Reviewerly/exHarmony huggingface dataset:
Federated learning based futuristic biomedical big-data analysis and standardization

Medical data processing and analytics exert significant influence in furnishing dependable decision support for prospective biomedical applications. Given the sensitive nature of medical data, specialized techniques and frameworks tailored for application-centric processing are imperative. This article presents a conceptualization for the analysis and uniformitarian of datasets through the implementation of Federated Learning (FL). The realm of medical big data stems from diverse origins, necessitating the delineation of data provenance and attribute paradigms to facilitate feature extraction and dependency assessment. The architecture governing the data collection framework is intricately linked to remote data transmission, thereby engendering efficient customization oversight. The operational methodology unfolds across four strata: the data origin layer, data acquisition layer, data classification layer, and data optimization layer. Central to this endeavor are multi-objective optimal datasets (MooM), characterized by attribute-driven feature cartography and cluster categorization through the conduit of federated learning models. The orchestration of feature synchronization and parameter extraction transpires across multiple tiers of neural networking, culminating in the provisioning of a steadfast remedy through dataset standardization and labeling. The empirical findings reflect the efficacy of the proposed technique, boasting an impressive 97.34% accuracy rate in the disentanglement and clustering of telemedicine data, facilitated by the operational servers within the ambit of the federated model.
Toward automatic generation of control structures for process flow diagrams with large language models

Abstract Developing Piping and Instrumentation Diagrams (P&amp;IDs) is a crucial step during process development. We propose a data‐driven method for the prediction of control structures. Our methodology is inspired by end‐to‐end transformer‐based human language translation models. We cast the control structure prediction as a translation task where Process Flow Diagrams (PFDs) without control structures are translated to PFDs with control structures. We represent the topology of PFDs as strings using the SFILES 2.0 notation. We pretrain our model using generated PFDs to learn the grammatical structure. Thereafter, the model is fine‐tuned leveraging transfer learning on real PFDs. The model achieved a top‐5 accuracy of 74.8% on 10,000 generated PFDs and 89.2% on 100,000 generated PFDs. These promising results show great potential for AI‐assisted process engineering. The tests on a dataset of 312 real PFDs indicate the need for a larger PFD dataset for industry applications and hybrid artificial intelligence solutions.
How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events

Social media moderation policies are often at the center of public debate, and their implementation and enactment are sometimes surrounded by a veil of mystery. Unsurprisingly, due to limited platform transparency and data access, relatively little research has been devoted to characterizing moderation dynamics, especially in the context of controversial events and the platform activity associated with them. Here, we study the dynamics of account creation and suspension on Twitter during two global political events: Russia's invasion of Ukraine and the 2022 French Presidential election. Leveraging a large-scale dataset of 270M tweets shared by 16M users in multiple languages over several months, we identify peaks of suspicious account creation and suspension, and we characterize behaviors that more frequently lead to account suspension. We show how large numbers of accounts get suspended within days of their creation. Suspended accounts tend to mostly interact with legitimate users, as opposed to other suspicious accounts, making unwarranted and excessive use of reply and mention features, and sharing large amounts of spam and harmful content. While we are only able to speculate about the specific causes leading to a given account suspension, our findings contribute to shedding light on patterns of platform abuse and subsequent moderation during major events.
Construction Risk Assessment of Yellow River Bridges Based on Combined Empowerment Method and Two-Dimensional Cloud Model

(1) In recent years, the economy of the Yellow River basin in China has developed rapidly, and a series of large bridges across the Yellow River have been built on both sides of the Yellow River, which has brought great convenience to regional socio-economic activities. However, risk events are prone to occur during the construction of bridges across the Yellow River, which affect the safety of the bridges’ structure. Therefore, it is necessary to establish a new scientific risk assessment system for the construction safety of bridges across the Yellow River. (2) Methods: Firstly, based on the construction safety risk assessment index system of bridges across the Yellow River, the cloud AHP method and the cloud entropy weight method are used to determine the subjective weight and objective weight of risk indexes, and then the game theory combination weighting method is used to determine the comprehensive weight of each risk index, and then the digital characteristic values of the risk probability cloud and the consequence cloud are calculated and input into the forward cloud generator algorithm. MATLAB was used to generate a two-dimensional comprehensive cloud map, which was visually compared with the standard cloud map, and the probability level and consequence level of each risk index were preliminarily obtained. Finally, the risk assessment matrix was used for comprehensive risk evaluation. (3) Results: Applying the method to the construction safety risk assessment of the Jiaoping Expressway, the overall construction safety risk level of the Yellow River bridge was determined as level 4, and the risk levels of the four primary indicators were: personnel risk (level 3), material and equipment risk (level 4), construction technology risk (level 5), and construction environment risk (level 4). (4) Conclusions: The results of the risk evaluation are consistent with the actual construction state of the bridge, which shows that game theory’s combination of empowerment with a two-dimensional cloud model is scientific and effective when applied to the construction safety risk evaluation of Yellow River bridges.
An experimental study on reading in high‐immersion virtual reality

Abstract High‐immersion virtual reality (VR) is an increasingly valued environment for language learners. Although reading constitutes a core language skill, practicing reading in VR has received little attention. In this between‐subject, quantitative study, 79 intermediate learners of English at a German university were randomly assigned to view an interactive, multimedia‐rich story under two conditions. In the experimental condition, subjects (the VR group) experienced the story using a high‐immersion VR headset. In the comparison condition, subjects (the video group) watched a video recording (ie, screencast) of the same VR experience. In both conditions, the story was presented using identical captions in English, which served as the reading comprehension text. In addition to measuring the impact of VR on reading comprehension, validated questionnaires yielded data on the subjects' intrinsic motivation, sense of presence and cognitive load. The analysis produced several findings. First, t ‐tests revealed the VR group was associated with statistically significantly higher reading comprehension scores compared with the video group ( p = 0.03). Second, Wilcoxon signed‐rank tests showed that the VR treatment elicited statistically significantly higher levels of motivation ( p ≤ 0.0001) and sense of presence ( p ≤ 0.0001). Cognitive load was not significantly different between the groups ( p = 0.22). Our main implication is that VR can be beneficial for reading practice. Practitioner notes What is already known about this topic High‐immersion virtual reality (VR) is engaging and motivational. Engaging students to practice reading is important. Research on practicing reading in VR has received little attention. What this paper adds It examines the effectiveness of using VR for practicing reading. It shows that VR enhances motivation and the sense of presence when reading. Students report equal levels of cognitive load while reading subtitles in VR and reading subtitles in videos. Implications for practice and/or policy Our main implication is that VR can be beneficial for reading practice. VR could be used as a motivational tool to engage students in reading activities.
Industrial data monetization: A blockchain-based industrial IoT data trading system

The Internet of Things (IoT) devices utilized in manufacturing industries produce vast volumes of valuable data that can revolutionize productivity and sustainability. While existing centralized data marketplaces facilitate data trading, they are often marred by trust issues, a single point of failure, and significant security and privacy vulnerabilities. Addressing these critical shortcomings, this paper introduces a blockchain-based industrial data trading system that not only ensures secure and transparent data trading but also offers advantages over conventional systems. Unlike traditional marketplaces, our proposed system emphasizes trustworthiness by mitigating third-party risks, enhancing data integrity through decentralized storage, and employing graph technology for efficient blockchain querying. Further, with integrated access control, our system elevates security standards. Preliminary evaluations reveal the system's potential to offer a more secure, transparent, auditable, and trustworthy data trading environment, distinctly outpacing the capabilities of current marketplaces.
HAM-Transformer: A Hybrid Adaptive Multi-Scaled Transformer Net for Remote Sensing in Complex Scenes

The quality of remote sensing images has been greatly improved by the rapid improvement of unmanned aerial vehicles (UAVs), which has made it possible to detect small objects in the most complex scenes. Recently, learning-based object detection has been introduced and has gained popularity in remote sensing image processing. To improve the detection accuracy of small, weak objects in complex scenes, this work proposes a novel hybrid backbone composed of a convolutional neural network and an adaptive multi-scaled transformer, referred to as HAM-Transformer Net. HAM-Transformer Net firstly extracts the details of feature maps using convolutional local feature extraction blocks. Secondly, hierarchical information is extracted, using multi-scale location coding. Finally, an adaptive multi-scale transformer block is used to extract further features in different receptive fields and to fuse them adaptively. We implemented comparison experiments on a self-constructed dataset. The experiments proved that the method is a significant improvement over the state-of-the-art object detection algorithms. We also conducted a large number of comparative experiments in this work to demonstrate the effectiveness of this method.
Small Latency Variations Do Not Affect Player Performance in First-Person Shooters

In interactive systems high latency affects user performance and experience. This is especially problematic in video games. A large number of studies on this topic investigated the effects of constant, high latency. However, in practice, latency is never constant but varies by up to 100 ms due to variations in processing time and delays added by polling between system components. In a large majority of studies, these variations in latency are neither controlled for nor reported. Thus, it is unclear to which degree small, continuous variations in latency affect user performance. If these unreported variations had a significant impact, this might cast into doubt the findings of some studies. To investigate how latency variation affects player performance and experience in games, we conducted an experiment with 28 participants playing a first-person shooter. Participants played with two levels of base latency (50 ms vs. 150 ms) and variation (0 ms vs. 50 ms). As expected, high base latency significantly reduces player performance and experience. However, we found strong evidence that small variations in latency in the order of 50 ms, do not affect player performance significantly. Thus, our findings mitigate concerns that previous latency studies might have systematically ignored a confounding effect.
Dual conditional GAN based on external attention for semantic image synthesis

Although the existing semantic image synthesis methods based on generative adversarial networks (GANs) have achieved great success, the quality of the generated images still cannot achieve satisfactory results. This is mainly caused by two reasons. One reason is that the information in the semantic layout is sparse. Another reason is that a single constraint cannot effectively control the position relationship between objects in the generated image. To address the above problems, we propose a dual-conditional GAN with based on an external attention for semantic image synthesis (DCSIS). In DCSIS, the adaptive normalization method uses the one-hot encoded semantic layout to generate the first latent space and the external attention uses the RGB encoded semantic layout to generate the second latent space. Two latent spaces control the shape of objects and the positional relationship between objects in the generated image. The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. Experiments on several challenging datasets demonstrate the advantages of our method over existing approaches, regarding both visual quality and the representative evaluating criteria.
Extractive social media text summarization based on MFMMR-BertSum

The advancement of computer technology has led to an overwhelming amount of textual information, hindering the efficiency of knowledge intake. To address this issue, various text summarization techniques have been developed, including statistics, graph sorting, machine learning, and deep learning. However, the rich semantic features of text often interfere with the abstract effects and lack effective processing of redundant information. In this paper, we propose the Multi-Features Maximal Marginal Relevance BERT (MFMMR-BertSum) model for Extractive Summarization, which utilizes the pre-trained model BERT to tackle the text summarization task. The model incorporates a classification layer for extractive summarization. Additionally, the Maximal Marginal Relevance (MMR) component is utilized to remove information redundancy and optimize the summary results. The proposed method outperforms other sentence-level extractive summarization baseline methods on the CNN/DailyMail dataset, thus verifying its effectiveness.
Classification of Lung and Colon Cancer Histopathological Images Using Global Context Attention Based Convolutional Neural Network

The malignant neoplastic malady known as cancer appears to exhibit a significantly elevated rate of mortality owing to its virulence and pronounced propensity for metastasis. To augment the diagnostic efficacy, research endeavors have been undertaken utilizing complex deep learning architectures. However, the performance of these efforts remains circumscribed by smaller dataset size, quality of the data, the interclass variations present between lung adenocarcinoma and lung squamous cell carcinoma, and the complexity of deploying to mobile devices and failure to address both image and patient level accuracy measurements. To surmount these obstacles, the present study proposes a stage-based method for enhancing the images, in conjunction with utilizing a global context attention-guided convolutional neural network that effectively captures both channel and spatial information and semantic information extracted from the input image. Implementing the proposed methodology increased total image level accuracy to 99.76% and a patient level accuracy of 96.5%, a metric that has yet to be previously quantified. The addition of the global context attention module decreases the model’s parameter count by 0.47 million, reduces the computational costs by saving 10.54 million floating point operations per second (FLOPs) and 10.72 million multiply-accumulate operations (MACs), and results in a 0.03s improvement in inference time. Furthermore, this module enhances both image level and patient level accuracy, boosting them by 2.84% and 3.17%, respectively, compared to using only the convolutional block attention module in the baseline convolutional neural network. Consequently, this modification renders the model highly suitable for deployment on mobile devices due to its adaptability. Our findings provide supporting evidence for the potential of this method to serve as a noninvasive screening tool capable of reliably classifying lung and colon cancer subtypes.
Estimation of the distribution patterns of heavy metal in soil from airborne hyperspectral imagery based on spectral absorption characteristics

Though soil is widely known as one of the most valuable resources for the world, its quality is going to be lower because of unsustainable economic development and social progress. Therefore, it is important for us to monitor and evaluate the quality of soil, especially its heavy metal contents which is too scarce to identify in soil spectra easily but poisonous enough to affect human health in a long run. Most of the existing estimation methods have based the characteristic bands on statistical analysis to a large extent, which is hard to accurately explain the retrieval mechanism. In this paper, the absorption characteristics of heavy metal are studied based on the soil spectra, and the distribution pattern is mapped in a large-scale continuous space, for environmental monitoring and further decision support. Taking Yitong County, China as the study area. After spectra continuum removal, the heavy metal contents were estimated by 11 features including the absorption depth, absorption area, and band ratio around 2200 nm, which showed the best performance. For arsenic (As), the best model yields Rp2 value of 0.8474, and the RMSEP value is 36.1542 (mg/kg). It is concluded that As is adsorbed by organic matter, clay minerals, and iron/manganese oxides in soil, and the adsorption of As by first two components is greater than that of the last. For airborne spectra after continuum removal, combining the spectral absorption characteristic parameters and the highly correlated bands is more accurate than using the spectral absorption characteristic parameters or bands alone. AdaBoost is presented for the heavy metal estimation, and the fitting ability of the method is found to be stronger than that of the traditional classical methods, with the Rp2 values of 0.6242 and the RMSEP value of 43.6481 (mg/kg). In summary, these results will provide a prospective basis for the rapid estimation of soil heavy metals, the risk assessment of soil heavy metals and soil environmental monitoring in a large scale.
Separation of organic compound from water using membrane process: Hybrid machine learning-based modeling and validation

Modeling was performed for separation of organic compounds from aqueous solution using porous membranes. Ensemble methods based on decision trees have gained significant attention in the field of predictive modeling due to their capability to capture complex relationships and handle high-dimensional data. In this study, we explore the effectiveness of three popular ensemble methods, namely Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB), for predicting the concentration of a chemical compound (C) in membrane system, using the radial distance (r) and axial distance (z) as input features. To optimize the performance of the ensemble models, we employ a novel hyper-parameter meta-heuristic technique called Political Optimizer (PO). Furthermore, to ensure the robustness of the models, we incorporate an outlier detection mechanism based on the Z-Score. The experimental results demonstrate the efficacy of the ensemble methods in predicting the concentration of the chemical compound. The PO-ET model achieves an impressive coefficient of determination (R2) score of 0.999, with a root mean squared error (RMSE) of 0.021 and a mean absolute percentage error (MAPE) of 6.60e+11. Similarly, the PO-GB and PO-RF models exhibit strong predictive performance, with R2 scores of 0.998 and 0.997, respectively, and corresponding RMSE and MAPE values.
dMCLAEKS: Pairing-free designated-tester multi-recipient certificateless authenticated encryption with keyword search for concealing search patterns

Modern Industrial Internet of Things (IIoT) technologies, such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.0, can be leveraged to promote intelligent management. Yet, IIoT systems create enormous volumes of data that must be outsourced to the cloud for storage and to provide end-users with search capabilities. The outsourcing of IIoT data to a third-party cloud service provider (CSP) creates a number of data leakage issues. Recently, Certificateless Authenticated Encryption with Keyword Search (CLAEKS) was proposed to address the issue of encrypted data querying in cloud-assisted IIoT. But so far, the majority of existing techniques depend on expensive bilinear pairing, and they are all single-receiver. In addition, these methods are extremely vulnerable to keyword guessing attacks and do not hide the search pattern, making it simple for an attacker to deduce the search content. In this paper, we present a rapid and trustworthy designed-tester multi-recipient CLAEKS (dMCLAEKS) protocol with the numerous advantages: (1) no expensive bilinear pairing procedures; (2) supporting for ciphertexts with multiple recipients; (3) defending against Inside Keyword Guess Attacks (IKGA); (4) incapability to determine search pattern. A comparative between our suggested scheme and other similar schemes is also offered to demonstrate that the proposed method is stronger overall.
System Software Architecture for Enhancing Human-robot Interaction by Conversational AI

Conversational AI combines natural language processing (NLP) with machine and deep learning models so that people can interact in human-like manner with the digital devices. The quality of social interactions can be additionally improved by utilizing the physical presence of the robot and prompting context derived from the robot's hardware. Therefore, we propose a modular software architecture to integrate Conversational AI into Socially Assistive Robots (SARs). It follows a flow-based approach with shared repositories and direct or message-based input/output channels. We conducted two experiments to test the architecture's modularity and adaptability. The first experiment focused on the performance of different NLP cloud services and their associated modules, while the second experiment tested the integration of the Conversational AI in two different SARs - NAO and Pepper. Our experimental results demonstrate that the architecture is general enough to be applied for various SARs and different NLP use cases.