Reviewerly/exHarmony huggingface dataset:
Wave Environment Decomposition with Adaptive Tri-Objective Particle Swarm Optimization for Mobile Robot Path Planning

Mobile robots are being used more and more in various fields of agriculture, industry, military as well as disaster relief. However, the robot's limited battery energy power has resulted in many failed missions. To utilize this limited energy of the robot, this paper focuses on solving the problem of multi-objective path planning for a robot that satisfies a free- collision constraint and tri-optimization objectives: path length, path safety, and smoothness of the path. An original environment decomposition method is proposed, which is inspired by wave propagation in physics. The working environment is divided into arcs with the same center. Then, an Adaptive Tri-Objective Particle Swarm Optimization algorithm (denoted, ATOPSO) that combines the advantages between the multi-objective Particle Swarm Optimization and the Differential Evolution, is also introduced to solve the multi-objective problem above. The performance of the proposed decomposition method and the path planning algorithm ATOPSO is compared to existing methods and experimented with various scenarios and data. Obtained results show that our proposed method is more effective, it can solve the multi-objective problem in many complex environments including obstacles of different shapes.
Convolutional Ensemble Network for Image Classification

Convolutional Neural Networks (CNNs) have been widely acclaimed for image classification tasks in the last decade. Despite the recent success, training a CNN for large datasets from scratch is still a challenging task due to the long training time and the uncertain iterative learning process of the fully connected layer. In this paper, a novel Convolutional Ensemble Network (CEN) is proposed which can overcome some of the CNN's problems due to slow iterative training process. The proposed network aims to improve two crucial things (1) training time and (2) classification accuracy. In the proposed approach, images are passed to network's convolution and pooling layers to extract the features and then features are passed to an ensemble layer for ensemble weight calculation. In ensemble layer, bootstrapped samples are created randomly using extracted features, further diverse classifiers are trained on each of the sampled features. Finally, the network's output, errors are calculated. The whole process is repeated for different number of iterations and a systematic comparative analysis of results is conducted for standard CNN and proposed CEN. Three benchmark datasets such as MNIST, F-MNNIST and CIFAR-10 are used to evaluate the proposed approach. The accuracy scores achieved for MNIST, F-MNIST and CIFAR10 are 99.08%, 91.5%, 89.76% respectively. Extensive experiments and detailed comparative analysis of results in conjunction with the statistical experiments show that CEN can achieve better accuracy than the standard CNN in lesser iterations.
Online learning with binary feedback for multi-class problems

Online learning methods often focus on data selection, as human labeling is a noted bottleneck in resources needed to train a model. This work chooses to focus on reducing the human effort necessary for providing labels by attempting to usefully utilize a binary feedback method where the human indicates whether the prediction is correct or incorrect. More specifically, this work investigates methods for using and labeling training data in the absence of complete information. Various methods to generate labels in response to human feedback are proposed and then compared. These methods are tested on a variety of common classification tasks and results showing their usefulness are presented. Although the maximum accuracy achieved is not as high, the methods presented allow a model to learn faster, in terms of number of human interactions required.
A Methodology for Evaluating Tightly-integrated and Disaggregated Accelerated Architectures

Tighter integration of computational resources can foster superior application performance by mitigating communication bottlenecks. Unfortunately, not every application can use every compute or accelerator all the time. As a result, co-locating resources often leads to under-utilization of resources. To mitigate this challenge, architects have proposed disaggregation and ad hoc pooling of computational resources. In the next five years, HPC system architects will be presented with a spectrum of accelerated solutions ranging from tightly coupled, single package APUs to a sea of disaggregated GPUs interconnected by a global network. In this paper, we detail NEthing, our methodology and tool for evaluating the potential performance implications of such diverse architectural paradigms. We demonstrate our methodology on today’s and projected 2026 technologies for three distinct workloads: a compute-intensive kernel, a tightly-coupled HPC simulation, and an ensemble of loosely-coupled HPC simulations. Our results leverage NEthing to quantify the increased utilization disaggregated systems must achieve in order to match superior performance of APUs and on-board GPUs.
Playing a Multi-action, Adversarial Game in a Dynamic Environment

Strategic decision-making in an adversarial environment is an open problem that poses major challenges in various domains. The traditional adversarial game-playing programs that work by searching in the space of game states have been actively explored in the literature for this purpose. However, most approaches proposed for playing adversarial games have been tested only in static game environments with fixed goals for winning. In this study, we extend the static environment of Hero Academy, a turn-based, multi-action, adversarial game, to a more dynamic game-playing environment, and study the behavior and performance of tree search and evolutionary algorithms in playing that game. Our simulations show that, while evolutionary algorithms continue to dominate tree search algorithms, tree search algorithms can become relatively more competitive under certain dynamic scenarios. Equally important is that evolutionary algorithms are able to alter their approach to playing and winning a game in the face of dynamic changes in the environment, whereas tree search algorithms are unable to do so. The findings of this study should contribute to further advance our understanding of strategic decision-making in adversarial situations, particularly when goals and targets may change dynamically.
Redundant robot control with learning from expert demonstrations

This paper proposes a biased exploration based reinforcement learning, which uses expert experiences to avoid the exploration of all states. The method is applied to control redundant robots with expert experiences. A 7-degree-of-freedom robot manipulator is used in experiments. The results show that expert demonstrations based robot control works well.
Feature reduction for simplified handwritten digit classification model

In this paper we present a model for feature reduction of handwritten digits in simple classifiers and comparison of performance of said classifiers. Classification will be done on modified databases of 28x28 px images containing handwritten numbers from 0 to 9. Modifications in databases were obtained by creating vectors from pixels, also later modified by using PCA and in some cases down-sampling. Comparison of KNN, Naive Bayes, Fuzzy Clustering, Centroids - McQueen classifiers will be done, based on standard metrics and process time.
Reinforcement Learning for Solving Communication Problems in Shepherding

Swarm guidance is one of the modern applications of autonomous systems. In it, the controller acts as the transmitter (Tx), and the actuator acts as the receiver (Rx), exchanging control commands through a wireless communication channel to complete the task. The success of the shepherding task, which is a swarm control application, depends on the success of these transmissions through the wireless communication channel that may vary through time due to the Tx's and Rx's mobility. Therefore, to save time and energy, the Tx and Rx must adapt to the communication channel changes while ensuring the task's success in the shortest possible time. In this paper, we model the problem of optimising the mobility of the Tx (controller) to overcome the noise in the commands sent to the Rx (actuator) that acts as a robotic sheepdog in a shepherding scenario with a Markov decision process (MDP). Then, we propose an incremental search method to find near-optimal actions in real-time. Furthermore, two different reward functions are used to help the CU improve its mobility through online learning using Q-learning. The results proved that using Q-learning combined with the incremental search method improved the task's success rate ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SR$</tex> ) sometimes to exceed the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SR$</tex> in the ideal communication link scenario, which is double its <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SR$</tex> in the presence of noise.
Physics Compliance as a Metric for Neural Network Uncertainty

Machine learning models are gradually gaining acceptance for deployment in science and engineering in recent years as industries mature and become more comfortable with data-driven methods, spanning the whole spectrum from data collection through to data engineering and data modelling. However, unease remains about the accuracy of predictions from these surrogate models when applied in industry, especially as these methods are often utilized as a black-box. Demand for uncertainty quantification has thus increased, especially in critical processes where one requires information about the degree of uncertainty in the prediction to better manage associated risks. Statistical methods such as Bayesian networks and deep ensembles have correspondingly been developed as possible solutions in recent years. While these methods are applicable across multiple domains, they can be computationally expensive or difficult to implement, and fail to capitalize on inherent physics present in many real-world problems. In this work, we thus propose to exploit our prior knowledge of the system, and use physics-based rules and relationships to estimate the degree of uncertainty in the model predictions. We compare the obtained results on a metasurface model system to state-of-the-art methods such as deep ensembles and hyperparameter ensembles. The promising results on this model system suggest that metrics to measure compliance to physics-based rules and relationships can indeed be a useful proxy for estimating these data-driven models' uncertainty, with these relations performing as well as conventional ensemble-based approaches.
Impact-Driven Sampling Strategies for Hybrid Attack Graphs

Cyber-Physical Systems (CPSs) have a large input space, with discrete and continuous elements across multiple layers. Hybrid Attack Graphs (HAGs) provide a flexible and efficient approach to generate attack sequences for a CPS. Analysis and testing of large-scale HAGs are prohibitively costly. To address scalability and analysis challenges of HAG generation, it is required to reduce the HAG size via sampling. Existing sampling techniques provide probabilistic sampling and do not consider the complete coverage of different types of vulnerabilities. Moreover, they do not consider the impact of successful attacks. In this work, we propose a sampling algorithm that is impact driven and coverage aware. In addition, we provide several sampling strategies for cyber-security experts to inquire about potential attacks. The experimental results performed on both synthetic and real-world graphs show that our sampling method reduces up to 50% of nodes and 86% edges while retaining all vulnerability types across different attack graph representations and graph generators.
Military Uniform Identification for Search And Rescue (SAR) through Machine Learning

Object classification is a rapidly growing topic that is proving to serve many uses in both civilian and military professions. With continued development in this field, the Army and its units can accomplish tasks in more safe and efficient manners as unmanned drones and other technologies can carry out missions that have not been possible in the past. The purpose of this project is to develop a classifier model that can autonomously identify and track personnel during search and rescue (SAR) missions. The implementation of this technology would potentially improve the efficiency and reduce the risk of SAR missions in the Army by allowing soldiers to send out dispensable robots instead of risking indispensable lives.
Kernel Matrix-based Spd Representation For Graph Learning

Graph pooling is the most crucial step designed to handle node representation from graph data and then produce appropriate graph representation which can be used for graph-level learning tasks. Inspired by studies demonstrating the superiority of kernel matrix in capturing nonlinear information thus better interpreting data representation in computer vision tasks, this paper explores the feasibility of kernel matrix applied in graph classification tasks. To be specific, we investigate the Deep Kernel SPD as a graph pooling strategy, namely Kernel SPD graph pooling. The overall experimental results reveal that the Kernel SPD graph pooling outperforms the current state-of-the-art baseline graph pooling strategy on specific benchmark datasets and is at least comparable to the baseline graph pooling strategy on multiple benchmark datasets, enabling us to further consider the potential of the kernel matrix-based SPD graph representation.
Towards Automatic Mapping of Vulnerabilities to Attack Patterns using Large Language Models

Cyber-attack surface of an enterprise continuously evolves due to the advent of new devices and applications with inherent vulnerabilities, and the emergence of novel attack techniques that exploit these vulnerabilities. Therefore, security management tools must assess the cyber-risk of an enterprise at regular intervals by comprehensively identifying associations among attack techniques, weaknesses, and vulnerabilities. How-ever, existing repositories providing such associations are incomplete (i.e., missing associations), which increases the likelihood of undermining the risk of specific set of attack techniques with missing information. Further, such associations often rely on manual interpretations that are slow compared to the speed of attacks, and therefore, ineffective in combating the ever increasing list of vulnerabilities and attack actions. Therefore, developing methodologies to associate vulnerabilities to all relevant attack techniques automatically and accurately is critically important. In this paper, we present a framework - Vulnerabilities and Weakness to Common Attack Pattern Mapping (VWC-MAP) - that can automatically identify all relevant attack techniques of a vulnerability via weakness based on their text descriptions, applying natural language process (NLP) techniques. VWC-MAP is enabled by a novel two-tiered classification approach, where the first tier classifies vulnerabilities to weakness, and the second tier classifies weakness to attack techniques. In this work, we improve the scalability of the current state-of-the-art tool to significantly speedup the mapping of vulnerabilities to weaknesses. We also present two novel automated approaches for mapping weakness to attack techniques by applying Text-to-Text and link prediction techniques. Our experimental results are cross-validated by cyber-security experts and demonstrate that VWC-MAP can associate vulnerabilities to weakness-types with up to 87% accuracy, and weaknesses to new attack patterns with up to 80% accuracy.
Measuring Overfitting in Convolutional Neural Networks using Adversarial Perturbations and Label Noise

Although numerous methods to reduce the overfitting of convolutional neural networks (CNNs) exist, it is still not clear how to confidently measure the degree of overfitting. A metric reflecting the overfitting level might be, however, extremely helpful for the comparison of different architectures and for the evaluation of various techniques to tackle overfitting. Motivated by the fact that overfitted neural networks tend to rather memorize noise in the training data than generalize to unseen data, we examine how the training accuracy changes in the presence of increasing data perturbations and study the connection to overfitting. While previous work focused on label noise only, we examine a spectrum of techniques to inject noise into the training data, including adversarial perturbations and input corruptions. Based on this, we define two new metrics that can confidently distinguish between correct and overfitted models. For the evaluation, we derive a pool of models for which the overfitting behavior is known beforehand. To test the effect of various factors, we introduce several anti-overfitting measures in architectures based on VGG and ResNet and study their impact, including regularization techniques, training set size, and the number of parameters. Finally, we assess the applicability of the proposed metrics by measuring the overfitting degree of several CNN architectures outside of our model pool.
Generalwise Separable Convolution for Mobile Vision Applications

It is acknowledged that the depthwise separable convolution effectively reduces the computational complexity of a standard convolution. However, its depthwise convolution only performs on the spatial domain while neglecting to consider other domains such as the one formed by the channel and width/height dimensions. This paper specifically bridges the gaps by proposing the generalwise separable convolution to generalize the depthwise separable convolution beyond the spatial domain to recruit the widthwise and heightwise convolutions. A sequential combination of pointwise group convolution, channel shuffling, channel splitting, and dimension transposing is required to implement the generalwise separable convolution. By embedding the generalwise separable convolution into a stack of inverted residuals with linear bottlenecks, we propose GSCNet as a lightweight neural backbone for various embedded vision tasks. Our empirical evidence indicates that the generalwise separable convolution is superior to the depthwise separable convolution by feature extraction from domains complementing the spatial domain. Experimental results show that GSCNet outperforms other state-of-the-art mobile CNNs over multiply vision tasks. On ImageNet object classification benchmark, GSCNet achieves 75.5% top-1 accuracy with 216.98M multiply-adds, which is 28.1 % fewer than that of MobileNetv2 and 29.3% fewer than that of HBONet. GSCNet also yields better mAP quality than MobileNetv1/v2 and MnasNet on COCO object detection benchmark.
Graph embedded intuitionistic fuzzy weighted random vector functional link network

Random vector function link (RVFL) network with direct links and closed form solution has shown its strength among randomized neural networks (RNNs). RVFL assumes that all the samples are equally important, however, this may not be satisfied in the real world scenario. Moreover, RVFL ignores the geometric and discriminative information of the data. To overcome these shortcomings, we propose a graph embedded intuitionistic fuzzy weighted random vector functional link (GE-IFWRVFL) network. The proposed GE-IFWRVFL model assigns weights to each data point based on the membership and non membership functions. Membership values are assigned based on the distance of each sample from the class centre and non-membership values are assigned based on the sample distance as well as the heterogeneity of the samples. Also, the proposed GE-IFWRVFL exploits the geometric data relationship of the data under the Graph Embedding (GE) framework to enhance the generalization performance. A novel regularization term is incorporated in the proposed GE-IFWRVFL model to handle the topological structure of the data. Similar to intuitionistic fuzzy RVFL (IFRVFL) model, the proposed GE-IFWRVFL optimizes the output layer weights assigning appropriate weights to each sample. The performance of the proposed GE-IFWRVFL model is evaluated over 25 UCI and KEEL datasets. Experimental results demonstrate the better generalization performance of the proposed model compared to the baseline models.
A medical Q&amp;A system with entity linking and intent recognition

As a result of current dearth of medical physicians and medical resources in some countries, and also due to the growth of the Internet, an increasing number of people are holding medical question-and-answer sessions online. The goal of this research is to create a medical question-answering system based on a knowledge graph that will allow individuals to acquire answers to medical queries rapidly on the computer. To accomplish it, we propose a new mechanism of intent recognition and entity linking on a medical knowledge graph. After these processes, the system uses Cypher language to search the knowledge graph for the answer based on the extracted query-intent and entity, using the specified rule template that was formed during slot filling. Two models are used in this study to fulfill the tasks of intent recognition and named entity recognition, and the system also enhanced the model's results with information from the knowledge graph using the template matching method. Additionally, the entity linking procedure has been finished in order to better integrate models into a question-and-answer system. The entity linking function can link non-standard words in user questions to common disease names on the knowledge graph, to better obtain answers from the knowledge graph. The intent recognition used the BERT-TextCNN model, and the accuracy obtained on the CMID public dataset was 0.67. The named entity recognition used the BiLSTM-CRF model, and the accuracy obtained on the cMedQANER public dataset was 0.96. The entity linking process used the ESIM model, and the accuracy obtained on the Yidu-N7K public dataset was 0.96. The system used the above trained models and template matching methods to answer a total of 13 types of questions: ‘Definition’, ‘Cause’, ‘Prevention’, ‘Disease manifestations’, ‘Associated conditions’, ‘Treatment’, ‘Department’, ‘Infectious’, ‘Cure rate’, ‘Taboo’, ‘Physical Examination Program’, ‘Treatment time’ and ‘Food recommend’. To compare the effectiveness of this system with the system that only uses the template matching method, the experiment was designed with 200 questions divided into four groups. ‘whether the question uses the standard entity names in the knowledge graph’ and ‘whether the question uses the feature words in the intent template’ were controlled separately in different groups. The experimental results demonstrate that the correct answer rate of this system is much higher than that of the system based on template matching only when users enter questions without using standard entity names from the knowledge graph or without using feature words from the intent template.