Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering

Data clustering is among the commonly investigated types of unsupervised learning; owing to its ability for capturing the underlying information. Accordingly, data clustering has an increasing interest in various applications involving health, humanities, and industry. Assessing the goodness of clustering has been widely debated across the history of clustering analysis, which led to the emergence of abundant clustering evaluation measures. The aim of clustering evaluation is to quantify the quality of the potential clusters which is often referred to as clustering validation. There are two broad categories of clustering validations; the external and the internal measures. Mainly, they differ by relying on external true-labels of the data or not. This chapter considers the role of evolutionary and swarm intelligence algorithms for data clustering, which showed extreme advantages over the classical clustering algorithms. The main idea of this chapter is to present thoroughly the clustering validation indices that are found in literature, indicating when they were utilized with evolutionary clustering and when used as an objective function.
Robust Training of Graph Convolutional Networks via Latent Perturbation

Despite the recent success of graph convolutional networks (GCNs) in modeling graph structured data, its vulnerability to adversarial attacks has been revealed and attacks on both node feature and graph structure have been designed. Direct extension of defense algorithms based on adversarial samples meets with immediate challenge because computing the adversarial network costs substantially. We propose addressing this issue by perturbing the latent representations in GCNs, which not only dispenses with generating adversarial networks, but also attains improved robustness and accuracy by respecting the latent manifold of the data. This new framework of latent adversarial training on graphs is applied to node classification, link prediction, and recommender systems. Our empirical experimental results confirm the superior robustness performance over strong baselines.
A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.
A sensitivity analysis on effective parameters for sliding/melting prediction of snow cover on solar photovoltaic panels

The electricity generation of solar photovoltaic (PV) panels can be significantly affected by snow cover on the panels. This influence must be accurately predicted for PV systems to be considered a reliable source of electricity generation. Previous studies have shown the effectiveness of threshold-type empirical models in predicting the condition of snow cover on PV panels; these models use plane-of-array irradiance and present ambient temperature to determine if clearing will occur on the panels. This study conducts a sensitivity analysis that examines the effectiveness of absorbed and accumulated solar irradiances as well as the thermal capacitance of PV panels on the prediction process. The analysis has experimentally proven that front absorbed irradiance substantially improves prediction models compared to ones based only on plane-of-array irradiance. Further analyses were executed on the solar radiation absorbed by a panel's back surface and accumulated solar heat caused by the thermal capacitance of PV panels. Experimental data was used to develop a preliminary model that can generate time-series weighting factors to calculate the accumulated solar heat in panels. The results of this analysis would assist subsequent investigations in reducing the uncertainties of empirical threshold models that determine meteorological conditions of snow cover melting and enhance the forecasting of electricity generation from PV systems in regions that experience snowfall.
Crafting a Leather Self-tracking Device for Pollen Allergies

In this pictorial, we offer an overview of the design and step-by-step guidance on crafting a leather self-tracking device for pollen allergies. We designed the device to support patients’ daily lives allowing them to track symptoms and their severity and medicine intake while on the go, multiple times per day. The self-tracker sends the data to an accompanying smartphone application to support patients in understanding their illness, be used in doctors’ consultation, and collect data for allergy research. We chose natural leather as a material for this domestic medical device due to its associations with everyday artifacts, so it would be easier to integrate it into allergy patients’ everyday lives.
Time Adaptive Recurrent Neural Network

We propose a learning method that, dynamically modifies the time-constants of the continuous-time counterpart of a vanilla RNN. The time-constants are modified based on the current observation and hidden state. Our proposal overcomes the issues of RNN trainability, by mitigating exploding and vanishing gradient phenomena based on placing novel constraints on the parameter space, and by suppressing noise in inputs based on pondering over informative inputs to strengthen their contribution in the hidden state. As a result, our method is computationally efficient overcoming overheads of many existing methods that also attempt to improve RNN training. Our RNNs, despite being simpler and having light memory footprint, shows competitive performance against standard LSTMs and baseline RNN models on many benchmark datasets including those that require long-term memory.
Quantum Fisher Information and Bures Distance Correlations of Coupled Two Charge-Qubits Inside a Coherent Cavity with the Intrinsic Decoherence

The dynamics of two charged qubits containing Josephson Junctions inside a cavity are investigated under the intrinsic decoherence effect. New types of quantum correlations via local quantum Fisher information and Bures distance norm are explored. We show that we can control the quantum correlations robustness by the intrinsic decoherence rate, the qubit-qubit coupling as well as by the initial coherent states superposition. The phenomenon of sudden changes and the freezing behavior for the local quantum Fisher information are sensitive to the initial coherent state superposition and the intrinsic decoherence.
Reflection and Virtual Reality: From the Etymological Analysis of Concepts to the Understanding of Essential Relationships

The study of virtual reality in psychology and pedagogy requires an understanding of the essential connections and relationships of VR with various mental phenomena, and above all - reflection.The urgency of considering this problem is due to the search of VR developers and those who use virtual technologies in practice, including VR didactics, practical psychology, for a solution to the issue of self-management of systems with VR.Can VR exercise the reflective function of the mind represented in the processes of self-development, self-determination, self-management of the individual? The first step in solving this issue is to identify and describe the essential relations of reflection and virtual reality.For this, an etymological analysis of the concepts of reflection and virtual reality was carried out.The paper highlights the intersections in the semantic meanings of these concepts.Our analysis allowed us to employ the functional model of reflection (Anikina V.G.), within which VR can be considered as a ‘chronotope’ of reflection — the space-time continuum of the presence of Self ‘outside’ the situation, in relation to which the reflective process is meaningfully carried out.The paper outlines the possible ways of using VR in the development and formation of reflection and universal reflective actions in the educational process.
A Framework for Online Updates to Safe Sets for Uncertain Dynamics

Safety is crucial for deploying robots in the real world. One way of reasoning about safety of robots is by building safe sets through Hamilton-Jacobi (HJ) reachability. However, safe sets are often computed offline, assuming perfect knowledge of the dynamics, due to high compute time. In the presence of uncertainty, the safe set computed offline becomes inaccurate online, potentially leading to dangerous situations on the robot. We propose a novel framework to learn a safe control policy in simulation, and use it to generate online safe sets under uncertain dynamics. We start with a conservative safe set and update it online as we gather more information about the robot dynamics. We also show an application of our framework to a model-based reinforcement learning problem, proposing a safe model-based RL setup. Our framework enables robots to simultaneously learn about their dynamics, accomplish tasks, and update their safe sets. It also generalizes to complex high-dimensional dynamical systems, like 3-link manipulators and quadrotors, and reliably avoids obstacles, while achieving a task, even in the presence of unmodeled noise.
Feedback Enhanced Motion Planning for Autonomous Vehicles

In this work, we address the motion planning problem for autonomous vehicles through a new lattice planning approach, called Feedback Enhanced Lattice Planner (FELP). Existing lattice planners have two major limitations, namely the high dimensionality of the lattice and the lack of modeling of agent vehicle behaviors. We propose to apply the Intelligent Driver Model (IDM) [1] as a speed feedback policy to address both of these limitations. IDM both enables the responsive behavior of the agents, and uniquely determines the acceleration and speed profile of the ego vehicle on a given path. Therefore, only a spatial lattice is needed, while discretization of higher order dimensions is no longer required. Additionally, we propose a directed-graph map representation to support the implementation and execution of lattice planners. The map can reflect local geometric structure, embed the traffic rules adhering to the road, and is efficient to construct and update. We show that FELP is more efficient compared to other existing lattice planners through runtime complexity analysis, and we propose two variants of FELP to further reduce the complexity to polynomial time. We demonstrate the improvement by comparing FELP with an existing spatiotemporal lattice planner using simulations of a merging scenario and continuous highway traffic. We also study the performance of FELP under different traffic densities.
Toward Comprehensive User and Item Representations via Three-tier Attention Network

Product reviews can provide rich information about the opinions users have of products. However, it is nontrivial to effectively infer user preference and item characteristics from reviews due to the complicated semantic understanding. Existing methods usually learn features for users and items from reviews in single static fashions and cannot fully capture user preference and item features. In this article, we propose a neural review-based recommendation approach that aims to learn comprehensive representations of users/items under a three-tier attention framework. We design a review encoder to learn review features from words via a word-level attention, an aspect encoder to learn aspect features via a review-level attention, and a user/item encoder to learn the final representations of users/items via an aspect-level attention. In word- and review-level attentions, we adopt the context-aware mechanism to indicate importance of words and reviews dynamically instead of static attention weights. In addition, the attentions in the word and review levels are of multiple paradigms to learn multiple features effectively, which could indicate the diversity of user/item features. Furthermore, we propose a personalized aspect-level attention module in user/item encoder to learn the final comprehensive features. Extensive experiments are conducted and the results in rating prediction validate the effectiveness of our method.
Semantic Nonnegative Matrix Factorization with Automatic Model Determination for Topic Modeling

Non-negative Matrix Factorization (NMF) models the topics of a text corpus by decomposing the matrix of term frequency-inverse document frequency (TF-IDF) representation, X, into two low-rank non-negative matrices: W , representing the topics and H, mapping the documents onto space of topics. One challenge, common to all topic models, is the determination of the number of latent topics (aka model determination). Determining the correct number of topics is important: underestimating the number of topics results in a poor topic separation, under-fitting, while overestimating leads to noisy topics, over-fitting. Here, we introduce SeNMFk, a semantic-assisted NMF-based topic modeling method, which incorporates semantic correlations in NMF by using a word-context matrix, and employs a method for determination of the number of latent topics. SeNMFk first creates a random ensemble of matrices based on the initial TF-IDF matrix and a word-context matrix, and then applies a coupled factorization to acquire sets of stable coherent topics that are robust to noise. The latent dimension is determined based on the stability of these topics. We show that SeNMFk accurately determines the number of high-quality topics in benchmark text corpora, which leads to an accurate document clustering.
On the Role of Data Anonymization in Machine Learning Privacy

Data anonymization irrecoverably transforms the raw data into a protected version by eliminating direct identifiers and removing sufficient details from indirect identifiers in order to minimize the risk of re-identification when there is a requirement for data publishing. Nevertheless, data protection laws (i.e., GDPR) do not consider anonymized data as personal data thus allowing them to be freely used, analysed, shared and monetized without a compliance risk. Motivated by the above advantages, it is plausible that the data controllers anonymize the data before releasing them for any data analysis tasks such as machine learning (ML); which is applied in a wide variety of domains where personal data are used. Moreover, in recent research, it has shown that ML models are vulnerable to privacy attacks as they retain sensitive information from the training data. Taking all of these facts into consideration, in this work we explore the interplay between data anonymization and ML with the ultimate aim of clarifying whether data anonymization is sufficient to achieve privacy for ML under different adversarial scenarios. We also discuss the challenges and opportunities of integrating these two domains. As per our findings, it is conspicuous that in order to substantially minimize the privacy risks in ML, existing data anonymization techniques have to be applied with high privacy levels that cause a deterioration in model utility.
Hypergraph Attention Networks

Recently, graph neural networks have achieved great success on the representation learning of the graph-structured data. However, these networks just consider the pairwise connection between nodes which cannot model the complicated connections of data in the real world. Thus, researchers began to pay attention to the hypergraph modeling. In recent years, some hypergraph neural networks have been proposed to aggregate the information of the hypergraph for representation learning. In this paper, we present hypergraph attention networks (HGATs) to encode the high-order data relation in the hypergraph. Specifically, our proposed HGATs consist of two modules: attentive vertex aggregation module and attentive hyperedge aggregation module. These two modules can implicitly assign different aggregation weights to different connected hyperedge/vertex to characterize the complex relations among data. We stack these modules to pass the messages between the hyperedges and vertices to refine the vertex/hyperedge features. Experimental results on the ModelNet40 and NTU2012 datasets show that our proposed HGATs can achieve superior performance for the visual object recognition tasks. Furthermore, we employ our HGAT for multi-view representation learning and better object classification results are achieved.
Iron ORE Region Segmentation Using High-Resolution Remote Sensing Images Based on Res-U-Net

Deep learning has found many applications in high-resolution remote sensing image interpretation. In this study, an image analysis system is presented, consisting of image segmentation and mineral volume change estimation. In this system, a revised U-Net structure, called Res-U-Net, is proposed by combining U-Net and residual structure for image segmentation. Experiments are performed on the collected high-resolution remote sensing images, which were annotated with iron ore positive, iron ore negative, and background, and the results demonstrate the superiority of the proposed Res-U-Net over other image segmentation methods. Our proposed Res-U-Net outperforms the traditional U-Net by achieving pixel-wise accuracy of 92% and mean intersection over union (mIOU) 86%, as well as faster frame rate of 35 FPS on test dataset.
Self-supervised Object Tracking with Cycle-consistent Siamese Networks

Self-supervised learning for visual object tracking possesses valuable advantages compared to supervised learning, such as the non-necessity of laborious human annotations and online training. In this work, we exploit an end-to-end Siamese network in a cycle-consistent self-supervised framework for object tracking. Self-supervision can be performed by taking advantage of the cycle consistency in the forward and backward tracking. To better leverage the end-to-end learning of deep networks, we propose to integrate a Siamese region proposal and mask regression network in our tracking framework so that a fast and more accurate tracker can be learned without the annotation of each frame. The experiments on the VOT dataset for visual object tracking and on the DAVIS dataset for video object segmentation propagation show that our method outperforms prior approaches on both tasks.
Hybrid approach to the dynamic systems identification based on the self-configuring genetic programming algorithm and the differential evolution method

Abstract This paper considers a hybrid approach to the identification of dynamical systems based on a self-configuring genetic programming algorithm and a differential evolution method. The value of this approach is in the automatic determination of the order, structure and parameters of differential equation, i.e., a model of a dynamic system. The application of the differential evolution method can significantly increase the accuracy of the resulting model confirmed by the results of the experiments presented in this paper.
A generative adversarial neural network model for industrial boiler data repair

Many achievements have been made in using neural networks to predict complex nonlinear industrial activities. The assumption that these prediction models established is that the various data collected by the sensors are available and accurate. However, accidents always happen beyond assumptions. The interference and damage of sensors are common phenomena in actual industrial production. In this case, the data collected by the sensors is incorrect and unavailable and cannot be fed into the prediction model for work. Incorrect data leads to inaccurate predictions, and the predictions lead to incorrect actions, which ultimately affect the activities of the entire industrial assembly line. How is this kind of problem data dealt with? This is the first time that generative adversarial networks (GAN) technology has been used to repair missing boiler data. Although GAN technology was originally used for image processing, we took the lead in transferring this technology to the issue of boiler data repair. Furthermore, we proposed a novel model structure that is more suitable for boiler data repair. Our method has rigorous theoretical feasibility and has passed the experimental test.
Bottom-Up Foreground-Aware Feature Fusion for Practical Person Search

The key to efficient person search is jointly localizing pedestrians and learning discriminative representation for person re-identification (re-ID). Some recently developed models are built with separate detection and re-ID branches on top of shared region feature extraction networks. There are two factors that are detrimental to re-ID feature learning. One is the background information redundancy resulting from the large receptive field of neurons. The other is the body part missing and background clutter caused by inaccurate localization. In this work, a bottom-up fusion (BUF) subnet is proposed to fuse the bounding box features pooled from multiple network stages. With a few parameters introduced, BUF leverages the multi-level features with various sizes of receptive fields to mitigate the background-bias problem. To further suppress the non-pedestrian regions, the newly introduced segmentation head generates a foreground probability map as guidance for the network to focus on the foreground regions. The resulting foreground attention module (FAM) enhances the foreground features. Moreover, for robust feature learning in practical person search, we propose to adaptively smooth the labels of the pedestrian boxes with consideration of the detection quality. Extensive experiments on PRW and CUHK-SYSU validate the effectiveness of the proposals. Our Bottom-Up Foreground-Aware Feature Fusion (BUFF) network with ALS achieves considerable gains over the state-of-the-art on PRW and competitive performance on CUHK-SYSU.
An Explorative Analysis of the Feasibility of Implementing Metacognitive Strategies in Self-Regulated Learning with the Conversational Agents

With the prevalence of commercially available conversational agents (CAs), little research examined the capacities and constraints of these devices to support adults to learn new information on their own. The article conducted systematic analysis on the commercially available CAs (using work domain analysis and literature review), synthesized the metacognitive strategies that have been implemented in the computer-based learning environments, and examined the feasibility to implement these strategies on CAs to support self-regulated learning. This study has implications on designing scalable evidence-based theory-driven educational applications to support users to learn new information on their own using the off-the-shelf devices.