Reviewerly/exHarmony huggingface dataset:
An Enhanced Binary Particle Swarm Optimization (E-BPSO) algorithm for service placement in hybrid cloud platforms

Hybrid cloud platforms offer an attractive solution to organizations interested in implementing integrated private and public cloud applications to meet their profitability requirements. However, this can only be achieved by utilizing available resources while speeding up execution processes. Accordingly, deploying new applications entails dedicating some of these processes to a private cloud while allocating others to the public cloud. In this context, the current work aims to minimize relevant costs and deliver effective choices for an optimal service placement solution within minimal execution time. To date, several evolutionary algorithms have been applied to solve the challenging service placement problem by dealing with complex solution spaces to provide an optimal placement with relatively short execution times. In particular, the standard BPSO algorithm has been found to display a significant disadvantage, namely getting trapped in local optima and demonstrating a noticeable lack of robustness in dealing with service placement problems. Hence, to overcome the critical shortcomings associated with the standard BPSO, an enhanced binary particle swarm optimization (E-BPSO) algorithm is proposed, comprising a modification inspired by the continuous PSO for the particle position updating equation. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art approaches using a real benchmark task in terms of both cost and execution time.
Supporting Expressive and Faithful Pictorial Visualization Design With Visual Style Transfer

Pictorial visualizations portray data with figurative messages and approximate the audience to the visualization. Previous research on pictorial visualizations has developed authoring tools or generation systems, but their methods are restricted to specific visualization types and templates. Instead, we propose to augment pictorial visualization authoring with visual style transfer, enabling a more extensible approach to visualization design. To explore this, our work presents Vistylist, a design support tool that disentangles the visual style of a source pictorial visualization from its content and transfers the visual style to one or more intended pictorial visualizations. We evaluated Vistylist through a survey of example pictorial visualizations, a controlled user study, and a series of expert interviews. The results of our evaluation indicated that Vistylist is useful for creating expressive and faithful pictorial visualizations.
A solar forecasting framework based on federated learning and distributed computing

Solar forecasting is a crucial and cost-effective tool for better utilization of solar energy for smart environment design. Artificial intelligence (AI) technologies, such as machine learning (ML) and deep learning (DL), have gained great popularity and been widely applied in solar forecasting in recent years. However, conventional AI-based forecasting methods suffer from high variability and stochasticity of solar irradiation, making unreliable predictions due to heterogeneous solar resources. Moreover, the training process of DL models is less flexible and requires immense data. Even for a well-trained model, it can still yield deteriorated performances on other datasets of varying data distributions. To tackle the deficiencies of AI forecasting models, we present a flexible distributed solar forecasting framework based on a novel spatial and temporal attention-based neural network (STANN) in conjunction with federated learning (FL) technique, considering multi-horizon forecasting scenario from 5–30 min. The STANN model consists of a feature extractor and a forecaster, which can be respectively trained on various local datasets for better localization, and updated to further improve forecasting accuracy through global parameter aggregation under the proposed framework without data gathering. We evaluate effectiveness of the proposed method by conducting extensive experiments on real-world datasets and compare it to other popular forecasting models. The results demonstrate that our approach outperforms the other benchmarks with higher forecasting accuracy for all forecast horizons and better generalization on various datasets, achieving the highest forecast skill of 28.83% at 30 min horizon and an improvement of 11.2% compared with the centralized, localized, and conventional FL training methods.
Time-series anomaly detection using dynamic programming based longest common subsequence on sensor data

This study proposes a novel approach to time-series anomaly detection by solving the longest common subsequence (LCS) problem of two time-series data. The conventional LCS problem is used to measure the similarity of two input strings. However, it cannot be applied to real-time data collected from sensors in smart manufacturing, which are mostly real numbers instead of strings. Thus, two algorithms—an extension of fixed gap longest common subsequence (extent FGLCS) and the modified dynamic programming-based LCS (MDP-LCS) algorithm—are proposed to deal with real-time series data. Further, a gap search constraint is investigated to limit the search range in finding the LCS. This study also provides a threshold to define the matching of a pair that contains real numbers rather than a string in the conventional LCS or FGLCS problems. In addition, the proposed methods employ the gap search constraint and the threshold to reduce computational time. Furthermore, the proposed algorithms can be implemented on both univariate and multivariate time-series data. The LCS length, known as the similarity of the two time-series data, is used to detect the anomalies based on the user’s expectation. The accuracy and computational time of the proposed algorithm show a significant positive result compared with the exact solution provided by the dynamic programming-based LCS algorithm. Moreover, this study also uses the proposed methods for anomaly detection in a case study of big multisensory data in smart manufacturing. The result shows that the MDP-LCS can detect approximately 98% of normal data and more than 85% of anomalies with a shorter computational time.
Bool Network: An Open, Distributed, Secure Cross-Chain Notary Platform

With the advancement of blockchain technology, hundreds of cryptocurrencies have been deployed. The bloom of heterogeneous blockchain platforms brings a new emerging problem: typically, various blockchains are isolated systems, how to securely identify and/or transfer digital properties across blockchains? There are three main kinds of cross-chain approaches: sidechains/relays, notaries, and hashed time-lock contracts. Among them, notary-based cross-chain solutions have the best compatibility and user-friendliness, but they are typically centralized. To resolve this issue, we present Bool Network – an open, distributed, secure cross-chain notary platform powered by MPC-based distributed key management over evolving hidden committees. More specifically, to protect the identities of the committee members, we propose a Ring verifiable random function ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ring VRF</i> ) protocol, where the real public key of a VRF instance can be hidden among a ring, which may be of independent interest to other cryptographic protocols. Furthermore, all the key management procedures are executed in the TEE, such as Intel SGX, to ensure the privacy and integrity of partial key components. A prototype of the proposed Bool Network is implemented in Rust language, using Polkadot Substrate.
Targeting local orogenic gold mineralisation zones using data-driven evidential belief functions: the Godarsorkh area, Central Iran

Using GIS-based multi-criteria decision-making techniques, i.e. mineral prospectivity mapping (MPM), specific spatial problems can be solved by combining information from different sources. Orogenic gold mineralisation shows weak exploration signals on the surface that can challenge exploration geologists. This study investigates the ability of a MPM method called evidential belief functions (EBFs) to identify a local orogenic gold mineralisation in the Godaesorkh gold deposit, north of Sanandaj-Sirjan Zone (Central Iran). Several evidential layers such as geological, structural, geophysical, remote sensing, and geochemical layers were generated by applying various processing methods. These layers are integrated to attain a MPM by applying the EBFs based on Dempster-Shafer’s rules. Precise selection of evidential layers and higher number of known mineral occurrences may lead to a higher degree of belief in the mineral prospectivity map. Accordingly, high probability values were correlated with the gold mineralisation in the study area, and three separate areas were identified as promising with N40E and N15W trends. It is concluded that the orogenic gold mineralisation occurred in shear zones, which is mainly controlled by tectonic structures. Consequently, analysing these structures can be considered as a key feature for identifying potential orogenic gold mineralisation zones in the study area.
Fast Tunable Coupling Scheme of Kerr Parametric Oscillators Based on Shortcuts to Adiabaticity

Kerr parametric oscillators (KPOs), which can be implemented with superconducting parametrons possessing large Kerr nonlinearity, have been attracting much attention in terms of their applications to quantum annealing, universal quantum computation, and studies of quantum many-body systems. It is of practical importance for these studies to realize fast and accurate tunable coupling between KPOs in a simple manner. We develop a simple scheme of fast tunable coupling of KPOs with high tunability in speed and amplitude using the fast transitionless rotation of a KPO in the phase space based on the shortcuts to adiabaticity. Our scheme enables rapid switching of the effective coupling between KPOs, and can be implemented with always-on linear coupling between KPOs, by controlling the phase of the pump field and the resonance frequency of the KPO without controlling the amplitude of the pump field nor using additional drive fields and couplers. We apply the coupling scheme to a two-qubit gate, and show that our scheme realizes high gate fidelity compared with a purely adiabatic one, by mitigating undesired nonadiabatic transitions.
Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images

Domain shift is a problem commonly encountered when developing automated histopathology pipelines. The performance of machine learning models such as convolutional neural networks within automated histopathology pipelines is often diminished when applying them to novel data domains due to factors arising from differing staining and scanning protocols. The Dual-Channel Auto-Encoder (DCAE) model was previously shown to produce feature representations that are less sensitive to appearance variation introduced by different digital slide scanners. In this work, the Multi-Channel Auto-Encoder (MCAE) model is presented as an extension to DCAE which learns from more than two domains of data. Experimental results show that the MCAE model produces feature representations that are less sensitive to inter-domain variations than the comparative StaNoSA method when tested on a novel synthetic dataset. This was apparent when applying the MCAE, DCAE, and StaNoSA models to three different classification tasks from unseen domains. The results of this experiment show the MCAE model out performs the other models. These results show that the MCAE model is able to generalise better to novel data, including data from unseen domains, than existing approaches by actively learning normalised feature representations.
Interactive Visual Cluster Analysis by Contrastive Dimensionality Reduction

We propose a contrastive dimensionality reduction approach (CDR) for interactive visual cluster analysis. Although dimensionality reduction of high-dimensional data is widely used in visual cluster analysis in conjunction with scatterplots, there are several limitations on effective visual cluster analysis. First, it is non-trivial for an embedding to present clear visual cluster separation when keeping neighborhood structures. Second, as cluster analysis is a subjective task, user steering is required. However, it is also non-trivial to enable interactions in dimensionality reduction. To tackle these problems, we introduce contrastive learning into dimensionality reduction for high-quality embedding. We then redefine the gradient of the loss function to the negative pairs to enhance the visual cluster separation of embedding results. Based on the contrastive learning scheme, we employ link-based interactions to steer embeddings. After that, we implement a prototype visual interface that integrates the proposed algorithms and a set of visualizations. Quantitative experiments demonstrate that CDR outperforms existing techniques in terms of preserving correct neighborhood structures and improving visual cluster separation. The ablation experiment demonstrates the effectiveness of gradient redefinition. The user study verifies that CDR outperforms t-SNE and UMAP in the task of cluster identification. We also showcase two use cases on real-world datasets to present the effectiveness of link-based interactions.
Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.
Improving Hybrid CTC/Attention Architecture for Agglutinative Language Speech Recognition

Unlike the traditional model, the end-to-end (E2E) ASR model does not require speech information such as a pronunciation dictionary, and its system is built through a single neural network and obtains performance comparable to that of traditional methods. However, the model requires massive amounts of training data. Recently, hybrid CTC/attention ASR systems have become more popular and have achieved good performance even under low-resource conditions, but they are rarely used in Central Asian languages such as Turkish and Uzbek. We extend the dataset by adding noise to the original audio and using speed perturbation. To develop the performance of an E2E agglutinative language speech recognition system, we propose a new feature extractor, MSPC, which uses different sizes of convolution kernels to extract and fuse features of different scales. The experimental results show that this structure is superior to VGGnet. In addition to this, the attention module is improved. By using the CTC objective function in training and the BERT model to initialize the language model in the decoding stage, the proposed method accelerates the convergence of the model and improves the accuracy of speech recognition. Compared with the baseline model, the character error rate (CER) and word error rate (WER) on the LibriSpeech test-other dataset increases by 2.42% and 2.96%, respectively. We apply the model structure to the Common Voice-Turkish (35 h) and Uzbek (78 h) datasets, and the WER is reduced by 7.07% and 7.08%, respectively. The results show that our method is close to the advanced E2E systems.
Rethinking prediction alignment in one-stage object detection

Owing to their excellent performance and efficiency, one-stage detectors have been widely used in multimedia tasks, such as temporal action detection, object tracking, and video detection. However, misalignment between classification and regression branches limits the accuracy of the detector. Most existing works add an auxiliary branch or adopt a specific sample assignment strategy to alleviate this problem, but with little effect. In this paper, we attribute this to incomplete branch interactions and propose a comprehensive Predictive Aligned Object Detector (PAOD), which can better correlate two subtasks. Specifically, our proposed PAOD achieves a better trade-off between prediction-interactive and prediction-specific by adopting an Iterative Aggregation Module (IAM) and a Mutual Constraint Module (MCM). We also design an aligned label assignment with an adaptive metric and re-weighting mechanism to further narrow the misalignment between prediction heads. With negligible additional overhead, PAOD achieves 50.4 AP at single-model single-scale testing on the MS-COCO branch, which demonstrates the effectiveness of our proposal. Notably, PAOD consistently outperforms previous sota such as ATSS (47.7 AP), BorderDet (48.0 AP) and GFL (48.2 AP) by a large margin on COCO test-dev dataset, and achieves better performance than various dense detectors on Pascal VOC and CrowdHuman datasets. Code is available at https://github.com/JunruiXiao/PAOD.
Digital technology and teacher digital competency in physical education: a holistic view of teacher and student perspectives

Digital technology is growing in popularity in the enacted teaching and learning process. However, there is ongoing debate regarding the evidence of its impact on teaching and learning in physical education. With increasing use in education, digitally competent teachers are essential to the success of its integration. The primary aim of this study was to explore perceived teacher competency levels in applying digital technology to the physical education classroom. Teachers perceived significantly low competency levels in relation to digital technology in physical education. This was a result of both personal and school hindrances which teachers identified as impeding the integration of digital technology in their physical education classes. Drawing on a holistic view of the dichotomy of pedagogy and technology, we suggest that this relationship is more complex than the one stated in most digital competence frameworks, therefore a narrow understanding of teacher digital competency in physical education.
Framework for Improved Sentiment Analysis via Random Minority Oversampling for User Tweet Review Classification

Social networks such as twitter have emerged as social platforms that can impart a massive knowledge base for people to share their unique ideas and perspectives on various topics and issues with friends and families. Sentiment analysis based on machine learning has been successful in discovering the opinion of the people using redundantly available data. However, recent studies have pointed out that imbalanced data can have a negative impact on the results. In this paper, we propose a framework for improved sentiment analysis through various ordered preprocessing steps with the combination of resampling of minority classes to produce greater performance. The performance of the technique can vary depending on the dataset as its initial focus is on feature selection and feature combination. Multiple machine learning algorithms are utilized for the classification of tweets into positive, negative, or neutral. Results have revealed that random minority oversampling can provide improved performance and it can tackle the issue of class imbalance.
Hybrid Evolutionary Algorithm for Optimal Control Problem

The optimal control problem is well known long time ago, but there is no a general numerical method for it. More precisely researchers try always to solve this problem various numerical methods. Recently it was established, that if the optimal control problem has phase constraints included in quality criterion, then a functional is multimodal and this optimization problem belongs to global class optimization. Therefore, to solve the optimal control problem it is better to use evolutionary algorithms. But this proposition doesn’t give useful information for researcher, because now there are huge quantity evolutionary algorithms. In this paper the best evolutionary algorithm for the optimal control problem is proposed. It is constructed on the base three evolutionary algorithms, genetic algorithm, particle swarm optimization, and grey wolf optimizer. It is shown computational experiments, where this hybrid algorithm is compared with everyone from listed above on the complex problem with phase constraints.
Sustainable versus Conventional Cryptocurrencies in the Face of Cryptocurrency Uncertainty Indices: An Analysis across Time and Scales

Are conventional and sustainable cryptocurrencies effective hedging instruments for high cryptocurrency uncertainty? This paper examines co-movements between conventional (Bitcoin, Ethereum, Binance Coin, Tether) and sustainable (Cardano, Powerledger, Stellar, Ripple) cryptocurrencies and two cryptocurrency uncertainty indices (UCRY price and UCRY policy). Using weekly returns from 1 October 2017 to 30 March 2021, the paper employs the bivariate wavelet coherence method considering three investment horizons, short-term, medium-term, and long-term. The results confirm that conventional and sustainable cryptocurrencies show consistent positive and identical co-movements with both cryptocurrency uncertainty indices at the short-term horizon during COVID-19 and negative co-movement at the medium-term investment horizon, suggesting the short-term hedging ability of dirty/green cryptocurrencies for high UCRY price and policy. Evidence of negative coherences shows that higher cryptocurrency prices and policy uncertainties lead to lower cryptocurrency returns, reflecting the adverse impact of higher uncertainties on the trust of crypto traders and investors. Weak co-movement is found between dirty/green cryptocurrencies and UCRY price/policy indices, which suggests the possible role of dirty/green cryptocurrencies as a weak hedge for UCRY price and policy indices. These findings provide potential avenues to hedge cryptocurrency uncertainties using conventional and sustainable cryptocurrencies across multiple investment horizons.