. After the widely tested and implemented Quantum Key Distribution (QKD) protocol for the generation of symmetric encryption keys, other protocols are being explored, such as Quantum Digital Signatures (QDS). Their classic analogues based on asymmetric cryptography will be vulnerable to Shor's quantum algorithm. Given the applicability that QDS could have in multi-domain operations, in this work we have analyzed and implemented a quantum-safe QDS protocol in a simulated environment provided with real QKD-generated keys. First, we recreated a basic three-user scenario composed of one signer and two verifiers, and later generalized by increasing the number of users by simulating a scheme with one signer and multiple verifiers. Our simulation also allows one participating user to be malicious, thus playing with the number of attack vectors and the breaking points of the forwarding chain. In addition, a use case is presented in which the QDS protocol is performed to confirm orders along the command chain, demonstrating its applicability. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200RSY — the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023.
A graph reasoning method for multi-object unordered stacking scenarios

In order to enable the robot to safely pick the target object from it in the face of a scene with multiple objects stacked in a disorderly manner. It is of great importance for intelligent robot grasping to get the operation order by obtaining the position relationship between the target object and other objects in space. In this paper, an end-to-end detection inference network is proposed, in which features are extracted from the input RGB information by EfficientNet-B0 combined with BiFPN in terms of feature extraction, and in terms of operation relationships, object pairs without up-down position relationships are first eliminated to reduce the amount of operations, and graph attention networks (GAT) are later used to reason about the positions between objects. The network model is trained and tested on the VMRD dataset, and the experiments show that this model has good results in inference of object spatial position relationships for multi-object unordered stacked scenes.
Grammar Error Correction for Less Resourceful Languages: A Case Study of Sinhala

Grammatical Error Correction (GEC) is crucial for improving the readability and comprehension of text. Although substantial advancements have been achieved in this area for widely-spoken languages such as English, the focus on the development of GEC tools for less common languages such as Sinhala has been inadequate. Sinhala is a language spoken by more than 16 million people in Sri Lanka, known for its rich morphology, and complex grammar structures that pose a challenge for Sinhala GEC systems. This paper presents a novel GEC approach that utilizes Google machine translation, cross-linguistic knowledge and rule-based techniques augmented by machine learning to analyze complex Sinhala sentences. We focus on analyzing Sinhala verb agreement rules, and object validation rules in Sinhala active voice sentences. Additionally, we address the major challenges in Sinhala GEC, such as subject and object detection and the detection of grammatical features of nouns, including animacy, gender, and number. Our findings indicate that the GEC methodology presented achieved an accuracy of 75.61 %. Additionally, the gender and number detection components produced an accuracy of 90.89% and 92.33%, respectively. These results demonstrate the effectiveness of our approach in identifying and correcting errors in complex Sinhala sentences. Our approach is particularly useful in languages with rich morphology and limited annotated data.
Focused Bidirectional Search Trees: A Bidirectional Optimal Fast Matching Method for Robot Path Planning

Robot path planning in high-dimensional continuous space is of great significance in robotics. The conventional bidirectional fast marching method utilizes brute-force search to explore the space, which is computationally expensive. Though improving the planning efficiency, heuristic-based bidirectional fast marching search results in a suboptimal path under a predetermined number of random samples. To address these issues, we propose Focused Bidirectional Fast Marching Trees (FBFMT*), developing a novel vertex exploring sequence to constrain the forward and backward searches within the brute-force searched regions. It enables the search trees to meet in the middle of the solution path, which guarantees the optimality under the current set of samples and searches faster than the bidirectional brute-force strategy. Extensive simulations in different scenarios validate the superiority of the proposed method, showing that FBFMT* can speed up the planning process and find a better solution. We also extend the proposed method to general conditions so that the forward and backward searches of FBFMT* can meet at any portion of the path. FBFMT* reveals that the heuristic-based bidirectional fast marching search strategy can be a general and efficient technique for robot path planning in high-dimensional continuous spaces.
Enabling Activity Based Intelligence with Visual Analytics

ThornViz is a visual analytics tool presenting data along multiple perspectives for discovering the unexpected and performing Activity Based Intelligence (ABI) analysis. It leverages a flexible data model along with a graph database to support ABI principles through interactive visual exploration of intelligence data including dynamic filtering and social network analysis techniques. ThornViz was applied to explore adversarial communication network data, to investigate crypto-currency transactions for suspicious financial activity, as well as to look for potential collaborations between NATO scientific activities, which demonstrated its multi-domain support. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY — the ICMCIS, held in Skopje, North Macedonia, 16—;17 May 2023
A Noise-Resilient SLAM for Perception-Degraded Scenes Leveraging on Lightweight Denoising Neural Networks

Visual SLAM has been widely used in the field of robotics. However, images captured by camera sensors often contain various noises and seriously affect the autonomy of robots, especially in low-light environments. Consequently, numerous methods are proposed for image denoising and better performance in challenging environments. Nonetheless, many do not achieve a balance between efficiency and robustness, which severely limits the application of these methods on resource-limited platforms such as UAVs. To solve this dilemma, we propose a noise-resilient SLAM with a tailored image enhancement module, composing of a lightweight denoising neural network and a dynamic switching mechanism. First, we employ the lightweight denoising network based on NB-Net for successive frames. Then, we propose a simple yet efficient image noise evaluation method based on the Gaussian filter, achieving no-reference noise identification. It adaptively turns the neural network on and off according to different conditions, optimizing the system’s performance. Finally, we integrate the proposed enhancement module into ORB-SLAM2 for practical tests. Extensive experiments on public datasets (EuRoC and TUM) and synthetic sequences demonstrate that the proposed method balances efficiency and accuracy fairly.
Person Following with Dynamic Obstacle Avoidance

Person following is essential for many human-robot interaction applications. However, many existing person-following methods do not consider obstacles that have a serious impact on the person-following. In this paper, we focus on the obstacle avoidance problem in the person-following process and propose a dynamic obstacle avoidance path planning method. Our method integrated trajectory prediction into global and local planning to follow a person and avoid obstacles. The proposed method is applied to a practical robot person-following task as an experiment. The experimental results show that the proposed method can achieve the target person following robustly in the case of obstacles.
Skeleton-Embedded Network for action recognition

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained.
Knowledge Distillation on Driving Intention Generator: Learn Human-like Semantic Reasoning

Semantic information plays a significant role in autonomous vehicles. However, it has remained open how to economically compute and efficiently incorporate semantic information into other frequently applied processes, such as construction of local navigation map, driving decision making and trajectories planning. To address this issue, we introduce Knowledge Distillation to convey semantic reasoning capability. Following the existing intention-guided framework, a semantically incorporated module associates given navigation instruction with acquired visual perception to synthesize driving intention. Our major contribution is to teach the driving intention generator how to propagate considering semantics implicitly. Specifically, we construct a semantic supervisor identical to the driving intention generator. The supervisor transfers semantic reasoning ability to the generator during training process, with which the generator is expected to perform stably and reliably even in extremely sophisticated surroundings or under frequently inaccurate navigation instruction. Through exhaustive experimental validation, our model demonstrates a superior robustness and adaptability compared to the state-of-the-art. Finally, we explain the intrinsic mechanism of our model with visualization.
An Integration and accurate calibration method of 3D scanning system for in-situ shape measurement *

In-situ automated measurement with industrial robot has become a core technology of large component inspection. This paper proposes an in-situ 3D scanning system by integrating laser sensor with dual linear rail and robot and simultaneously develops a accurate calibration method of systematic orientation parameters. Firstly, according to the mechanism analysis, 3D measurement model based on 2D scanning data are presented. Then, a calibration model of orientation parameters is established based on multiple geometric constraints without additional measurement device. In this model, the parallel constraints and distance constraints can be obtained by only scanning a single high-precision sphere from multiple perspectives. Then, the analytical solution of unknown orientation parameters is derived with consideration of redundant measurement data. Experimental results show that the root mean square error and the standard deviation are less than 15 μm and 7μm within 200 mm × 200 mm respectively, which verifies the accuracy and reliability.
TGCN-P: A TCN-GCN Network With Weighted Graph Constructed by Pearson Correlation Coefficient for Human Motion Tracking

Neural networks based on graph convolution are simple and effective network structures that have better performance in some fields compared to traditional convolutional networks. However, when graph convolution networks (GCN) are applied to other fields, how to transform data or features into graph edges is a challenge needed to be studied. In previous studies of non-Euclidean spatial data such as relationship studies, people achieve graph construction by indicating the connection or not of nodes with binary values. However, when graph network is applied to some multi-sensor data scenarios, especially human motion tracking tasks, binarization is obviously not the best solution for the weights of edges. In addition, relying on manual graph construction may ignore some cross-space sensor relationships, which makes the update information of nodes far away slower or unavailable. Therefore, we try to use Pearson correlation coefficient (PCC) as the estimation of node correlation, and construct a complete graph to enhance the connectivity of nodes, which achieve faster signal transmission at remote nodes. After combining it with the temporal convolutional network (TCN), we propose a new network structure TGCN-P. Experimentally, this approach is more effective compared to other baseline models.
D2SR: Transferring Dense Reward Function to Sparse by Network Resetting

In Reinforcement Learning (RL), most algorithms use a fixed reward function, and few studies discuss transferring the reward function during learning. Actually, different types of reward functions have different characteristics. In general, a shaped dense reward function has the advantage of quickly guiding the agent to a high-value state but has the disadvantage of being difficult to design a well-shaped function and susceptible to noise. The sparse reward has the advantages of being robust and consistent with the task, but less efficient in early exploration. Therefore, this paper proposes an algorithm called Dense2Sparse by Network Resetting (D2SR), which simultaneously satisfies the efficiency of dense reward functions and the robustness of sparse rewards. Specifically, the D2SR method can rescue the agent from being misled by suboptimal dense rewards by network resetting parameters and transferring experience to sparse rewards, thereby achieving significant improvements in the direction of the global optimum. In this study, through a series of ablation experiments on challenging robot manipulation tasks, we find that D2SR can reduce the requirement of dense reward function design, which can also balance efficiency and performance in tasks with noisy rewards.
Real-Time Multispectral Pedestrian Detection with Weakly Aligned Cross-Modal Learning

Over the past ten years, multispectral pedestrian detection has attracted a lot of interest. The RGB-thermal image pairs used in existing methods are well-aligned by default, but there is a weak alignment issue between both image pairs captured by different sensors, which leads to the inaccuracy of pedestrian detection. To alleviate the problem of weak alignment in multispectral tasks, a cross-modal learning network (CMLNet) is proposed in this paper. A novel spatial-semantic alignment strategy is firstly designed to align the RGB-thermal features with the spatial transformation and semantic mapping between both modalities. A feature reselection module is implemented to filter the redundant features before the fusion. Finally, YOLOX is chosen as the detection framework. The open KAIST dataset is used to validate the suggested technique. Experimental results demonstrate that the proposed method can be applied in real-time applications, i.e., the pedestrian can be detected in 16 ms for each pair of RGB-thermal images. And the miss rate of pedestrian detection can reach 18.12% with competitive performance, compared with the state-of-the-art approaches.
Learning Adaptive Mapless Navigation Skills Through Evidential Deep Learning

Traditional navigation methods tend to design and train navigation skills based on the consideration of multi-scenario applicability and perform well in general scenarios. However, this results in sub-optimal navigation performance in some context-specific scenarios, such as navigating through open or crowded scenarios. While some existing navigation skills can achieve satisfactory performance in context-specific scenarios, e.g., using PID in open scenarios and reinforcement learning-based training methods in crowded scenarios. Most methods currently do without consideration of the integration of navigation skills and contextual understanding. To address this gap, we propose an adaptive mapless navigation method to utilize the optimal capabilities of each skill corresponding to the context-specific scenario. Specifically, we package several existing navigation skills and train a scenario classifier to enable the agent to automatically select the most appropriate skill based on the current scenario. Furthermore, we incorporate evidential deep learning (EDL) to assess classification uncertainty and ensure the safe switching of navigation skills. Our method was tested in both simulated and real scenarios, and the results of the experiments confirmed the effectiveness and generalization of our method. Moreover, the proposed method exhibits notably enhanced, context-sensitive efficiency within a hybrid multi-scenario setting, in comparison to a single navigation skill.
Underwater Object Detection Based on DN-DETR

We present a method for underwater object detection with DN-DETR (DeNoising-DEtection TRansformer) and DN-Deformable-DETR. Both DN-DETR and DN-Deformable-DETR are DETR-like detectors that introduce a denoising acceleration training method. Denoising is capable of accelerating the convergence rate and reducing the training instability of the native DETR detector. In addition, DETR has a strong ability on learning global image context and performs better in the detection of large and small targets. The results show that DN-Deformable-DETR has better average precision (+3.7AP ) and better performance on large targets (+5.1AP L) and small targets (+11.5AP S) than Faster R-CNN. Compared with DN-Deformable-DETR, DN-DETR obtained the optimal value in a single metric (+0.8AP 50).
Evaluation of Robustness Metrics for Defense of Machine Learning Systems

In this paper we explore some of the potential applications of robustness criteria for machine learning (ML) systems by way of tangible “demonstrator” scenarios. In each demonstrator, ML robustness metrics are applied to real-world scenarios with military relevance, indicating how they might be used to help detect and handle possible adversarial attacks on ML systems. We conclude by sketching promising future avenues of research in order to: (1) help establish useful verification methodologies to facilitate ML robustness compliance assessment; (2) support development of ML accountability mechanisms; and (3) reliably detect, repel, and mitigate adversarial attack.
Can Publicly Available Models Understand NATO Language? A Named Entity Recognition Case Study

In this research we address how well publicly available language models can generalize to NATO context. Expanding on the Natural Language Processing (NLP) task of named entity recognition (NER), we test a BERT fine-tuning strategy using different combinations of pre-trained models, tuning datasets and NER problem complexity. We demonstrate how these choices have a fundamental impact on out-of-domain and in-domain task performances and demonstrate a trade-off between generalization and memorization in our NER model design. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY — the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023.
Reinforcement Learning with Goal Relabeling and Dynamic Model for Robotic Tasks

Improving sample efficiency is crucial for reinforcement learning, especially for the robot manipulation tasks. The model-based method could improve the sample efficiency by introducing a dynamic model to generate a large number of samples, so that the requirement of interaction with the environment can be reduced. However, as the dynamic model is constructed by the deep network, the model error is inevitable. This error will increase the errors of the data generated by the model, which may damage the policy training process to a certain extent. To overcome the limitations of the above data augmentation methods, this paper proposes a new reinforcement learning method based on goal relabeling and dynamic (GMRL) model. In the GMRL, the quality of the explored data will be improved by the goal relabeling at first, followed by introducing a dynamic model to further increase the data quantity. The proposed method has been tested in a reinforcement learning benchmark environment, and the results show that the performance of the proposed method is significantly better than that both of the goal relabeling and standard model-based methods. At the same time, the proposed method has a higher sample efficiency than other existing combined schema of goal relabeling and dynamic model.