Reviewerly/exHarmony huggingface dataset:
Contour wavelet diffusion – a fast and high-quality facial expression generation model

Authors:
raw_author_name: Chenwei Xu
raw_affiliation_string: School of Design and Art, Communication University of Zhejiang, Hangzhou, China

Abstract: Facial expressions are important for conveying information in human interactions. The diffusion model can generate high-quality images for clearer and more discriminative faces, but its training and inference time is often prolonged, hampering practical application. Latent space diffusion models have shown promise in speeding up training by leveraging feature space parameters, but they require additional network structures. To address these limitations, we propose a contour wavelet diffusion model that accelerates both training and inference speeds. We use a contour wavelet transform to extract components from images and features, achieving substantial acceleration while preserving reconstruction quality. A normalised random channel attention module enhances the quality of generated images by focusing on high-frequency information. We also include a reconstruction loss function to enhance convergence speed. Experimental results demonstrate the effectiveness of our approach in boosting the training and inference speeds of diffusion models without sacrificing image quality. Fast generation of facial expressions can provide a smoother and more natural user experience, which is important for real-time applications. In addition, the increase in inference speed can save the use of computational resources, reduce system cost and improve energy efficiency, which is conducive to promoting the development and application of this technology.
Research on 3D Face Reconstruction Algorithm Based on ResNet and Transformer

Authors:
raw_author_name: Yilihamu Yaermaimaiti
raw_affiliation_string: School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830017, P. R. China
raw_author_name: Tianxing Yan
raw_affiliation_string: School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830017, P. R. China
raw_author_name: Yuhang Zhao
raw_affiliation_string: School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830017, P. R. China

Abstract: In view of the problems of high production cost, scarcity and lack of diversity of 3D face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction algorithm with a single 2D face image as input, which only uses 2D face datasets to complete model training. First, the improved ResNet module is introduced to preprocess the input face image. The deep residual neural network has strong feature extraction and characterization ability for the image, which can provide rich high-level semantic feature maps for the subsequent subnetwork. Then, add transformer module completely based on self-attention mechanism to the parameter prediction subnetwork, which can make different parameters of the subnetwork focus on self-related feature map information and avoid interference from invalid feature map information, so as to further improve the parameter prediction accuracy of the subnetwork. Next, training, ablation and comparison experiments were conducted on CelebA, BFM and Photoface datasets, and the combined function of pixel loss function and perceptual loss function was selected as the loss function. The experimental results show that: compared with the historical optimal results of the same network structure, the scale-invariant depth error (SIDE) and mean angle deviation (MAD) are improved by 5.9% and 10.8%, respectively, which strongly proves the effectiveness of the algorithm. Finally, in order to verify the actual effect of the 3D face reconstruction algorithm, examples are selected in this paper for reconstruction. The 3D faces generated by the algorithm all have a good sense of reality, which intuitively and effectively proves the advancement of the algorithm.
Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Authors:
raw_author_name: Dhayanithi Jaganathan
raw_affiliation_string: Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India
raw_author_name: Sathiyabhama Balasubramaniam
raw_affiliation_string: Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India
raw_author_name: Seshathiri Dhanasekaran
raw_affiliation_string: Department of Computer Science, UiT the Arctic University of Norway, 9037 Tromsø, Norway

Abstract: Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121-each Convolutional Neural Network architecture designed for classification tasks-this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.
The Research on the Application of Artificial Intelligence in Visual Art- based on Souvenir Design

Authors:
raw_author_name: Liping Qiu
raw_affiliation_string: Faculty of Design and Architecture, University Putra Malaysia, Serdang 43400, Selangor, MALAYSIA
raw_author_name: Ahmad Rizal Abdul Rahman
raw_affiliation_string: Faculty of Design and Architecture, University Putra Malaysia, Serdang 43400, Selangor, MALAYSIA

Abstract: This paper will introduce the application of artificial intelligence (AI), machine learning, and deep learning in art design and visual arts, and how these technologies can be used to create unique souvenirs. In the field of art design, AI and machine learning can be used to automatically generate artwork and patterns, providing more inspiration and creativity, and can also help artists better understand their audience and market. The application of deep learning in the field of visual arts includes image recognition, image classification, image generation, and so on. In the field of souvenir design, the use of AI and machine learning can help designers better understand market needs and consumer trends to create unique souvenirs. Taken together, the application of AI, machine learning, and deep learning technologies has great potential and creativity in the fields of art design and souvenirs.
Variable Selection in Data Analysis: A Synthetic Data Toolkit

Authors:
raw_author_name: Rohan Mitra
raw_affiliation_string: Department of Computer Science and Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
raw_author_name: Eyad Ali
raw_affiliation_string: Department of Computer Science and Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
raw_author_name: Dara Varam
raw_affiliation_string: Department of Computer Science and Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
raw_author_name: Hana Sulieman
raw_affiliation_string: Department of Computer Science and Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
raw_author_name: Firuz Kamalov
raw_affiliation_string: Department of Electrical Engineering, Canadian University of Dubai, Dubai P.O. Box 117781, United Arab Emirates

Abstract: Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs’ resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study.
Video-Based Gaze Detection for Oculomotor Abnormality Measurements

Authors:
raw_author_name: Israel Rosset
raw_affiliation_string: NeuraLight Inc., 8 The Green #16790, Dover, DE 19901, USA

Abstract: Measuring oculomotor abnormalities in human subjects is challenging due to the delicate spatio-temporal nature of the oculometric measures (OMs) used to assess eye movement abilities. Some OMs require a gaze estimation accuracy of less than 2 degrees and a sample rate that enables the detection of movements lasting less than 100 ms. While past studies and applications have used dedicated and limiting eye tracking devices to extract OMs, recent advances in imaging sensors and computer vision have enabled video-based gaze detection. Here, we present a self-calibrating neural network model for gaze detection that is suitable for oculomotor abnormality measurement applications. The model considers stimuli target locations while the examined subjects perform visual tasks and calibrate its gaze estimation output in real time. The model was validated in a clinical trial and achieved an axial accuracy of 0.93 degrees and 1.31 degrees for horizontal and vertical gaze estimation locations, respectively, as well as an absolute accuracy of 1.80 degrees. The performance of the proposed model enables the extraction of OMs using affordable and accessible setups—such as desktop computers and laptops—without the need to restrain the patient’s head or to use dedicated equipment. This newly introduced approach may significantly ease patient burden and improve clinical results in any medical field that requires eye movement measurements.
Detecting Fake Social Media Profiles Using the Majority Voting Approach

Authors:
raw_author_name: Dharmaraj R Patil
raw_affiliation_string: R.C. Patel Institute of Technology

Abstract: INTRODUCTION: The rise of social media platforms has brought about a concerning surge in the creation of fraudulent user profiles, with intentions ranging from spreading false information and perpetrating fraud to engaging in cyberbullying. The detection of these deceptive profiles has emerged as a critical imperative to safeguard the trustworthiness and security of online communities.OBJECTIVES: This paper focused on the detection and identification of fake social media profiles.METHODS: This paper introduces an innovative approach for discerning and categorizing counterfeit social media profiles by leveraging the majority voting approach. The proposed methodology integrates a range of machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost and K-Nearest Neighbors each tailored to capture distinct facets of user behavior and profile attributes. This amalgamation of diverse methods results in an ensemble of classifiers, which are subsequently subjected to a majority voting mechanism to render a conclusive judgment regarding the legitimacy of a given social media profile.RESULTS: We conducted thorough experiments using a dataset containing both legitimate and fake social media profiles to determine the efficiency of our methodology. Our findings substantiate that the Majority Voting Technique surpasses individual classifiers, attaining an accuracy rate of 99.12%, a precision rate of 99.12%, a recall rate of 99.12%, and an F1-score of 99.12%.CONCLUSION: The results show that the majority vote method is reliable for detecting and recognising fake social media profiles.
Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning

Authors:
raw_author_name: Haojie Wang
raw_affiliation_string: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
raw_author_name: Yansong Wang
raw_affiliation_string: School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Abstract: The intelligent identification of coal gangue on industrial conveyor belts is a crucial technology for the precise sorting of coal gangue. To address the issues in coal gangue detection algorithms, such as high false negative rates, complex network structures, and substantial model weights, an optimized coal gangue detection algorithm based on YOLOv5s is proposed. In the backbone network, a feature refinement module is employed for feature extraction, enhancing the capability to extract features for coal and gangue. The improved BIFPN structure is employed as the feature pyramid, augmenting the model’s capability for cross-scale feature fusion. In the prediction layer, the ESIOU is utilized as the bounding box regression loss function to rectify the misalignment issue between predicted and actual box angles. This approach expedites the convergence speed of the network while concurrently enhancing the accuracy of coal gangue detection. Channel pruning is implemented on the network to diminish model computational complexity and weight, consequently augmenting detection speed. The experimental results demonstrate that the refined YOLOv5s coal gangue detection algorithm outperforms the original YOLOv5s algorithm, achieving a notable accuracy enhancement of 2.2% to reach 93.8%. Concurrently, a substantial reduction in model weight by 38.8% is observed, resulting in a notable 56.2% increase in inference speed. These advancements meet the detection requirements for scenarios involving mixed coal gangue.
Dialogue System for Casual and Continuous Word Learning

Authors:
raw_author_name: Yosuke Seki
raw_affiliation_string: Research Center for Higher Education Tokushima University, 2-24, Shinkura-cho, Tokushima-shi Tokushima 770-8501 Japan

Abstract: Facilitating causal and continuous learning in individuals with low motivation presents a formidable challenge owing to the diverse methods and systems available for vocabulary acquisition. Therefore, this study developed a non‐task‐oriented dialogue system to facilitate casual and continuous word learning. Specifically, dialogue systems capable of processing text‐based inputs, encompassing words registered by learners, were formulated. Learners could engage in the acquisition of these words through informal conversation. The evaluation results demonstrated the effectiveness of the proposed method for fostering casual and continuous learning. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Beyond the pages: unleashing holograms and holographic technology in Caribbean academic libraries

Authors:
raw_author_name: Adrian St. Patrick Duncan
raw_affiliation_string: Department of Library, University of the West Indies at Mona, Mona, Jamaica

Abstract: Purpose This paper is intended to spark discourse on the impact of holograms and holographic technology on libraries and the possibility of their usage in Caribbean academic libraries for assisting users, especially those from the physically challenged community, as well as distance library users. Design/methodology/approach A thorough assessment of literature was done of all academic and higher institution libraries across the English-speaking Caribbean to determine the effective use of holograms and holographic technology in Caribbean libraries. Literature from several developed countries was studied, paying keen attention to the emerging holograms and holographic technology and the plausibility for inclusion and exploitation by academic libraries. Findings Revealing the possibilities for fostering great levels of efficacy, as well as saving time and reducing the possibility for inefficiencies in the system through smoother check-in and check-out while offering more accountability. Research limitations/implications This research focused primarily on holograms and holographic technology that can assist libraries and their users in the near future. It further attested to the need for there to be greater consideration for physically challenged individuals and their access to material from the library. Originality/value This paper is the genesis for discussion surrounding holograms and holographic technology in the English-speaking Caribbean. It sought to bring awareness to the possible uses of holograms while exploring the practical implications of its use in libraries across the English-speaking Caribbean.
Optimal trajectory planning of robot energy consumption based on improved sparrow search algorithm

Authors:
raw_author_name: Yaosheng Zhou
raw_affiliation_string: School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan, China
raw_author_name: Ziang Wei
raw_affiliation_string: Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, China; School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, China
raw_author_name: Zixin Huang
raw_affiliation_string: Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan, China; School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, China
raw_author_name: Xubing Chen
raw_affiliation_string: School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan, China

Abstract: In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.
WeightedSLIM: A Novel Item-Weights Enriched Baseline Recommendation Model

Authors:
raw_author_name: Haiyang Zhang
raw_affiliation_string: Department of Computing, Xi’an Jiaotong, Liverpool University, Suzhou, CHINA
raw_author_name: Xinyi Zeng
raw_affiliation_string: College of Artificial Intelligence, North China University of Science and Technology, Tangshan, CHINA
raw_author_name: Ivan Ganchev
raw_affiliation_string: University of Plovdiv “Paisii Hilendarski”, Plovdiv, BULGARIA

Abstract: This paper proposes a novel weight-enriched ranking-based baseline model, WeightedSLIM, aiming to provide more accurate top-N recommendations from implicit user feedback. The model utilizes ItemRank to calculate the ranking score of each item, which is then used as an item weight within the Sparse Linear Model (SLIM), while using Bayesian Personalized Ranking (BPR) to optimize the item similarity matrix. Experiments, conducted for performance comparison of the proposed model with existing recommendation models, demonstrate that it can indeed provide better recommendations and can be used as a strong baseline for top-N recommendations.
<i>Clingraph</i>: A System for ASP-based Visualization

Authors:
raw_author_name: SUSANA HAHN
raw_affiliation_string: University of Potsdam, Potsdam, Germany Potassco Solutions, Potsdam, Germany (e-mail:
raw_author_name: ORKUNT SABUNCU
raw_affiliation_string: TED University, Ankara, Turkey Potassco Solutions, Potsdam, Germany (e-mail:
raw_author_name: TORSTEN SCHAUB
raw_affiliation_string: University of Potsdam, Potsdam, Germany Potassco Solutions, Potsdam, Germany (e-mails:
raw_author_name: TOBIAS STOLZMANN
raw_affiliation_string: University of Potsdam, Potsdam, Germany Potassco Solutions, Potsdam, Germany (e-mails:

Abstract: Abstract We present the Answer Set Programming (ASP)-based visualization tool clingraph , which aims at visualizing various concepts of ASP by means of ASP itself. This idea traces back to the aspviz tool and clingraph redevelops and extends it in the context of modern ASP systems. More precisely, clingraph takes graph specifications in terms of ASP facts and hands them over to the graph visualization system graphviz . The use of ASP provides a great interface between logic programs and/or answer sets and their visualization. Also, clingraph offers a Python application programming interface (API) that extends this ease of interfacing to clingo ’s API and in turn to connect and monitor various aspects of the solving process.