Reviewerly/exHarmony huggingface dataset:
Research and Prediction of Influential Factors of Film Box Office: Based on Machine Learning Algorithms Such as XGB, LGB and CAT

In response to the issue of film box office predictions, in this paper, we first built XGBoost, and used the training set to optimise the model by grid search and cross-validation for many times of tuning.Then we obtained the model prediction results. After that, we built the LightGBM model, tried to add text features, variable combinations, and interactions in turn, introduced the SHAP model to judge the specific impact of each important feature, and used the tuned model for training and comparison to evaluate its performance on the test set. In addition, we built the CatBoost model following the same method. According to the rmse comparison it was found that lightGBM had the best prediction. Finally, the models were fused by the Blending method, and the fused models were found to have better prediction results.
OOD Problem Research in Biochemistry Based on Backdoor Adjustment

Due to its ability to deal well with the features of graph structure (social network, molecular structure), graph neural network (GNN) has recently shown amazing capabilities in many fields (biology, chemistry), which has aroused the attention of a large number of researchers on the operation mechanism of GNN model. In order to apply graph neural network to real environment, the problem of out-of-distribution generalization is solved. The difference of data distribution between training environment and real environment is an urgent problem to be solved. In the present study, from the point of view of data, the input graph data is processed to find the core part and filter out the noise part; From the perspective of model method, the parts related to this task (graph classification) in the model are extracted, so as to improve the accuracy and efficiency of the model. However, while these methods are valid from a theoretical point of view, they are using methods that are too simple to actually cut off the effects of non-causal components. However, the current methods of model Angle are to cut the network directly, without considering the information transfer between the model parameters. To solve these problems, the main work of this paper is to propose a distance-based environment selection method, which enables the backdoor adjustment to be implemented to the maximum extent and ensures the robustness of the model. At the same time, it proposes a way for the network to squeeze information during the clipping process, so that the effect of the model pruning algorithm can be optimized. Reduce the complexity of the model and improve the generalization of the model. The method presented in this paper has been validated on data sets in several biochemical fields, and the best performance has been achieved.
Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Sensor-based human activity recognition (HAR) plays a fundamental role in various mobile application scenarios, but the model performance of HAR heavily relies on the richness of the dataset and the completeness of data annotation. To address the shortage of comprehensive activity types in collected datasets, we adopt the domain adaptation technique with a graph neural network-based approach by incorporating an adaptive learning mechanism to enhance the action recognition model’s generalization ability, especially when faced with limited sample sizes. To evaluate the effectiveness of our proposed approach, we conducted experiments using three well-known datasets: MHealth, PAMAP2, and TNDA. The experimental results demonstrate the efficacy of our approach in sensor-based HAR tasks, achieving impressive average accuracies of 98.88%, 98.58%, and 97.78% based on the respective datasets. Furthermore, we conducted transfer learning experiments to address the domain adaptation problem. These experiments revealed that our proposed model exhibits exceptional transferability and distinguishing ability, even in scenarios with limited available samples. Thus, our approach offers a practical and viable solution for sensor-based HAR tasks.
FSN: Joint Entity and Relation Extraction Based on Filter Separator Network

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.
Gaussian boson sampling with click-counting detectors

Gaussian boson sampling constitutes a prime candidate for an experimental demonstration of quantum advantage within reach with current technological capabilities. The original proposal employs photon-number-resolving detectors, however, these are not widely available. Nevertheless, inexpensive threshold detectors can be combined into a single click-counting detector to achieve approximate photon-number resolution. We investigate the problem of sampling from a general multimode Gaussian state using click-counting detectors and show that the probability of obtaining a given outcome is related to a matrix function which is dubbed as the Kensingtonian. We show how the Kensingtonian relates to the Torontonian and the Hafnian, thus bridging the gap between known Gaussian boson sampling variants. We then prove that, under standard complexity-theoretical conjectures, the model cannot be simulated efficiently.
g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested that involved generalizing the uniform shrinkage of ridge regression to non-uniform shrinkage; this was shown to perform well in sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (first version published on 7 December 2023) that implements both the ridge estimator and generalized ridge estimator. The package is equipped with generalized cross-validation for the automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on the R package “glmnet”. Hence the generalized ridge estimator may be the most recommended estimator for sparse and high-dimensional models. We demonstrate the package using intracerebral hemorrhage data.
The role of Open Science in our research

This editorial considers Open Science, what it is, what are its potential benefits, what are the pillars of engagement upon which it rests, and what are some of the main challenges facing its further adoption by research communities. At its core, Open Science involves sharing not only the contents of a traditional research article, but also of any source data and methodologies upon which the reported findings are based. Though some extra work may be required, usually without anyone providing additional resources to do that work, continuous developments in digital technology are making Open Science easier to implement. While not all data is suitable to be shared, Open Science practices are widely supported within the wider research community and funding organizations.
Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique

Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year’s orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results.
High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models

With the extensive adoption of generative models across various domains, the protection of copyright for these models has become increasingly vital. Some researchers suggest embedding watermarks in the images generated by these models as a means of preserving IP rights. In this paper, we find that existing generative model watermarking introduces high-frequency artifacts in the high-frequency spectrum of the marked images, thereby compromising the imperceptibility and security of the generative model watermarking system. Given this revelation, we propose an innovative image watermarking technology that takes into account frequency-domain imperceptibility. Our approach abandons the conventional convolutional neural network (CNN) structure typically used as the watermarking embedding network in popular watermarking techniques. This helps the image watermarking system to avoid the inherent high-frequency artifacts commonly produced by CNNs. In addition, we design a frequency perturbation generation network to generate low-frequency perturbations. These perturbations are subsequently added as watermarks to the low-frequency components of the carrier image, thus minimizing the impact of the watermark embedding process on the high-frequency properties of the image. The results show that our proposed watermarking framework can effectively embed low-frequency perturbation watermarks into images and effectively suppress high-frequency artifacts in images, thus significantly improving the frequency-domain imperceptibility and security of the image watermarking system. The introduced approach enhances the average invisibility performance in the frequency domain by up to 24.9% when contrasted with prior methods. Moreover, the method attains superior image quality (&gt;50 dB) in the spatial domain, accompanied by a 100% success rate in watermark extraction in the absence of attacks. This underscores its capability to uphold the efficacy of the protected network and preserve the integrity of the watermarking process. It always maintains excellent imperceptibility and robustness. Thus, the framework shows great potential as a state-of-the-art solution for protecting intellectual property.
Ethics reviews in the European Union. Implications for the governance of scientific research in times of data science and Artificial Intelligence

This article builds on discussions on peer review in science and the role of ethics in the governance of technologies to achieve a two-fold goal. First, it shows the process of co-production of the European Commission’s (EC’s) standardised ‘ethics appraisal process’ with the development of the EC’s Framework Programmes (FPs), by looking at the standardisation of the process and the mutual shaping of data protection law and risk-based discourses around Artificial Intelligence. Second, it investigates the political and epistemic implications arising from this process for the EU governance of scientific research regarding (1) sponsorship of science and technology by the EU and (2) how many limitations or constraints researchers have in practice when carrying out their research. After sketching the origins of the ethics appraisal process and its co-production with data protection law and risk-based jargon, the article outlines the implications for the governance of scientific research and draws recommendations.
MOTC: Abdominal Multi-objective Segmentation Model with Parallel Fusion of Global and Local Information

Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional operations restricts the modeling of long-term dependencies. The introduction of Transformer enables the modeling of long-term dependencies and partially eliminates the local inductive bias in convolutional operations, thereby improving the accuracy of tasks such as segmentation and classification. Researchers have proposed various hybrid structures combining Transformer and Convolutional Neural Networks. One strategy is to stack Transformer blocks and convolutional blocks to concentrate on eliminating the accumulated local bias of convolutional operations. Another strategy is to nest convolutional blocks and Transformer blocks to eliminate bias within each nested block. However, due to the granularity of bias elimination operations, these two strategies cannot fully exploit the potential of Transformer. In this paper, a parallel hybrid model is proposed for segmentation, which includes a Transformer branch and a Convolutional Neural Network branch in encoder. After parallel feature extraction, inter-layer information fusion and exchange of complementary information are performed between the two branches, simultaneously extracting local and global features while eliminating the local bias generated by convolutional operations within the current layer. A pure convolutional operation is used in decoder to obtain final segmentation results. To validate the impact of the granularity of bias elimination operations on the effectiveness of local bias elimination, the experiments in this paper were conducted on Flare21 dataset and Amos22 dataset. The average Dice coefficient reached 92.65% on Flare21 dataset, and 91.61% on Amos22 dataset, surpassing comparative methods. The experimental results demonstrate that smaller granularity of bias elimination operations leads to better performance.
Lagrangian particle tracking in the presence of obstructing objects

Abstract Volumetric flow measurement techniques have become the state-of-the-art for characterizing a broad range of different flow fields. Still, certain major limitations are present, which hinders the application of these techniques for some of the more complex flow configurations. In particular, flow measurements involving the presence of obstructing objects require time consuming measurement strategies and careful adjustment of the experimental equipment to avoid inaccurate measurement results. Within this study, these limitations are mitigated by the use of a known object's shape and position in the form of depth maps for commonly used Lagrangian particle tracking (LPT) schemes like Shake-the-Box (STB) as well as in volume self-calibration (VSC) methods. The incorporation of these depth maps is computationally inexpensive and straight forward to implement. In order to evaluate the performance of this novel object-aware Lagrangian particle tracking (OA-LPT) approach, synthetic as well as experimental test data is created and the reconstruction quality is evaluated. It is shown, that OA-LPT is capable of providing full flow-field information, whereas the default STB implementation fails to correctly reconstruct particles in the partly-occluded regions.
Applying Findable, Accessible, Interoperable, and Reusable Principles to Biospecimens and Biobanks

The importance of stimulating greater sharing of data for use and reuse in health research is widely recognized. To this end, the findable, accessible, interoperable, and reusable (FAIR) principles for data have been developed and widely accepted in the research community. Research biospecimens are a resource that leads to much of this health research data but are also a form of data. Therefore, the FAIR principles should apply to biospecimens. Nevertheless, there is a widespread problem of not sharing biospecimen resources that is clearly visible within the research arena. The impacts of this are likely to include diversion of precious research funds into compiling duplicate biospecimen cohorts, detraction from research productivity as researchers compete for and create duplicate resources, and deterrence of attempts to assess research reproducibility. This article explores some of the barriers that may limit availability of FAIR biospecimens. These barriers relate to the type of biospecimen collections and the characteristics of the custodians that influence their intention and interest in sharing. Barriers also relate to the ethical, legal, and social issues concerning collections, the research context of the collections, and cost and expertise involved in repurposing collections to enable sharing. Several solutions to increase sharing are identified. Some have recently been implemented, including enhancing biospecimen locators with tools to guide researchers and facilitating transfer of research collections to centralized biobank infrastructures at the conclusion of projects. New proposed solutions include improving search capabilities within publication databases, and introduction of evidence-based justifications for all new collections into peer-reviewed grant competition processes. It is recognized that there are both scientific factors and practical reasons that can impose limits to sharing biospecimens. However, funding availability, productivity, and progress in health research all stand to benefit from improved sharing of research biospecimen collections.
Smart Voting Web Based Application Using Aadhar

Abstract: Elections are a crucial part of any democratic system, and it is essential to ensure that the voting process is conducted in a fair and transparent manner. In traditional paper-based elections, the process is time-consuming, resource-intensive, and prone to errors. In this context, the use of technology can significantly improve the efficiency, security, and accuracy of the voting process. In this project, we propose an online voting system that uses face recognition technology to identify and authenticate voters. The proposed system allows voters to cast their votes remotely, eliminating the need for physical ballot boxes and reducing the cost and time involved in the voting process. The system works by capturing the facial image of the voter and passing it to the server unit for verification. The server compares the facial image with the information stored in the database and verifies the identity of the voter. If the identity is verified, the voter is allowed to cast the vote; otherwise, an error message is displayed on the screen, and the person is not allowed to poll the vote. The system is designed to be secure and tamper-proof. The use of face recognition technology makes it difficult for anyone to impersonate another person and cast a fraudulent vote. The system also ensures that each voter can cast only one vote, and the voting process is conducted in a transparent and fair manner. Overall, the proposed online voting system offers numerous advantages over traditional paper-based voting systems, including increased efficiency, reduced cost, improved security, and transparency.
An Innovative KOT Ordering System for Restaurants

Abstract: This study proposes a digital menu ordering system to address challenges in the restaurant industry's digital transformation. Traditional paper-based methods lead to losses and inefficiencies. The system, using QR codes, enables customers to register, log in, and order online, with features like suggesting popular dishes and online payments. Managers gain real-time oversight of orders, tables, and payments, supported by sales data visualization. By automating tasks, the system reduces errors, enhances service, and streamlines operations, offering a transformative solution to industry challenges in the digital era.
A New Novel Approach for Sentiments Analysis using Contextual Mining and Supervised Learning

Textual data mining is used to anticipate the sentiment of a user based on a similar book. Using conditional probability distributions, the rating similarity between books can be quantified by textual mining. In supervised learning, the input and output data are sent to the machine learning model in tandem to maximize accuracy. In this paper, a cloud-based suggested system is incorporated which detects and recommends similar types of content based on the ranking of books. A cloud recommendation system is a means of determining which service is best suited to the user's tastes and requirements. The work uses specific formats such as graphics or text, and the networks built throughout the process may help uncover the links between the words. Textual mining is used to calculate book-ranging similarities and recommendations. From the comparison result, the proposed logistic regression technique has maximum accuracy which is 82% as compared to the existing technique The model shows all recommended books that are like the input book are displayed, while all other types of books are hidden from view. Also, positive, or negative masking can be determined by comparing the intensity of the extracted images.