Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Framework for Supervision of Livelihood Issues: Proof of Concept With Optimized Consensus

Supervision of Livelihood Issues (SLI) refers to the supervision and participation of the general public in government actions and public affairs, with a particular focus on issues closely related to people’s lives. However, current regulatory systems often suffers from information imbalances, low authenticity, and credibility. To address these challenges, this study proposes a blockchain-based SLI framework that leverages the decentralized, tamper-proof, traceable, and transparent nature of blockchain technology to provide a reliable platform for SLI. Furthermore, we designed an information data structure on the blockchain and presented the information flow process. We developed smart contracts that utilize automation capability to automatically aggregate, analyze, and publish warnings based on on-chain data, enabling the evaluation of relevant departments’ performance. Moreover, in the context of SLI applications, we propose an optimized consensus algorithm called the SLI-PBFT. This algorithm is based on the PBFT consensus protocol and incorporates dynamic scalability mechanisms, scoring models, and simplified consistency protocols to enhance the consensus efficiency of SLI applications. Finally, the experiments were conducted. We developed an SLI prototype system and compared and analyzed the performance of SLI-PBFT with other PBFT-based consensus algorithms within the SLI prototype system. The experimental results demonstrated the feasibility of the SLI framework. Moreover, SLI-PBFT exhibits significant improvements in throughput and reduced latency compared to other algorithms in the context of SLI applications. In addition, they possess strong fault tolerances and security capabilities. This study provides theoretical and practical guidance for the SLI in real-world scenarios.
An Intelligent Decision Support System Based on Multi Agent Systems for Business Classification Problem

The development of e-systems has given consumers and businesses access to a plethora of information, which has complicated the process of decision making. Document classification is one of the main decisions that any business adopts in their decision making to categorize documents into groups according to their structure. In this paper, we combined multi-agent systems (MASs), which is one of the IDSS systems, with Bayesian-based classification to filter out the specialization, collaboration, and privacy of distributed business sources to produce an efficient distributed classification system. Bayesian classification made use of MAS to eliminate distributed sources’ specialization and privacy. Therefore, incorporating the probabilities of various sources is a practical and swift solution to such a problem, where this method works the same when all the data are merged into a single source. Each intelligent agent can collaborate and ask for help from other intelligent agents in classifying cases that are difficult to classify locally. The results demonstrate that our proposed technique is more accurate than those of the non-communicated classification, where the results proved the ability of the utilized productive distributed classification system.
Collective prompt tuning with relation inference for document-level relation extraction

Document-level relation extraction (RE) aims to extract the relation of entities that may be across sentences. Existing methods mainly rely on two types of techniques: Pre-trained language models (PLMs) and reasoning skills. Although various reasoning methods have been proposed, how to elicit learnt factual knowledge from PLMs for better reasoning ability has not yet been explored. In this paper, we propose a novel Collective Prompt Tuning with Relation Inference (CPT-RI) for Document-level RE, that improves upon existing models from two aspects. First, considering the long input and various templates, we adopt a collective prompt tuning method, which is an update-and-reuse strategy. A generic prompt is first encoded and then updated with exact entity pairs for relation-specific prompts. Second, we introduce a relation inference module to conduct global reasoning overall relation prompts via constrained semantic segmentation. Extensive experiments on two publicly available benchmark datasets demonstrate the effectiveness of our proposed CPT-RI as compared to the baseline model (ATLOP (Zhou et al., 2021)), which improve the 0.57% on the DocRED dataset, 2.20% on the CDR dataset, and 2.30 on the GDA dataset in the F1 score. In addition, further ablation studies also verify the effects of the collective prompt tuning and relation inference.
Powered education based on Metaverse: Pre- and post-COVID comprehensive review

Abstract In recent years, an increasing interest has been in applying the Metaverse to create unique educational settings. This article reports a review of the literature on Metaverse and its application in higher educational settings to understand its definitive features, evolution from ideation to higher educational use cases, accessibility and affordability, ethical issues, current trends, and future research needs for effective and efficient Metaverse application in higher educational settings. A total of 58 publications between 1992 and 2022 retrieved from Google Scholar and other online portals were reviewed. The review was carried out using Nvivo software for qualitative data analysis to identify and extract the main themes from the reviewed papers, which were then organized thematically, discussed, and presented in the current article. The findings from this review provide an overview of the development of the Metaverse and its application in higher education from a pre-Covid and post-Covid perspective. It highlights the current state of research on applications of the Metaverse in higher education and the development of ten Metaversities funded by Meta, formerly Facebook. Furthermore, the article discusses the main concerns around adopting the Metaverse, which is mainly ethical issues such as data privacy, network security, netiquette, digital citizenship, copyright, and policies.
Interpretation of Drop Size Predictions from a Random Forest Model Using Local Interpretable Model-Agnostic Explanations (LIME) in a Rotating Disc Contactor

Drop size is a crucial parameter for the efficient design and operation of the rotating disc contactor (RDC) in liquid–liquid extraction. The current work focuses on providing local and global explanations for the prediction of the drop size in a rotating disc contactor (RDC). The Random Forest (RF) regression model is a robust machine learning algorithm that can accurately capture complex relationships in the data. However, the interpretability of the model is limited. In order to address the issue of interpretability of the developed RF model, in the current work, we employed Local Interpretable Model-Agnostic Explanations (LIME) of the predictions of the RF model. This provides both local and global views of the model and thereby helps one to gain insights into the factors influencing predictions. We have provided local explanations depicting the impact of different attributes on the prediction of the output for any given input example. We have also obtained global feature importance, providing the top subset of informative attributes. We have also developed local surrogate models incorporating second order attribute interactions. This has provided important information about the effect of interactions on the drop size prediction. By augmenting the random forest model with LIME, it is possible to develop a more accurate and interpretable model for estimating the drop size in RDCs, ultimately leading to improved performance and efficiency.
Effect of temporal distance and goal type on predictions of future information security: Focus on moderation of self-efficacy and social responsibility

We investigate how predictions of future information security vary according to temporal distance, goal type, and personal characteristics. A total of 368 participants rated the likelihood of achieving the two-goal types at present, and then one and ten years later. The future information security bias was found to be greater for a distant place than shortly, and it was found to be greater for private goal types than public goal types. Personal characteristics were classified into self-efficacy and social responsibility, and their effectiveness was verified. A self-efficacy test revealed that the information security bias for the two types of goals was higher when it was higher than the low self-efficacy group. In the case of social responsibility, the bias was found in the higher group than the lower group only in the public goal type, but not the private goal type. We conclude by presenting some implications for information security policy and suggestions for further research.
Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers

<h2>Summary</h2><h3>Background</h3> For patients with early-stage breast cancers, neoadjuvant treatment is recommended for non-luminal tumors instead of luminal tumors. Preoperative distinguish between luminal and non-luminal cancers at early stages will facilitate treatment decisions making. However, the molecular immunohistochemical subtypes based on biopsy specimens are not always consistent with final results based on surgical specimens due to the high intra-tumoral heterogeneity. Given that, we aimed to develop and validate a deep learning radiopathomics (DLRP) model to preoperatively distinguish between luminal and non-luminal breast cancers at early stages based on preoperative ultrasound (US) images, and hematoxylin and eosin (H&E)-stained biopsy slides. <h3>Methods</h3> This multicentre study included three cohorts from a prospective study conducted by our team and registered on the Chinese Clinical Trial Registry (ChiCTR1900027497). Between January 2019 and August 2021, 1809 US images and 603 H&E-stained whole slide images (WSIs) from 603 patients with early-stage breast cancers were obtained. A Resnet18 model pre-trained on ImageNet and a multi-instance learning based attention model were used to extract the features of US and WSIs, respectively. An US-guided Co-Attention module (UCA) was designed for feature fusion of US and WSIs. The DLRP model was constructed based on these three feature sets including deep learning US feature, deep learning WSIs feature and UCA-fused feature from a training cohort (1467 US images and 489 WSIs from 489 patients). The DLRP model's diagnostic performance was validated in an internal validation cohort (342 US images and 114 WSIs from 114 patients) and an external test cohort (270 US images and 90 WSIs from 90 patients). We also compared diagnostic efficacy of the DLRP model with that of deep learning radiomics model and deep learning pathomics model in the external test cohort. <h3>Findings</h3> The DLRP yielded high performance with area under the curve (AUC) values of 0.929 (95% CI 0.865–0.968) in the internal validation cohort, and 0.900 (95% CI 0.819–0.953) in the external test cohort. The DLRP also outperformed deep learning radiomics model based on US images only (AUC 0.815 [0.719–0.889], <i>p =</i> 0.027) and deep learning pathomics model based on WSIs only (AUC 0.802 [0.704–0.878], <i>p =</i> 0.013) in the external test cohort. <h3>Interpretation</h3> The DLRP can effectively distinguish between luminal and non-luminal breast cancers at early stages before surgery based on pretherapeutic US images and biopsy H&E-stained WSIs, providing a tool to facilitate treatment decision making in early-stage breast cancers. <h3>Funding</h3> Natural Science Foundation of Guangdong Province (No. 2023A1515011564), and National Natural Science Foundation of China (No. 91959127; No. 81971631).
SMS Spam Detection Using Deep Learning Techniques: A Comparative Analysis of DNN Vs LSTM Vs Bi-LSTM

SMS spam detection is crucial for ensuring text messaging systems' security and usability. Spam messages, also known as unwanted text messages, can be a nuisance and be used for phishing scams and other malicious activities. In this research, we analyze various SMS spam detection methods and their efficiency in recognizing spam messages. Three machine learning models-LSTM, a DNN, and a Bi-LSTM model have been used to examine the data. We found that our proposed methods are highly effective at detecting spam messages with high accuracy through our experiments on a dataset of SMS text messages. Of the three models, the DNN has produced the highest accuracy of 95.6522%, followed by Bi-LSTM with an accuracy of 93.9799% and LSTM with an accuracy of 92.9766%.
Advanced Genetic Programming vs. State-of-the-Art AutoML in Imbalanced Binary Classification

The objective of this article is to provide a comparative analysis of two novel genetic programming (GP) techniques, differentiable Cartesian genetic programming for artificial neural networks (DCGPANN) and geometric semantic genetic programming (GSGP), with state-of-the-art automated machine learning (AutoML) tools, namely Auto-Keras, Auto-PyTorch and Auto-Sklearn. While all these techniques are compared to several baseline algorithms upon their introduction, research still lacks direct comparisons between them, especially of the GP approaches with state-of-the-art AutoML. This study intends to fill this gap in order to analyze the true potential of GP for AutoML. The performances of the different tools are assessed by applying them to 20 benchmark datasets of the imbalanced binary classification field, thus an area that is a frequent and challenging problem. The tools are compared across the four categories average performance, maximum performance, standard deviation within performance, and generalization ability, whereby the metrics F1-score, G-mean, and AUC are used for evaluation. The analysis finds that the GP techniques, while unable to completely outperform state-of-the-art AutoML, are indeed already a very competitive alternative. Therefore, these advanced GP tools prove that they are able to provide a new and promising approach for practitioners developing machine learning (ML) models. Doi: 10.28991/ESJ-2023-07-04-021 Full Text: PDF
Reservoir Computing and Task Performing through Using High-β Lasers with Delayed Optical Feedback

Nonlinear photonic sources including semiconductor lasers have been recently utilized as ideal computation elements for information processing.They supply energy-efficient way and rich dynamics for classification and recognition tasks.In this work, we propose and numerically study the dynamics of complex photonic systems including high-β laser element with delayed feedback and functional current modulation, and employ nonlinear laser dynamics of near-threshold region for the application in time-delayed reservoir computing.The results indicate a perfect (100%) recognition accuracy for the pattern recognition task and an accuracy about 98% for the Mackey-Glass chaotic sequences prediction.Therefore, the system shows an improvement of performance with low-power consumption.In particular, the error rate is an order of magnitude smaller than previous works.Furthermore, by changing the DC pump, we are able to modify the number of spontaneous emission photons of the system, which then allows us to explore how the laser noise impacts the performance of the reservoir computing system.Through manipulating these variables, we show a deeper understanding on the proposed system, which is helpful for the practical applications of reservoir computing.
Hybrid Deep Learning Model for Mean Hourly Irradiance Probabilistic Forecasting

For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with measured meteorological data in order to account for the short-term variability of solar irradiance, which is mostly caused by the presence of clouds in the sky. Additionally, deep learning models like the multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridized forms are widely used for deterministic solar irradiance forecasting. The implementation of probabilistic solar irradiance forecasting, which is gaining prominence in grid management since it offers information on the likelihood of different outcomes, is another task we carry out using quantile regression. The novelty of this paper lies in the combination of a hybrid deep learning model (CNN-LSTM) with quantile regression for the computation of prediction intervals at different confidence levels. The training of the different machine learning algorithms is performed over a year’s worth of sky images and meteorological data from the years 2019 to 2020. The data were measured at the University of French Polynesia (17.5770° S, 149.6092° W), on the island of Tahiti, which has a tropical climate. Overall, the hybrid model (CNN-LSTM) is the best performing and most accurate in terms of deterministic and probabilistic metrics. In addition, it was found that the CNN, LSTM, and ANN show good results against persistence.
Cross-Domain Disentangled Learning for E-Commerce Live Streaming Recommendation

E-commerce live streaming as an increasingly popular sales model has generated a significant amount of gross merchandise value (GMV) for e-commerce platforms. Live streaming recommendation systems (LSRS) of e-commerce aim to recommend the most appropriate live channels for users to motivate them to buy products. Existing LSRS methods focus only on the user’s interaction behaviors on the live channel (live domain) while ignoring the user’s behaviors and intentions on the e-commerce product (product domain). As a result, the user’s consistent purchase intentions in the cross-domain are not being fully captured, especially when user present differentiated purchase intentions in the cross-domain. How to disentangle user’s consistent intentions and domain-specific intentions in the cross-domain poses a challenge to the LSRS of e-commerce platforms. In this paper, we present a live channel recommendation method, named eLiveRec, developed for Taobao, one of the largest e-commerce platform in the world. Specifically, eLiveRec employs the disentangled encoder module to learn user’s cross-domain consistent intentions and domain-specific intentions. Then, an adaptive multi-task learning framework is developed to jointly optimize the multiple objectives (e.g., stay time, click goods bag, and click products after entering channel) related to live streaming recommendation. In this way, the performance of live streaming recommendation can be further improved and con-form to standard industry RS paradigms. Extensive experiments are conducted on a large-scale industry dataset collected from Taobao Live platform have been performed. Both online and offline experimental results indicate that eLiveRec consistently outperforms existing state-of-the-art baseline methods.
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation

Longitudinal analysis in medical imaging is crucial to investigate the progressive changes in anatomical structures or disease progression over time. In recent years, a novel class of algorithms has emerged with the goal of learning disease progression in a self-supervised manner, using either pairs of consecutive images or time series of images. By capturing temporal patterns without external labels or supervision, longitudinal self-supervised learning (LSSL) has become a promising avenue. To better understand this core method, we explore in this paper the LSSL algorithm under different scenarios. The original LSSL is embedded in an auto-encoder (AE) structure. However, conventional self-supervised strategies are usually implemented in a Siamese-like manner. Therefore, (as a first novelty) in this study, we explore the use of Siamese-like LSSL. Another new core framework named neural ordinary differential equation (NODE). NODE is a neural network architecture that learns the dynamics of ordinary differential equations (ODE) through the use of neural networks. Many temporal systems can be described by ODE, including modeling disease progression. We believe that there is an interesting connection to make between LSSL and NODE. This paper aims at providing a better understanding of those core algorithms for learning the disease progression with the mentioned change. In our different experiments, we employ a longitudinal dataset, named OPHDIAT, targeting diabetic retinopathy (DR) follow-up. Our results demonstrate the application of LSSL without including a reconstruction term, as well as the potential of incorporating NODE in conjunction with LSSL.
The Impact of Leadership Styles on Employee Loyalty and Engagement

The behavior and characteristics of a managerial leader play a crucial role in fostering employee loyalty and enhancing engagement. Effective people-centered communication boosts employees' confidence and facilitates their ongoing development, while trust in management encourages open and transparent communication. As a result, employees feel a sense of pride in the organization and are motivated to demonstrate unwavering dedication to their work. To investigate the impact of leadership styles on employee loyalty and engagement, a survey was conducted by randomly selecting participants from the employees of two insurance companies. The survey involved 153 employees, and the statistical analyses involved factor analysis, reliability analysis, correlation analysis, and structural equation models (SEMs). Based on the analysis results, it was observed that among the study participants, the transformational leadership style exhibited a strong and positive impact on both employee loyalty and engagement. Similarly, the transactional leadership style demonstrated a moderate yet positive influence on employee loyalty and engagement. Furthermore, a strong positive relationship was identified between employee loyalty and engagement, indicating that higher levels of loyalty contribute significantly to increased employee engagement.
An Exploratory Approach for Game Engine Architecture Recovery

Game engines provide video game developers with a wide range of fundamental subsystems for creating games, such as 2D/3D graphics rendering, input device management, and audio playback. Developers often integrate these subsystems with other applications or extend them via plugins. To integrate or extend correctly, developers need a broad system architectural understanding. However, architectural information is not always readily available and is often overlooked in this kind of system. In this work, we propose an approach for game engine architecture recovery and explore the architecture of three popular open-source game engines (Cocos2d-x, Godot, and Urho3D). We perform manual subsystem detection and use Moose, a platform for software analysis, to generate architectural models. With these models, we answer the following questions: Which subsystems are present in game engines? Which subsystems are more often coupled with one another? Why are these subsystems coupled with each other? Results show that the platform independence, resource management, world editor, and core subsystems are frequently included by others and therefore act as foundations for the game engines. Furthermore, we show that, by applying our approach, game engine developers can understand whether subsystems are related and divide responsibilities. They can also assess whether relationships among subsystems are appropriate for the game engine.