Reviewerly/exHarmony huggingface dataset:
Security Analysis and Improvement of Dual Watermarking Framework for Multimedia Privacy Protection and Content Authentication

The demand for using multimedia network infrastructure for transmission grows with each passing day. Research scholars continue to develop new algorithms to strengthen the existing network security framework in order to ensure the privacy protection and content authentication of multimedia content and avoid causing huge economic losses. A new technology for multimedia image copyright protection and content authentication has been proposed. The innovations lie in the use of an inter-block coefficient difference algorithm to embed robust watermarking in the transform domain, and the same fragile watermark is embedded twice in the spatial domain so that any tiny tampering can be identified and located. A new encryption algorithm combined with Arnold transform is used to encrypt data before embedding. However, some security vulnerabilities were found, and successful cryptanalysis and attack were conducted. Subsequently, an improved scheme was proposed to improve the security and tamper detection ability of the original watermarking scheme and recover the tampered robust watermark. The results show that the improved scheme is safer and more reliable and shows good performance in tampering detection and the recovery robustness of the watermark.
A survey on the pipeline evolution of facial capture and tracking for digital humans

Abstract With the introduction of concepts for virtual interaction and digital doubles, a rich scenario has been created for embodied avatars to strive. These avatars, more recently referred to as digital humans, have become a popular area of research, resulting in various techniques and methods that focus on improving the perception of their realism, fidelity, emphatic response, and interactivity. This survey aims to explore the literature and recent advancements on the key processes behind the creation and animation of digital human faces through the view of a general pipeline. The extensive review carried out in this study explores the usual data collection protocols, the main facial codification paradigms and databases, the approaches for digital human asset creation, facial tracking solutions for performance-driven animation, the solving process, and the final rendering delivery. Different quantitative evaluation methods, visual perception tests, and empathetic response evaluations for digital humans are also included in the survey. Additionally, the paper presents an updated summary of public and private frameworks for digital humans that go through the complete general pipeline presented. Finally, the condensed knowledge is discussed, inquiring into the possible direction of future developments in the field.
Simulation of Diffuse Solar Radiation with Tree-Based Evolutionary Hybrid Models and Satellite Data

Diffuse solar radiation (Rd) provides basic data for designing and optimizing solar energy systems. Owing to the notable unavailability in many regions of the world, Rd is traditionally estimated by models through other easily available meteorological factors. However, in the absence of ground weather station data, such models often need to be supplemented according to satellite remote sensing data. The performance of Himawari-7 satellite inversion of Rd was evaluated in the study, and hybrid models were established (XGBoost_DE, XGBoost_FPA, XGBoost_GOA, and XGBoost_GWO), so as to improve the satellite data and achieve a better utilization effect. The meteorological data of 14 Rd stations in mainland China from 2011 to 2015 were used. Four input combinations (L1–L4) and eight input combinations (S1–S8) of meteorological factors corresponding to satellite remote sensing data were used for model simulation, while two optimal combinations (S7 and S8) were selected for cross-station application. The results revealed that the accuracy of Himawari-7 satellite Rd data was low, with RMSE, R2, MAE, and MBE values of 2.498 MJ·m−2·d−1, 0.617, 1.799 MJ·m−2·d−1, and 0.323 MJ·m−2·d−1, respectively. The performance of these coupled models based on satellite data was significantly improved. The RMSE and MAE values increased by 15.5% and 9.4%, respectively, while the R2 value decreased by 10.9 %. Compared with others based on satellite data, the XGBoost_GOA model exhibited optimal performance. The mean values of RMSE, R2, and MAE were 1.63 MJ·m−2·d−1, 0.76 and 1.21 MJ·m−2·d−1, respectively. The XGBoost_GWO model exhibited optimal performance in the cross-station application, and the average RMSE value was reduced by 2.3–10.5% compared with the other models. The meteorological factors input by the models exhibited different levels of significance in different scenarios. Rd_s was the main meteorological parameter that affected the model based on satellite data, while RH exhibited a significant improvement in the XGBoost_FPA and XGBoost_GWO models based on ground weather stations data. Accordingly, the present authors believe that the XGBoost_GOA model has excellent ability for simulating Rd, while the XGBoost_GWO model allows for cross-station simulation of Rd from satellite data.
Rapid mapping and spatial analysis on the distribution of photovoltaic power stations with Sentinel-1&amp;2 images in Chinese coastal provinces

Photovoltaic (PV) solar energy generation attracts considerable attention to archive carbon neutrality goals worldwide. Geospatial data describing the PV system based on satellite images are critical for PV deployment. However, it remains challenging to obtain relative data in coastal zones due to frequent cloud cover. There have also been relatively few studies on spatial analysis of PV development in terms of solar resources and the energy demands. Therefore, this study took Chinese coastal provinces as example, and proposed a random forest classification method with information on morphology, optics, and SAR to address cloud pollution. The results obtained were analyzed with geographical and socioeconomic factors to determine its development status. The results have shown that (1) by October 2022, the total PV area in Chinese coastal provinces reached 837.3 km2, with an overall accuracy of 96.9 % and a Kappa coefficient of 0.91. Water-based PV represents 33.7 % which tended to be distributed near the shoreline. (2) Bounded by the Huai River, the PV density in the north was 9.0 km2 per 104 km2, which is more than twice that in the south. The development of water-based PV is a key reason for the high PV construction density in coastal areas. (3) PV distribution was slightly mismatched with solar resource and power demand, especially in Liaoning and Guangdong. Liaoning has relatively high potential for PV development. The study findings can play a key role in evaluation of PV power generation, development potential, and ecological environmental impacts.
Human and machine translation of occasionalisms in literary texts

Abstract Literary occasionalisms, new words coined by writers with a particular poetic aim in view, often pose a great challenge for translators. Given recent advances in machine translation (MT), could literary translators benefit from MT when it comes to the translation of occasionalisms? We address this question by considering the work of Austria’s most important nineteenth-century comedy writer, Johann Nestroy (1801–1862). We compare how human translators and two generic neural MT systems (Google Translate, DeepL) translated occasionalisms (compounds, derivations, and blends) in Nestroy’s play Der Talisman into English. While human translators largely refrained from creating new target expressions, the two MT systems generated a number of viable new coinages, most of them by literal translation procedures. In an interactive human-computer environment, using MT output as a repository from which to retrieve novel target solutions or derive inspiration might open up new avenues in the practice of literary translation.
Predicting neural network confidence using high-level feature distance

Neural networks have achieved state-of-the-art performance in many fields. However, they are often reported to produce overconfident predictions, especially for misclassifications. Therefore, confidence prediction is vitally important in practical applications to enable models to provide reasonable confidence. The objective of this paper is to address the problem of overconfidence in neural networks. This is achieved by constructing a detector that can predict the probability of incorrect output of the neural network. The goal of the detector is to identify incorrect outputs and adjust their raw confidences to reduce the overconfidence of misclassification. The idea of the detector is to learn the relationship between high-level features of inputs and their classification correctness. The high-level feature is the output of the deep hidden layer in the network, and for the CNNs, we chose the last convolutional layer. The training of the detector requires a hold-out validation set, which in practice can be the same set used for hyperparameter tuning. The detector predicts which inputs are likely to be misclassified by neural networks and estimates the probability of misclassification which is then used to adjust the raw softmax confidence, thereby reducing the confidence of misclassification. The detector is learned by deeply mining the classification results of the validation data produced during the training process of the neural network, no additional data such as disturbance samples needs to be collected. Experimental results on the CIFAR-10 dataset and two typical neural network structures, ResNet20 and VGG16, show that our method is effective in reducing the confidence of misclassifications and maintaining the confidence of correct classifications. The effectiveness of our method is demonstrated on the non-disturbance i.i.d test set and three types of disturbance sets. It outperforms two baseline methods on all test sets, especially for the i.i.d set, on the misclassification identification task. This new method is proven to perform well on both misclassification identification and out-of-distribution detection tasks. In contrast to previous softmax calibration methods that aim to decrease the confidence of all classifications, the method proposed in this paper innovatively reduces the confidence of misclassification straightforwardly. As a result, it becomes feasible to visually interpret the correctness of classifications using confidence scores. This will lead to a better understanding of the model’s behavior and facilitate more reliable decision-making. Overall, our proposed confidence prediction method represents a promising step towards addressing the overconfidence problem in classification tasks in deep learning, especially image classification, and is of great value for real-world deep learning applications.
A Comprehensive Consent Management System for Electronic Health Records in the Healthcare Ecosystem

The recent trends in the healthcare sector have catalyzed the emergence of several healthcare ecosystems. The inevitable and innate health data exchange amongst ecosystem components for provisioning of healthcare services as well as secondary use has raised several trust issues. The consent is a predominant mechanism to ensure privacy preserving, secure and ethical exchange, as well as sharing of health records of a patient by establishments. The proposed consent management system supports the life cycle of consent given by the patient or their authorized entity as well as validation and delegation of consent by a provider for health records requested by a user in an ecosystem. It reinforces different types as well as flavors of consent to strike a balance between privacy protection, ease of use, and patient safety. It supports the simple, flexible, and efficient management of consent at different granularity of health resource, user, and purpose. This is enabled by embedding consent structure in tree representation of EHR and a tree traversal algorithm for conflict resolution.
RoughSet based Feature Selection for Prediction of Breast Cancer

Breast cancer is the most deadly cancer and has highest mortality rate in women all over the world. Early prediction of breast cancer can improve the survival rate of the patient. Consequently, high accuracy in cancer prediction is important to avoid any mis-diagnosis. Machine learning algorithms can contribute in early prediction and diagnosis of breast cancer. In this study, we have used rough set based feature selector to extract relevant features from the breast cancer feature set and classify them using machine learning algorithm like Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Random Forest, Adaboost. The main aim is to predict cancerous breast nodules, using rough set driven feature selection and machine learning classification algorithms. The results were evaluated pertaining to accuracy, sensitivity and specificity and positive predictive value. It is observed that random forest outperformed all other classifiers and achieved the highest accuracy using the proposed approach (95.23%).
AE-DIL: A double incremental learning algorithm for non-stationary time series prediction via adaptive ensemble

Many dynamic processes in the real world can be modeled as time series, so time series prediction is significant for social and economic development. The inherent non-stationarity of time series obtained from actual projects may make it difficult to predict accurately. To alleviate this problem, in this paper, a Double Incremental Learning algorithm via Adaptive Ensemble, termed as AE-DIL for short, is proposed for non-stationary time series prediction. AE-DIL provides a general online prediction framework consisting of two modules. The first detects changes based on the statistical hypothesis test and self-adaptive sliding window technology. The second updates the prediction model based on double incremental learning and adaptive ensemble learning. The effectiveness of the proposed algorithm is empirically underpinned by the experiments conducted on seven benchmark time series datasets, compared with several baselines and state-of-the-art models.
Engineering for emergency response: Türkiye and Syria earthquakes

Sakthy Selvakumaran, Mark Scorer and Josh Macabuag of the charity Saraid provide their insights from recently being on the ground as part of the emergency search and rescue efforts after the 2023 earthquakes.
Tight lower bounds and optimal constructions of anonymous broadcast encryption and authentication

Abstract Broadcast Encryption (BE) is public-key encryption allowing a sender to encrypt a message by specifing recipients, and only the specified recipients can decrypt the message. In several BE applications, since the privacy of recipients allowed to access the message is often as important as the confidentiality of the message, anonymity is introduced as an additional but important security requirement for BE. Kiayias and Samari (IH 2013) presented an asymptotic lower bound on the ciphertext sizes in BE schemes satisfying anonymity (ANO-BE for short). More precisely, their lower bound is derived under the assumption that ANO-BE schemes have a special property. However, it is insufficient to show their lower bound is asymptotically tight since it is unclear whether existing ANO-BE schemes meet the special property. In this work, we derive asymptotically tight lower bounds on the ciphertext size in ANO-BE by assuming only properties that most existing ANO-BE schemes satisfy. With a similar technique, we first derive asymptoticallyPlease provide MSC codes. For more details, please visit http://www.ams.org/msc/. tight lower bounds on the authenticator sizes in Anonymous Broadcast Authentication (ABA). Furthermore, we extend the above result and present (non-asymptotically) tight lower and upper bounds on thePlease check and confirm the Running title. ciphertext sizes in ANO-BE. We show that a variant of ANO-BE scheme proposed by Li and Gong (ACNS 2018) is optimal. We also provide tight bounds on the authenticator sizes in ABA via the same approach as ANO-BE, and propose an optimal construction for ABA.
Unsupervised Forgery Detection of Documents: A Network-Inspired Approach

The area of forgery detection of documents is considered an active field of research in digital forensics. One of the most common issues that investigators struggle with is circled around the selection of the approach in terms of accuracy, complexity, cost, and ease of use. The literature includes many approaches that are based on either image processing techniques or spectrums analysis. However, most of the available approaches have issues related to complexity and accuracy. This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents’ matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms. The detection rate is measured according to the number of network clusters. Based on the obtained results, our approach provides high accuracy using the Louvain clustering algorithms, while the use of the updated version of the DBSAN was more successful when testing many documents at the same time. Additionally, the suggested framework is considered simple to implement and does not require professional knowledge to use.
Wireless Intelligent Sensor Network in Dynamic Environmental Monitoring of Archaeological Excavation Site

This paper intends to apply the Intelligent Wireless Sensor Network (IWSN) to the archaeological excavation site and make the dynamic site of archaeological excavation can be monitored intelligently in real-time. This paper firstly discusses the structure and function of Wireless Sensor Network (WSN). Secondly, according to the problem of limited dynamic monitoring of WSN, an IWSN is established. The dynamic environmental monitoring of archaeological excavation sites is achieved by using Geographic Information System (GIS) and remote sensing technology combined with IWSN. Finally, this paper analyses the environmental monitoring results of the archaeological excavation site, the dynamic environmental monitoring location of the archaeological excavation site of the IWSN, and the simulation results of the Intelligent Wireless Sensor Network-Geographic Information System (IWSN-GIS) in the dynamic environmental monitoring of the archaeological excavation site. The results show that the Inverse Distance to a Power (IDP) algorithm in temperature and humidity monitoring data on the overall downward trend, the algorithm performance is relatively stable, and the algorithm performance is better than the other two algorithms. The monitoring performance of IWSN-GIS system is better than that of IWSN system. It shows that with the increase in the communication distance of sensor nodes, the relative error of different monitoring systems for the dynamic monitoring position of the archaeological excavation site is decreasing. IWSN-GIS can monitor the environment before, during, and after the archaeological site. This paper realizes the intelligent, scientific, and technological archaeology of archaeological excavation through IWSN, GIS, and remote sensing image technology, and provides technical support for digital archaeology in archaeological research.
<i>Microcell‐Net</i>: A deep neural network for <scp>multi‐class</scp> classification of microscopic blood cell images

Abstract Blood‐related diseases are one of the major concerns in the biomedical domain and most of the disease symptoms are reflected through the analysis of blood cells. The diagnosis by experts in laboratories is very costly and time‐consuming, thus artificial intelligence‐based systems can help in the automatic diagnosis and monitoring of an individual's health. In this study, a CNN‐based architecture Microcell‐Net is proposed which is trained on a microscopic image dataset of peripheral blood cells in eight different classes. The images have several inter‐class and intra‐class diversity with different magnification levels and the noise present in the images makes the classification task significantly challenging. Experimental results indicated that the proposed model can efficiently classify various types of microscopic blood cells with good accuracy. The experimental findings accomplished 98.76% validation accuracy and 97.65% test accuracy in complex background conditions. The performance of the model is compared with other state‐of‐the‐art models and the proposed deep neural network performs significantly better than others. The proposed model can be utilized in a real‐time diagnosis system because it is fast, automatic and efficient, which can assist in taking clinical decisions and early diagnosis of haematological disorders.
Online Purchase Environment Using Blockchain-Based Solutions: An acceptance of online grocers

The Internet has enabled e-commerce activities, including online grocery purchases. Although all users dream of a hassle-free and safe transaction, not all online payment methods are safe and acceptable. Thus, one of the solutions is to integrate blockchain technology. However, the acceptance of Blockchain among urban users still needs to be discovered. This paper aims to determine the factors affecting the acceptance of Blockchain and online delivery networks for grocery purchases in urban areas. A total of 384 responses were collected among urban online purchasers through questionnaires. The independent variables, namely Accuracy, online payment, and Cybersecurity, significantly impact the dependent variable.
Reducing the Reality Gap Using Hybrid Data for Real-Time Autonomous Operations

This paper presents an ablation study aimed at investigating the impact of a hybrid dataset, domain randomisation, and custom-designed neural network architecture on the performance of object localisation. In this regard, real images were gathered from the Boeing 737-400 aircraft while synthetic images were generated using the domain randomisation technique involved randomising various parameters of the simulation environment in a photo-realistic manner. The study results indicated that the use of the hybrid dataset, domain randomisation, and the custom-designed neural network architecture yielded a significant enhancement in object localisation performance. Furthermore, the study demonstrated that domain randomisation facilitated the reduction of the reality gap between the real-world and simulation environments, leading to a better generalisation of the neural network architecture on real-world data. Additionally, the ablation study delved into the impact of each randomisation parameter on the neural network architecture’s performance. The insights gleaned from this investigation shed light on the importance of each constituent component of the proposed methodology and how they interact to enhance object localisation performance. The study affirms that deploying a hybrid dataset, domain randomisation, and custom-designed neural network architecture is an effective approach to training deep neural networks for object localisation tasks. The findings of this study can be applied to a wide range of computer vision applications, particularly in scenarios where collecting large amounts of labelled real-world data is challenging. The study employed a custom-designed neural network architecture that achieved 99.19% accuracy, 98.26% precision, 99.58% recall, and 97.92% mAP@.95 trained using a hybrid dataset comprising synthetic and real images.