Reviewerly/exHarmony huggingface dataset:
Evaluation Model of Football Players’ Training and Teaching Actions Based on Artificial Intelligence

Football is a sport that needs to combine the physical stamina and physical characteristics of athletes. It needs to pay attention to the differences in different individuals and then conduct targeted training. In this regard, this article introduces artificial intelligence technology into the teaching actions of football player training and analyzes the teaching elements according to the characteristics of the football player’s body. Through the analysis of fog computing under artificial intelligence, this article aimed to study the related effects of combining intelligent technology on the basis of athletes’ original training. This article proposes the establishment of a system model and quantitative analysis of different frames of football players’ movements. According to the combination of two-phase analysis, it can be concluded that after the introduction of artificial intelligence technology, the ability of football players in various indexes has increased by 20%.
Tooth CT Image Segmentation Method Based on the U-Net Network and Attention Module

Traditional image segmentation methods often encounter problems of low segmentation accuracy and being time-consuming when processing complex tooth Computed Tomography (CT) images. This paper proposes an improved segmentation method for tooth CT images. Firstly, the U-Net network is used to construct a tooth image segmentation model. A large number of feature maps in downsampling are supplemented to downsampling to reduce information loss. At the same time, the problem of inaccurate image segmentation and positioning is solved. Then, the attention module is introduced into the U-Net network to increase the weight of important information and improve the accuracy of network segmentation. Among them, subregion average pooling is used instead of global average pooling to obtain spatial features. Finally, the U-Net network combined with the improved attention module is used to realize the segmentation of tooth CT images. And based on the image collection provided by West China Hospital for experimental demonstration, compared with other algorithms, our method has better segmentation performance and efficiency. The contours of the teeth obtained are clearer, which is helpful to assist the doctor in the diagnosis.
MfdcModel: A Novel Classification Model for Classification of Benign and Malignant Breast Tumors in Ultrasound Images

Automatic classification of benign and malignant breast ultrasound images is an important and challenging task to improve the efficiency and accuracy of clinical diagnosis of breast tumors and reduce the rate of missed and misdiagnosis. The task often requires a large amount of data to train. However, it is difficult to obtain medical images, which contradicts the large amount of data needed to obtain good diagnostic models for training. In this paper, a novel classification model for the classification of breast tumors is proposed to improve the performance of diagnosis models trained by small datasets. The method integrates three features from medical features extracted from segmented images, features selected from the pre-trained ResNet101 output by principal component analysis (PCA), and texture features. Among the medical features that are used to train the naive Bayes (NB) classifier, and the PCA-selected features are used to train the support vector machine (SVM) classifier. Subsequently, the final results of boosting are obtained by weighting the classifiers. A five-fold cross-validation experiment yields an average accuracy of 89.17%, an average precision of 90.00%, and an average AUC value of 0.95. According to the experimental results, the proposed method has better classification accuracy compared to the accuracy obtained by other models trained on only small datasets. This approach can serve as a reliable second opinion for radiologists, and it can also provide useful advice for junior radiologists who do not have sufficient clinical experience.
Experience Report: Running and Participating in a Multi-Institutional Research in Practice Project Activity (RIPPA)

In this paper we report on our experiences of being involved in the first Research in Practice Project Activity (RIPPA) which was established at UKICER 2021. The purpose of the RIPPA was to bring practitioners and researchers together from multiple institutions to collaboratively perform a practical research task at their own institutions over several months. The intended RIPPA outcomes were to strengthen the knowledge and skills of participants, expand their network of collaborators and to produce high quality research. As the first RIPPA draws to a close, we highlight the main successes, failures and other learning moments of the RIPPA to provide guidance for aspiring RIPPA leaders and participants. We hope that in sharing our experiences, we can encourage and support the launch and success of other RIPPA projects across the field of CER.
Motion Planning and Tracking Control of Autonomous Vehicle Based on Improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> Algorithm

The traditional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm, applied to the motion planning of autonomous vehicles, easily causes high computational costs and excessive turning points generated in the planning path. In addition, the vehicle cannot track the path due to the unsmooth inflection point. To overcome these potential limitations, an improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm-based motion planning algorithm and a tracking control strategy based on model predictive control theory were proposed in this work. The method of expanding the search neighborhood is adopted to improve the planning efficiency of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm. The artificial potential field method is also incorporated into the proposed <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm. The resultant force generated by each potential field is further introduced into the evaluation function of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msup> <mrow> <mi>A</mi> </mrow> <mi>∗</mi> </msup> </math> algorithm to plan the driving path, which could be suitable for autonomous vehicles. The sharp nodes in the path are smoothed by cubic quasi-uniform B-spline curve. The tracking control strategy is designed based on model predictive control theory to realize the accurate tracking of the planned path. Typical obstacle avoidance conditions were selected for co-simulation test verification. The experimental results show that the proposed motion planning algorithm and tracking control strategy can effectively plan the obstacle avoidance path and accurately track the path in different environments.
Local Bubble Dilation Functions: Hypersphere-bounded Landscape Deformations Simplify Global Optimization

Solving optimization problems is one of the most complex and widespread task in Computer Science. In many scenarios, finding the global optimum of a function is hampered by several features that characterize the fitness landscapes, such as noisiness, multi-modality, non-convexity, non-separability, and non-differentiability. In order to facilitate the optimization process, a variety of methods have been proposed to manipulate either the search space or the fitness landscape. Among these, Dilation Functions (DFs) were introduced to expand regions of the search space that are characterized by promising fitness values. In this work, we extend the family of DFs by introducing Local Bubble Dilation Functions (LBDFs), a novel approach that generates local distortions bounded by hyper-spheres. By performing an appropriate mapping of the search space, LBDFs can improve the optimization performance, since they expand and reveal the promising regions around the global optimum, while leaving the rest of the fitness landscape untouched. The additional advantage of LBDFs, with respect to DFs, is that different dilations can be applied to each dimension of the search space, which is useful in the case of asymmetric landscapes. In order to show the benefits of local dilations, we executed several tests on the Michalewicz benchmark function, with different settings for the LBDFs. Our results show that a properly designed LBDF can lead to statistically significant better results than using vanilla optimization. Finally, we investigated the use of LBDFs to facilitate the solution of the parameter estimation problem in Systems Biology by analyzing the landscape related to a stochastic model of enzyme kinetics.
Unsupervised Domain Adaptation Image Dehazing with Contrastive Nearest-Farthest Subspace Distance

Haze can cause significant changes in the data domain of the image. Due to domain-shift problem, the model trained on the synthetic haze images has a weak or even invalid dehazing ef-fect on the real data. This paper proposes an unsupervised domain adaptation dehazing method based on the nearest-farthest subspace distance in response to this problem. For the middle convolved features, the matrix singular value de-composition is used to obtain the basis vectors of the support subspace. Narrowing the angle between the basis vectors of natural and synthetic haze domains could reduce the differ-ence between the data domains. In subspace, a newly defined distance with a new penalty term named subspace measure distance is employed to constrain the model. Furthermore, to avoid search blindness of the proposed method in the real data training phase, we propose the nearest-farthest subspace distance inspired by contrastive learning. In addition, we use a new training strategy. Finally, the experimental tests on the synthetic and real images prove the effectiveness of the pro-posed method.
Double Attention Network Based on Sparse Sampling

Locating action segments in long untrimmed videos is a sub-task of video understanding, which more and more scholars pay attention to. Boundary ambiguity and over-segmentation errors are two difficult problems. To handle them, we propose a network called Double Attention Network based on Sparse Sampling (DASS) on the basis of MS-TCN series. First, we design a Seq2Seq Convolution Sampling Network (SCSN) to reduce feature redundancy, which also works on over-fitting. Second, we devise a Global Temporal Attention Module (GTAM) to help predict action boundaries and improve the effect of post-processing from a global perspective. Third, we propose Local Temporal Attention Module (LTAM), which both casts attention to local frames and complements details lost in high dilated layers. We perform experiments on three challenging datasets: 50Salads, GTEA and Breakfast and prove our model is state-of-the-art.
A Channel Mix Method for Fine-Grained Cross-Modal Retrieval

In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities. Specifically, in order to enhance information interaction in different modalities for fine-grained objects, a channel mix method is developed and performed upon the channels of deep activations across dif-ferent modalities. After that, a 1 x 1 convolution is employed to aggregate the mixed channels into a unified feature vector. Moreover, equipped with a novel fine-grained cross-modal cen-ter loss, our method can further improve the intra-class separa-bility as well as inter-class compactness for multi-modalities. Experiments are conducted on the fine-grained cross-modal benchmark dataset and show our superiority over competing methods. Meanwhile, ablation studies also demonstrate the effectiveness of our proposals.
Background Matting via Recursive Excitation

We propose a simple yet effective technique that significantly improves the performance of the current state-of-the-art background matting model without compromising its original speed. We achieve this by carefully exciting the proper neural activations using an excitation map in the training phase and performing recursive inference in the testing phase. To avoid being over-reliant on perfect excitations, we follow the idea of curriculum learning to divide the training phase into three easy-to-hard stages and gradually shift the excitation map from GT alpha matte to pseudo GT alpha matte. In the testing phase, we propose a recursive inference mechanism that uses the output alpha matte as the excitation map to further refine the output alpha matte. Our method is a simple plug-in for arbitrary matting models. Compared with the original ones, the enhanced models alleviate the problem of performance degradation with complex background and thus boosts the matting accuracy.
End-to-End Image Compression via Attention-Guided Information-Preserving Module

Deep learning-based end-to-end image compression has achieved significant compression performance in recent years. However, current learning-based image compression methods are designed considering the characteristic of RGB color space, which is not suitable for image compression in YUV 420 color space because of the variance between color formats. To achieve efficient image compression in YUV 420 color space, we propose an information-preserving compression framework using the attention mechanism. Specifically, we design an information-preserving module (IPM), where we utilize the dual-branch architecture to prevent changes in data distribution and propose the feature attention block (FAB) to preserve information. Furthermore, a cross-channel progressive enhancement (CPE) network is designed by taking advantage of the relations among different channels. Ex-perimental results show that the proposed framework outper-forms state-of-the-art compression standard Versatile Video Coding (VVC) with 2.52% BD-rate reduction on common test conditions (CTC) sequences on average.
A Lightweight Image Entropy-Based Divide-and-Conquer Network for Low-Light Image Enhancement

Images captured in low-light conditions usually suffer from degradation problems. Based on the observation, we found that different image regions have different enhancement difficulties and can be processed by networks with different capacities. Hence, in this work, we propose a lightweight image entropy-based divide-and-conquer network called IEDCN for low-light image enhancement. Our network consists of Pre-processing, Enhancement, and Refinement three stages. In the Pre-processing Stage, we crop the low-light image into patches, and classify them into “simple”, “medium” and “hard” groups according to their image entropy. Then patches in each group are enhanced separately by corresponding branches with the divide-and-conquer strategy in the Enhancement Stage. Finally, the combined segments from the branches are refined by the last stage as the final output. Compared with other state-of-the-art methods, our IEDCN with only 0.73M parameters can effectively improve the quality of enhanced images, while saving up to 53% Flops on the LOL dataset.
Primed: Private Federated Training and Encrypted Inference on Medical Images in Healthcare

In healthcare, patient information is a sparse critical asset considered as private data and is often protected by law. It is also the domain which is least explored in the field of Machine Learning. The main reason for this is to build efficient artificial intelligence (AI) based models for preliminary diagnosis of various diseases, it would require a large corpus of data which can be obtained by pooling in patient information from multiple sources. However, for these sources to agree to sharing their data across distributed systems for training algorithms and models, there has to be an assurance that there will be no disclosure of the personally identifiable information (PII) of the respective Data Owners. This paper proposes PriMed, an approach to build robust privacy preserving additions to convolutional neural networks (CNN) for training and performing inference on medical images without compromising privacy. Since privacy of the data is preserved, large amounts of data can be effectively accumulated to increase the accuracy and efficiency of AI models in the field of healthcare. This involves implementing a hybrid of privacy-enhancing techniques like Federated Learning, Differential Privacy, and Homomorphic Encryption to provide a private and secure environment for learning through data.
A Virtual Reality Serious Game for Children with Dyslexia: DixGame

Children with reading and writing difficulties, such as dyslexia, have been directly affected by the Covid-19 situation because they could not have the teacher’s face-to-face support. Consequently, new devices and technological applications are being used in educational contexts to improve the interest of learning. This paper presents the design of a Virtual Reality Serious Game called DixGame. This game is a pedagogical tool specifically oriented to children between 8 and 12 years old with dyslexia. Two immersive mini-games are included in this game: a Whack-a-mole and a Memory, which try to improve different skills keeping the children focused on tasks. Whack-a-mole aims to work on the attention and visual and reading agility by recognizing correct letters and words. Memory aims to improve memory and attention ability by pairing letter-cards. The mini-game structure permits to incorporate new levels or games and the progressive increment of difficulty allows the autonomous treatment.
A Comparative Analysis for Extracting Facial Features to Detect Deepfake Videos by Various Machine Learning Methods

Generative adversarial networks (GANs) have become famous for their image synthesis capabilities from random input in deep learning models. This model generates realistic videos known as deepfake that have been misused on social media. The challenging task is to distinguish these fake videos from the public through naked eyes, causing disconcerting problems. This study proposes a supervised machine learning approach to successfully differentiate between real and fake videos by detecting visual artefacts to address this problem. For this purpose, two facial features are extracted i.e., eye blinking and nose position based on landmark detection. Both features were trained on supervised machine learning classifiers and evaluated on the publicly available UADFV and Celeb-DF deepfake datasets. Experiments successfully demonstrate that the proposed method shows the promising performance of AUC -97% for deepfake detection.
Information Architecture: Using Best Merge Method, Category Validity, and Multidimensional Scaling for Open Card Sort Data Analysis

Open card sorting is a widely used method in HCI for the design of user-centered Information Architectures (IAs).This article proposes a new algorithm that combines the best merge method (BMM), category validity technique (CVT), and multidimensional scaling (MDS) to explore, analyze and visualize open card sort data.A study involving 20 participants and 41 cards explored the IA redesign of a university's website.The collected data were analyzed using two popular methods employed in the quantitative analysis of open card sort data (i.e., hierarchical clustering, K-means) and the proposed algorithm.It was found that the latter provides increased IA insights compared to the existing methods.Specifically, the proposed algorithm can expose hidden patterns and relationships amongst cards and identify complexities.We also found that the proposed algorithm produces better initial clusters, which have a direct effect on the final clustering quality.
Type 2 Fuzzy PID for Robot Manipulator

This paper consists in developing a robust control device for a nonlinear dynamic robot manipulator subjected to external disturbances. We propose an approach combining the fuzzy logic type 2 with the proportional-integral-derivative (PID) controller to control the joint-angle position of the two-link robot manipulator in order to apply an optimization mechanism for the gains of the PID. The type-2 fuzzy system is applied to improve the efficiency and robustness of the traditional PID controller, allowing for improving the classical regulator, by introducing a certain degree of intelligence in the control strategy. For the purpose of verifying the effectiveness of the approach developed in this study, all the simulation results attest that the (T2FL-PID) compared with (T1FL-PID) and (PID) controller produces a better response. The feasibility, as well as the performances of this controller, have been validated in simulation in the control of the two-link robot manipulator.
A Proposed Big Data Architecture Using Data Lakes for Education Systems

Nowadays, educational data can be defined through the 3Vs of Big Data: volume, variety and velocity. Data sources produce massive and complex data, which makes knowledge extraction with traditional tools difficult for educational organizations. Indeed, the actual architecture of data warehouses do not possess the capability of storing and managing this huge amount of varied data. The same goes for analytical processes; which no longer satisfy business analysts; in terms of data availability and speed of execution of queries. These constraints have implied an evolution towards more modern architectures, integrating Big Data solutions capable of promoting smart learning to students. In this context, the present paper proposes a new big data architecture for education systems covering multiple data sources. Using this architecture, data is organized through a set of layers, starting with the management of the different data sources to their final consumption. The proposal approach includes data lake as a means of modernizing decision-making processes, in particular data warehouses and OLAP methods. It will be used as a means for data consolidation for the integration of heterogeneous data sources.
Agenda Setting on FAIR Guidelines in the European Union and the Role of Expert Committees

Abstract The FAIR Guidelines were conceptualised and coined as guidelines for Findable, Accessible, Interoperable and Reusable (FAIR) data at a conference held at the Lorentz Centre in Leiden in 2014. A relatively short period of time after this conference, the FAIR Guidelines made it onto the public policy agenda of the European Union. Following the concept of Kingdon, policy entrepreneurs played a critical role in creating a policy window for this idea to reach the agenda by linking it to the policy of establishing a European Open Science Cloud (EOSC). Tracing the development from idea to policy, this study highlights the critical role that expert committees play in the European Union. The permeability of the complex governance structure is increased by these committees, which allow experts to link up with the institutions and use the committees to launch new ideas. The High Level Expert Groups on the EOSC provided the platform from which the FAIR Guidelines were launched, and this culminated in the adoption of the FAIR Guidelines as a requirement for all European-funded science. As a result, the FAIR Guidelines have become an obligatory part of data management in European-funded research in 2020 and are now followed by other funders worldwide.