Reviewerly/exHarmony huggingface dataset:
Role Engine Implementation for a Continuous and Collaborative Multirobot System

Authors:
raw_author_name: Behzad Akbari
raw_affiliation_string: Department of Computer Science and Mathematics, Collaborative Systems Laboratory, Nipissing University, North Bay, Canada
raw_author_name: Zikai Wang
raw_affiliation_string: Department of Computer Science and Mathematics, Collaborative Systems Laboratory, Nipissing University, North Bay, Canada
raw_author_name: Haibin Zhu
raw_affiliation_string: Department of Computer Science and Mathematics, Collaborative Systems Laboratory, Nipissing University, North Bay, Canada
raw_author_name: Ryan Adderson
raw_affiliation_string: Department of Mechanical Engineering, Advanced Control and Mechatronics Laboratory, Dalhousie University, Halifax, Canada
raw_author_name: Ya-Jun Pan
raw_affiliation_string: Department of Mechanical Engineering, Advanced Control and Mechatronics Laboratory, Dalhousie University, Halifax, Canada

Abstract: In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream of actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multirobot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment’s skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach’s feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots.
Privacy-Preserving Realization of Fuzzy Clustering and Fuzzy Modeling Through Vertical Federated Learning

Authors:
raw_author_name: Xiubin Zhu
raw_affiliation_string: School of Electro-Mechanical Engineering, Xidian University, Xi&#x2019;an, China
raw_author_name: Dan Wang
raw_affiliation_string: College of Computer Science and Technology, Xi&#x2019;an University of Science and Technology, Xi&#x2019;an, China
raw_author_name: Witold Pedrycz
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
raw_author_name: Zhiwu Li
raw_affiliation_string: Institute of Systems Engineering, Macau University of Science and Technology, Macau, China

Abstract: In this study, we elaborate on a realization of fuzzy clustering and the construction of fuzzy rule-based models on the basis of vertically partitioned datasets in a privacy-preserving federated learning approach. The main focus of the overall design process is to construct a family of information granules (clusters) and the corresponding fuzzy rules in the presence of a collection of vertically partitioned datasets without compromising data privacy. These datasets are composed of the same data but are described by different features, and due to security considerations, data cannot be shared. The vertical federated fuzzy clustering can be realized as an iterative optimization process composed of successive cycles: 1) computation (update) of the prototypes and partition matrices performed on the basis of local datasets and 2) an integration of the local sources of knowledge carried out on a central coordinator-server. The update of the partition matrices can be completed using a distance-based or gradient-based approach. The communication of findings between local clients and the coordinator-server is realized through exchanging partition matrices, which are more general than numeric data and can avoid leakage of data privacy. Fuzzy models are optimized in a similar manner through exchanging the gradients of the performance index computed with respect to the parameters between the clients and the global coordinator. The proposed mechanism exhibits significant originality since the realization of fuzzy modeling in a vertical federated learning environment has not been studied. Experimental studies show that the proposed federated clustering and fuzzy model design could effectively reveal the structure of the entire dataset and achieve high performance compared with the results obtained in a centralized manner.
Optical voice security scheme for anticounterfeiting

Authors:
raw_author_name: Sudheesh K. Rajput
raw_affiliation_string: Faculty of Electrical Engineering and Electronics, Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, Kyoto 606-8585, Japan
raw_author_name: Kenzo Nishio
raw_affiliation_string: Advanced Technology Center, Kyoto Institute of Technology, Matsugasaki, Sakyo-ku, Kyoto 606-8585, Japan
raw_author_name: Osamu Matoba
raw_affiliation_string: Organization for Advanced and Integrated Research, Kobe University, Rokkodai 1-1, Nada, Kobe 657-850, Japan

Abstract: In this work, we propose an optical voice security scheme for anticounterfeiting that uses voice watermark recording by an optical setup of parallel phase shifting digital holography (PPSDH) and an approach of converting the recorded voice into a suitable 2D voice watermark. The converted voice watermark is further encrypted in the optical domain in order to provide enhanced security in terms of physical keys for watermarking and other applications. The proposed scheme makes it difficult for counterfeiting in order to claim the ownership of voice or image data in case of watermarking. We present the experimental results of voice watermark recorded by optical microphone based on PPSDH and simulation results to verify the proposed optical security scheme for watermarking of optically recorded data. Best of our knowledge, this is the first proposal towards voice watermark generation in optical domain and the first demonstration of it for watermarking.
Ensemble of ghost convolution block with nested transformer encoder for dense object recognition

Authors:
raw_author_name: Ponduri Vasanthi
raw_affiliation_string: Vignan’s Foundation for Science, Technology, and Research, Guntur, Andhra Pradesh, India
raw_author_name: Laavanya Mohan
raw_affiliation_string: Vignan’s Foundation for Science, Technology, and Research, Guntur, Andhra Pradesh, India

Abstract: The Technological advancement and innovation is happening at rapid pace and within the ambit of computerized era, the recognition models have displayed exceptionally outstanding performance in object detection but still the technology relies on ‘Dense-packed object detection”. This object detection remains one of the greatest challenges of the present situation due to redundant feature map calculation complexity, diverse shapes, and alignment of the object in various directions. This paper suggests a GCB (Ghost Convolution Block) based on nested-transformer encoder block in the feature refinement network to overcome these intricacies. The GCB effectively alleviates the redundant feature maps calculation complexity by using DW (Depth-Wise separable) convolution operation. Whereas, the nested-transformer encoder block extracts in-depth information from diverse shaped objects and misaligned objects based on the MHSA (Multi-Head-Self-Attention) mechanism's query, key, and value parameter information. We propose to perform quantitative evaluations on the VOC, GWHD (Global Wheat Head Detection), and SKU-110K data sets and carried out an ablation study by using the YOLOv5 model with GCB and GCBTR (Ghost Convolution Block-based Transformer) modules. We compared this model with conventional other models, our model achieved 84.2% mAP, 80% precision, 78.1% recall, and 79% F1-score on VOC, 81.8% mAP, 91.6% precision, 73.1% recall, and 81.3% F1-score on SKU-110K and also achieved 95.7% mAP, 94.4% precision, 90.2% recall on GWHD datasets. Thus, obtained reliable results prove that the suggested model has shown superior performance when compared with other existing models as well as the YOLOv5 model in detecting dense objects.
An objective quality indicator for evaluating filtering of microscopic video in CASA system

Authors:
raw_author_name: Ying Zhang
raw_affiliation_string: School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271000, China
raw_author_name: Liyang Xue
raw_affiliation_string: Department of Medical Engineering, the First Affiliated Hospital of Shandong First Medical University, Jinan 250000, China
raw_author_name: Wei Chen
raw_affiliation_string: School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271000, China
raw_author_name: Guangyu Zhang
raw_affiliation_string: School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271000, China
raw_author_name: Lixia Hou
raw_affiliation_string: School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271000, China

Abstract: Computer-Aided Sperm Analysis (CASA) has received great attention in the diagnosis of male reproductive health for its objectivity and accuracy. But its accuracy may be greatly affected by algorithms used in microscopic video preprocessing, especially algorithms for microscopic videos filtering. Owing to the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) indicators are not suitable for microscopic images, objective indicators for evaluating filtering of microscopic videos are scarce at present, leading to filtering algorithms are chosen arbitrarily. In addition, it is critical to establish quality control indicators for CASA from the perspective of image processing, but they are rarely mentioned for the reason that algorithms adopted in image processing aren’t public. To reduce influences of filtering on diagnostic results, it is imperative to establish an objective quality control indicator for evaluating filtering of microscopic video from the perspective of image preprocessing. The principle of formatting sperm tracking trajectory and the rules of grading trajectory continuity were introduced firstly. Then, impacts of filtering algorithms on the continuity of sperm trajectory were investigated and the relation between the continuity of sperm trajectory and the fluctuation of sperm segmentation thresholds was further explored. Finally, an objective indicator for appraising the quality of filtering for microscopic video was established based on SADTM (Sum of Absolute Difference between Threshold and the Mean). The proposed indicator can objectively evaluate the quality of microscopic video filtering and provide a guideline for algorithm selection, laying foundation for quality control for CASA.
HTC-Net: A hybrid CNN-transformer framework for medical image segmentation

Authors:
raw_author_name: Hui Tang
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
raw_author_name: Yuanbo Zhou
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
raw_author_name: Qinquan Gao
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China; Imperial Vision Technology, Fuzhou, China
raw_author_name: Min Du
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
raw_author_name: Tao Tan
raw_affiliation_string: Faulty of Applied Science, Macao Polytechnic University, Macao Special Administrative Region of China
raw_author_name: Xinlin Zhang
raw_affiliation_string: College of Physics and Information Engineering, Fuzhou University, Fuzhou, China; Imperial Vision Technology, Fuzhou, China

Abstract: Automated medical image segmentation is a crucial step in clinical analysis and diagnosis, as it can improve diagnostic efficiency and accuracy. Deep convolutional neural networks (DCNNs) have been widely used in the medical field, achieving excellent results. The high complexity of medical images poses a significant challenge for many networks in balancing local and global information, resulting in unstable segmentation outcomes. To address the challenge, we designed a hybrid CNN-Transformer network to capture both the local and global information. More specifically, deep convolutional neural networks are introduced to exploit the local information. At the same time, we designed a trident multi-layer fusion (TMF) block for the Transformer to fuse contextual information from higher-level (global) features dynamically. Moreover, considering the inherent characteristic of medical image segmentation (e.g., irregular shapes and discontinuous boundaries), we developed united attention (UA) blocks to focus on important feature learning. To evaluate the effectiveness of our proposed approach, we performed experiments on two publicly available datasets, ISIC-2017, and Kvasir-SEG, and compared our results with state-of-the-art approaches. The experimental results demonstrate the superior performance of our approach. The codes are available at https://github.com/Tanghui2000/HTC-Net.
Linear codes of larger lengths with Galois hulls of arbitrary dimensions and related entanglement-assisted quantum error-correcting codes

Authors:
raw_author_name: Yang Li
raw_affiliation_string: School of Mathematics, Hefei University of Technology, Hefei, China
raw_author_name: Shixin Zhu
raw_affiliation_string: School of Mathematics, Hefei University of Technology, Hefei, China

Abstract: Galois hulls are generalizations of Euclidean and Hermitian hulls, which have attracted interest because of their important applications in determining the complexity of some algorithms about linear codes and in constructing entanglement-assisted quantum error-correcting codes (EAQECCs). In this paper, the whole contribution is two-folded. On one hand, we propose explicit methods to construct linear codes of larger lengths with Galois hulls of arbitrary dimensions from given Galois self-orthogonal codes. Conditions required in our approach are proven to be relatively weak. On the other hand, we apply these results to construct EAQECCs. Two bounds for EAQECCs constructed from linear codes with prescribed dimensional Galois hull are given and EAQECCs with rates greater than or equal to 12 and positive net rates can be obtained. We also present many interesting examples to explain visually how these two aspects work.
Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization

Authors:
raw_author_name: Josef Stumpfegger
raw_affiliation_string: Technical University of Munich (TUM), Germany
raw_author_name: Rüdiger Westermann
raw_affiliation_string: Technical University of Munich (TUM), Germany

Abstract: Visualizing spatial correlations in 3D ensembles is challenging due to the vast amounts of information that need to be conveyed. Memory and time constraints make it unfeasible to pre-compute and store the correlations between all pairs of domain points. We propose the embedding of adaptive correlation sampling into chord diagrams with hierarchical edge bundling to alleviate these constraints. Entities representing spatial regions are arranged along the circular chord layout via a space-filling curve, and Bayesian optimal sampling is used to efficiently estimate the maximum occurring correlation between any two points from different regions. Hierarchical edge bundling reduces visual clutter and emphasizes the major correlation structures. By selecting an edge, the user triggers a focus diagram in which only the two regions connected via this edge are refined and arranged in a specific way in a second chord layout. For visualizing correlations between two different variables, which are not symmetric anymore, we switch to showing a full correlation matrix. This avoids drawing the same edges twice with different correlation values. We introduce GPU implementations of both linear and non-linear correlation measures to further reduce the time that is required to generate the context and focus views, and to even enable the analysis of correlations in a 1000-member ensemble.
PoSE: Suppressing Perceptual Noise in Embodied Agents for Enhanced Semantic Navigation

Authors:
raw_author_name: Benhui Zhuang
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Chunhong Zhang
raw_affiliation_string: Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China
raw_author_name: Zheng Hu
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

Abstract: Embodied agents navigating unknown environments face the challenge of optimizing exploration based on semantic information. Conventional methods, reliant on collected data or pre-defined rules, have limitations in scalability and applicability, while pretrained language models based methods focusing on textual modality encounter perceptual noise, which affects decision-making. To mitigate these problems, this paper presents Prompt-based Vision Context Semantic Exploration (PoSE), an innovative method that leverages prior knowledge from vision language models (VLMs) to suppress perceptual noise. Through its unique design of prompts that use existential logic, PoSE reduces misidentifications of target objects within the observed environment. It also introduces a unique exploration map to translate target locations into exploration coordinates. The proposed methodology is evaluated on the ALFRED benchmark, demonstrating improved performance against previous rulebased and task-specific data-driven exploration policies. Furthermore, PoSE's semantic exploration policy offers enhancements over pretrained language-model based exploration methods that focus on text modality, showcasing its effectiveness and generality.
Heterogeneous Graph Contrastive Learning With Metapath-Based Augmentations

Authors:
raw_author_name: Xiaoru Chen
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Yingxu Wang
raw_affiliation_string: Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
raw_author_name: Jinyuan Fang
raw_affiliation_string: School of Computing Science, University of Glasgow, Glasgow, U.K.
raw_author_name: Zaiqiao Meng
raw_affiliation_string: School of Computing Science, University of Glasgow, Glasgow, U.K.
raw_author_name: Shangsong Liang
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

Abstract: Heterogeneous graph contrastive learning is an effective method to learn discriminative representations of nodes in heterogeneous graph when the labels are absent. To utilize metapath in contrastive learning process, previous methods always construct multiple metapath-based graphs from the original graph with metapaths, then perform data augmentation and contrastive learning on each graph respectively. However, this paradigm suffers from three defects: 1) It does not consider the augmentation scheme on the whole metapath-based graph set, which hinders them from fully leveraging the information of metapath-based graphs to achieve better performance. 2) The final node embeddings are not optimized from the contrastive objective directly, so they are not guaranteed to be distinctive enough. It leads to suboptimal performance on downstream tasks. 3) Its computational complexity for contrastive objective is high. To tackle these defects, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> eterogeneous <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontrastive learning model with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> etapath-based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ugmentations ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HGCMA</b> ), which is designed for downstream tasks with a small amount of labeled data. To address the first defect, both semantic-level and node-level augmentation schemes are proposed in our HGCMA for augmentation, where a metapath-based graph and a certain ratio of edges in each metapath-based graph are randomly masked, respectively. To address the second and third defects, we utilize a two-stage attention aggregation graph encoder to output final node embedding and optimize them with contrastive objective directly. Extensive experiments on three public datasets validate the effectiveness of HGCMA when compared with state-of-the-art methods.
Alternatives for the q-matroid axioms of independent spaces, bases, and spanning spaces

Authors:
raw_author_name: Michela Ceria
raw_affiliation_string: Dept. of Mechanics, Mathematics & Management, Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy
raw_author_name: Relinde Jurrius
raw_affiliation_string: Faculty of Military Sciences, Netherlands Defence Academy, the Netherlands

Abstract: It is well known that in q-matroids, axioms for independent spaces, bases, and spanning spaces differ from the classical case of matroids, since the straightforward q-analogue of the classical axioms does not give a q-matroid. For this reason, a fourth axiom has been proposed. In this paper we show how we can describe these spaces with only three axioms, providing two alternative ways to do that. As an application, we show direct cryptomorphisms between independent spaces and circuits and between independent spaces and bases.
EsArCost: Estimating repair costs of software architecture erosion using slice technology

Authors:
raw_author_name: Tong Wang
raw_affiliation_string: School of Computer Science and Technology, Anhui University of Technology, Maanshan, China
raw_author_name: BiXin Li
raw_affiliation_string: School of Computer Science and Engineering, Southeast University, Nanjing, China

Abstract: Software architecture erosion has a negative effect on software quality, software performance and evolution cost, so repairing architecture erosion is an important task. However, in the actual evolution process, due to the pressure of cost, it is not practical to repair all erosion problems. To repair more erosion problems at a certain total cost, developers would better know the appropriate repair cost of each erosion problem. In this paper, we propose an approach called EsArCost, which can locate the reasons for architecture erosion and estimate the repair cost of each erosion problem. To this end, EsArCost detects multi-level changes of software architecture and calculates the architecture erosion degree, then locates which changed codes cause architecture erosion. EsArCost further calculates the full slice of each erosion problem to estimate the difficulty and repair costs. We evaluate our approach on small and large open source programs, and the experiment results indicate that EsArCost can effectively and efficiently estimate repair costs.