Reviewerly/exHarmony huggingface dataset:
Evaluation and Forecasting Models for GGDP

Authors:
raw_author_name: Jueying Huang
raw_affiliation_string: School of Psychology, Jiangxi Normal University, Nanchang, Jiangxi, China, 330022

Abstract: The evaluation and prediction of GGDP has important implications for global development. We derived positive and negative factors affecting climate change through indicator analysis. We then also developed a grey prediction model and a regression analysis model to predict global climate change. The analysis of the results shows that GGDP benefits from a reduction in carbon emissions of 1791.67665767 Mt C02/year. In addition, we used a quadratic non-linear regression model to analyze the environmental impact of the specific use of GGDP. The model proposed in this paper facilitates the evaluation and prediction of GGDP.
Detection of Suspicious Front Yard-View Burglary Aided with Moving Service

Authors:
raw_author_name: Shilong Wang
raw_affiliation_string: Faculty of Information Communication and Technology (FICT), Universiti Tunku Abdul Rahman, Kampar Campus, 31900 Kampar, Perak, Malaysia; School of Mathematics & Computer Science, Guangxi Science & Technology Normal University Laibin, Guangxi, 546199, China
raw_author_name: Maylor Karhang Leung
raw_affiliation_string: Faculty of Information Communication and Technology (FICT), Universiti Tunku Abdul Rahman, Kampar Campus, 31900 Kampar, Perak, Malaysia
raw_author_name: Siak Wang Khor
raw_affiliation_string: Faculty of Information Communication and Technology (FICT), Universiti Tunku Abdul Rahman, Kampar Campus, 31900 Kampar, Perak, Malaysia
raw_author_name: Hasnain Sultan
raw_affiliation_string: Faculty of Information Communication and Technology (FICT), Universiti Tunku Abdul Rahman, Kampar Campus, 31900 Kampar, Perak, Malaysia

Abstract: This work is devoted to the implementation and evaluation of a front-yard human abnormal/crime activity detection system. This system identifies abnormal/crime activities (burglary emptying site with moving service) in the front yard and gives an early warning message in time. In this way, the system realizes the maximum protection of the owner’s life and property. The system combines video surveillance systems and computer vision technology to detect abnormal human behavior in videos. Based on the human action understanding of burglary emptying sites with moving services in the real world, this paper designs an abnormal behavior detection system in the front yard scene. The system mainly uses YOLO5 and OpenCV for computer vision-related design. First, YOLO is used to identify the objects in the video scene, and then the OpenCV color comparison algorithm is used to track the relevant people in the scene. The activity relationship between people, vehicles, and buildings in the scene is combined to evaluate whether abnormal behavior occurs.
A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

Authors:
raw_author_name: Yongqi Chen
raw_affiliation_string: School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 201400, China
raw_author_name: Huailin Zhao
raw_affiliation_string: School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 201400, China

Abstract: Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively. However, challenges like scare-variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this article, we propose a weakly supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The incorporation of a modified Swin-Transformer block addresses the need for effective global context information. A Pyramid Pooling Aggregation Module handles the inherent scale variation problem in crowd-counting tasks in a more computation-efficient way. This module, along with the cross-attention module, serves as bridges to promote the feature information flow between local features and global context information. Finally, a simplified regressor module is designed to allow the proposed model with weakly supervised guidance for training to avoid precise location-level annotations, noting that the omission of density map generation makes the proposed network more lightweight. Our results on the UCF-QNRF dataset indicate our model is 8.73% and 12.17% more accurate on MAE and MSE metrics, respectively, than the second-best ARNet, in which the parameters decrease by 4.52%. On the ShanghaiTech A dataset, MAE and MSE drop 1.5% and 3.2%, respectively, compared to the second-best PDDNet. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of our model.
Research and Prediction of Influential Factors of Film Box Office: Based on Machine Learning Algorithms Such as XGB, LGB and CAT

Authors:
raw_author_name: Boning Jiang
raw_affiliation_string: School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China 200043

Abstract: 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

Authors:
raw_author_name: Pengfei Gu
raw_affiliation_string: School of Data Science University of Science and Technology of China Hefei, China

Abstract: 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

Authors:
raw_author_name: Jing Yang
raw_affiliation_string: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
raw_author_name: Tianzheng Liao
raw_affiliation_string: Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 519041, China
raw_author_name: Jingjing Zhao
raw_affiliation_string: School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China
raw_author_name: Yichun Huang
raw_affiliation_string: School of Mechatronic Engineering and Automation, Foshan University, Foshan 528010, China
raw_author_name: Zhijia Zhao
raw_affiliation_string: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
raw_author_name: Jing Xiong
raw_affiliation_string: Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
raw_author_name: Changhong Liu
raw_affiliation_string: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China

Abstract: 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

Authors:
raw_author_name: Qicai Dai
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China
raw_author_name: Wenzhong Yang
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China
raw_author_name: Fuyuan Wei
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Multilingual Information Technology, Xinjiang University, Urumqi 830017, China
raw_author_name: Liang He
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China; School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
raw_author_name: Yuanyuan Liao
raw_affiliation_string: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China

Abstract: 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

Authors:
raw_author_name: Gabriele Bressanini
raw_affiliation_string: QOLS, Blackett Laboratory, Imperial College London, London SW7 2AZ, United Kingdom
raw_author_name: Hyukjoon Kwon
raw_affiliation_string: Korea Institute for Advanced Study, Seoul 02455, South Korea
raw_author_name: M. S. Kim
raw_affiliation_string: Korea Institute for Advanced Study, Seoul 02455, South Korea; QOLS, Blackett Laboratory, Imperial College London, London SW7 2AZ, United Kingdom

Abstract: 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

Authors:
raw_author_name: Takeshi Emura
raw_affiliation_string: Biostatistics Center, Kurume University, Kurume 830-0011, Japan; Research Center for Medical and Health Data Science, The Institute of Statistical Mathematics, Tokyo 190-8562, Japan
raw_author_name: Koutarou Matsumoto
raw_affiliation_string: Biostatistics Center, Kurume University, Kurume 830-0011, Japan
raw_author_name: Hirofumi Michimae
raw_affiliation_string: Department of Clinical Medicine (Biostatistics), School of Pharmacy, Kitasato University, Tokyo 108-8641, Japan

Abstract: 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.
Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique

Authors:
raw_author_name: Ugur Alganci
raw_affiliation_string: Department of Geomatics Engineering, Istanbul Technical University, Istanbul 34469, Türkiye
raw_author_name: Ozan Arslan
raw_affiliation_string: Department of Geomatics Engineering, Kocaeli University, Kocaeli 41040, Türkiye
raw_author_name: Dursun Zafer Seker
raw_affiliation_string: Department of Geomatics Engineering, Istanbul Technical University, Istanbul 34469, Türkiye

Abstract: 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

Authors:
raw_author_name: Li Zhang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Xinpeng Zhang
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
raw_author_name: Hanzhou Wu
raw_affiliation_string: School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Abstract: 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

Authors:
raw_author_name: Niels van Dijk
raw_affiliation_string: Faculty of Law and Criminology, Law, Science, Technology and Society research group, Vrije Universiteit Brussel, Brussels, Belgium

Abstract: 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.