Reviewerly/exHarmony huggingface dataset:
Edge Resource Prediction and Auction for Distributed Spatial Crowdsourcing With Differential Privacy

Traditional spatial crowdsourcing (SC) systems employ a centralized server platform to provide services for requesters. Such a centralized design requires powerful resource capacity and often cannot accomplish the urgent demands due to the unpredictable network latency. In order to ensure the scalability of systems and the quality of services, we study the distributed SC (DSC), where a diversity of location-relative services provided by various service providers (SPs) can deploy on edge clouds (ECs) with low time latency. Since the edge resources are limited, SPs need to compete for edge resources so as to deploy their desired SC services, and the requested resources must be allocated together to meet the demand of the service. We first design a gated recurrent unit with particle filter (GRUPF) network for SPs to predict future resource demands so as to participate in the competitions judiciously. Then, we model the competitive edge resource allocation problem between SPs and ECs as a combinatorial auction process. Due to the NP-hardness of this problem, an approximation algorithm is proposed to tackle it. Moreover, the leakage of private information such as bids may incur severe economic damage, and most existing studies usually rely on a trusted third party to provide rigorous privacy protection. Therefore, we customize a novel differentially private resource auction (DRA) mechanism, and design a bid confusion strategy based on differential privacy. Through theoretical analysis, we prove that the DRA mechanism meets some desired properties, including <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -differential privacy, individual rationality, computational efficiency, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> -truthfulness. Additionally, we corroborate the significant performances of DRA through extensive simulations on synthetic and real-world data sets.
PistonNet: Object Separating From Background by Attention for Weakly Supervised Ship Detection

Object detection under weakly supervised learning is a challenging issue. In remote sensing ship detection task, the weakly supervised learning method requires that the training set only has image-level class annotations. In the absence of location information, it is difficult to locate ships and extract features. Moreover, when the detector extracts more than one candidate region, the image-level annotation makes it difficult to determine whether the candidate regions are multiple ships or mixed with the detected background. To address these issues, this article analyzes the interaction between class information and location information, and proposes a weakly supervised detection method, PistonNet, based on Data-efficient image Transformers (DeiT). PistonNet proposes an artificial point, which is inserted into the feature map. Artificial point can suppress the background and enhance the object by interfering in the weight distribution of object and background in Self-Attention calculation. PistonNet also proposes joint confidence probability to improve detection accuracy. Experiments on the GF1-LRSD and NWPU VHR-10 dataset show that the proposed method boosts detection performance effectively. The main improvements of PistonNet as well as the contributions of this article are threefold. Firstly, PistonNet is a specific weakly supervised object detection (WSOD) method for single-class detection, which provides an innovative approach to classifying target and background. Secondly, PistonNet reaches the level of advanced supervised detectors on detection accuracy with fewer parameters. Finally, the objects&#x2019; locations detected by PistonNet are obtained by segmenting regions on the heatmap. PistonNet&#x2019;s background suppression ability makes it free from dependence on segmentation threshold.
Cyl-IRRT*: Homotopy Optimal 3D Path Planning for AUVs by Biasing the Sampling Into a Cylindrical Informed Subset

In a complex 3-D environment, efficiently and safely reaching the target position is of great significance for autonomous underwater vehicles. This article proposes a cylinder-based informed rapid exploration random tree (Cyl-iRRT*) algorithm, which seeks to find the homotopy optimal path by focusing the search space on the designed gradually shrinking cylinder. The proportional shrinkage method and obstacle-based sampling strategy are presented to yield a fast convergence response and robust stability. Furthermore, the probabilistic completeness and homotopic optimality of Cyl-iRRT* are proven to be effective. Finally, both simulation and real-world experimental results reveal the superiorities of the Cyl-iRRT* algorithm.
Time-aware Context-Gated Graph Attention Network for Clinical Risk Prediction

Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in better judgment and can make sense of early diagnosis. However, the prediction performance heavily relies on effective representations from multi-dimensional time-series EHR data. Existing solutions usually focus on temporal features or inherent relations between clinical event variables or extract both information in two separate phases. This usually leads to insufficient patient feature information and results in poor prediction performance. Moreover, existing methods based on Heterogeneous Graph Neural Network usually require manual selection of proper Meta-Paths. To solve these problems, we propose the Time-aware Context-Gated Graph Attention Network (T-ContextGGAN). Specifically, we design a GNN based module with Time-aware Meta-Paths and self-attention mechanism to extract both temporal semantic information and inherent relations of EHR data simultaneously and perform automatic Meta-Path selection. To evaluate the proposed model, we extract the first 48 hour EHR data in the first Intensive Care Unit (ICU) admission of three different tasks from two open-source datasets and model various clinical variables on the proposed EHRGraph. Extensive experimental results show the proposed model can effectively extract informative features, and outperform existing state-of-art models in terms of various prediction measures. Our code is available in https://github.com/OwlCitizen/TContext-GGAN.
The Performance Analysis of Complex-Valued Neural Network in Radio Signal Recognition

Many techniques have been developed for wireless signal recognition in many fifth generation (5G) enabled derivatives. Many harsh constraints, such as the large amount of model parameters and complex signal characteristics, drives intelligent recognition method in real-world settings. In this paper, we propose a generalizable, practical method for raw IQ signal recognition. Specifically, deep complex-valued convolutional neural network models, including a Complex-valued Visual Geometry Group (VGG) (CxVGG) model and a Complex-valued ResNet (CxRN) model, are proposed for handling raw signal IQ data. We examine the merit of complex-valued neural networks (CxNN) and validate their performance with experiments using two public datasets. With an SNR of 10dB, the proposed algorithm achieves a recognition accuracy of 96% on the RadioML2018.10a dataset. When performing drone recognition, CvNN can achieve a recognition accuracy of 99%. Our experimental results verify that deep complex-valued neural network models can achieve considerably improved accuracy with lower computation complexity and fewer model parameters than their real-valued counterparts.
Blockchain and Federated Learning Based Bidding Applications in Power Markets

Currently, power generators are facing an imperfectly competitive power market with relatively limited information, and they can increase their own economic returns by raising their quotations through game theory, which leads to an inflated price in the wholesale power market. In this paper, we propose a bidding application for the wholesale power market called Federated Learning Power Quotation System (FedLPQS), which combines federated learning and blockchain to consider the characteristics of the future power trading market of ”Liberalizing both ends”. FedLPQS using federated learning aggregates cost data of each power plant to achieve a certain degree of transparency and records the data in a blockchain platform based on the consortium chain to reduce the price of power traded in the wholesale power market.
A Robust and Optional Privacy Data Aggregation Scheme for Fog-Enhanced IoT Network

With the advances in fog computing, various users’ data, collected by smart devices in the Internet of Things (IoT), are published to facilitate services and efficiency. This leads to the users’ worry about security and privacy issues in fog-enhanced IoT. Although privacy protection can ease such worry, users need to pay some extra price, such as more computation costs. Besides, there are some users who want convenience rather than privacy. They prefer to publish their raw data to exchange for better convenience or benefit. In this article, an optional privacy-preserving data aggregation scheme based on BGN homomorphic encryption is proposed, without any trusted third party. In the proposed scheme, each user can choose either no privacy encryption or privacy encryption to upload its own data according to its own privacy sensitivity. With the help of the fog node, the control center can get the aggregated data of all smart devices and the single data of smart devices that choose the no privacy option. In addition, the proposed scheme is analyzed to achieve correctness, privacy preservation, and robustness. Experimental results and comparisons show the proposed scheme is satisfactory in terms of computation cost and communication overhead.
Ascend: A Scalable and Energy-Efficient Deep Neural Network Accelerator With Photonic Interconnects

The complexity and size of recent deep neural network (DNN) models have increased significantly in pursuit of high inference accuracy. Chiplet-based accelerator is considered a viable scaling approach to provide substantial computation capability and on-chip memory for efficient process of such DNN models. However, communication using metallic interconnects in prior chiplet-based accelerators poses a major challenge to system performance, energy efficiency, and scalability. Photonic interconnects can adequately support communication across chiplets due to features such as distance-independent latency, high bandwidth density, and high energy efficiency. Furthermore, the salient ease of broadcast property makes photonic interconnects suitable for DNN inference which often incurs prevalent broadcast communication. In this paper, we propose a scalable chiplet-based DNN accelerator with photonic interconnects named ASCEND. ASCEND introduces (1) a novel photonic network that supports seamless intra- and inter- chiplet broadcast communication, and flexible mapping of diverse convolution layers, and (2) a tailored dataflow that exploits the ease of broadcast property and maximizes parallelism by simultaneously processing computations with shared input data. Simulation results using multiple DNN models show that ASCEND achieves 71% and 67% reduction in execution time and energy consumption, respectively, as compared to other state-of-the-art chiplet-based DNN accelerators with metallic or photonic interconnects.
Deep Transfer Learning for Wall Bulge Endpoints Regression for Autonomous Decoration Robots

Wall bulge maintenance and repairing is an essential task for autonomous decoration robots.The problem of the wall bulge endpoints regression refers to identifying the position of the wall bulge endpoints in spatial coordinates.This problem is of significant importance for autonomous decoration robots as these robots target automatic maintenance and repairing of wall bulges, they must automatically recognize where to start and stop the repairing process.Training deep convolutional neural networks for supervised computer vision tasks requires a large number of annotated images.Since gathering annotated images for this task is difficult, laborious, and time-consuming, we proposed a model for detecting the wall bulge endpoints position based on deep transfer learning.Our proposed model is capable of classifying the wall bulge into one of four classes according to its orientation.Deep transfer learning transfers the knowledge acquired by deep learning models trained for a specific task and domain to another different but related task and domain.Our proposed model is mainly based on deep convolutional neural networks pre-trained on large datasets for tasks of object classification and detection.We transfer the knowledge acquired by the model from these tasks to solve both problems in our new task.
Progressive Domain Translation Defogging Network for Real-World Fog Images

Images captured in bad weather are affected by atmospheric scattering. To remove image degradation caused by scattering, many defogging methods have been proposed. However, due to the lack of consideration of high-frequency signal loss in real-world fog (RF) images, few existing methods are able to enhance RF images that have a large domain distance from mist images or synthetic fog images in the style feature space. Therefore, in this paper, a progressive domain translation defogging network is proposed to achieve the coordination of fog removal and target contour refinement for RF images. Firstly, a fog inversion module is trained with the newly built synthetic foggy images, and the high-frequency signal is truncated to shorten the distance between the training sample and RF images. The module realizes the restoration of fog density of RF images. Secondly, an image translation module is trained by an unsupervised loss to further eliminate fog degradation and enhance the style of defogged images toward clear images. The image style enhanced by the module includes the overall image style related to color, contrast, and the local image style related to target contours. Experimental results show that the proposed network can obtain high-quality RF images, which is beyond state-of-the-art methods in terms of visual quality indices and target detection indices. The code of the proposed network is found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yeyekurong/Guoqiang_PTD-Net/</uri> .
Participatory Systems Mapping

Abstract This chapter introduces Participatory Systems Mapping, a method for building and analysing causal system models in groups, developed by us. The method uses tools from network analysis and focuses on chains of causal connections to develop meaningful and actionable insights with stakeholders. This chapter describes in detail what it is and how to use it, considers what it is good and bad at, as well as describes some of the history of its development. We also point to resources and tips for getting started with the method yourself.
Analog Secret Sharing With Applications to Private Distributed Learning

single,double We consider the critical problems of distributed computing and learning over data while keeping it private from the computational servers. The state-of-the-art approaches to this problem rely on quantizing the data into a finite field, so that the cryptographic approaches for secure multiparty computing can then be employed. These approaches, however, can result in substantial accuracy losses due to fixed-point representation of the data and computation overflows. To address these critical issues, we propose a novel algorithm to solve the privacy-preserving distributed computing problem when data is in the analog domain, e.g., the field of real/complex numbers. We characterize the privacy of the data from both information-theoretic and cryptographic perspectives, while establishing a connection between the two notions in the analog domain. More specifically, the well-known connection between the distinguishing security (DS) and the mutual information security (MIS) metrics is extended from the discrete domain to the analog domain. This is then utilized to bound the amount of information about the data leaked to the servers in our protocol, in terms of the DS metric, using well-known results on the capacity of single-input multiple-output (SIMO) channel with correlated noise. It is shown how the proposed framework can be adopted to do computation tasks when data is represented using floating-point numbers. We then show that this leads to a fundamental trade-off between the privacy level of data and accuracy of the result. By extending the setup to distributed learning, we show how to train a machine learning model using the proposed algorithm while keeping the data as well as the trained model private. Then numerical results are shown for experiments on several datasets. Furthermore, experimental advantages are shown comparing to fixed-point implementations over finite fields.
MobileNetV3 With CBAM for Bamboo Stick Counting

This study aims to solve the problems of inaccurate weighing of bamboo sticks and inefficient manual counting.To overcome this problem, an improved MobileNetV3 model and a counting algorithm suitable for bamboo sticks-combined with a spatial-temporal attention mechanism-are proposed in this paper.Inspired by the idea of EfficientNet, scaling coefficients are used to scale the MobileNetV3 network structure as a whole in terms of width and height.The optimal model for bamboo-stick recognition is screened initially, and then the algorithm uses the convolutional block attention module (CBAM) attention mechanism to replace the squeeze-and-excitation (SE) attention mechanism in the MobileNetV3 network structure to allow the network to extract features in the two dimensions of channel and space.Since the number of bamboo sticks in a single image is extremely dense-generally around 1000-3000-it is difficult to effectively count them with existing algorithms.The proposed algorithm divides the image into multiple equally sized blocks and then uses the boundary processing algorithm to merge the cut bamboo stick images and count the number of sticks.Experimental results show that the proposed algorithm can effectively perform near real-time detection on a mobile terminal and its accuracy can reach approximately 97%, which is in line with actual production applications.
Hyperspectral Estimation of Soil Copper Concentration Based on Improved TabNet Model in the Eastern Junggar Coalfield

China is the largest coal consumer in the world. The massive exploitation and utilization of coal resources has resulted in serious problems of heavy metal pollution and environmental contamination, such as soil degradation, water pollution, crop damage, and even threatening human lives. Therefore, monitoring soil heavy metal pollution quickly and in real time is an urgent task at present. This research not only formulated a new preprocessing method enlightened by few-shot learning for soil hyperspectral data, but also combined it with other soil-related auxiliary information to extract effective information from the soil hyperspectrum, at the end of which different regression methods were adopted to predict soil heavy metal contamination. This test used 168 actual soil samples from the Eastern Junggar coalfield in Xinjiang for verification. Since copper in the soil is a trace element and the corresponding spectral characteristics are affected by other impurities, improper use of hyperspectral preprocessing methods may introduce interference information or may delete useful information, which makes the model effect unsatisfied. To effectively address the above problems, the preprocessing method of this experiment combined the second-order differential derivation, data enhancement method together with the addition of auxiliary information to allow more effective features to be entered into the model. Next, the Attentive Interpretable Tabular Learning (TabNet) model was improved in three different ways using the original TabNet model and three improved TabNet models to create regression models. One of the improved TabNet models had the best effect, with a list of the top 30 features according to the degree of importance. Meanwhile, the regression prediction of Cu content using four different convolutional neural networks (CNN) revealed that the model with the residual block was the strongest and slightly outperformed the improved TabNet model, but lacked interpretation of the input data. Besides, this experiment also employed different pre-processing methods for regression prediction on various models, and found that the traditional pre-processing methods performed best in traditional regression models (e.g., PLSR) and underperformed in deep learning models. The selected optimal model was compared with partial least square regression (PLSR), and convolutional neural network (CNN) models. The results indicated that both the improved TabNet model and improved CNN model had better performance using the new preprocessing approach proposed in this paper, with improved TabNet yielding a coefficient of determination (R<sup>2</sup>), root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) of 0.94, 1.341 and 4.474, respectively. The improved CNN model had a coefficient of determination of 0.942, a root mean square error of 1.324 and an interquartile range of 4.531 in the test dataset.
An Intention Prediction Based Shared Control System for Point-to-Point Navigation of a Robotic Wheelchair

Shared control approaches for robotic wheelchairs aim to provide navigation assistance to humans by utilizing robot’s intelligence in environment perception and motion planning. They can be broadly classified into two categories based on human intention prediction. Without human intention prediction, control authority lies with humans and assistance is provided only to avoid collisions. This can cause difficulty in cases where fine motor control is required, such as when entering narrow doorways, especially for users with severe upper limb disability. Intention prediction based approaches are able to better assist with such tasks but do not give enough control authority to the user as possible user intentions are pre-defined. In this work, we present an intention prediction based shared control system for point-to-point navigation of wheelchair which gives control authority to the user and also assists in fine motor control tasks. We compute various possible user intentions online using generalized Voronoi diagram, link them across time steps using their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homotopy class</i> and thus are able to calculate their probability given user input history. A shared local path planner then steers user towards the most likely path. This allows the user to follow any path. Our simulation experiments with 18 healthy subjects and both simulation and real wheelchair experiments, with 2 Cerebral Palsy (CP) subjects, show that our system can improve navigation outcome for people with disability and in general leads to around 10% faster completion of the task for even healthy people as compared to a local obstacle avoidance system while allowing users to follow their desired path with similar accuracy.