Reviewerly/exHarmony huggingface dataset:
Image Inpainting Based Multi-scale Gated Convolution and Attention

For image inpainting tasks, details and semantics are very important. Most methods based deep learning will make the result lack structure and texture features or even lose rationality. Additionally, many algorithms are step-by-step inpainting algorithms, which complicates the whole process. To avoid these, following the previous work, we propose an end-to-end inpainting model based on a multi-scale residual convolutional network, using MSGC to replace the conventional convolutional layer in encoder. This method combines multi-scale information and expands the receptive field, which can capture long-term information. In addition, we exploit SAB, a new scale attention block to capture important features from previous scale. At the same time, for getting richer detailed information, various loss functions are used to constrain the model during the training process. The test results of CelebA-HQ and Paris street view show that our proposed method is superior to other classic image inpainting algorithms.
Model-Based Offline Adaptive Policy Optimization with Episodic Memory

Offline reinforcement learning (RL) is a promising direction to apply RL to real-world by avoiding online expensive and dangerous exploration. However, offline RL is challenging due to extrapolation errors caused by the distribution shift between offline datasets and states visited by behavior policy. Existing model-based offline RL methods set pessimistic constraints of the learned model within the support region of the offline data to avoid extrapolation errors, but these approaches limit the generalization potential of the policy in out-of-distribution (OOD) region. The artificial fixed uncertainty calculation and the sparse reward problem of low-quality datasets in existing methods have weak adaptability to different learning tasks. Hence, a model-based offline adaptive policy optimization with episodic memory is proposed in this work to improve generalization of the policy. Inspired by active learning, constraint strength is proposed to trade off the return and risk adaptively to balance the robustness and generalization ability of the policy. Further, episodic memory is applied to capture successful experience to improve adaptability. Extensive experiments on D4RL datasets demonstrate that the proposed method outperforms existing state-of-the-art methods and achieves superior performance on challenging tasks requiring OOD generalization.
Data mining of public opinion: An overview

The United Nations recently published the “E-government survey 2020” with the main aim of assessing the e-government development status of all United Nations member states. The survey outlines 14 leading countries in e-government development (out of 193 member states) some of them claiming to utilize technologies as artificial intelligence (AI), big data and blockchain. Moreover, with the burst of the COVID-19 pandemic the topic on development and implementation of e-government services becomes even hotter. However, along with the research on the process of digitalization of public services, it is important to develop tools measuring how these rapid changes are perceived by the users. Consequently, this paper examines the most recent research devoted on public opinion data mining. On the basis of extensive literature review, we outline the latest developments and trends in the field of public opinion data mining with special focus on sentiment analysis. Our main goal is to provide a self-contained comprehensive summary that might be used as a basis for design and development of AI systems aimed to mine the public opinion.
Creating 3D models of the intersections of two surfaces of revolution

The contribution describes the usage of the software Rhinoceros for creating the 3D models of two intersecting surfaces of revolution. We distinguish three different cases, in which there are couples of surfaces of revolution with one coincident axis, next to it with two non-coincident parallel axes and finally, with two intersecting axes. There exists the possibility to save the created 3D models of two intersecting surfaces of revolution in the *.stl format in the Rhinoceros. The files saved in the *.stl format are used as bases for 3D printing. We use 3D printing as a tool for creating realistic models of such types of spatial situations or constructions that are hard to image for the students of the first year of the Bachelor study at the Faculty of Mechanical Engineering at TUL. In the case of the contact way of teaching, we can lend the printed 3D models during the lectures or seminars to students. Consequently, the manipulative activities can lead to developing the imaginations of the real visibility of the spatial situations or constructions drawn on the paper by the ruler and the compass. We also introduce the possibilities of using 3D printing in the online way of teaching the course titled Constructive Geometry.
Using an interactive floor system for game-based learning of human emotions

Game-based learning is a method on how to teach a group of people certain skills and experiences. In this paper we demonstrate how to design and develop an educational game that teaches young children to recognize human emotions. We use an interactive floor as our medium. These devices are relatively new augmented reality systems that project interactive content on a flat surface. Furthermore, we demonstrate how effective this method of teaching is when applied to children with special educational needs and draw conclusions.
Design of ML-based AI system for mining public opinion on e-government services in Bulgaria

Digitalization affects all fields of the modern world including the economic, political, and social aspects of our life. Governments are also involved in this process. Considering the rapid transition towards digital e-services that we evidence since the COVID-19 outbreak, the detection and analysis of public opinion on e-government services becomes even more important. The sentiments and opinions of users on digital public services might be used to foster improvements in the development and implementation of the e-services. In this paper we engage the advances in NLP so as to examine the possibilities to analyze in an automated manner the opinions and sentiments towards e-government services expressed by citizens in various social networks in Bulgaria and this is the main goal of our research. For this purpose, we design an integrated ML-based AI system that aims to support decision makers in e-government and public services provision. The system utilizes a variety of data sources - news websites, web forums and other online social networks. To the best of the authors’ knowledge, this is the first study that develops a methodology for mining public opinion in Bulgaria based on a combination of NLP and ML techniques, rather than relying on surveys or in-depth interviews.
Wavelet analysis for biomedical data

The following aper deals with a concept for biomedical image classification based on the wavelet analysis. For the goal of the concept implementation Covid-19 image database is applied. For image analysis and description, the Dual-Tree Complex Wavelet Transform is applied in combination with the Euclidean distance to compute the similarity distance. The proposed concept is evaluated through accuracy, precision and recall metric. The results are presented and analysed showing the advantages of the concept for biomedical image classification and its application.
Mixed data modelling of transportation and incidents

To create and maintain a well-functioning transportation system is a very complex task, that requires knowledge from different domains. As the number of different means of transportation grows new dependencies and dynamic behavior patterns emerge. In this paper we analyze different options for modelling transportation systems with the use of heterogeneous data inputs and sources. Our study builds on use of machine learning and big data analysis for extracting information on characteristics of transportation systems and reducing the number of incidents. We aim at developing a flexible and extensible approach, that can be adapted to transportation problems of different complexity.
Bounds for regular induced subgraphs of strongly regular graphs

Given feasible strongly regular graph parameters (v,k,λ,μ) and a non-negative integer d, we determine upper and lower bounds on the order of a d-regular induced subgraph of any strongly regular graph with parameters (v,k,λ,μ). Our new bounds are at least as good as the bounds on the order of a d-regular induced subgraph of a k-regular graph determined by Haemers. Further, we prove that for each non-negative integer d, our new upper bound improves on Haemers' upper bound for infinitely many strongly regular graphs.
Achieving Revocable Attribute Group-Based Encryption for Mobile Cloud Data: A Multi-Proxy Assisted Approach

Although proxy-assisted revocable attribute-based encryption provides fine-grained privacy protection and reduces the decryption cost of data users, it inherits several disadvantages in outsourcing decryption. The decryption capability in outsourcing decryption is divided into two parts: the outsourcing transformation key and the user decryption key. The proxy server utilizes transformation keys to transform the ciphertext which can only be decrypted by the users who generated the transformation key. When multiple users with the same attributes request outsourcing transformation, the proxy server must do numerous transformation operations, increasing the computing overhead of the proxy server and the user request response time. To address the aforementioned issues, we propose a multi-proxy assisted revocable attribute group-based encryption (MP-RAGBE) scheme. We form a user group out of users who have the same attributes and send the transformation keys directly to the proxy server. Users in the same group can decrypt the transformed ciphertext, and only a small number of transformation keys need to be updated when revocation occurs. The security and experimental analyses show that the proposed scheme meets the defined security requirements while significantly reducing user request response time, the overhead of transformation key generation, transmission, and ciphertext transformation.
Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey

This survey reviews and organizes existing methods for integrating constraints into dimensionality reduction (DR). In the world of high-dimensional data, DR methods help to reduce dimensionality while preserving important structures to facilitate subsequent tasks, such as data visualization. While DR methods only reveal hidden structures from the original data, additional information, such as class labels, external features, or even feedback or prior knowledge from users can help to enrich low-dimensional representations. We consider all these types of additional information as constraints. Integrating constraints into classification and clustering methods is well studied, yet, a systematic review on constraint integration in DR methods for visualization is still lacking. We contribute to the literature of constraints in DR visualizations with a novel categorization focusing on constraint types. This survey also introduces new perspectives on the subject, and suggests new trends and future research directions for combining constraints and DR methods.
Language Model-Based Paired Variational Autoencoders for Robotic Language Learning

Human infants learn language while interacting with their environment in which their caregivers may describe the objects and actions they perform. Similar to human infants, artificial agents can learn language while interacting with their environment. In this work, first, we present a neural model that bidirectionally binds robot actions and their language descriptions in a simple object manipulation scenario. Building on our previous Paired Variational Autoencoders (PVAE) model, we demonstrate the superiority of the variational autoencoder over standard autoencoders by experimenting with cubes of different colours, and by enabling the production of alternative vocabularies. Additional experiments show that the model's channel-separated visual feature extraction module can cope with objects of different shapes. Next, we introduce PVAE-BERT, which equips the model with a pretrained large-scale language model, i.e., Bidirectional Encoder Representations from Transformers (BERT), enabling the model to go beyond comprehending only the predefined descriptions that the network has been trained on; the recognition of action descriptions generalises to unconstrained natural language as the model becomes capable of understanding unlimited variations of the same descriptions. Our experiments suggest that using a pretrained language model as the language encoder allows our approach to scale up for real-world scenarios with instructions from human users.
View-Adaptive Graph Neural Network for Action Recognition

Skeleton-based recognition of human actions has received attention in recent years because of the popularity of 3-D acquisition sensors. Existing studies use 3-D skeleton data from video clips collected from several views. The body view shifts from the camera perspective when humans perform certain actions, resulting in unstable and noisy skeletal data. In this article, we developed a view-adaptive (VA) mechanism that identifies the viewpoints across the sequence and transforms the skeleton view through a data-driven learning process to counteract the influence of variations. Most existing methods use fixed human-defined prior criterion to reposition skeletons. We utilized an unsupervised reposition approach and jointly designed a VA neural network based on the graph neural network (GNN). Our VA-GNN model can transform the skeletons of distinct views into a considerably more consistent virtual perspective over preprocessing approach. The VA module learns the best observed view because it determines the most suitable view and transforms the skeletons from the action sequence for end-to-end recognition along with suited graph topology with adaptive GNN. Thus, our strategy reduces the influence of view variance, allowing networks to focus on learning action-specific properties and resulting in improved performance. The accuracy achieved by the experiments on the four benchmark data sets.
Towards Run-time Efficient Hierarchical Reinforcement Learning

This paper investigates a novel method combining Scalable Evolution Strategies (S-ES) and Hierarchical Reinforcement Learning (HRL). S-ES, named for its excellent scalability, was popularised with demonstrated performance comparable to state-of-the-art policy gradient methods. However, S-ES has not been tested in conjunction with HRL methods, which empower temporal abstraction thus allowing agents to tackle more challenging problems. We introduce a novel method merging S-ES and HRL, which creates a highly scalable and efficient (compute time) algorithm. We demonstrate that the proposed method benefits from S-ES's scalability and indifference to delayed rewards. This results in our main contribution: significantly higher learning speed and competitive performance compared to gradient-based HRL methods, across a range of tasks.
Regularized Differentiable Architecture Search

Differentiable architecture search (DARTS) transforms architectural optimization into a super network optimization by stacking two cells (2 c.). However, repeatedly stacking two cells is a suboptimal operation since cells in different depths should be various. Besides, we find that the performance is slightly improved by increasing the number of searched cells (e.g., from 2 c. to 5 c.), but it leads to uneven resource allocation. This letter proposes a regularized DARTS (RDARTS) to adjust the architectural differences and balance degrees of freedom and resource allocation. Specifically, we use separate architectural parameters for two reduction cells and three normal cells, and then propose an Reg distance to calculate the difference between cells. We design a new validation loss which is the weighting of cross-entropy and Reg loss and introduce an adaptive adjustment method. Results show that RDARTS achieves the top-1 accuracy of 97.64% and 75.8% on CIFAR and ImageNet.
Step-Size Individualization: a Case Study for The Fish School Search Family

This study proposes a new strategy to improve the performance of the algorithms of the Fish School Search (FSS) family via the individualization of the step-size of each fish. We propose to be calculated in two different manners: using individual weight or using individual fitness, depending on the chosen variation of the proposed technique. Our methods were tested on the original FSS, on the Weight based Fish School Search (wFSS) and on the Multi Objective Fish School Search (MOFSS) algorithms. The benchmark functions of the Congress on Evolutionary Computation, The Genetic and Evolutionary Computation Conference (CEC'2020, CEC'2013, and GECCO'2016) and the DTLZ test suite were used to assess the experimental results, which yielded that all variants of the FSS algorithm tested have been improved in the majority of the scenarios.
A Multifactorial Cellular Genetic Algorithm for Multimodal Multitask Optimization

In multimodal optimization problems the main goal is to find as many global optima as possible by using a single search process. This type of optimization tasks emerges in many real-world scenarios in assorted fields including medicine, physics, and aerospace, among many others. However, addressing several multimodal optimization problems simultaneously has received little attention from the multitask optimization community to date. Even though solving different multimodal problems at the same time can largely benefit from the existing synergies among the modes of different tasks, this setup has been less studied than other optimization tasks. This work finds its inspiration in the incipient concepts of Evolutionary Multitasking and Multifactorial Optimization to propose a multifactorial Cellular Genetic Algorithm for solving multimodal optimization problems. Our designed algorithm expedites the search for the global optima of different problems at a time by including several algorithmic steps aimed at adapting the search itself as per the synergies found over the exploration of the problems' landscape. An extensive experimentation has been designed using 14 different functions from the CEC‘2013 competition on multimodal optimization benchmark. Besides evaluating the performance of the devised algorithm to retain the global optima of every function in the benchmark, we also conduct an analysis of the transfer of knowledge among such functions. Finally, we compare its performance to that of a winning proposal in this CEC‘2013 competition so as to reflect on the suitability of the multitasking paradigm to solve multimodal optimization tasks.
An Enhanced Multi-Phase Stochastic Differential Evolution Framework for Numerical Optimization

Real-life problems can be expressed as optimization problems. These problems pose a challenge for researchers to design efficient algorithms that are capable of finding optimal solutions with the least budget. Stochastic Fractal Search (SFS) proved its powerfulness as a metaheuristic algorithm through the large research body that used it to optimize different industrial and engineering tasks. Nevertheless, as with any meta-heuristic algorithm and according to the “No Free Lunch” theorem, SFS may suffer from immature convergence and local minima trap. Thus, to address these issues, a popular Differential Evolution variant called Success-History based Adaptive Differential Evolution (SHADE) is used to enhance SFS performance in a unique three-phase hybrid framework. Moreover, a local search is also incorporated into the proposed framework to refine the quality of the generated solution and accelerate the hybrid algorithm convergence speed. The proposed hybrid algorithm, namely eMpSDE, is tested against a diverse set of varying complexity optimization problems, consisting of well-known standard unconstrained unimodal and multimodal test functions and some constrained engineering design problems. Then, a comparative analysis of the performance of the proposed hybrid algorithm is carried out with the recent state of art algorithms to validate its competitivity.
QL-SSA: An Adaptive Q-Learning based Squirrel Search Algorithm for Feature Selection

Machine learning techniques are widely used for discovering meaningful patterns and classifying real-world data. These datasets may be large and complex, so feature selection is the primary strategy for reducing the dimension of the data, with the general goal of reducing the amount of redundant and disruptive features in a dataset for fast and efficient data analysis without sacrificing significant predictive model performance. Due to exponentially high search space, feature selection is a complex optimization problem. It is practically impossible to evaluate all of the feature subsets manually. Nature-inspired optimization is widely used for this due to its inherent capability, and it solves feature selection tasks as a single objective optimization problem. However, the main issue is their frequent premature convergence, which results in an inadequate contribution to data mining. Even the majority of existing optimizers are not adaptive in nature. As a result, in this paper, we proposed QL-SSA, which combines Reinforcement Learning and the Squirrel Search Algorithm, making it more adaptive and robust for feature selection by maintaining a good balance between exploration and exploitation steps. It is tested on 20 real-world benchmark datasets using two classifiers, and the results show that it outperforms the baseline optimizer in most cases.
Gabo: Gene Analysis Bitstring Optimization

This paper analyzes bitstring functions, character-izes genes according to their contribution to the genome's fitness, and proposes an optimization algorithm (G ABO) that uses this characterization for directing the optimization process. We define a gene's contribution as the difference between the genome's fitness when the gene takes a value of 1 and its fitness when the gene takes a value of 0. We characterize a gene as intron-like if it does not contribute to the genome's fitness (zero difference) and as separable-like if its contribution to the fitness of both the genome and genome's complement is the same. Gabo divides genes into two groups coding-like and intron-like genes. Then it searches for an optimal solution by reducing intron-like genes (IOSA) and analyzing coding-like genes (COSA). G Aborepeats these two steps while there are intron-like genes, not all genes are separable-like, and function evaluations are available. We test the performance of Gabo on well-known binary-encoding functions and a function that we define as the mix of them. Our results indicate that G Aboproduces the optimal or near to the optimal solution on the tested functions expending a reduced number of function evaluations and outperforming well-established optimization algorithms.