Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence and Pathomics

Authors:
raw_author_name: Puria Azadi Moghadam
raw_affiliation_string: Department of Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada
raw_author_name: Ali Bashashati
raw_affiliation_string: Department of Pathology and Laboratory Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z7, Canada; School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, British Columbia V6T 1Z3, Canada
raw_author_name: S. Larry Goldenberg
raw_affiliation_string: Department of Urologic Sciences, University of British Columbia, 2775 Laurel Street, Vancouver British Columbia V5Z 1M9, Canada

Abstract: Artificial intelligence (AI) has the potential to transform pathologic diagnosis and cancer patient management as a predictive and prognostic biomarker. AI-based systems can be used to examine digitally scanned histopathology slides and differentiate benign from malignant cells and low from high grade. Deep learning models can analyze patient data from individual or multimodal combinations and identify patterns to be used to predict the response to different therapeutic options, the risk of recurrence or progression, and the prognosis of the newly diagnosed patient. AI-based models will improve treatment planning for patients with prostate cancer and improve the efficiency and cost-effectiveness of the pathology laboratory.
Federated Learning and NFT-Based Privacy-Preserving Medical-Data-Sharing Scheme for Intelligent Diagnosis in Smart Healthcare

Authors:
raw_author_name: Siva Sai
raw_affiliation_string: Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science-Pilani, Pilani, India
raw_author_name: Vikas Hassija
raw_affiliation_string: School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India
raw_author_name: Vinay Chamola
raw_affiliation_string: Department of Electrical and Electronics Engineering and APPCAIR, Birla Institute of Technology and Science-Pilani, Pilani, India
raw_author_name: Mohsen Guizani
raw_affiliation_string: Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

Abstract: Historical medical data of the patients have an important place in the healthcare industry for providing the best care to patients through intelligent health diagnosis and prediction of diseases. The existing intelligent health diagnosis systems collect data from medical institutions or laboratories and then use machine learning algorithms to predict diseases. But, in most cases, the medical institutions have incomplete medical data of the patients since a patient may consult different specialists (from various hospitals) during the treatment process. To overcome this problem, we build a smart and secure federated learning framework for intelligent health diagnosis with a blockchain-based incentive mechanism and NFT(Non-Fungible Tokens)-based marketplace. We make use of NFTs to develop clear demarkations on the ownership and accessibility of the data of patients. We create an NFT marketplace that manages access to the historical medical data of patients. A comprehensive incentive mechanism based on several factors, including the quality and relevance of the data, the frequency, and regularity of data uploading, etc., is incorporated to encourage and penalize the patients based on their contributions to the global model. We used the Polyak-averaging technique for aggregating local models to form a global model. The extensive analysis shows that the proposed model achieves comparable performance with the centralized machine learning models while affording better security and access to better data. The results also show the efficacy of the proposed blockchain-based incentive mechanism.
Fedcs: Efficient communication scheduling in decentralized federated learning

Authors:
raw_author_name: Yunchuan Qin
raw_affiliation_string: Hunan University, Changsha, China
raw_author_name: Fan Wu
raw_affiliation_string: Hunan University, Changsha, China
raw_author_name: Zhuo Tang
raw_affiliation_string: Hunan University, Changsha, China; National Supercomputing Changsha Center, Changsha, China
raw_author_name: Kenli Li
raw_affiliation_string: Hunan University, Changsha, China; National Supercomputing Changsha Center, Changsha, China

Abstract: Decentralized federated learning is a training approach that prioritizes user data privacy protection, while also offering improved scalability and robustness. However, as the number of edge devices participating in training increases, a significant communication overhead arises among devices located in different geographical locations. Therefore, designing a well-thought-out gradient synchronization strategy is crucial for minimizing the overall communication overhead of training. To tackle this issue, this article introduces a 2D-Ring network structure based parameter synchronization strategy and a 2D-attention-based device placement algorithm, aiming to minimize communication overhead. The parameter synchronization strategy devises a two-layer circular communication architecture for the devices involved in training, thereby reducing the overall frequency of parameter synchronization in decentralized federated learning. By taking into account the total communication overhead and the device placement strategy, an optimization problem is formulated. Specifically, a 2D-attention neural network is constructed to optimize the device placement solution based on 2D-Ring network structure, leading to reduced communication overhead. Moreover, an evaluation model is designed to assess the communication overhead in a complex decentralized system during federated training. This enables precise determination of the total communication overhead throughout the training process, providing valuable insights for devising the device placement strategy. Extensive simulations confirm that the proposed approach achieves a substantial reductions of 55% and 64% in the total communication overhead for decentralized federated learning training with 50 and 100 devices, respectively.
Towards Scalable Multi-View Clustering via Joint Learning of Many Bipartite Graphs

Authors:
raw_author_name: Jinghuan Lao
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, China
raw_author_name: Dong Huang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, China
raw_author_name: Chang-Dong Wang
raw_affiliation_string: Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangzhou, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Jian-Huang Lai
raw_affiliation_string: Guangdong Key Laboratory of Information Security Technology, Guangzhou, China; School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

Abstract: This paper focuses on two limitations to previous multi-view clustering approaches. First, they frequently suffer from quadratic or cubic computational complexity, which restricts their feasibility for large-scale datasets. Second, they often rely on a single graph on each view, yet lack the ability to jointly explore many versatile graph structures for enhanced multi-view information exploration. In light of this, this paper presents a new Scalable Multi-view Clustering via Many Bipartite graphs (SMCMB) approach, which is capable of jointly learning and fusing many bipartite graphs from multiple views while maintaining high efficiency for very large-scale datasets. Different from the one-anchor-set-per-view paradigm, we first produce multiple diversified anchor sets on each view and thus obtain many anchor sets on multiple views, based on which the anchor-based subspace representation learning is enforced and many bipartite graphs are simultaneously learned. Then these bipartite graphs are efficiently partitioned to produce the base clusterings, which are further re-formulated into a unified bipartite graph for the final clustering. Note that SMCMB has almost linear time and space complexity. Extensive experiments on twenty general-scale and large-scale multi-view datasets confirm its superiority in scalability and robustness over the state-of-the-art.
Combining variational mode decomposition with regularisation techniques to denoise MRI data

Authors:
raw_author_name: Krzysztof Brzostowski
raw_affiliation_string: Department of Computer Science and Systems Engineering, Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Wrocław 50-370, Poland
raw_author_name: Rafał Obuchowicz
raw_affiliation_string: Department of Diagnostic Imaging, Jagiellonian University Medical College, Kraków 31-501, Poland

Abstract: In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.
CT-Net: Asymmetric compound branch Transformer for medical image segmentation

Authors:
raw_author_name: Ning Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China
raw_author_name: Long Yu
raw_affiliation_string: College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China; College of Network Center, Xinjiang University, Urumqi 830000, China
raw_author_name: Dezhi Zhang
raw_affiliation_string: People’s Hospitial of Xinjiang Uygur Autonomous Region, Xinjiang University, China
raw_author_name: Weidong Wu
raw_affiliation_string: People’s Hospitial of Xinjiang Uygur Autonomous Region, Xinjiang University, China
raw_author_name: Shengwei Tian
raw_affiliation_string: College of Software, Xinjiang University, Urumqi 830000, China
raw_author_name: Xiaojing Kang
raw_affiliation_string: People’s Hospitial of Xinjiang Uygur Autonomous Region, Xinjiang University, China
raw_author_name: Min Li
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha 410083, China

Abstract: The Transformer architecture has been widely applied in the field of image segmentation due to its powerful ability to capture long-range dependencies. However, its ability to capture local features is relatively weak and it requires a large amount of data for training. Medical image segmentation tasks, on the other hand, demand high requirements for local features and are often applied to small datasets. Therefore, existing Transformer networks show a significant decrease in performance when applied directly to this task. To address these issues, we have designed a new medical image segmentation architecture called CT-Net. It effectively extracts local and global representations using an asymmetric asynchronous branch parallel structure, while reducing unnecessary computational costs. In addition, we propose a high-density information fusion strategy that efficiently fuses the features of two branches using a fusion module of only 0.05M. This strategy ensures high portability and provides conditions for directly applying transfer learning to solve dataset dependency issues. Finally, we have designed a parameter-adjustable multi-perceptive loss function for this architecture to optimize the training process from both pixel-level and global perspectives. We have tested this network on 5 different tasks with 9 datasets, and compared to SwinUNet, CT-Net improves the IoU by 7.3% and 1.8% on Glas and MoNuSeg datasets respectively. Moreover, compared to SwinUNet, the average DSC on the Synapse dataset is improved by 3.5%.
An Algorithm for Finding Optimal k-Core in Attribute Networks

Authors:
raw_author_name: Jing Liu
raw_affiliation_string: Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610299, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Yong Zhong
raw_affiliation_string: Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610299, China; School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: As a structural indicator of dense subgraphs, k-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of k-core. However, relevant algorithms often set k values based on empirical analysis of datasets or require users to input manually. Once users are not familiar with the graph network structure, they may miss the optimal solution due to an improper k setting. Especially in attribute social networks, characterizing communities with only k-cores may lead to a lack of semantic interpretability of communities. Consequently, this article proposes a method for identifying the optimal k-core with the greatest attribute score in the attribute social network as the target community. The difficulty of the problem is that the query needs to integrate both structural and textual indicators of the community while fully considering the diversity of attribute scoring functions. To effectively reduce computational costs, we incorporate the topological characteristics of the k-core and the attribute characteristics of entities to construct a hierarchical forest. It is worth noting that we name tree nodes in a way similar to pre-order traversal and can maintain the order of all tree nodes during the forest creation process. In such an attribute forest, it is possible to quickly locate the initial solution containing all query vertices and reuse intermediate results during the process of expanding queries. We conducted effectiveness and performance experiments on multiple real datasets. As the results show, attribute scoring functions are not monotonic, and the algorithm proposed in this paper can avoid scores falling into local optima. With the help of the attribute k-core forest, the actual query time of the Advanced algorithm has improved by two orders of magnitude compared to the BaseLine algorithm. In addition, the average F1 score of our target community has increased by 2.04 times and 26.57% compared to ACQ and SFEG, respectively.
Application of Metaverse as an Immersive Teaching Tool in Engineering Technology Classrooms

Authors:
raw_author_name: Iftekhar Basith
raw_affiliation_string: Sam Houston State University

Abstract: Abstract Application of Metaverse as an Immersive Teaching Tool in Engineering Technology Classroom The last two years have presented an ever challenging classroom environment for both students and educators, especially when it comes to topics in engineering based on hands-on or case based learning requiring highly interactive class sessions. Online education platforms like Blackboard and Canvas have provided some level of classroom stability by providing a centralized repository for class content, delivery and administration, however whether this translates into a more engaging experience is relatively unknown, especially as classes have moved to a combination of online, hybrid, synchronous and asynchronous models. The Department of Engineering Technology and in particular the "Technology Innovations" class will leverage the metaverse environment in virtual reality (VR) and other highly immersive tools with students. Through the class we want to define a framework for education curriculum design and content delivery in a virtual and highly interactive environment. The development of the metaverse curriculum framework will aim to establish values and principles outlining the parameters for classroom methodology focused on leveraging both current and near future tools to deliver classroom education. Students will have the ability to take the course online through Blackboard and also have the ability to take the course in the metaverse using VR. The course content for both online and metaverse will be the same however the environment and the content delivery methods will be different to test out the level of engagement and knowledge transfer experienced by the student. It is expected, at the end of the course, students will have a valuable skill set in their ability to both learn, present and interact in the metaverse. Finally, as educators we will have an initial evaluation of classroom educational tools for the metaverse.
commonmeta-py now supports metadata lists

Authors:
raw_author_name: Martin Fenner

Abstract: This week the commonmeta-py Python library adds an important new feature: metadata lists. With this feature commonmeta-py no longer only operates on metadata for a single scholarly work (e.g. a journal article, book, dataset, software, or blog post), but can handle lists of scholarly works.
A Technical Review of the State-of-the-Art Methods in Aspect-Based Sentiment Analysis

Authors:
raw_author_name: Emeka Ogbuju
raw_author_name: Taiwo Abiodun
raw_author_name: Francisca Oladipo

Abstract: With the advent and rapid advancement of text mining technology, a computer-based approach used to capture sentiment standpoints from data in textual form is increasingly becoming a promising field. Detailed information about sentiment can be provided using aspect-based sentiment analysis, which can be used in better decision-making. This study aims to study, observe, and classify previous methods used in aspect-based sentiment analysis. A systematic review is adopted as the method used to collect and review papers to achieve this research's aim. Papers focused on sentiment analysis, aspect extraction, and aspect aggregation from different academic databases such as Scopus, ScienceDirect, IEEE Explore, and Web of Science were gathered based on the inclusion and exclusion criteria of the study. The gathered papers were further reviewed to answer the stated research questions. The findings from the research show the most used methods for aspect extraction, sentiment analysis, and aspect aggregation in aspect-based sentiment analysis. This research offers a robust synthesis of evidence to guide further academic exploration in sentiment analysis.
STR: Secure Computation on Additive Shares Using the Share-Transform-Reveal Strategy

Authors:
raw_author_name: Zhihua Xia
raw_affiliation_string: College of Cyber Security, Jinan University, Guangzhou, China
raw_author_name: Qi Gu
raw_affiliation_string: Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science &#x0026; Technology, Nanjing, China
raw_author_name: Wenhao Zhou
raw_affiliation_string: Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science &#x0026; Technology, Nanjing, China
raw_author_name: Lizhi Xiong
raw_affiliation_string: Engineering Research Center of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science &#x0026; Technology, Nanjing, China
raw_author_name: Jian Weng
raw_affiliation_string: College of Cyber Security, Jinan University, Guangzhou, China
raw_author_name: Neal N. Xiong
raw_affiliation_string: School of Computer Science and Technology, Hainan University, Haikou, China

Abstract: The rapid development of cloud computing has probably benefited each of us. However, the privacy risks brought by untrustworthy cloud servers arise the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks while ensuring the security of private data. The tasks to be outsourced are countless; however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) in arbitrary $n\geq 2$ servers. All protocols only require constant rounds of interactions and achieve the low computation complexity. Moreover, the proposed $n$-party protocols ensure the security of private data even though $n-1$ servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols.
Adaptive Collaboration With Training Plan Considering Role Correlation

Authors:
raw_author_name: Libo Zhang
raw_affiliation_string: College of Artificial Intelligence, Southwest University, Chongqing, China
raw_author_name: Shiyu Wu
raw_affiliation_string: College of Artificial Intelligence, Southwest University, Chongqing, China
raw_author_name: Haibin Zhu
raw_affiliation_string: Department of Computer Science and Mathematics, Nipissing University, North Bay, Canada
raw_author_name: Yin Sheng
raw_affiliation_string: College of Internet of Things Engineering, Hohai University, Changzhou, China

Abstract: Based on role-based collaboration (RBC), group role assignment (GRA) optimizes a team’s overall performance by assigning the most appropriate individual agents from the team’s viewpoint based on agents’ role-playing abilities. As an extension of GRA, GRA with a training plan (GRATP) deals with the impact of training on team management. Considering the correlation between roles, the training of one agent on one role also affects the performance of the agent in other roles. Moreover, in the adaptive collaboration (AC) problem, the training time also affects significantly the agent’s ability, as an agent’s ability changes over time. However, the existing GRATP models fail to consider these factors in the collaboration process. Therefore, we aim to address the role-correlation-based adaptive GRATP (RCA-GRATP) in this article. This article contributes two aspects to the literature on AC. 1) RCA-GRATP problem is abstracted based on RBC and GRA. To the best of the authors’ knowledge, this is the first article that explicitly considers role correlation in the RBC problems. 2) A comprehensive formalization of RCA-GRATP and two solving algorithms for diverse situations are proposed to solve the formalized problems. Experiments are carried out to verify the effectiveness of the proposed algorithms in diverse scenarios.