Reviewerly/exHarmony huggingface dataset:
Research on laser-based mobile robot SLAM and autonomous navigation

Authors:
raw_author_name: Zhihong Huang
raw_affiliation_string: School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
raw_author_name: Jianping Geng
raw_affiliation_string: School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China

Abstract: Abstract SLAM (Simultaneous Localization and Mapping) and autonomous navigation systems, as fundamental components of mobile robots, largely determine their ability to accomplish tasks. While research on laser-based SLAM and autonomous navigation algorithms in simulated environments has achieved significant success, there is limited research on deploying these algorithms on physical mobile robots, conducting real-world experiments to provide practical data for validating the accuracy and applicability of theoretical models. This paper begins by presenting the system architecture of the mobile robot used in this study. Subsequently, it delves into the research on SLAM algorithms, analyzing and selecting the Gmapping algorithm as the solution to the SLAM problem. Furthermore, the paper introduces the research on autonomous navigation, providing a detailed explanation of the principles behind the A* global path planning algorithm combined with the Dynamic Window Approach (DWA) for local path planning. Finally, by controlling the mobile robot through a distributed communication network, the paper tests the performance of the mobile robot, constructs maps in a real indoor environment, achieving a relative map accuracy error of less than 3%, thus validating the effectiveness of the Gmapping algorithm for practical mapping. Autonomous navigation tasks are also performed, testing the mobile robot’s local obstacle avoidance capabilities using temporary obstacles. Overall, this research demonstrates that the designed mobile robot can effectively accomplish SLAM and autonomous navigation tasks. The results of this study can also assist beginners in rapidly and clearly building physical mobile robots and deploying SLAM systems.
Hyperledger sawtooth based supplychain traceability system for counterfeit drugs

Authors:
raw_author_name: Anum Nawaz
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China; Turku Intelligent and Embedded Robotic Systems Lab, Faculty of Technology, University of Turku, Finland
raw_author_name: Liguan Wang
raw_affiliation_string: Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China
raw_author_name: Muhammad Irfan
raw_affiliation_string: Turku Intelligent and Embedded Robotic Systems Lab, Faculty of Technology, University of Turku, Finland
raw_author_name: Tomi Westerlund
raw_affiliation_string: Turku Intelligent and Embedded Robotic Systems Lab, Faculty of Technology, University of Turku, Finland

Abstract: Drug supply chains have been facing severe issues as counterfeit product cases are increasing exponentially. A supply chain management system that ensures transparency, reliability, and provenance of drugs would increase the trustworthiness of the whole industry. One solution to all three needs is utilizing blockchain-based distributed ledger technologies (DLTs). Even though DLTs are emerging as an ideal infrastructure for multi-stakeholder supply chain applications, they still need to be more mature to address the specific challenges specific to each use case. In this article, we propose a distributed blockchain-based framework, PHTrack, leveraging hyperledger sawtooth as a drug supply chain traceability system. Hyperledger sawtooth addresses scalability issues by offering a robust foundation to support large-scale drug supply chain operations in a modular way for it’s each participating stakeholder. Furthermore, it simplifies the integration process with existing systems, even those employing different technologies, thereby facilitating a smoother transition to DLTs. The design of PHTrack is oriented towards minimizing resource consumption throughout the process, particularly within hyperledger sawtooth nodes. Additionally, it incorporates quantum secure off-chain communication for peer-to-peer (P2P) communication. A set of experiments was conducted to validate the proposed framework. Experiments have shown that PHTrack provides reliable and comprehensive drug provenance as well as real-time drug supply chain tracking.
FCSG-Miner: Frequent closed subgraph mining in multi-graphs

Authors:
raw_author_name: Xinyang Chen
raw_affiliation_string: School of Science, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
raw_author_name: Jiayu Cai
raw_affiliation_string: School of Science, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China
raw_author_name: Guoting Chen
raw_affiliation_string: School of Science, Great Bay University, Dongguan, Guangdong 523000, China
raw_author_name: Wensheng Gan
raw_affiliation_string: College of Cyber Security, Jinan University, Guangzhou 510632, China

Abstract: Graph data-based mining is vital in various fields, such as business management, chemistry, and social networks. Frequency-based frameworks have limitations regarding large mining output. In many real-world scenarios, not all frequent patterns hold significant meaning. To address this issue, concise representation such as closed patterns is proposed. Unfortunately, existing methods utilize support or occurrence as frequency measurements, which have drawbacks. Support overlooks the isomorphic quantity of a single graph, while occurrence lacks the downward closure property. In this paper, we introduce an approach for mining frequent closed subgraphs. A novel measure MMNI (multiple minimum node image) is introduced to strike a balance between support and occurrence measures. Additionally, we design a novel structure to store occurrence information. We develop a pruning strategy and employ an early termination strategy to enhance efficiency. To evaluate the performance of our algorithm, we conduct experiments on seven real datasets, considering four aspects. The results demonstrate that our algorithm has high efficiency and performance compared to the state-of-the-art algorithm for closed subgraph mining. In several cases, our method requires only 50% of the time consumed by previous approaches. We also present a real-world application example in the domain of bike-sharing systems.
Structure-aware contrastive hashing for unsupervised cross-modal retrieval

Authors:
raw_author_name: Jinrong Cui
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
raw_author_name: Zhipeng He
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
raw_author_name: Qiong Huang
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
raw_author_name: Yulu Fu
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
raw_author_name: Yuting Li
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China
raw_author_name: Jie Wen
raw_affiliation_string: Shenzhen Key Laboratory of Visual Object Detectionand Recognition , Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China

Abstract: Cross-modal hashing has attracted a lot of attention and achieved remarkable success in large-scale cross-media similarity retrieval applications because of its superior computational performance and low storage overhead. However, constructing similarity relationships among samples in cross-modal unsupervised hashing is challenging because of the lack of manual annotation. Most existing unsupervised methods directly use the representations extracted from the backbone of their respective modality to construct instance similarity matrices, leading to inaccurate similarity matrices and resulting in suboptimal hash codes. To address this issue, a novel unsupervised hashing model, named Structure-aware Contrastive Hashing for Unsupervised Cross-modal Retrieval (SACH), is proposed in this paper. Specifically, we concurrently employ both high-dimensional representations and discriminative representations learned by the network to construct a more informative semantic correlative matrix for multiple modalities. Moreover, we design a Multimodal Structure-aware Alignment Network to minimize heterogeneity in the high-order semantic space of each modality, effectively reducing disparities within heterogeneous data sources and enhancing the consistency of semantic information across modalities. Extensive experimental results on two widely utilized datasets demonstrate the superiority of our proposed SACH method in cross-modal retrieval tasks over existing state-of-the-art methods.
TAILOR: inTer-feAture distinctIon fiLter fusiOn pRuning

Authors:
raw_author_name: Xuming Han
raw_affiliation_string: Engineering Research Center of Trustworthy AI, Ministry of Education, Jinan University, Guangzhou 510632, China
raw_author_name: Yali Chu
raw_affiliation_string: School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
raw_author_name: Ke Wang
raw_affiliation_string: College of Information Science and Technology, Jinan University, Guangzhou 510632, China
raw_author_name: Limin Wang
raw_affiliation_string: School of Information Science, Guangdong University of Finance & Economics, Guangzhou 510320, China
raw_author_name: Lin Yue
raw_affiliation_string: University of Newcastle, Australia
raw_author_name: Weiping Ding
raw_affiliation_string: School of Information and Science, Nantong University, Nantong, China

Abstract: Filter pruning is an effective method for reducing the size of convolutional neural networks without sacrificing performance. Most filter pruning methods prioritize filters with high information content, but fail to consider that filters with low information content might capture essential features. Moreover, we have discovered that the distinctions among feature maps generated by filters can identify crucial features. Based on this insight, we propose a novel pruning method called inTer-feAture distinctIon fiLter fusiOn pRuning (TAILOR), which fuses the feature distinctions between filters. TAILOR randomly selects multiple filter sets within a convolutional layer and calculates the output feature maps of the next convolutional layer of these sets. Subsequently, an intelligent distinction optimization scheme is proposed to obtain the optimal filter set for filter pruning, which supplants the original convolutional layer. Experimental results indicated that the inter-feature distinctions among filters significantly affect filter pruning. TAILOR outperforms state-of-the-art filter-pruning methods in terms of model prediction accuracy, floating-point operations, and parameter scale. For instance, with VGG-16, TAILOR achieves a 73.89% FLOPs reduction by removing 91.85% of the parameters, while improving accuracy by 0.36% on the CIFAR-10.
Towards blockchain-enabled decentralized and secure federated learning

Authors:
raw_author_name: Du Xu
raw_affiliation_string: University of Electronic Science and Technology of China, Chengdu, 611731 China
raw_author_name: Katinka Wolter
raw_affiliation_string: Freie Universität Berlin, Berlin, 14195 Germany

Abstract: The conventional machine learning process typically operates under the premise of centralized data aggregation, where all data is collected at a central location for model training. However, this raises substantial privacy concerns when the data contains private information. In this context, federated learning has emerged as a prominent solution for preserving privacy in machine learning. This innovative paradigm allows multiple data owners, or clients, to collaboratively train a machine learning model while keeping their local data unshared. A federated learning task is typically initiated by companies, often referred to as model owners, who do not possess enough training data and are willing to financially remunerate clients who contribute to the development of the federated learning model. This situation demands a trading platform that enables model owners to effectively select clients, while ensuring robustness against malicious clients who execute poisoning attacks for unfair financial gain. To address these issues, we design a contribution-based exploration-exploitation mechanism implemented as a smart contract. This mechanism cherry-picks clients with high data quality based on the Shapley value, which is calculated based on local models to evaluate the contribution of each client. Unlike other state-of-the-art security mechanisms, the proposed mechanism can adapt to various scenarios with heterogeneous data distribution and various attacks, while mitigating the effect of malicious behaviors without compromising training accuracy. To accelerate the time-consuming calculation of the Shapley value, we design a parallel computing algorithm that partitions blockchain nodes into multiple shards and distributes calculation tasks among them. The algorithm improves efficiency and tolerates potential false calculation results from malicious nodes.
MAInt: A multi-task learning model with automatic feature interaction learning for personalized recommendations

Authors:
raw_author_name: Pu Yin
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Ziyi Gao
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Rui Wang
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Yuan Yao
raw_affiliation_string: Institute of Hospital Management, Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, China

Abstract: Nowadays, more and more recommender systems apply multi-task learning to provide users with more accurate personalized recommendations. However, most of the existing multi-task learning models ignore the importance of explicit feature interactions in different tasks, which are an important part of recommender system models. In addition, the knowledge sharing mechanism of the existing model is difficult to meet the needs of all tasks, resulting in the performance degradation of some tasks. To address these issues, we propose a Multi-task learning model based on Automatic feature Interaction learning (MAInt). The Tasks Interaction Layer in MAInt automatically learns explicit feature interactions in different tasks through self-attention neural networks. At the same time, this process allows each task to interact with each other, so that each task can adaptively extract helpful information from other task features. We conducted multiple experiments on two popular public datasets: Census-income Dataset and Ali-CCP Dataset, and the experimental results verified the effectiveness of our proposed model.
Graph-based social relation inference with multi-level conditional attention

Authors:
raw_author_name: Xiaotian Yu
raw_affiliation_string: Department of AI Technology Center, Shenzhen Intellifusion Ltd., China
raw_author_name: Hanling Yi
raw_affiliation_string: Department of AI Technology Center, Shenzhen Intellifusion Ltd., China
raw_author_name: Kun Huang
raw_affiliation_string: Department of AI Technology Center, Shenzhen Intellifusion Ltd., China
raw_author_name: Wenze Hu
raw_affiliation_string: Department of AI Technology Center, Shenzhen Intellifusion Ltd., China
raw_author_name: Shiliang Zhang
raw_affiliation_string: Department of Computer Science, Peking University, China
raw_author_name: Xiaoyu Wang
raw_affiliation_string: Department of AI Technology Center, Shenzhen Intellifusion Ltd., China; The Chinese University of Hong Kong (Shenzhen), China

Abstract: Social relation inference intrinsically requires high-level semantic understanding. In order to accurately infer relations of persons in images, one needs not only to understand scenes and objects in images, but also to adaptively attend to important clues. Unlike prior works of classifying social relations using attention on detected objects, we propose a MUlti-level Conditional Attention (MUCA) mechanism for social relation inference, which attends to scenes, objects and human interactions based on each person pair. Then, we develop a transformer-style network to achieve the MUCA mechanism. The novel network named as Graph-based Relation Inference Transformer (i.e., GRIT) consists of two modules, i.e., a Conditional Query Module (CQM) and a Relation Attention Module (RAM). Specifically, we design a graph-based CQM to generate informative relation queries for all person pairs, which fuses local features and global context for each person pair. Moreover, we fully take advantage of transformer-style networks in RAM for multi-level attentions in classifying social relations. To our best knowledge, GRIT is the first for inferring social relations with multi-level conditional attention. GRIT is end-to-end trainable and significantly outperforms existing methods on two benchmark datasets, e.g., with performance improvement of 7.8% on PIPA and 9.6% on PISC.
A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms

Authors:
raw_author_name: Mustafa Ibrahim Khaleel
raw_affiliation_string: University of Sulaimani, College of Science, Computer Department, Sulaimani, 00964, Iraq

Abstract: As the geographically distributed cloud infrastructure continues to grow in scale and the intricacy of workflow applications increases, there is a growing threat to the operational efficiency of the system. This poses the risk of resource inefficiencies and elevated energy consumption. Load balancing becomes a critical concern for this category of applications, particularly when they encounter overwhelming demand surges and resource outages. Numerous comprehensive methods still overlook certain aspects, such as the inclusion of multiple objectives, addressing local minimum problems, and optimizing resource utilization to its fullest potential. Our research focus is on exploring cloud-deployed three-tier applications to address these challenges. This paper presents a new approach to load balancing, introducing a novel weight assignment algorithm. The algorithm effectively distributes tasks among virtual machines using three carefully optimized algorithms. To begin with, we convert the user tasks into the directed acyclic graph (DAG) structured workflow, organizing them according to their execution sequence. The iterative critical path search algorithm, implemented with a layer-based priority assigning technique, addresses the critical path challenge. This process involves finding a critical path, dividing tasks into distinct layers, classifying them based on their computational complexity and priorities, and distinguishing between critical and non-critical tasks. Following the classification process, the tasks are organized and prioritized using an improved Moth Flame Optimization approach based on multiple criteria, which consider criticality, priority, and makespan. This approach significantly reduces scheduling latency and delays. Finally, task allocation is proposed using the high convergence rock hyrax optimization algorithm with a divide-and-conquer strategy, enabling effective exploration and exploitation of overcrowded resources for improved load balancing. The CloudSim simulation tool is used to carry out the simulation, where it assesses various performance aspects such as latency, bandwidth, completion time, throughput, energy usage, instances of not meeting QoS violations, and overhead.
Improving small sample medical image segmentation using CBAM: Insights from two datasets

Authors:
raw_author_name: Yusheng Tan
raw_affiliation_string: North China Electric Power University, Baoding, Hebei, 071000, China

Abstract: Abstract Medical image segmentation is one of the key tasks in the medical field and is crucial for accurate lesion detection and treatment planning. However, the small sample problem has been one of the challenges in medical image segmentation. In this study, the small sample medical image segmentation problem is evaluated experimentally based on two different datasets, the VOC dataset and the esophageal medical images. In the VOC dataset experiments, we used the code from the BAM project on GitHub as a benchmark and compared it. Additionally, we improved the benchmark code by adding the Attention Mechanism (CBAM), placing its position before the support set undergoes global average pooling. By comparing the experimental results, we found that the model with the added CBAM achieved better segmentation results under small sample conditions. In the esophageal medical image experiments, the code from the BAM project was also used as a benchmark and a similar experimental design was performed. We further added CBAM by placing it before the global average pooling of the support set. The experimental results show that the model with the addition of CBAM achieves a significant improvement in the small sample segmentation task of esophageal medical images. To summarize, this study validates the effectiveness of using the BAM model in the small-sample medical image segmentation task on two different datasets. By adding CBAM, our model exhibits better segmentation performance under small sample conditions. These findings provide useful insights for small-sample medical image segmentation and are important for future research and practice.