Reviewerly/exHarmony huggingface dataset:
Automated design of feature extraction for unsupervised image clustering using grammatical evolution

When doing image processing it has been shown that the use of an appropriate feature extraction function can result in a significant boost in performance. Selecting or creating the correct feature extraction function is however non-trivial. This work uses grammatical evolution (GE) to evolve a feature extraction function for images. The goal is to evolve feature extraction functions that are able to improve the clustering results of three different unsupervised clustering algorithms: KMeans++, DBSCAN and Mean Shift. In order to test whether the feature extraction functions evolved by the GE are effective, the clustering algorithms are also used with no feature extraction and with randomly generated feature extraction functions. The results of experimentation show that using a GE to evolve a feature extraction function is more effective than using no feature extraction or randomly generated feature extraction, with the exception of K-Means++ when applied to the Oral lesion dataset where GE feature extraction performed equally as good as when no feature extraction is used. A possible reason for this might be that the grammar for the GE needs additional operators to generate color specific features. Future work includes applying transfer learning to the GE.
Secure Robotic Vehicles: Vulnerabilities and Mitigation Strategies

Robotic vehicles are becoming more widespread and used in many industries, including agriculture, manufacturing, and defense. They are safety-critical systems because of the fact that they are mobile, autonomous, and can operate in hazardous environments. The focus on robotic systems in the last several decades has been to add new complex functionality, in many cases using artificial intelligence. Many of these new technologies are fairly sophisticated and expose robotic vehicles to new vulnerabilities, especially when vehicles need to operate autonomously. Security and safety are connected and preventing intentional attacks on mobile robots improves safety and allows robots to complete their missions in challenging and hostile environments. In addition, robots that move can and need to adapt and counteract adversarial attacks and adapt to sensor and actuator faults. Robotic vehicles are also real-time systems; their ability to function is determined by their ability to maintain these characteristics all the time. In this work, we present the major classes of attacks on robotic vehicles and analyze the existing and propose some new mitigation strategies to counteract the attacks. Our scope is on robotic vehicles in general, with a specialized focus on UAVs as a class of vehicles receiving more attention and presenting significant security challenges. We discuss strategies based on simplex architecture, enforcers, partitioning, redundancy, self-adaptation, and dynamic architectures during run-time.
Building Actionable Personas Using Machine Learning Techniques

Personas are quantifiable and describable ways of grouping people based on their behaviours. They are valuable to businesses as it enables them to better understand their customer base. The creation of personas from survey data requires establishing the client requirements, building a quantifiable personality scale, developing personality questions for a survey, and human subjective analysis. In this work, we have utilised clustering to automate the persona development process. We have developed a real-world survey for children (from 17 countries) which included 25 personality-based questions (based on the OCEAN model), 22 questions that captured purchase behaviour, and other general features from the children's land-scape. There were 63,969 completed questionnaires with a high proportion of categorical features, which were preprocessed to allow different segmentation methods to be tested. Preliminary results with simple K-means and a Euclidean distance function demonstrated that this was inappropriate for the survey data set. A novel distance function for K-means clustering has been developed, which can handle a mixture of feature types and to allow the importance of each feature to be varied, using a linearly weighted distance method. The function also incorporates the haversine distance function to provide a distance between two locations, enabling potential cultural differences to be exam-ined.We have also implemented Gaussian Mixture Model on the same feature set to compare the results and see the limitations of Gaussian Models Our novel approach generated clusters based on a combination of features including personality, consumer behaviour and location which has demonstrated key cultural differences across the globe. Results from our novel approach show that location distance is one of the key features when constructing personas as culture (location) has a significant effect on the way children answer survey questions.
Juvenile Morph Dataset: A Study of Attack Detectability and Recognition Vulnerability

A morph is an image of an ambiguous subject generated by combining multiple individuals. The morphed image can be submitted to a facial recognition system and erroneously verified with the contributing bad actors. When submitted as a passport image, a morphed face poses a national security threat because a passport can then be shared between the individuals. As morphed images become easier to generate, it is vital that the research community expands available datasets in order to contentiously improve current technology. Children are a challenging paradigm for facial recognition systems and morphing children takes advantage of this disparity. In this paper, we morph juvenile faces in order to create a unique, high-quality dataset to challenge FRS. To the best of our knowledge, this is the first study on the generation and evaluation of juvenile morphed faces. The evaluation of the generated morphed juvenile dataset is performed in terms of vulnerability analysis and presentation attack error rates.
Learning Deep Representations via Contrastive Learning for Instance Retrieval

Instance-level Image Retrieval (IIR), or simply Instance Retrieval, deals with the problem of finding all the images within an dataset that contain a query instance (e.g. an object). This paper makes the first attempt that tackles this problem using instance-discrimination based contrastive learning (CL). While CL has shown impressive performance for many computer vision tasks, the similar success has never been found in the field of IIR. In this work, we approach this problem by exploring the capability of deriving discriminative representations from pre-trained and fine-tuned CL models. To begin with, we investigate the efficacy of transfer learning in IIR, by comparing off-the-shelf features learned by a pre-trained deep neural network (DNN) classifier with features learned by a CL model. The findings inspired us to propose a new training strategy that optimizes CL towards learning IIR-oriented features, by using an Average Precision (AP) loss together with a fine-tuning method to learn contrastive feature representations that are tailored to IIR. Our empirical evaluation demonstrates significant performance enhancement over the off-the-shelf features learned from a pre-trained DNN classifier on the challenging Oxford and Paris datasets.
Robustness Analysis of Data-Driven Self-Learning Controllers for IoT Environmental Monitoring Nodes based on Q-learning Approaches

Today we are seeing a significant need for efficient control of operating cycles to deliver improvements in services provided by Internet of Things (IoT) devices embedded with environmental monitoring. To design an algorithm which provides sufficient duty-cycle control, we can apply machine learning approaches. The present study investigates the reinforcement learning (RL) algorithm family, especially Q-learning (QL) and Double Q-learning (DQL) algorithms and their suitability for devices deployed in a range of locations. We present a comprehensive analysis of the implemented RL approaches for regulating data-driven self-learning (DDSL) controllers. We tested QL and DQL algorithms on various datasets and evaluated their performance with a statistical analysis. The results indicated that the QL and the DQL approaches were highly dependent on the nature of the environmental parameters which the DDSL controller detected and recorded.
Investigating The Scalability of Kernel Minimum Enclosing Balls for Novelty Detection: Algorithms with Empirical Evaluations

Though being a family of powerful representation learning methods, Kernel Minimum Enclosing Balls (KMEBs), as for many kernel based methods, require careful attention when analyzing large scale data. In this standalone paper we take a look at the algorithms for Kernel Minimum Enclosing Balls and examine their scalability. To that end, we firstly present a detailed investigation of the recently existing scalable methods to compute the kernel minimum enclosing balls. Those methods feature sub-matrices, coresets and density-based sampling approaches. The strength of those methods lies in their simplicity and theoretically proven approximation of the decision boundaries. Secondly, we bring additional value to the scalability of KMEBs by introducing a principle of uniting decision boundaries obtained from multiple kernel balls. This principle can be described as sampling batches of data, performing independent training on those batches and using models fitted to the batches to create the ensemble approach. We present two simple and efficient algorithms that rely on the above-described principle: randomized sampling speedup and characteristic function based partition. We show that former speedup is highly efficient, and we assume that the reason for it lies in KMEBs' outstanding abilities in few-shot learning. Additionally, we compare two introduced algorithms against recent methods from literature mentioned above. We perform the comparison of novelty detection scores on the example of two popular image datasets.
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning

Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration. Often, SAR coordination strategies are manually designed by human experts who can remotely control the multi-robot system and enable semi-autonomous operations. However, in remote environments where connectivity is limited and human intervention is often not possible, decentralized collaboration strategies are needed for fully-autonomous operations. Nevertheless, decentralized coordination may be ineffective in adversarial environments due to sensor noise, actuation faults, or manipulation of inter-agent communication data. In this paper, we propose an algorithmic approach based on adversarial multi-agent reinforcement learning (MARL) that allows robots to efficiently coordinate their strategies in the presence of adversarial inter-agent communications. In our setup, the objective of the multi-robot team is to discover targets strategically in an obstacle-strewn geographical area by minimizing the average time needed to find the targets. It is assumed that the robots have no prior knowledge of the target locations, and they can interact with only a subset of neighboring robots at any time. Based on the centralized training with decentralized execution (CTDE) paradigm in MARL, we utilize a hierarchical meta-learning framework to learn dynamic team-coordination modalities and discover emergent team behavior under complex cooperative-competitive scenarios. The effectiveness of our approach is demonstrated on a collection of prototype grid-world environments with different specifications of benign and adversarial agents, target locations, and agent rewards.
Robust Denoising in Graph Neural Networks

In recent years, Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCN) have been gaining momentum in machine learning for graph structured data. Although a majority of GNN architectures are designed in a way that is sustainable to node feature attack, they still suffer from performance degradation under the presence of extreme outlier noise in node features. This is because most strategies dealing with noise rely on the Gaussian noise assumption where an intrinsic Euclidean norm regularization is added to the objective. In this paper, we propose a novel GNN module that is more robust to extreme noise with a Laplace noise assumption. Our proposed module can be used alongside with any existing GNN model to improve the robustness. Numerical experiments show the effectiveness of the new module in enhancing the performance for various GNN models including GCN, GAT, APPNP under the case of extreme noise, and show great robustness under various types of noise added into the datasets.
Supplementary-architecture weight-optimization neural networks for advanced weight optimizers

The research presented in this paper contributes to attempts by the community to improve learning in neural networks. Traditionally, neural networks learn using a single architecture, which we refer to as the primary architecture. Previous work has illustrated the effectiveness of using supplementary architectures in addition to the primary architecture, referred to as supplementary-architecture weight-optimization neural networks (SAWO-NNs) in neural network learning. This was illustrated using the stochastic gradient descent optimizer. The research presented in this paper extends this work by investigating the use of SAWO-NN with more advanced weight optimizers, namely, gradient descent with momentum, RMSprop and Adam. A genetic algorithm was used to evolve the primary and supplementary architecture sets and which weight updates they are used for. The approach was evaluated for data classification (UCI benchmarks and KDD Cup 1999 intrusion detection dataset) and regression (U CI benchmarks and UCLA graduate admission dataset) applications. SAWO-NN was found to improve the performance of neural networks using these optimizers, with statistically significant results. SAWO-NN's performance was also found to be competitive to state of the art approaches applied to the same datasets.
LSTM based Algorithmic Trading model for Bitcoin

Cryptocurrencies have emerged as an alternative financial asset in the last decade, with their market growing exponentially in recent years. The price of cryptocurrencies is highly volatile and is prone to rapid swings within short periods of time. This behaviour makes them a high-risk and high-return financial asset. The efficacy of neural networks in forecasting the high frequency financial time series has become widely accepted in the research community. This work explored the use of Long Short Term Memory (LSTM), a neural network based non-linear sequence model, to propose a novel algorithmic trading strategy for cryptocurrencies. The proposed novel high frequency algorithmic trading strategy built over an LSTM based short-term price forecasting is used for Bitcoin and Ethereum. This simple, yet effective trading algorithm uses the network's price forecasts to make buy and short selling decisions for cryptocurrency based on certain set criteria. The proposed trading strategy gives positive returns when backtested on Bitcoin hourly prices taken from yahoo! finance. We also verified the effectiveness of the trading strategy for Ethereum, the second largest cryptocurrency, based on the positive backtesting returns. As an extension to the study, the proposed strategy is applied on an even higher frequency (minute by minute) Bitcoin price data, and the strategy gives positive backtesting returns in this extended study. We also provide fuzzy intervals for the algorithmic return of our strategy and compare those with corresponding intervals on a simple buy and hold strategy.
Advanced Ensemble Deep Random Vector Functional Link for Eye-Tracking-based Situation Awareness Recognition

Situation awareness (SA) plays a significant role in takeover transitions from autonomous to manual driving. Previous researchers have shown that eye movement signals can be used for SA recognition. Moreover, ensemble deep random vector functional link (edRVFL) has demonstrated its superiority in different applications. Therefore, this work proposes an advanced edRVFL (AedRVFL) to perform eye-tracking (ET)-based SA recognition, including improvements in two aspects. Specifically, pruning in the direct links is implemented to improve the efficiency of linear features. Then, a weighting method based upon the regression errors of samples is developed. The experiment was conducted on a public conditionally automated driving dataset. Results showed that the proposed AedRVFL out-performed the baseline methods, demonstrating the effectiveness of using AedRVFL for ET-based SA recognition. Ablation studies were conducted to validate the improvements in the edRVFL.
An Optimistic Approach to the Temporal Difference Error in Off-Policy Actor-Critic Algorithms

The balance between exploration and exploitation is a protracted challenge in reinforcement learning (RL). High dimensional continuous action domains increase the time and resources required to find an optimal policy due to the intractable actions, i.e., infinitely many possible. In this paper, we transfer the existing theories on animal motivational systems to the deep RL paradigm to constitute an efficient, directed exploration strategy for off-policy actor-critic algorithms. We introduce a novel and scalable high-frequency perturbation framework that directs agents to state-action pairs with large value errors through an additive deep explorer network. By maximizing the error induced by the Q-value estimation, our method forces the approximated critic function to learn the values of less-known or unselected actions and prevents premature behavior before sufficiently learning the environment. Moreover, the presented algorithm can benefit from the off-policy setting by extracting useful information from the experiences contained in the experience replay buffer, in contrast to the state-of-the-art exploration techniques, which exhibit poor performance in the off-policy setting due to the separated policy and exploration strategy updates. An extensive set of empirical studies demonstrates that our method considerably diversifies the visited states, outperforms the baselines, and improves the state-of-the-art off-policy actorcritic by a significant margin.
Improved Genetic Programming for Symbolic Regression: Case Studies on Practical Applications

Genetic Programming (GP), especially Semantic GP (SGP), has shown significant potential in solving numerical benchmarks in Symbolic Regression (SR) domain in recent years. However, its application on real-world problems has been less explored due to the large sizes of the resulting expressions, which are prone to over-fitting and are difficult to interpret. In this paper, we propose a method that incorporates customization for real-world data sets based on a combination of two operators of GP. The first operator utilizes the concept of Semantic Backpropagation, a noteworthy method in SGP, to create short expressions which are highly correlated with the outputs. The second operator makes use of Mixed Integer Linear Programming (MILP) to combine short expressions into the overall expression with good accuracy. The proposed approach is tested on one synthetic data set and two practical applications which are challenging for conventional GP. The experimental results are very promising, with further scope of improvement.
Water surface target detection and recognition of USV based on YOLOv5

Affected by wave, illumination intensity, water mist, and other factors, the traditional target detection algorithm is difficult to accurately and robustly identify water surface targets. To solve this problem, this paper proposes water surface targets detection method of USV based on deep learning. First of all, the ASFF was introduced to improve the basic YOLOv5s model, and then the improved YOLOv5s model was trained and tested with self-made surface data set. Experimental results show that the improved model has stronger feature extraction ability, the model detection accuracy is improved by 9.4%, and the actual detection speed reaches 138 FPS.
A Hybrid Image Restoration Method based on Partition for Industrial X-ray images

The balance between image denoising and detail-maintaining is a significant index of image restoration. X-ray images of integrated circuits (IC) are characterized by high level of noise and low contrast, so that tiny details preserving is one of the key points to the internal defect detection of IC. Therefore, a hybrid image restoration method is proposed. First of all, by using Fourier transform-based image partition method, restored results of iterative denoising and backward projections (IDBP) and adaptive total generalized variation method (ATGV) are divided into detail areas and smooth areas. Then, smooth area of IDBP and detail area of ATGV are combined to obtain a new restored X-ray image, which can make full use of the excellent denoising effect of IDBP and the detail-preserving performance of ATGV. Experimental results of X-ray images show that the proposed method has the ability to attain the balance between denoising and detail-maintaining.
High-Performance Computing based Scalable Online Fuzzy Clustering Algorithms for Big Data

With the rise of big data trends so quickly, real-time stream data processing has become very important. Stream data is a type of big, fast, and unreliable dataset that cannot be handled well by traditional algorithms. Designing the algorithm that can efficiently process streaming data is a challenging task. This paper shows how important is to make a real-time clustering algorithm for data streams with high concept drift and an algorithm that can adapt to different dimensions. We propose Scalable Random Sampling Online Optimization Weighted Fuzzy c-Means (SRSOO-WFCM) algorithms for handling Big Data in a High-Performance Computing (HPC) environment using an Apache Spark cluster. To compare SRSOO-WFCM with the traditional Online Fuzzy c-Means (OFCM) algorithm, we made a scalable version of OFCM named SOFCM. The proposed SRSOO-WFCM and SOFCM are incremental algorithms that involve making one sequential run through the data subsets. We employ both loadable and very large datasets to perform extensive experiments that facilitate comparing the proposed SRSOO-WFCM and SOFCM algorithms. The proposed SRSOO-WFCM algorithm performs better than the SOFCM in terms of Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and F-score, respectively.
Adaptive Online Rough C-Means Clustering and Its Application to Collaborative Filtering

Content recommendation systems found in e-commerce sites and video streaming services are realized by collaborative filtering (CF). In clustering-based CF, clusters composed of users with similar preference pattern are extracted and items with high preference degree in the cluster to which user belongs are recommended. Rough C-means (RCM) is a clustering method that incorporates a perspective of rough set theory into hard C-means (k-means) to deal with the uncertainty inherent in data. Furthermore, online RCM (ORCM) and its application to CF (ORCM-CF) have been proposed for large-scale datasets. ORCM introduces online learning to RCM and loads an object one by one and gradually updates the parameters. In RCM and ORCM, although it is necessary to set the number of clusters in advance, it is difficult to set the appropriate number of clusters. In this study, we propose adaptive ORCM (AORCM), which aims to automatically generate the appropriate number of clusters by adaptively adding and merging clusters during online learning. Furthermore, we propose AORCM-CF as its application to CF. We verified the recommendation performance of the proposed AORCM-CF through the numerical experiments using real-world datasets.