Reviewerly/exHarmony huggingface dataset:
Sentiment analysis: an empirical comparison between various training algorithms for artificial neural network

The proliferated increase in the commercial benefits of sentiment analysis accumulated a huge interest in the domain of sentiment classification. Sentiment analysis categorises a given text into positive or negative class. In this paper, we present an empirical comparison between different training algorithms gradient descent (GD), gradient descent with momentum backpropagation (GDM), gradient descent adaptive learning rate backpropagation (GDA), gradient descent with momentum and adaptive learning rate backpropagation (GDX), and Levenberg-Marquardt backpropagation (LM), used for training the neural network for the domain of sentiment classification. The performance of all the methods is compared and evaluated using three balanced binary datasets from various domains with different features using various performance metrics such as accuracy, precision, recall, f-score, mean squared error, and training time. The experiments are performed five times with different random seed values using 10-fold cross-validation. The results indicate that GDX and LM outperform other methods in terms of classification accuracy.
Review on Strain Localization Phenomena Studied by High‐Resolution Digital Image Correlation

Strain localization phenomena are well known for numerous materials occurring under various loading conditions on different length scales. The digital image correlation (DIC) is a full-field measuring technique which allows to study these phenomena with high lateral and strain resolution. In this review, article number 2001409 by Anja Weidner and Horst Biermann, an overview on developments and applications of DIC is provided and case studies on the application of high-resolution DIC in combination with scanning electron microscopy are presented.
OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series

Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining methods to discover interesting patterns in time series. The discretization will not only cause the loss of some important information, which partially destroys the continuity of time series, but also ignore the order relations between time-series values. Inspired by order-preserving matching, this article explores a new method called order-preserving sequential pattern (OPP) mining, which does not need to discretize time series into symbolic sequences and represents patterns based on the order relations of time series. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath time series. We propose an OPP-Miner algorithm to mine frequent patterns in time series with the same relative order. OPP-Miner employs the filtration and verification strategies to calculate the support and uses the pattern fusion strategy to generate candidate patterns. To compress the result set, we also study to find the maximal OPPs. Experimental results validate that OPP-Miner is not only efficient but can also discover similar subsequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuc567/Pattern-Mining/tree/master/OPP-Miner</uri> .
They May Not Work! An evaluation of eleven sentiment analysis tools on seven social media datasets

Sentiment analysis is an important method for understanding emotions and opinions expressed through social media exchanges. Little work has been done to evaluate the performance of existing sentiment analysis tools on social media datasets, particularly those related to health, healthcare, or public health. This study aims to address the gap. We evaluated 11 commonly used sentiment analysis tools on five health-related social media datasets curated in previously published studies. These datasets include Human Papillomavirus Vaccine, Health Care Reform, COVID-19 Masking, Vitals.com Physician Reviews, and the Breast Cancer Forum from MedHelp.org. For comparison, we also analyzed two non-health datasets based on movie reviews and generic tweets. We conducted a qualitative error analysis on the social media posts that were incorrectly classified by all tools. The existing sentiment analysis tools performed poorly with an average weighted F1 score below 0.6. The inter-tool agreement was also low; the average Fleiss Kappa score is 0.066. The qualitative error analysis identified two major causes for misclassification: (1) correct sentiment but on wrong subject(s) and (2) failure to properly interpret inexplicit/indirect sentiment expressions. The performance of the existing sentiment analysis tools is insufficient to generate accurate sentiment classification results. The low inter-tool agreement suggests that the conclusion of a study could be entirely driven by the idiosyncrasies of the tool selected, rather than by the data. This is very concerning especially if the results may be used to inform important policy decisions such as mask or vaccination mandates.
A Blockchain-Based Access Control Scheme for Zero Trust Cross-Organizational Data Sharing

Multi-organization data sharing is becoming increasingly prevalent due to the interconnectivity of systems and the need for collaboration across organizations (e.g., exchange of data in a supply chain involving multiple upstream and downstream vendors). There are, however, data security concerns due to lack of trust between organizations that may be located in jurisdictions with varying security and privacy legislation and culture (also referred to as a zero trust environment). Hence, in such a zero trust setting, one should introduce strengthened, yet efficient, access control mechanisms to facilitate cross-organizational data access and exchange requests. Contemporary access control schemes generally focus on protecting a single objective rather than multiple parties, due to higher security costs. In this article, we propose a blockchain-based access control scheme, designed to facilitate lightweight data sharing among different organizations. Specifically, our approach utilizes the consortium blockchain to establish a trustworthy environment, in which a Role-Based Access Control (RBAC) model is then deployed using our proposed multi-signature protocol and smart contract methods. Evaluation of our proposed approach is performed on the HyperLedger Fabric consortium blockchain platform using both Caliper and BFT-SMaRT benchmarks, and the findings demonstrate the utility of our approach.
Text-Based Chatbot in Financial Sector: A Systematic Literature Review

&lt;abstract&gt; &lt;p&gt;Text-based chatbots are implemented in the financial sector to enhance the relationship between the customer and services provided by the sector, and also to address external challenges and customer requirements. The chatbot technology in the financial sector serves to examine customers' frequently asked questions and the representation of the process using machine learning. In light of this, this study presents a comprehensive systematic literature review of articles focused on text-based chatbots in the financial sector. It describes the understanding of chatbots in the financial sector in terms of implementation, adoption intention, attitude toward use and acceptance; it also describes how people experience chatbots, specifically in terms of perception, expectation and trust, as well as how they are engaging and emotionally motivated; management of the security and privacy vulnerabilities of the chatbots; and identifies the potential strategies that can hinder the efficient, successful evolution of chatbots in the financial sector. Finally, the main findings regarding the use of text chatbots in the financial sector are presented; additionally, the open issues in current research are highlighted and a number of research opportunities that can be pursued in the future are suggested.&lt;/p&gt; &lt;/abstract&gt;
AI Service and Emotion

AI in service can be for routine mechanical tasks, analytical thinking tasks, or empathetic feeling tasks. We provide a conceptual framework for the customer, firm, and interactional use of AI for empathetic tasks at the micro-, meso-, and macro-levels. Emotions resulting from AI service interactions can include basic emotions (e.g., joy, sadness, and fear), self-conscious emotions (e.g., pride, guilt, embarrassment), and moral emotions (e.g., contempt, righteous anger, social disgust). These emotions are mostly likely to occur during frontline interactions in which both firms and customers use AI, a phenomenon called “AI as customer.” The analysis level of AI service and emotion can be at the macro-level in which AI is transforming the service economy into a feeling economy, at the meso-level in which firms can use “thoughtful AI” to make the employees’ and customers’ lives a little bit better by brightening their days, and at the micro-level in which customers can experience basic, self-conscious, and moral emotions from interactions with service AI.
Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection

This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms.
A Multi-AUV Maritime Target Search Method for Moving and Invisible Objects Based on Multi-Agent Deep Reinforcement Learning

Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving and invisible objects. In the MATSMI algorithm, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we add spatial and temporal information to the reinforcement learning state and set up specialized rewards in conjunction with a maritime target search scenario. Additionally, we construct a simulation environment to simulate a multi-AUV search for the floating object. The simulation results show that the MATSMI method has about 20% higher search success rate and about 70 steps shorter search time than the traditional search method. In addition, the MATSMI method converges faster than the MADDPG method. This paper provides a novel and effective method for solving the maritime target search problem.
DeconNet: End-to-End Decontaminated Network for Vision-Based Aerial Tracking

Vision-based aerial tracking has proven enormous potential in the field of remote sensing recently. However, challenges such as occlusion, fast motion, and illumination variation remain crucial issues for realistic aerial tracking applications. These challenges, frequently occurring from the aerial perspectives, can easily cause object feature pollution. With the contaminated object features, the credibility of trackers is prone to be substantially degraded. To address this issue, this work proposes a novel end-to-end decontaminated network, i.e., DeconNet, to alleviate object feature pollution efficiently and effectively. DeconNet mainly consists of downsampling and upsampling phases. Specifically, the decontaminated downsampling network first decreases the polluted object information with two convolution branches, enhancing the object location information. Subsequently, the decontaminated upsampling network applies the super-resolution technology to restore the object scale and shape information, with the low-to-high (LTH) encoder for further decontamination. In addition, the pooling distance (PD) loss function is carefully designed to improve the decontamination effect of the decontaminated downsampling network. Comprehensive evaluations on four well-known aerial tracking benchmarks validate the effectiveness of DeconNet. Especially, the proposed tracker has superior performance on the sequences with feature pollution. Besides, real-world tests on an aerial platform have proven the efficiency of DeconNet with 30.6 fps.
Day-ahead probabilistic forecasting at a co-located wind and solar power park in Sweden: Trading and forecast verification

This paper presents a first step in the field of probabilistic forecasting of co-located wind and photovoltaic (PV) parks. The effect of aggregation is analyzed with respect to forecast accuracy and value at a co-located park in Sweden using roughly three years of data. We use a fixed modelling framework where we post-process numerical weather predictions to calibrated probabilistic production forecasts, which is a prerequisite when placing optimal bids in the day-ahead market. The results show that aggregation improves forecast accuracy in terms of continuous ranked probability score, interval score and quantile score when compared to wind or PV power forecasts alone. The optimal aggregation ratio is found to be 50%–60% wind power and the remainder PV power. This is explained by the aggregated time series being smoother, which improves the calibration and produces sharper predictive distributions, especially during periods of high variability in both resources, i.e., most prominently in the summer, spring and fall. Furthermore, the daily variability of wind and PV power generation was found to be anti-correlated which proved to be beneficial when forecasting the aggregated time series. Finally, we show that probabilistic forecasts of co-located production improve trading in the day-ahead market, where the more accurate and sharper forecasts reduce balancing costs. In conclusion, the study indicates that co-locating wind and PV power parks can improve probabilistic forecasts which, furthermore, carry over to electricity market trading. The results from the study should be generally applicable to other co-located parks in similar climates.
The role of blockchain-enabled supply chain applications in improving supply chain performance: the case of Jordanian manufacturing sector

Purpose Blockchain technology has the potential to enhance information and knowledge management among members of the supply chain. This study aims to demonstrate the direct and indirect impact of blockchain adoption on achieving supply chain competitive advantage and improving innovation capabilities to achieve greater supply chain performance. Design/methodology/approach The study opted for a quantitative research approach. A structured questionnaire was used to collect data from 284 respondents from various Jordanian manufacturing firms. Smart PLS software was used to conduct structural equation modelling to test the proposed hypotheses. Findings The analysis revealed that investing in and using blockchain-enabled supply chain applications improves the ability to achieve higher levels of productivity, lead times, customer service and relationships with supply chain members. Moreover, blockchain technology integration with supply chain management processes has a positive impact on innovative activities that support supply chain operations. Finally, blockchain adoption has enhanced supply chain performance by reducing transaction costs, improving customer service and increasing the speed of supply chain operations. Research limitations/implications The study provides guidance for decision-makers and academicians on the use of blockchain adoption to improve supply chain performance. Future studies should use a larger sample size and random sampling techniques to achieve better generalizability of the results. Originality/value The study fulfils an identified gap in the application of blockchain technology to improving supply chain performance within a broader context that encompasses supply chain innovation capabilities and competitive advantage.
NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory

The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wireless communication and artificial intelligence. Federated learning (FL) as a privacy preserving machine learning framework has received extensive attention. From the perspective of game theory, FL can be regarded as a process in which multiple participants play games against each other to maximize their own interests. This requires that the user’s data is not leaked during the training process. However, existing studies have proved that the privacy protection capability of FL is insufficient. In addition, the existing way of realizing privacy protection through multiple rounds of communication between participants increases the burden of wireless communication. To this end, this paper considers the security model of FL based on game theory, and proposes our scheme, NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme in wireless communication environments. The NVAS can protect user privacy during FL training without unnecessary interaction between participants, which can better motivate more participants to join and provide high-quality training data. Furthermore, we designed a concise and efficient verification algorithm to ensure the correctness of model aggregation. Finally, the security and feasibility of the scheme are analyzed.
Estimating High-resolution Surface Normals via Low-resolution Photometric Stereo Images

Acquiring high-resolution 3D surface structures is a crucial task in computer vision as it provides more detailed surface textures and clearer structures. Photometric stereo can measure per-pixel surface normals of a 3D object using various shading cues. However, obtaining high-resolution images in a linear response photometric stereo imaging system can be challenging. Additionally, photometric stereo, as a per-pixel reconstruction method, requires higher-resolution surface normal maps to accurately depict complex surface structures, particularly in regions that demand more attention and precise reconstruction. Therefore, measuring high-resolution surface normals via low-resolution photometric stereo images is of great importance. Motivated by these, we propose a Super-resolution Photometric Stereo Network, namely SR-PSN. In order to address the issues of measuring the high-resolution surface normals from low-resolution photometric images, we mainly (1) apply a dual-position threshold normalization pre-processing scheme to effectively handle the spatially-varying reflectance of non-Lambertian surfaces, (2) adopt a local affinity feature module to learn the rich structural representation by explicitly revealing the neighbor relationships, (3) employ a parallel multi-scale feature extractor, which preserves high-resolution representations and deep feature extraction, and (4) propose a shared-weight regressor to handle the multi-scale features, to prevent the model collapsing into learning non-important features related to a certain fixed scale. Extensive ablation experiments validate the effectiveness of our proposed modules. Furthermore, quantitative experiments conducted on public benchmarks demonstrate that SR-PSN outperforms state-of-the-art calibrated photometric stereo methods. Notably, SR-PSN achieves superior results while utilizing photometric stereo images with only half the resolution of other methods. It effectively restores the structure of complex surfaces, producing a high-resolution normal map.
ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics

The grammar of graphics is ubiquitous, providing the foundation for a variety of popular visualization tools and toolkits. Yet support for uncertainty visualization in the grammar graphics-beyond simple variations of error bars, uncertainty bands, and density plots-remains rudimentary. Research in uncertainty visualization has developed a rich variety of improved uncertainty visualizations, most of which are difficult to create in existing grammar of graphics implementations. ggdist, an extension to the popular ggplot2 grammar of graphics toolkit, is an attempt to rectify this situation. ggdist unifies a variety of uncertainty visualization types through the lens of distributional visualization, allowing functions of distributions to be mapped to directly to visual channels (aesthetics), making it straightforward to express a variety of (sometimes weird!) uncertainty visualization types. This distributional lens also offers a way to unify Bayesian and frequentist uncertainty visualization by formalizing the latter with the help of confidence distributions. In this paper, I offer a description of this uncertainty visualization paradigm and lessons learned from its development and adoption: ggdist has existed in some form for about six years (originally as part of the tidybayes R package for post-processing Bayesian models), and it has evolved substantially over that time, with several rewrites and API re-organizations as it changed in response to user feedback and expanded to cover increasing varieties of uncertainty visualization types. Ultimately, given the huge expressive power of the grammar of graphics and the popularity of tools built on it, I hope a catalog of my experience with ggdist will provide a catalyst for further improvements to formalizations and implementations of uncertainty visualization in grammar of graphics ecosystems. A free copy of this paper is available at https://osf.io/2gsz6. All supplemental materials are available at https://github.com/mjskay/ggdist-paper and are archived on Zenodo at doi:10.5281/zenodo.7770984.