Reviewerly/exHarmony huggingface dataset:
Reducing echo state network size with controllability matrices

Echo state networks are a fast training variant of recurrent neural networks excelling at approximating nonlinear dynamical systems and time series prediction. These machine learning models act as nonlinear fading memory filters. While these models benefit from quick training and low complexity, computation demands from a large reservoir matrix are a bottleneck. Using control theory, a reduced size replacement reservoir matrix is found. Starting from a large, task-effective reservoir matrix, we form a controllability matrix whose rank indicates the active sub-manifold and candidate replacement reservoir size. Resulting time speed-ups and reduced memory usage come with minimal error increase to chaotic climate reconstruction or short term prediction. Experiments are performed on simple time series signals and the Lorenz-1963 and Mackey-Glass complex chaotic signals. Observing low error models shows variation of active rank and memory along a sequence of predictions.
Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks. However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles. RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.
Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests

Fast, autonomous flight in unstructured, cluttered environments such as forests is challenging because it requires the robot to compute new plans in realtime on a computationally-constrained platform. In this paper, we enable this capability with a search-based planning framework that adapts sampling density in realtime to find dynamically-feasible plans while remaining computationally tractable. A paramount challenge in search-based planning is that dense obstacles both necessitate large graphs (to guarantee completeness) and reduce the efficiency of graph search (as heuristics become less accurate). To address this, we develop a planning framework with two parts: one that maximizes planner completeness for a given graph size, and a second that dynamically maximizes graph size subject to computational constraints. This framework is enabled by motion planning graphs that are defined by a single parameter-dispersion-which quantifies the maximum trajectory cost to reach an arbitrary state from the graph. We show through real and simulated experiments how the dispersion can be adapted to different environments in realtime, allowing operation in environments with varying density. The simulated experiment demonstrates improved performance over a baseline search-based planning algorithm. We also demonstrate flight speeds of up to 2.5m/s in real-world cluttered pine forests.
Enhancing Deep Reinforcement Learning Approaches for Multi-Robot Navigation via Single-Robot Evolutionary Policy Search

Recent Multi-Agent Deep Reinforcement Learning approaches factorize a global action-value to address non-stationarity and favor cooperation. These methods, however, hinder exploration by introducing constraints (e.g., additive value-decomposition) to guarantee the factorization. Our goal is to enhance exploration and improve sample efficiency of multi-robot mapless navigation by incorporating a periodical Evolutionary Policy Search (EPS). In detail, the multi-agent training “specializes” the robots' policies to learn the collision avoidance skills that are mandatory for the task. Concurrently, in this work we propose the use of Evolutionary Algorithms to explore different regions of the policy space in an environment with only a single robot. The idea is that core navigation skills, originated by the multi-robot policies using mutation operators, improve faster in the single-robot EPS. Hence, policy parameters can be injected into the multi-robot setting using crossovers, leading to improved performance and sample efficiency. Experiments in tasks with up to 12 robots confirm the beneficial transfer of navigation skills from the EPS to the multi-robot setting, improving the performance of prior methods.
UnDAF: A General Unsupervised Domain Adaptation Framework for Disparity or Optical Flow Estimation

Disparity and optical flow estimation are respectively 1D and 2D dense correspondence matching (DCM) tasks in nature. Unsupervised domain adaptation (UDA) is crucial for their success in new and unseen scenarios, enabling networks to draw inferences across different domains without manually-labeled ground truth. In this paper, we propose a general UDA framework (UnDAF) for disparity or optical flow estimation. Unlike existing approaches based on adversarial learning that suffers from pixel distortion and dense correspondence mismatch after domain alignment, our UnDAF adopts a straightforward but effective coarse-to-fine strategy, where a co-teaching strategy (two networks evolve by complementing each other) refines DCM estimations after Fourier transform initializes domain alignment. The simplicity of our approach makes it extremely easy to guide adaptation across different domains, or more practically, from synthetic to real-world domains. Extensive experiments carried out on the KITTI and MPI Sintel benchmarks demonstrate the accuracy and robustness of our UnDAF, advancing all other state-of-the-art UDA approaches for disparity or optical flow estimation. Our project page is available at https://sites.google.com/view/undaf.
Estimation of Upper Limb Kinematics with a Magnetometer-Free Egocentric Visual-Inertial System

Most human activities in daily living or professional work rely on upper body motion. Measuring upper body motion is essential for many applications such as health evaluation, rehabilitation, human power augmentation, skill transferring, etc. Computer vision-based systems have been widely used to directly capture upper limb motion but are usually constrained in a restricted area. Wearable sensors such as inertial measurement units (IMUs) are promising to enable ambulant and out-of-lab measurements but also suffer from issues such as magnetic distortion and drifting. Some visual-inertial systems have been proposed recently to fuse these two complementary measurements but mostly apply in a restricted area. In this paper, we propose a fully wearable egocentric visual-inertial system to estimate the upper-limb pose. Magnetometers are not used to allow the system to work in complex industrial and daily living scenarios or to be integrated with motorized assistive devices. Methods to automatically calibrate the sensor-to-segment alignment and estimate upper body motion is presented and validated with an optical motion capture system. Experimental results showed the system can estimate the joint angles without drift and obtain accurate wrist position even with occlusion, verifying the efficacy of the proposed system and method.
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation

We propose a semi-supervised learning framework for monocular depth estimation. Compared to existing semi-supervised learning methods, which inherit limitations of both sparse supervised and unsupervised loss functions, we achieve the complementary advantages of both loss functions, by building two separate network branches for each loss and distilling each other through the mutual distillation loss function. We also present to apply different data augmentation to each branch, which improves the robustness. We conduct experiments to demonstrate the effectiveness of our framework over the latest methods and provide extensive ablation studies.
A Method for Designing Autonomous Robots that Know Their Limits

For assisting humans in their daily lives, robots need to perform long-horizon tasks, such as tidying up a room or preparing a meal.One effective strategy for handling a long-horizon task is to break it down into short-horizon subgoals, that the robot can execute sequentially.In this paper, we propose extending a predictive learning model using deep neural networks (DNN) with a Subgoal Proposal Module (SPM), with the goal of making such tasks realizable.We evaluate our proposed model in a case-study of a long-horizon task, consisting of cutting and arranging a pizza.This task requires the robot to consider: (1) the order of the subtasks, (2) multiple subtask selection, (3) coordination of dual-arm, and (4) variations within a subtask.The results confirm that the model is able to generalize motion generation to unseen tools and objects arrangement combinations.Furthermore, it significantly reduces the prediction error of the generated motions compared to without the proposed SPM.Finally, we validate the generated motions on the dual-arm robot Nextage Open.
RE:BT-Espresso: Improving Interpretability and Expressivity of Behavior Trees Learned from Robot Demonstrations

Behavior trees (BTs) are hierarchical agent control architectures popular for robot task-level planning that can be autonomously learned from robot demonstrations via decision tree (DT) intermediaries, making them accessible to non-expert users. Conversion algorithms from DTs to BTs, such as the BT-Espresso algorithm, focus on replicating DT logic in a BT format but do not exploit the strengths of the BT architecture. We introduce the Representation Exploitation of BT-Espresso (RE:BT-Espresso) algorithm, which builds on BT-Espresso and improves the learned BT's interpretability and expressivity. RE:BT-Espresso improves interpretability by removing logical redundancies in the generated BTs and improves expressivity by exploiting desired BT structures, such as adding Inverter nodes, Repeater sequences, and Parallel Selector Action nodes that gives the user a choice of actions for state spaces that did not resolve to a concise action in the DT. The RE:BT-Espresso algorithm was evaluated against BT-Espresso using demonstration data synthesized by BTs. When compared to the synthesized BTs using graph edit distance (GED), RE:BT-Espresso outscored BT-Espresso on 54 subtrees, tied on 178, and lost on 2. Further, the proposed reduction strategies reduced the number of nodes in a generated tree by a median of 7.82%. The results validate improved interpretability and expressivity of learned RE:BT-Espresso task-level BT policies from robot demonstration.
FishGym: A High-Performance Physics-based Simulation Framework for Underwater Robot Learning

Bionic underwater robots have demonstrated their superiority in many applications. Yet, training their intelligence for a variety of tasks that mimic the behavior of underwater creatures poses a number of challenges in practice, mainly due to lack of a large amount of available training data as well as the high cost in real physical environment. Alternatively, simulation has been considered as a viable and important tool for acquiring datasets in different environments, but it mostly targeted rigid and soft body systems. There is currently dearth of work for more complex fluid systems interacting with immersed solids that can be efficiently and accurately simulated for robot training purposes. In this paper, we propose a new platform called “FishGym”, which can be used to train fish-like underwater robots. The framework consists of a robotic fish modeling module using articulated body with skinning, a GPU-based high-performance localized two-way coupled fluid-structure interaction simulation module that handles both finite and infinitely large domains, as well as a reinforcement learning module. We leveraged existing training methods with adaptations to underwater fish-like robots and obtained learned control policies for multiple benchmark tasks. The training results are demonstrated with reasonable motion trajectories, with comparisons and analyses to empirical models as well as known real fish swimming behaviors to highlight the advantages of the proposed platform.
Federated-Learning-Aided Next-Generation Edge Networks for Intelligent Services

Nowadays, federated learning (F1) has been proposed as an emerging technology to store sensory data and train the edge networks using a set of computing devices with minimum training time. However, a collection of heterogeneous participating devices with different processing power and energy usage are used to analyze the model parameters locally. Therefore, an intelligent service provisioning mechanism with the F1 technique needs to be developed at the edge networks. This strategy can increase the security and privacy of the network while minimizing the training time on resource-constrained edge devices. In this magazine, we describe the importance of the FL-aided hybrid edge intelligent framework for next-generation Internet of Things applications. Moreover, to enhance the critical service provisioning functionality, we highlight two use case studies along with their potential research directions, including intelligent transportation systems and intelligent healthcare systems. Finally, this work concludes with a set of potential future research directions of FL-aided edge networks.
The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM

Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks. And it has been effectively used to accomplish tasks that are thought to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and it is not good at capturing local information. The Bidirectional Encoder Representation from Transformers (BERT) has the ability to capture contextual information in sentences or documents, but it is limited in capturing global information about vocabulary in a language, which is the advantage of GCN. Therefore, this paper proposes an improved model named Improved Mutual Graph Convolution Networks (IMGCN) to solve the above problems. The original GCN uses word co-occurrence relationships to build text graphs. Word connections are not rich enough and cannot capture context dependencies well, so we introduce semantic dictionary (WordNet) and dependencies. While the model enhances the ability to capture contextual dependencies, it lacks the ability to capture sequences. Therefore, we introduced BERT and Bi-directional Long Short-Term Memory (BiLSTM) Network to perform deeper learning on the features of text, thereby improving the classification effect of the model. The experimental results show that our model is more effective than previous research reports on four text classification datasets.
Cross-Modal Cross-Domain Dual Alignment Network for RGB-Infrared Person Re-Identification

RGB-Infrared cross-modal person re-identification (Re-ID) has drawn increasing attention due to its application value in practice. Most of the current works rely on a supervised training manner. However, in real-world applications, manual collection of pair-wise RGB-Infrared (IR) person data is labor-intensive and time-consuming. Moreover, when a trained model is directly used in another domain, there is usually a significant performance drop. To overcome the above problems, we make the first attempt to transfer the learned model to a new RGB-IR domain which is unlabeled. The practical problem covers two kinds of challenges, i.e., cross-modal (RGB-Infrared) and cross-domain (different dataset) person Re-ID. Previous works have often considered only one of them either cross-modal or cross-domain. In this work, we propose a dual alignment network (DAN) to solve the RGB-Infrared cross-modal cross-domain person Re-ID problem. This network consists of three parts: Domain Adversarial Alignment component (DAA), Pseudo Label Generation module for target domain (PLG), and Cross-Modal Alignment component (CMA). These three modules complement and promote the model to learn domain-invariant and modality-invariant person representations. Further, we propose a protocol of cross-modal cross-domain person Re-ID by synthesizing target domains by adding random noise, adjusting the lighting intensity, and changing the background color, respectively. Experiments on real and synthetic datasets under the same cross-modalities across domains demonstrate the effectiveness of our method.
Isolation Forest Based on Minimal Spanning Tree

Detecting anomalies in data sets has been one of the most studied issues in modern data analysis.Therefore, there is a plethora of applications in a very wide range of fields of science and technology.One of the most frequently used anomaly detection methods is Isolation Forest.In this study, we propose a novel efficient approach based on this technique.In order to improve the classification accuracy of the base method, we make two-fold modifications.First, we propose a change of the technique of building isolation trees to merge nodes by minimal spanning tree algorithm.The second change is based on a modification of the function assessing the anomaly of the analyzed element (data record) to sum of factors correlated with tree height and nearest point distance.In the series of comprehensive computational experiments, the proposed method has proven to produce better results than other compared state-of-the-art methods available in popular data mining programming libraries.It is worth stressing that the final version of the new method in comparison to original Isolation Forest is 2.9% better in terms of AUC measure.
The impact of lexical and grammatical processing on generating code from natural language

Considering the seq2seq architecture of Yin and Neubig (2018) for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms.To study the impact of these components, we use a state-of-the-art architecture that relies on BERT encoder and a grammar-based decoder for which a formalization is provided.The paper highlights the importance of the lexical substitution component in the current natural language to code systems.
A New Hybrid Cryptocurrency Returns Forecasting Method Based on Multiscale Decomposition and an Optimized Extreme Learning Machine Using the Sparrow Search Algorithm

The return series of cryptocurrencies, which are emerging digital assets, exhibit nonstationarity, nonlinearity, and volatility clustering compared to other traditional financial markets, making them exceptionally difficult to forecast. Therefore, accurate cryptocurrency price forecasting is important for both market participants and regulators. It has been demonstrated that improved data forecasting accuracy can be achieved through decomposition, but few researchers have performed information extraction on the residual series generated by data decomposition. Based on the construction of a "decomposition-optimization-integration" hybrid model framework, in this paper, we propose a multi-scale hybrid forecasting model that combines the residual components after primary decomposition for secondary decomposition and integration. This model uses the variational modal decomposition (VMD) method to decompose the original return series into a finite number of components and residual terms; then, the residual terms are decomposed and the features are extracted using the completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. The components are predicted by an extreme learning machine optimized by the sparrow search algorithm, and the final predictions are summed to obtain the final results. Forecasts for the returns of Bitcoin and Ethereum, which are major cryptocurrency assets, are compared with other benchmark models constructed based on different ideas, and we find that the proposed quadratic decomposition VMD-Res.-CEEMDAN-SSA-ELM hybrid model demonstrates the optimal and most stable forecasting performance in both one-step and multi-step ahead prediction of the cryptocurrency return series.
Predicting CVSS Metric via Description Interpretation

Cybercrime affects companies worldwide, costing millions of dollars annually.The constant increase of threats and vulnerabilities raises the need to handle vulnerabilities in a prioritized manner.This prioritization can be achieved through Common Vulnerability Scoring System (CVSS), typically used to assign a score to a vulnerability.However, there is a temporal mismatch between the vulnerability finding and score assignment, which motivates the development of approaches to aid in this aspect.We explore the use of Natural Language Processing (NLP) models in CVSS score prediction given vulnerability descriptions.We start by creating a vulnerability dataset from the National Vulnerability Database (NVD).Then, we combine text pre-processing and vocabulary addition to improve the model accuracy and interpret its prediction reasoning by assessing word importance, via Shapley values.Experiments show that the combination of Lemmatization and 5,000-word addition is optimal for DistilBERT, the outperforming model in our experiments of the NLP methods, achieving state-of-the-art results.Furthermore, specific events (such as an attack on a known software) tend to influence model prediction, which may hinder CVSS prediction.Combining Lemmatization with vocabulary addition mitigates this effect, contributing to increased accuracy.Finally, binary classes benefit the most from pre-processing techniques, particularly when one class is much more prominent than the other.Our work demonstrates that DistilBERT is a state-of-the-art model for CVSS prediction, demonstrating the applicability of deep learning approaches to aid in vulnerability handling.
A Deep Learning Based Framework for Malaria Diagnosis on High Variation Data Set

Malaria is a globally widespread disease caused by parasitic protozoa transmitted by infected female Anopheles mosquitoes. It is caused in humans only by the parasite Plasmodium, further classified into four different species. Identifying malaria parasites is possible by analyzing digital microscopic blood smears, which is tedious, time-consuming, and error-prone. Therefore, automation of the process has assumed great importance as it helps the laborious manual process of review and diagnosis. This work proposes a real-time malaria parasite detector and classification system after studying the YOLOv5 detector and comparing different off-the-shelf convolutional neural network architectures for four-class classification on Plasmodium Falciparum life stages. The results show that the use of the networks YOLOv5 and DarkNet-53 reaches great accuracy in detecting and classifying the life stages of Plasmodium Falciparum, achieving an accuracy of 95.2% and 96.02%, respectively, and outperforming the state-of-art. The obtained results enable broad improvements geared explicitly towards recognizing types and life stages of less common species of malaria parasites, even in mobile environments.