Reviewerly/exHarmony huggingface dataset:
VertexShuffle-Based Spherical Super-Resolution for 360-Degree Videos

Authors:
raw_author_name: Na Li
raw_affiliation_string: Rutgers University, Piscataway, USA
raw_author_name: Yao Liu
raw_affiliation_string: Rutgers University, Piscataway, USA

Abstract: 360-degree video is an emerging form of media that encodes information about all directions surrounding a camera, offering an immersive experience to the users. Unlike traditional 2D videos, visual information in 360-degree videos can be naturally represented as pixels on a sphere. Inspired by state-of-the-art deep-learning-based 2D image super-resolution models and spherical CNNs, in this paper, we design a novel spherical super-resolution (SSR) approach for 360-degree videos. To support viewport-adaptive and bandwidth-efficient transmission/streaming of 360-degree video data and save computation, we propose the Focused Icosahedral Mesh to represent a small area on the sphere. We further construct matrices to rotate spherical content over the entire sphere to the focused mesh area, allowing us to use the focused mesh to represent any area on the sphere. Motivated by the PixelShuffle operation for 2D super-resolution, we also propose a novel VertexShuffle operation on the mesh and an improved version VertexShuffle_V2. We compare our SSR approach with state-of-the-art 2D super-resolution models and show that SSR has the potential to achieve significant benefits when applied to spherical signals.
Research on joint model relation extraction method based on entity mapping

Authors:
raw_author_name: Hongmei Tang
raw_affiliation_string: School of Computer Science and Engineering, Beihang University, Beijing, China.
raw_author_name: Dixiongxiao Zhu
raw_affiliation_string: School of Computer Science and Engineering, Beihang University, Beijing, China.
raw_author_name: Wenzhong Tang
raw_affiliation_string: School of Computer Science and Engineering, Beihang University, Beijing, China.
raw_author_name: Shuai Wang
raw_affiliation_string: School of Computer Science and Engineering, Beihang University, Beijing, China.
raw_author_name: Yanyang Wang
raw_affiliation_string: School of Aeronautic Science and Engineering, Beihang University, Beijing, China.
raw_author_name: Lihong Wang
raw_affiliation_string: National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China.

Abstract: Relationship Extraction (RE) is a central task in information extraction. The use of entity mapping to address complex scenarios with overlapping triples, such as CasRel, is gaining traction, yet faces challenges such as inadequate consideration of sentence continuity, sample imbalance and data noise. This research introduces an entity mapping-based method CasRelBLCF building on CasRel. The main contributions include: A joint decoder for the head entity, utilizing Bi-LSTM and CRF, integration of the Focal Loss function to tackle sample imbalance and a reinforcement learning-based noise reduction method for handling dataset noise. Experiments on relation extraction datasets indicate the superiority of the CasRelBLCF model and the enhancement on model's performance of the noise reduction method.
A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents

Authors:
raw_author_name: Eric Rohmer
raw_author_name: Paula Dornhofer Paro Costa
raw_author_name: Esther Luna Colombini
raw_author_name: Alexandre da Silva Simões
raw_author_name: Ricardo Ribeiro Gudwin

Abstract: Abstract Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.
On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition

Authors:
raw_author_name: Jascha Kolberg
raw_affiliation_string: da/sec–Biometrics and Security Research Group, Hochschule Darmstadt, Darmstadt, Germany
raw_author_name: Christian Rathgeb
raw_affiliation_string: da/sec–Biometrics and Security Research Group, Hochschule Darmstadt, Darmstadt, Germany
raw_author_name: Christoph Busch
raw_affiliation_string: da/sec–Biometrics and Security Research Group, Hochschule Darmstadt, Darmstadt, Germany

Abstract: With the rise of deep neural networks, the performance of biometric systems has increased tremendously. Biometric systems for face recognition are now used in everyday life, e.g., border control, crime prevention, or personal device access control. Although the accuracy of face recognition systems is generally high, they are not without flaws. Many biometric systems have been found to exhibit demographic bias, resulting in different demographic groups being not recognized with the same accuracy. This is especially true for facial recognition due to demographic factors, e.g., gender and skin color. While many previous works already reported demographic bias, this work aims to reduce demographic bias for biometric face recognition applications. In this regard, 12 face recognition systems are benchmarked regarding biometric recognition performance as well as demographic differentials, i.e., fairness. Subsequently, multiple fusion techniques are applied with the goal to improve the fairness in contrast to single systems. The experimental results show that it is possible to improve the fairness regarding single demographics, e.g., skin color or gender, while improving fairness for demographic subgroups turns out to be more challenging.
A topic relevance-aware click model for web search

Authors:
raw_author_name: Liu Jianping
raw_affiliation_string: College of Computer Science and Engineering, North Minzu University, Yinchuan, China; The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, China
raw_author_name: Wang Jian
raw_affiliation_string: Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
raw_author_name: Wang Meng
raw_affiliation_string: College of Computer Science and Engineering, North Minzu University, Yinchuan, China

Abstract: To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks.
Diffusion models in text generation: a survey

Authors:
raw_author_name: Qiuhua Yi
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University of Technology, HangZhou, China
raw_author_name: Chenwei Zhang
raw_affiliation_string: School of Faculty of Education, University of Hong Kong, Hong Kong, China
raw_author_name: Zehai Zhou
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University of Technology, HangZhou, China
raw_author_name: Linan Zhu
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University of Technology, HangZhou, China
raw_author_name: Xiangjie Kong
raw_affiliation_string: College of Computer Science and Technology, Zhejiang University of Technology, HangZhou, China

Abstract: Diffusion models are a kind of math-based model that were first applied to image generation. Recently, they have drawn wide interest in natural language generation (NLG), a sub-field of natural language processing (NLP), due to their capability to generate varied and high-quality text outputs. In this article, we conduct a comprehensive survey on the application of diffusion models in text generation. We divide text generation into three parts (conditional, unconstrained, and multi-mode text generation, respectively) and provide a detailed introduction. In addition, considering that autoregressive-based pre-training models (PLMs) have recently dominated text generation, we conduct a detailed comparison between diffusion models and PLMs in multiple dimensions, highlighting their respective advantages and limitations. We believe that integrating PLMs into diffusion is a valuable research avenue. We also discuss current challenges faced by diffusion models in text generation and propose potential future research directions, such as improving sampling speed to address scalability issues and exploring multi-modal text generation. By providing a comprehensive analysis and outlook, this survey will serve as a valuable reference for researchers and practitioners interested in utilizing diffusion models for text generation tasks.
Study of real-time parameter measurement of ring rolling pieces based on machine vision

Authors:
raw_author_name: Han Li
raw_affiliation_string: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China.
raw_author_name: Zhijiang Zuo
raw_affiliation_string: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China.
raw_author_name: Libo Pan
raw_affiliation_string: State Key Laboratory of Precision Blasting, Jianghan University, Wuhan, 430056, China.

Abstract: Real time parameter measurement cannot be carried out to dynamic ring parts during automation ring rolling processes so that rolling process parameters cannot be adjusted in time. Considering effects of shaping of ring rolling parts, a visual measurement platform was set up and a machine vision-based non-contact real -time measurement method was put forward. This article improves the subpixel level edge extraction algorithm to extract edge data information of circular rolling pieces. Based on the characteristics of circular rolling pieces, an RG-Hough transform method is proposed to fit the detected edge data information. The conversion relationship between pixel and actual sizes were determined in combination with the camera calibration to gain parameters of ring rolling parts. Measurements of ring parts (OD: 462.12mm; and ID: 315.53mm) were applied to verify the effectiveness of our method. Our measurement error is ±0.25mm and our average speed can be up to 104ms/frame. Our study can provide powerful technical supports for intelligent control of ring rolling pieces.
Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Authors:
raw_author_name: Marie Hubálovská
raw_affiliation_string: Department of Technics, Faculty of Education, University of Hradec Kralove, 50003 Hradec Králové, Czech Republic
raw_author_name: Štěpán Hubálovský
raw_affiliation_string: Department of Technics, Faculty of Education, University of Hradec Kralove, 50003 Hradec Králové, Czech Republic
raw_author_name: Pavel Trojovský
raw_affiliation_string: Department of Technics, Faculty of Education, University of Hradec Kralove, 50003 Hradec Králové, Czech Republic

Abstract: This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA's ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA's superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA's effectiveness in real-world optimization tasks.
Quantitative assessment of H&amp;E staining for pathology: development and clinical evaluation of a novel system

Authors:
raw_author_name: Catriona Dunn
raw_affiliation_string: Department of Pathology and Data Analytics, University of Leeds, Leeds, UK. catriona.dunn@nhs.net.; National Pathology Imaging Co-operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK. catriona.dunn@nhs.net.
raw_author_name: David Brettle
raw_affiliation_string: National Pathology Imaging Co-operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
raw_author_name: Darren Treanor
raw_affiliation_string: Department of Clinical Pathology and Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.; Department of Pathology and Data Analytics, University of Leeds, Leeds, UK.; National Pathology Imaging Co-operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK.

Abstract: Staining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation.The stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels.Stain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98-0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period.The proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.
Multi-sensor fusion federated learning method of human posture recognition for dual-arm nursing robots

Authors:
raw_author_name: Huanyu Deng
raw_affiliation_string: College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161000, China; Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, China
raw_author_name: Yulong Wang
raw_affiliation_string: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
raw_author_name: Jiexin Xie
raw_affiliation_string: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
raw_author_name: Hui Zhang
raw_affiliation_string: College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161000, China
raw_author_name: Yang Li
raw_affiliation_string: Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, China
raw_author_name: Shijie Guo
raw_affiliation_string: Academy for Engineering and Technology, Fudan University, Shanghai 200433, China; Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, China

Abstract: Human posture estimation is a significant pre-imperative for intelligent nursing robot. However, most existing studies suffer from the driven data deficiencies, because of the concerns about poor information safety in the private healthcare environment. Federated Learning is able to solve the problem, as it can improve the learning ability of the models as well as protecting the data privacy. In this paper, we proposed a Federated Learning Human Posture Recognition (FL-HPR) framework according to image and point cloud fusion. FL-HPR benefits the information flow in the global model while ensuring the data privacy of local models. Furthermore, FL-HPR optimizes the local dynamic graph edge convolution network of robot, which improves the recognition accuracy of each body limb and enhance robustness. Experiments on Non-IID datasets illustrate that the presented FL-HPR remarkably outperforms non-federated learning methods. The developed frame improves the accuracy of human joint estimation, which indicates the proposed FL-HPR can be integrated into the intelligent nursing robot in practical. The open-source code and videos are available at https://github.com/Hebut-LEVO/.
Melanoma Prevention using an Augmented Reality-based Serious Game

Authors:
raw_author_name: Catarina Ferreira
raw_affiliation_string: I3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal, Ipatimup – Instituto de Patologia e Imunologia Molecular da Universidade do Porto, Portugal, Faculdade de Engenharia da Universidade do Porto, Portugal
raw_author_name: António Coelho
raw_affiliation_string: Faculdade de Engenharia da Universidade do Porto, Portugal, INESC TEC – Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Portugal

Abstract: The purpose of this study was to field-test a recently developed AR-based serious game designed to promote SSE self-efficacy, called Spot. Thirty participants played the game and answered 3 questionnaires: a baseline questionnaire, a second questionnaire immediately after playing the game, and a third questionnaire 1 week later (follow-up). The majority of participants considered that the objective quality of the game was high, and considered that the game could have a real impact in SSE promotion. Participants showed statistically significant increases in SSE self-efficacy and intention at follow-up. Of the 24 participants that had never performed a SSE or had done one more than 3 months ago, 12 (50.0%) reported doing a SSE at follow-up. This study provides supporting evidence to the use of serious games in combination with AR to educate and motivate users to perform SSE. Spot seems to be an inconspicuous but effective strategy to promote SSE, a cancer prevention behavior, among healthy individuals. Patient education is essential to tackle skin cancer, particularly melanoma. Serious games, such as Spot, have the ability to effectively educate and motivate patients to perform a cancer prevention behavior.
High invisibility image steganography with wavelet transform and generative adversarial network

Authors:
raw_author_name: Ye Yao
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Junyu Wang
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Qi Chang
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Yizhi Ren
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Weizhi Meng
raw_affiliation_string: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark

Abstract: Recently, extensive research is showing that deep learning has enormous potential in image steganography. However, most steganography methods based on deep learning are not sufficiently invisible. In this paper, a novel end-to-end deep neural network for image steganography based on generative adversarial network (GAN) and discrete wavelet transformation (DWT) is proposed, which can greatly improve the invisibility of the method. Firstly, the cover image is transformed from the RGB domain to the DWT domain to embed information in frequency. Furthermore, a multi-scale attention convolution to fuse different scales of feature information is proposed, which can help the model to find a better location to embed the information. Finally, the fusion module is used to embed the information into the cover image at different modification intensities. In addition, a new Discriminator that compares cover images and stego images in a patch-to-patch way to improve the ability of the Discriminator. We analyze our algorithm from multiple aspects (e.g., security and invisibility) to verify the superior performance of the proposed method. To demonstrate generalization ability, experiments are conducted on three widely used datasets and show the superior performance of our method compared to other steganography methods based on deep learning.