Reviewerly/exHarmony huggingface dataset:
Graphical Consensus-Based Sharding for Efficient and Secure Sharings in Blockchain-Enabled Internet of Vehicles

Authors:
raw_author_name: Wenqi Li
raw_affiliation_string: College of Artificial Intelligence, Dalian Maritime University, Dalian, China
raw_author_name: Zheng Zhao
raw_affiliation_string: College of Artificial Intelligence, Dalian Maritime University, Dalian, China
raw_author_name: Pingchuan Ma
raw_affiliation_string: College of Artificial Intelligence, Dalian Maritime University, Dalian, China
raw_author_name: Zeqiang Xie
raw_affiliation_string: College of Artificial Intelligence, Dalian Maritime University, Dalian, China
raw_author_name: Vasile Palade
raw_affiliation_string: Centre of Computational Science and Mathematical Modeling, Coventry University, Coventry, U.K.
raw_author_name: Hongbo Liu
raw_affiliation_string: College of Artificial Intelligence, Dalian Maritime University, Dalian, China

Abstract: Blockchain-enabled Internet of Vehicles provides a reliable collaboration environment for traffic entities and promotes road safety and traffic efficiency through real-time sharings between vehicles and infrastructure. However, a bottleneck is likely to emerge as the Internet of Vehicles scales up. In this paper, we propose a novel Graphical Consensus-based Sharding (GCS) framework, which is underpinned by four important strategies: 1) A graphical consensus is adopted as its intra-shard consensus, where the consensus group is set up according to its maximal connected subgraph, and the leader is elected by its reliability weight. The consensus group is refreshed intermittently by alternating the leader role. 2) Within GCS, the intra-shard data are stored in the local chain, while a block-based directed acyclic graph, rather than a chained structure, is employed as the main chain. The local chain is used to respond to requests within each shard, and the main chain supports the cross-shard sharings. GCS will parallelly optimize the throughput of the blockchain-enabled Internet of Vehicles. 3) GCS further introduces the shard backup and the node scheduling to handle shard failure and overheating by using new backup strategies and a temporal-spatial graph convolutional network prediction model, respectively. 4) An off-chain transmission algorithm is presented for secure sharing between the infrastructure and the vehicles. Simulation results show that the number of Transactions Per Second is 1.69 times higher than that of the non-sharding blockchain, and the pending time is dramatically reduced compared to the mainstreaming sharding approach, which is 1.02s.
Path Planning for the Gantry Welding Robot System Based on Improved RRT*

Authors:
raw_author_name: Xuewu Wang
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
raw_author_name: Xin Zhou
raw_affiliation_string: Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

Abstract: In the shipbuilding industry, various workpieces are often produced in small batches. A new program must be written for new workpiece to be processed, which leads to the inefficiency of the traditional teaching programming method. In particular, some manufacturers have applied large gantry structures to robots to improve their handling space. Although the external positioning device enhances the robot's flexibility, it also increases the difficulty of path planning. The RRT* algorithm based on sampling is widely used in the path planning of manipulator for its efficient expansibility and probability completeness. However, in the robot system equipped with gantry structure, the increase of freedom makes its efficiency relatively low. Therefore, this article presents an improved RRT* algorithm for autonomous path planning of welding robots with a large gantry structure. This method introduces the sampling pool mechanism, and selects the node nearest to the connection line between the starting node and the target node in the sampling pool, which effectively shortens the length of the search path. In addition, it adopts the strategy of limiting the nearest node to prevent the transitional search of the configuration space. The improved RRT* algorithm proposed in this paper is verified in complex environment, and compared with improved algorithms such as IB-RRT*, the path cost and time cost are increased by 22.2% and 32.5%, respectively, and the success rate is relatively stable.
Imperceptible UAPs for Automatic Modulation Classification Based on Deep Learning

Authors:
raw_author_name: Dongwei Xu
raw_affiliation_string: Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Jiangpeng Li
raw_affiliation_string: Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Zhuangzhi Chen
raw_affiliation_string: Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Qi Xuan
raw_affiliation_string: Institute of Cyberspace Security, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
raw_author_name: Weiguo Shen
raw_affiliation_string: Innovation Studio of Academician Yang, National Key Laboratory of Electromagnetic Space Security, Jiaxing, China
raw_author_name: Xiaoniu Yang
raw_affiliation_string: Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China

Abstract: Automatic Modulation Classification (AMC), which based on deep learning has been extensively researched and implemented in wireless communication systems. Universal adversarial perturbation refers to a single perturbation that can cause most samples to be misclassified by deep learning models. In this paper, we aim to achieve imperceptible universal adversarial attacks on AMC models, and thus an imperceptible universal adversarial perturbations (imperceptible UAPs) framework was proposed. Specifically, loss functions were separately designed for target signals and non-target signals, and then a total loss was calculated. This total loss function can be modified by hyperparameters to achieve class-specific universal adversarial attacks (CUAAs), class-discriminative universal adversarial attacks (CD-UAAs), and class-discriminative target universal adversarial attacks (CD-TUAAs). Meanwhile, wavelet reconstruction was applied to the training data, thus further improving the discriminability of the generated UAPs. After CUAAs were implemented, the class dominant in radio signals was visualized and analyzed by confusion matrices. Furthermore, with the confusion matrices of CUAAs, CD-TUAAs were efficiently implemented. The experiments were conducted on two radio signal datasets and models. In most scenarios, CD-UAAs achieved excellent performance with an average ΔAcc of 58. 20% and CD-TUAAs achieved an average ΔK of 73.78%.
CB-DSL: Communication-Efficient and Byzantine-Robust Distributed Swarm Learning on Non-i.i.d. Data

Authors:
raw_author_name: Xin Fan
raw_affiliation_string: School of Information Science and Technology, Beijing Forestry University, Beijing, China
raw_author_name: Yue Wang
raw_affiliation_string: Department of Computer Science, Georgia State University, Atlanta, GA, USA
raw_author_name: Yan Huo
raw_affiliation_string: School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China
raw_author_name: Zhi Tian
raw_affiliation_string: Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA

Abstract: The valuable data collected by IoT devices together with the resurgence of machine learning (ML) stimulate the latest trend of artificial intelligence (AI) at the edge. However, traditional ML and recent federated learning (FL) face major challenges including communication bottleneck, data heterogeneity, and security concerns in edge IoT. Meanwhile, the swarm nature of IoT systems is overlooked by most existing literature, which calls for new designs of distributed learning algorithms. Inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with the non-i.i.d. data issues and Byzantine attacks, a small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which alleviates the local data heterogeneity effectively and enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Our convergence analysis theoretically demonstrates that the CB-DSL is superior to the standard FL with better convergence behavior. We also evaluate the model divergence of CB-DSL by deriving its upper bound, which measures the effectiveness of the introduction of the globally shared dataset.
Spoken Language Processing

Authors:
raw_author_name: Alex Acero
raw_author_name: Xuedong Huang

Abstract: From the Publisher: New advances in spoken language processing: theory and practice In-depth coverage of speech processing, speech recognition, speech synthesis, spoken language understanding, and speech interface design Many case studies from state-of-the-art systems, including examples from Microsoft’s advanced research labs Spoken Language Processing draws on the latest advances and techniques from multiple fields: computer science, electrical engineering, acoustics, linguistics, mathematics, psychology, and beyond. Starting with the fundamentals, it presents all this and more: Essential background on speech production and perception, probability and information theory, and pattern recognition Extracting information from the speech signal: useful representations and practical compression solutions Modern speech recognition techniques: hidden Markov models, acoustic and language modeling, improving resistance to environmental noises, search algorithms, and large vocabulary speech recognition Text-to-speech: analyzing documents, pitch and duration controls; trainable synthesis, and more Spoken language understanding: dialog management, spoken language applications, and multimodal interfaces To illustrate the book’s methods, the authors present detailed case studies based on state-of-the-art systems, including Microsoft’s Whisper speech recognizer, Whistler text-to-speech system, Dr. Who dialog system, and the MiPad handheld device. Whether you’re planning, designing, building, or purchasing spoken language technology, this is the state of the art—fromalgorithms through business productivity.
CotepRes-Net: An efficient U-Net based deep learning method of liver segmentation from Computed Tomography images

Authors:
raw_author_name: Jiahua Zhu
raw_affiliation_string: State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Str., Nangang District, Harbin 150080, China
raw_author_name: Ziteng Liu
raw_affiliation_string: School of Life Science and Technology, Harbin Institute of Technology, 2 Yikuang Str., Nangang District, Harbin, 150080, China
raw_author_name: Wenpeng Gao
raw_affiliation_string: State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Str., Nangang District, Harbin 150080, China
raw_author_name: Yili Fu
raw_affiliation_string: State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Str., Nangang District, Harbin 150080, China

Abstract: Automatic liver segmentation from CT images is challenging due to the indistinct boundaries between the liver and surrounding organs in the abdominal cavity CT. To address these limitations, we propose a deep learning model called CotepRes-Net, which is based on the U-Net architecture but with modified skip connection using a convolutional block attention module (CBAM). We introduce a modified inception block and a contextual transformer block (CoT) at the bottleneck to capture richer feature representations and leverage global contextual information to constrain the liver shape at the unclear boundary. To accelerate the network convergence in the training stage, we utilize an Efficient channel attention module (ECA) in the decoder layers and replace the convolution operation in each convolution layer with a residual structure. We evaluate the performance of the proposed model using the publicly available dataset LiTS (MICCAI 2017 Liver Tumor Segmentation Challenge) and show the segmentation results on the dataset Sliver07 (Segmentation of the Liver Competition 2007). The results show that our model achieves a dice similarity coefficient (DSC) of 0.9688, Jaccard index of 0.9422, volumetric overlap error (VOE) of 0.0578, relative absolute volume difference (RAVD) of 0.0039, average symmetric surface distance (ASSD) of 1.094 mm and maximum symmetric surface distance (MSSD) of 16.079 mm for liver segmentation. The experimental results show that the proposed method is comparable to or even outperform the state-of-the-art methods in terms of accuracy and computation cost, which can serve as a valuable tool for image-based liver analysis.
An efficient but effective writer: Diffusion-based semi-autoregressive transformer for automated radiology report generation

Authors:
raw_author_name: Yuhao Tang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Dacheng Wang
raw_affiliation_string: Medical School of Nanjing University, Nanjing University, Nanjing, China
raw_author_name: Liyan Zhang
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Ye Yuan
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract: It is firmly believed that manually diagnosing radiology images is clinically critical but labour-intensive and error-prone. Therefore, an automatic radiology report generation method is highly desired for alleviating the burden imposed on doctors. However, a typical report contains numerous template descriptions and only a few abnormal sentences. This unbalanced distribution makes the generation of template sentences more likely. Additionally, describing an entire report in a word-by-word manner can lead to significant latency during the inference step. Besides, the existing datasets are limited to conventional pneumonia, making them incomplete and one-sided. This work is concerned with forming a better trade-off between generation performance. One key design is an abnormal semantic diffusion module, which progressively absorbs the semantics of abnormal medical terminology and strengthens the linguistic coherence between local tokens. In detail, the generated report is refined by enhancing the incorporation of informative words with limited occurrence frequencies, which alleviates the monotony of template-based generation. Another design is a length-controllable self-attention decoder, which regulates the input length of the sentences used for target word generation. This framework preserves the autoregressive nature of word generation while also maintaining a controllable range, ensuring the efficiency of report generation. Moreover, a novel XRG-COVID-19 clinical dataset is tailored, which includes X-ray scans and professional diagnostic reports of 8676 patients. The experimental results demonstrate the proposed model achieves a better trade-off between performance and speed than those of carefully designed baselines on both the IU X-ray dataset and the proposed XRG-COVID-19 dataset.
Model-Driven IEP-GNN Framework for MIMO Detection With Bayesian Optimization

Authors:
raw_author_name: Dongxuan He
raw_affiliation_string: School of Information and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Nan Wu
raw_affiliation_string: School of Information and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Qinsiwei Yan
raw_affiliation_string: School of Information and Electronics, Beijing Institute of Technology, Beijing, China
raw_author_name: Yonghui Li
raw_affiliation_string: School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia

Abstract: In this letter, a model-driven detector called IEP-GNN is proposed for massive multiple-input and multiple-output (MIMO) system. Graph neural network (GNN) and improved moment matching (IMM) are integrated into the expectation propagation (EP) algorithm to improve the accuracy of posterior distribution approximation and leverage the self-correction ability of EP algorithm. Moreover, to acquire the training experiences and optimize initial parameters, hotbooting and Bayesian parameter optimization (BPO) are employed respectively, which can further improve the performance of the proposed IEP-GNN. Simulation results show that our proposed IEP-GNN with BPO outperforms other state-of-the-art EP-based detectors while maintaining an acceptable convergence and computational complexity.
A statistical model for computing global spectral solar irradiance under all-sky conditions at four stations situated in main regions of Thailand

Authors:
raw_author_name: I. Masiri
raw_affiliation_string: Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand
raw_author_name: S. Khakhu
raw_affiliation_string: Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand
raw_author_name: R. Wattan
raw_affiliation_string: Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand
raw_author_name: S. Janjai
raw_affiliation_string: Solar Energy Research Laboratory, Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand

Abstract: This paper presents a statistical model for computing global spectral solar irradiance under all-sky conditions at four stations situated in main regions of Thailand. The model expresses the global spectral irradiance under all-sky conditions as a multiplication of two functions, namely, a function of global spectral irradiance under clear-sky conditions and a cloud modification function. To develop the model, global spectral solar irradiance was measured at four stations in the main regions of Thailand, namely Chiang Mai (18.77˚ N, 98.97˚ E) in the northern region of Thailand, Ubon Ratchathani (15.25˚ N, 104.87˚ E) in the north-eastern region of the country, Nakhon Pathom (13.82˚ N, 100.04˚ E) in the central region of this country, and Songkhla (7.18˚ N, 100.60˚ E) in the southern region of Thailand. The spectral data from these stations were gathered and divided into two groups. The first group (January 2017 to December 2020) was used for modeling and the second group (January 2021 to December 2021) for validating the model. The first function was developed using the first group of data. Clear-sky conditions were detected using sky images obtained from a sky camera installed at each station. To develop the second function, a satellite-derived cloud index derived from Himawari-8 data at the stations was utilized. To validate the model of the global spectral solar irradiance under all-sky conditions, the model was employed to compute the global solar irradiance at the four stations for the year 2021, and the findings were compared to the global solar irradiance determined from the measurements taken at the four stations. It was discovered that the spectral irradiance calculated from the model and that acquired from the measurements was in good agreement. There was a discrepancy in the form of root mean square difference relative to the mean measured value (RMSD) of 9.48%. We concluded that the model performed well in computing the global spectral solar irradiance.
Dual mode information fusion with pre-trained CNN models and transformer for video-based non-invasive anaemia detection

Authors:
raw_author_name: Abhishek Kesarwani
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India
raw_author_name: Sunanda Das
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India
raw_author_name: Dakshina Ranjan Kisku
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India
raw_author_name: Mamata Dalui
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India

Abstract: Anaemia, caused by low haemoglobin levels, affects billions of people worldwide. India has one of the highest anaemia rates among developing nations, according to WHO data. Non-invasive, low-cost, and accurate anaemia screening technologies are in high demand because invasive methods are expensive and difficult to administer globally. The current effort aims to construct a reliable anaemia detection system by combining cutting-edge computational methods with the age-old practise of imprecise assessment of haemoglobin levels from pallor in palm. The proposed method utilises time-domain analysis to establish the connection between blood haemoglobin concentration and colour changes in the palm caused by pressure application and release. A smartphone camera sensor which records the full event of palm colour changes generated by a customised device is processed and analysed. Some key frames are selected from extracted frames and passed to the pre-trained deep learning models for feature extractions. These features are then passed through vision transformers for classification and regression tasks. This approach have the power of pre-trained models for feature extraction and utilises the flexibility of transformer encoders and MLP networks for efficient and effective representation learning, classification, and regression. Two different approaches are proposed i.e., decision level and feature level fusion. The suggested approach accurately estimates blood haemoglobin levels with RMSE and accuracy of 0.483 and 96.296%, respectively based on 531 video samples of palm evidence.
Improvement of Validated Manufacturing Processes with Fuzzy Logic

Authors:
raw_author_name: Marisol Hernández-Hernández
raw_affiliation_string: Autonomous Mexico State University, State of Mexico, Mexico
raw_author_name: Luis Alfonso Bonilla Cruz
raw_affiliation_string:  Technological of Higher Studies of Ixtapaluca, Mexico

Abstract: SMEs are essential entities for the economy of countries, so they need to implement strategies to maintain and achieve economic stability. Technology is a great support tool to achieve this. Still, entrepreneurs, generally acting empirically, need to determine which technology to select, how to do it, and its scope of implementation. Fuzzy logic is a technology adapted to human linguistic thinking, which served as a methodology in this case study to measure the degree of behavior given by the implementation of software and hardware in a company. The result of the research validated the benefits that the gradual implementation of the technology presented to the company in terms of utility, time, and quantity of production, which were related in degrees of uncertainty with variables that were labeled as “high,” “medium,” and “low.” The established membership was validated through fuzzy graphs, showing the company’s status, and adjusted with the appropriateness of the technology until profits were placed within the “high” range. Thus, fuzzy logic is a tool that helps measure variables in degrees of belonging, with words used by business people to make it more understandable. Furthermore, the data coding in fuzzy terms showed the prediction of the behavior of the variables adjusted with technological processes.