Reviewerly/exHarmony huggingface dataset:
Differential Evolution With Domain Transform

Although a significant advancement of differential evolution (DE) for global optimization has been witnessed in the past two decades, the problems of premature convergence and stagnation are still open questions that hinder the performance. Both are likely to occur on complicated multimodal functions, but the phenomena differ. Premature convergence refers to a rapid loss of population diversity when attracted to a local minimum while stagnation happens even though the population is diverse. To deal with these problems, this article proposes a domain transform (DT) methodology. Different from existing fitness analysis which mainly utilizes the original fitness landscape information, DT yields a transformed fitness landscape with transform operation to the frequency domain and inverse transform operation back to the solution domain, between which the first few highest frequencies are removed. With the deletion operation, the transformed fitness landscape becomes smoother and facilitates the escape from local minima and stagnation on complicated multimodal functions. Simulation results show that DT significantly improves the population successful update rate and population convergence. The constructed DTDE algorithm consequently exhibits remarkable improvements on the baseline algorithm and outperforms several state-of-the-art DE variants. DT has also been extended for noisy optimization and it performs better than the baseline, the classic resampling method, state-of-the-art DE variants, as well as several popular noisy evolutionary optimization algorithms.
GEnI: A framework for the generation of explanations and insights of knowledge graph embedding predictions

Knowledge Graphs (KGs) are among the most commonly used knowledge representation paradigms, being at the core of tasks such as question answering or recommendation systems. Knowledge Graph Completion (KGC) is one of the key tasks concerning KGs, where the goal is to extract new elements from the existing information. Different approaches have been proposed through the years to tackle this challenge. Among them, two analogous categories can be distinguished: rule-learning and Knowledge Graph Embeddings (KGE). Different methods have been subsequently proposed to unify both types under a single framework, such that the benefits of both proposals can be exploited. However, most of these methods consider using rule-learning models as a boosting agent for KGE models, but not as an explainability tool. This work presents GEnI1, a framework capable of generating insights and explanations for KGE models. GEnI follows a three-phase sequential process, generating a feasible explanation for a given prediction. Possible outcomes are rules, correlations, and influence detection. Moreover, the output is expressed in natural language to further extend the explainability of the proposal. GEnI has been successfully evaluated under three criteria: coherence, the meaningfulness of the output, and reliability. Moreover, it can be used by both translational and bilinear KGE models, offering broad coverage. Furthermore, this work also presents an in-depth review of existing integrative approaches between rule-learning and embedding models, providing a comparative framework between them.
Separable reversible data hiding in an encrypted image using the adjacency pixel difference histogram

Information security is a practice of encrypting data in movement and on hold, improving discretion and integrity. One can protect, encrypt and decrypt critical data in several ways. One of them is reversible data hiding in an encrypted image. The technique enables the user to encrypt images that need authentication and restore them to their original form of images. This technique returns a lossless image as an output making it the most suiTable for medical images and the military. Histogram shifting of pixel difference is an effectual reversible data hiding method in information security. Each image's pixel is encrypted when a user wants to safely store a digital image in an open environment like a cloud. The authentication or any other relevant information related to that image is embedded in the pixel difference histogram. The proposed approach's advantage is that the grayscale image transfer is carried out exceedingly safely, with near-zero correlation and Entropy closer to 8. The Peak Signal Noise Ratio (PSNR) for a directly decrypted image with an embedding capacity of 0.0807 bpp is 50.84 dB. Moreover, the secret and cover images are retrieved without error.
RGB-T object tracking via sparse response-consistency discriminative correlation filters

RGB-T object tracking is an emerging topic in the object tracking community owing to the complementarity of visual and thermal infrared images. However, existing RGB-T trackers usually focus on feature-level fusion and struggle to satisfy real-time requirements. In this study, we propose a real-time RGB-T object tracking algorithm based on sparse response-consistency discriminative correlation filters. First, to leverage the collaboration and heterogeneity of multi-modal images, we jointly learn discriminative correlation filters of visual and thermal infrared modalities via sparse response consistency. Second, we introduce an adaptive spatial regularization strategy to mitigate the boundary effects of the correlation filter-based tracking algorithm. Finally, to efficiently utilize the complementarity of visual and thermal infrared modalities, we design an adaptive decision-fusion-and-updating strategy, which can reduce the adverse impact of unreliable modalities on tracking performance and boost the running speed. Extensive experiments on four large-scale RGB-T tracking benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art trackers in terms of precision and robustness, achieving a tracking speed of up to 53.447 fps.
Application of wavelet transforms to the simulation of corrosion fields on buried pipelines

The present study presents a framework to analyze and simulate nonhomogeneous non-Gaussian corrosion fields on the external surface of buried in-service pipelines by using continuous and discrete wavelet transforms. The considered transforms are the two-dimensional continuous wavelet transform (CWT) using the complex Morlet wavelets, dual-tree complex discrete wavelet transform (DT-CDWT), and dual-tree complex discrete wavelet with hyperbolic wavelet transform scheme (DT-CHWT); the natural corrosion field is measured using a high-resolution laser scan. Scalograms and marginal distribution of the measured corrosion field are incorporated into the iterative power and amplitude correction (IPAC) algorithm to generate synthetic corrosion fields. The framework is explained and illustrated using a numerical example. The results indicate that the proposed framework can generate synthetic corrosion fields that effectively capture probabilistic characteristics of the measured corrosion field in terms of the scalogram, textural features, and burst capacity of the pipe segment containing the corrosion field. The results suggest that DT-CDWT and DT-CHWT combined with IPAC are efficient and viable options to simulate synthetic corrosion fields to facilitate the pipeline corrosion management practice, whereas the use of CWT in conjunction with IPAC is associated with high computational cost.
Multi-scale local-temporal similarity fusion for continuous sign language recognition

Continuous sign language recognition (cSLR) is a public significant task that transcribes a sign language video into an ordered gloss sequence. It is important to capture the fine-grained gloss-level details, since there is no explicit alignment between sign video frames and the corresponding glosses. Among the past works, one promising way is to adopt a one-dimensional convolutional network (1D-CNN) to temporally fuse the sequential frames. However, CNNs are agnostic to similarity or dissimilarity, and thus are unable to capture local consistent semantics within temporally neighboring frames. To address the issue, we propose to adaptively fuse local features via temporal similarity for this task. Specifically, we devise a Multi-scale Local-Temporal Similarity Fusion Network (mLTSF-Net) as follows: 1) In terms of a specific video frame, we firstly select its similar neighbours with multi-scale receptive regions to accommodate different lengths of glosses. 2) To ensure temporal consistency, we then use position-aware convolution to temporally convolve each scale of selected frames. 3) To obtain a local-temporally enhanced frame-wise representation, we finally fuse the results of different scales using a content-dependent aggregator. We train our model in an end-to-end fashion, and the experimental results on RWTH-PHOENIX-Weather 2014 datasets (RWTH) demonstrate that our model achieves competitive performance compared with several state-of-the-art models.
Predicting time series by data-driven spatiotemporal information transformation

Making an accurate prediction of an unknown system only from a short-term time series is difficult due to the lack of sufficient information, especially in a multistep-ahead manner. However, a high-dimensional short-term time series still contains rich dynamical information and is increasingly available in many fields. In this work, we exploit a spatiotemporal information (STI) scheme that transforms high-dimensional/spatial information into temporal information and develop a new method called multitask Gaussian process regression machine (MT-GPRM) to achieve accurate predictions from short-term time series. We first construct a specific multitask GPR comprising multiple linked STI mappings to transform high-dimensional/spatial information into temporal/dynamical information of any given target variable and then make multistep-ahead predictions of the target variable by solving those STI mappings. The multistep-ahead prediction results on various synthetic and real-world datasets show that MT-GPRM outperforms other existing approaches.
Strategic and Tactical Path Planning for Urban Air Mobility: Overview and Application to Real-World Use Cases

Urban air mobility requires safe and efficient airspace management, as well as effective path planning and decision-making capabilities to enable access to the urban airspace, which is predicted to be very densely populated. This paper tackles the problem of strategic and tactical path planning by presenting a framework specifically designed for accounting for several constraints and issues of the urban environment. Multi-objective and multi-constraint planner algorithms are developed to this aim, along with an innovative method for information simplification and manipulation. Navigation-aware and optimized trajectories were retrieved from the strategic approach. Tactical path planning was developed using three approaches that react differently to unpredicted conditions. The entire strategic–tactical pipeline was tested in two real-world use cases, representing common missions in urban environments, such as medical delivery and short-range air taxi. The results demonstrate the effectiveness of the proposed methodology in generating the strategic path and show the different outcomes of the proposed tactical approaches, thus highlighting their advantages and drawbacks.
An Improved Particle Swarm Optimization Algorithm for Data Classification

Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.
Building a Knowledge Graph for the History of Vienna with Semantic MediaWiki

While research on semantic wikis is declining, Semantic MediaWiki (SMW) can still play an important role in the emerging field of knowledge graph curation. The Vienna History Wiki, a large knowledge base curated by the city government in collaboration with other institutions and the general public, provides an ideal use case for demonstrating strengths and weaknesses of SMW as well as discussing the challenges of co-curation in a cultural heritage setting. This paper describes processes like collaborative editing, interlinking unique identifiers on the web, sharing data with Wikidata, making use of Schema.org, and other ontologies. It presents insights from a user survey, access statistics, and a knowledge graph analysis. This work contributes to the scarce research in wiki usage outside of the Wikipedia ecosystem as well as to the field of community-based knowledge graph curation. The availability of a now significantly improved RDF representation indicates future directions for research and practice.
Interactive Python Notebooks for Physical Chemistry

Chemistry simulations using interactive graphic user interfaces (GUIs) represent uniquely effective and safe tools to support multidimensional learning. Computer literacy and coding skills have become increasingly important in the chemical sciences. In response to both of these facts, a series of Jupyter notebooks hosted on Google Colaboratory were developed for undergraduate students enrolled in physical chemistry. These modules were developed for use during the COVID-19 pandemic when Millsaps College courses were virtual and only virtual or online laboratories could be used. These interactive exercises employ the Python programming language to explore a variety of chemical problems related to kinetics, the Maxwell–Boltzmann distribution, numerical versus analytical solutions, and real-world application of concepts. All of the modules are available for download from GitHub (https://github.com/Abravene/Python-Notebooks-for-Physical-Chemistry). Accessibility was prioritized, and students were assumed to have no prior programming experience; the notebooks are cost-free and browser-based. Students were guided to use widgets to build interactive GUIs that provide dynamic representations, immediate access to multiple investigations, and interaction with key variables. To evaluate the perceived effectiveness of this introduction to Python programming, participants were surveyed at the beginning and end of the course to gauge their interest in pursuing programming and data analysis skills and how they viewed the importance of programming and data analysis for their future careers. Student reactions were generally positive and showed increased interest in programming and its importance in their futures, so these notebooks will be incorporated into the in-person laboratory in the future.
Leader population learning rate schedule

The successful application of modern deep neural network models is deeply influenced by the choice of hyperparameters. As one of the most important hyperparameters, the Learning Rate (LR) needs to be fine-tuned. In order to train models that can perform better, finding a suitable LR schedule quickly has become a pressing problem. Current research often relies on manual use of parallel search methods such as grid search, or serial methods such as Bayesian. However, the variety and number of models and datasets result in a very large LR space, and these methods consume significant resources to find only sub-optimal static LR schedules within a given time frame. It was found that in the currently widely used Distributed Data Parallel (DDP) deep learning, where multiple nodes themselves constitute the population, it is possible to use population algorithms to optimise the searched LR schedule dynamically throughout the training process with little additional training time. To maximise the potential of both the searched LR schedule and the population optimisation of the participating multi-nodes, this paper proposes a Leader Population Learning Rate Schedule (LPLRS) for the DDP deep learning environment, which continuously explores for better learning rates in the neighbourhood of the searched LR schedule and guides the subsequent training process. LPLRS achieves higher model classification accuracy on the Cifar 10&100 test datasets on the state-of-the-art Wide Residual Network with Sharpness-Aware Minimization (WRN(SAM)) model compared to the latest SGDR, CLR, and StepLR learning rate schedules.
The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing-an emerging paradigm to overcome the shortcomings of cloud-based computing-could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator-a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
A Research On The New Generation Artificial Intelligence Technology Generative Pretraining Transformer 3

In the digitalizing world, Artificial Intelligence (AI) paves the way for the automation of routine work done by humans and makes life easier. Recently, there is no area where AI and its applications are not used in daily life, from health to education, from transportation to energy, and from agriculture to tourism. AI applications are making rapid progress in the direction of important and current developments, especially in Natural Language Processing (NLP) and Deep Learning (DL). A concrete example of progress in these areas is the GPT-3 (Generative Pre-trained Transformer 3) language model. AI-assisted GPT-3 technology is the DL model that is effectively used in many NLP fields, which can produce long and consistent content similar to the texts written by people using pre-trained algorithms. The GPT-3 architecture has reached a level that can compete with people in many areas by producing optimum solutions for all kinds of inputs by using the Transformer-based language model, which is an attention-based deep learning technique. This article aims to convey the efficiency, structure and potential of the Transformer-assisted GPT-3 model, which is one of the most up-to-date NLP technologies, to the reader. Since the number of Turkish studies in the field of GPT-3 AI is quite limited, it is considered that this study will contribute to the literature in terms of both quantity and quality. In addition, the performance parameters of the model were examined by making a customized fine-tuned sample application in the beta version of the GPT-3 model.
PCLUDA: A Pseudo-Label Consistency Learning- Based Unsupervised Domain Adaptation Method for Cross-Domain Optical Remote Sensing Image Retrieval

Recent advances in deep learning have dramatically improved the performance of content-based remote sensing image retrieval (CBRSIR) with the same distribution of training set (source domain) and test set (target domain). In fact, their distributions are inconsistent in most cases, which can lead to a dramatic decrease in retrieval performance. Currently, some unsupervised domain adaptation (DA) methods for other remote sensing applications have been proposed to eliminate the inconsistency. However, the current unsupervised DA methods do not make full use of the target domain’s distribution characteristics when delineating its decision boundary. This tends to degrade the cross-domain retrieval performance. In this article, a pseudo-label consistency learning-based unsupervised DA method (PCLUDA) is proposed for cross-domain CBRSIR. Our PCLUDA method minimizes the difference in probability distribution between the target domain and its perturbed output by a pseudo-label self-training and consistency regularization strategy, followed by adjusting the target domain’s decision boundaries to the low-density region. Besides, minimize class confusion (MCC) is introduced to reduce negative transfer caused by large intraclass variance of RSIs. Two cross-domain datasets with 12 cross-domain scenarios are constructed based on six open access datasets to measure DA methods. Experimental results show that our PCLUDA method achieves superior retrieval performances with average retrieval precision improvement by 4.9%–32.3% compared with eight state-of-the-art DA approaches in complex cross-domain scenarios. Furthermore, other experimental results indicate that our PCLUDA can also reach optimal retrieval performances in different kinds of deep learning networks [i.e., vision transformer (ViT) and convolutional neural networks (CNNs)].