Reviewerly/exHarmony huggingface dataset:
RSIn-Dataset: An UAV-Based Insulator Detection Aerial Images Dataset and Benchmark

Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. However, in the power line inspection based on computer vision, datasets have a significant impact on deep learning. The lack of public high-quality power scene data hinders the application of deep learning. To address this problem, we built a dataset for power line inspection scenes, named RSIn-Dataset. RSIn-Dataset contains 4 categories and 1887 images, with abundant backgrounds. Then, we used mainstream object detection methods to build a benchmark, providing reference for insulator detection. In addition, to address the problem of detection inefficiency caused by large model parameters, an improved YoloV4 is proposed, named YoloV4++. It uses a lightweight network, i.e., MobileNetv1, as the backbone, and employs the depthwise separable convolution to replace the standard convolution. Meanwhile, the focal loss is implemented in the loss function to solve the impact of sample imbalance. The experimental results show the effectiveness of YoloV4++. The mAP and FPS can reach 94.24% and 53.82 FPS, respectively.
A Multi-Stage Deep Reinforcement Learning with Search-Based Optimization for Air–Ground Unmanned System Navigation

An important challenge for air–ground unmanned systems achieving autonomy is navigation, which is essential for them to accomplish various tasks in unknown environments. This paper proposes an end-to-end framework for solving air–ground unmanned system navigation using deep reinforcement learning (DRL) while optimizing by using a priori information from search-based path planning methods, which we call search-based optimizing DRL (SO-DRL) for the air–ground unmanned system. SO-DRL enables agents, i.e., an unmanned aerial vehicle (UAV) or an unmanned ground vehicle (UGV) to move to a given target in a completely unknown environment using only Lidar, without additional mapping or global planning. Our framework is equipped with Deep Deterministic Policy Gradient (DDPG), an actor–critic-based reinforcement learning algorithm, to input the agents’ state and laser scan measurements into the network and map them to continuous motion control. SO-DRL draws on current excellent search-based algorithms to demonstrate path planning and calculate rewards for its behavior. The demonstrated strategies are replayed in an experienced pool along with the autonomously trained strategies according to their priority. We use a multi-stage training approach based on course learning to train SO-DRL on the 3D simulator Gazebo and verify the robustness and success of the algorithm using new test environments for path planning in unknown environments. The experimental results show that SO-DRL can achieve faster algorithm convergence and a higher success rate. We piggybacked SO-DRL directly onto a real air–ground unmanned system, and SO-DRL can guide a UAV or UGV for navigation without adjusting any networks.
E-DGAN: An Encoder-Decoder Generative Adversarial Network Based Method for Pathological to Normal Voice Conversion

In recent years, more and more people suffer from voice-related diseases. Given the limitations of current pathological speech conversion methods, that is, a method can only convert a single kind of pathological voice. In this study, we propose a novel Encoder-Decoder Generative Adversarial Network (E-DGAN) to generate personalized speech for pathological to normal voice conversion, which is suitable for multiple kinds of pathological voices. Our proposed method can also solve the problem of improving the intelligibility and personalizing custom speech of pathological voices. Feature extraction is performed using a mel filter bank. The conversion network is an encoder-decoder structure, which is used to convert the mel spectrogram of pathological voices to the mel spectrogram of normal voices. After being converted by the residual conversion network, the personalized normal speech is synthesized by the neural vocoder. In addition, we propose a subjective evaluation metric named "content similarity" to evaluate the consistency between the converted pathological voice content and the reference content. The Saarbrücken Voice Database (SVD) is used to verify the proposed method. The intelligibility and content similarity of pathological voices are increased by 18.67% and 2.60%, respectively. Besides, an intuitive analysis based on a spectrogram was done and a significant improvement was achieved. The results show that our proposed method can improve the intelligibility of pathological voices and personalize the conversion of pathological voices into the normal voices of 20 different speakers. Our proposed method is compared with five other pathological voice conversion methods, and our proposed method has the best evaluation results.
Geochemical survey data cube: A useful tool for lithological classification and geochemical anomaly identification

Geochemical survey data play a critical role in geological studies, mineral exploration, and environmental applications by providing information on geological events and processes such as mineralization and pollution. A typical geochemical survey dataset contains the analysis of multiple elements. For example, the National Geochemical Mapping Project of China comprises 39 major and trace element concentrations. Multiple geochemical maps can be generated by interpolating geochemical samples into raster maps to constitute a geochemical survey data cube in which elements are sorted by their atomic numbers. A geochemical spectrum can be created using these geochemical maps in which each pixel that records geochemical characteristics. In this study, a convolutional neural network (CNN) that considers the geochemical spectrum and spatial pattern of geological objects was employed to mine a geochemical survey data cube, aiming of geological mapping and geochemical anomalies identification associated with mineralization in the eastern part of Hubei Province of China. The results showed that (1) a geochemical survey data cube which built based on various geochemical exploration data provided vital information on mineralization process and the formation of geological features; and (2) a CNN had a strong ability to recognize high-level features in the geochemical survey data cube, and it showed excellent performance in mineral exploration and related geological studies.
Further to quantification of content, can reflectance spectroscopy determine the speciation of cobalt and nickel on a mine waste dump surface?

Toxic elements released due to mining activities are of the most important environmental concerns, characterised not only by their concentration, but also by their distribution among different chemical species, known as speciation. These are conventionally determined using chemical analysis and sequential extraction, which are expensive and time-demanding. In this study, the possibility of using visible-near-infrared-shortwave infrared (VNIR-SWIR) reflectance spectroscopy was investigated as an alternative technique to quantify the contents of cobalt (Co) and nickel (Ni) in soil samples collected from Sarcheshmeh copper mine waste dump surface, in Iran. As a novel approach, the capability of VNIR-SWIR spectroscopy was also investigated in speciation of those elements. Three machine learning (ML) techniques (i.e., extreme gradient boosting (EGB), random forest (RF) and support vector regression (SVR)) were used to make relationships between soil spectral responses and Co and Ni contents of the samples. For all ML algorithms, the best prediction accuracies were obtained by the models developed on the first derivative (FD) spectra (for Co: RMSEp values of 7.82, 8.03 and 9.22 mg·kg-1, and for Ni: RMSEp values of 9.88, 10.32 and 11.02 mg·kg-1, using EGB, RF and SVR, respectively). Spatial variability maps of elements showed relatively similar patterns between observed and predicted values. Correlation and ML (EGB, RF, SVR)-based methods revealed that the most important wavelengths for Co and Ni prediction were those related to iron oxides/hydroxides and clay minerals, as two main soil properties responsible for controlling their speciation. This study demonstrated that the EGB technique was successful at indirect quantification and spatial variability mapping of Co and Ni on the mine waste dump surface. In addition, it provided an inspiration for implementation of the VNIR-SWIR reflectance spectroscopy as a potentially fast and cost-effective method for speciation studies of toxic elements, especially in heterogeneous soil environments.
The Research of AHP-Based Credit Rating System on a Blockchain Application

NFT is a kind of virtual token derived from the blockchain. In 2019, the NFT transaction market became a new force in the field of the digital economy, while NFT fraud was also widespread. There is no efficient technology or methods to ensure the authenticity of the source data (which have not been stored on the blockchain yet) on a blockchain traceability system. To solve this problem and to safeguard the rights and interests of members of the blockchain application, we propose a method to measure the user’s credit degree by obtaining the data before it stores on the blockchain. We first analyze some NFT trading markets’ business processes and dealing models. Then, based on the analytic hierarchy process (AHP) in the operational research theory, some indexes of credit rating have been made. A credit rating system has been established by calculating the evaluation matrix and efficacy coefficient of each index. The experimental results show that the credit evaluation system can be used as a method to judge the user’s credit rating on a blockchain traceability system. This method provides a reference for the decision of whether to restrict the transaction of some users with abnormal behavior.
EC-GCN: A encrypted traffic classification framework based on multi-scale graph convolution networks

The sharp increase in encrypted traffic brings a huge challenge to traditional traffic classification methods. Combining deep learning with time series analysis techniques is a recent trend in solving this problem. Most of these approaches only capture the temporal correlation within a flow. The accuracy and robustness are unsatisfactory, especially in an unstable network environment with high packet loss and reordering. How to learn a representation with a strong generalization ability for each encrypted traffic flow remains a key challenge. Our detailed analysis indicates that there is a graph with particular local structures corresponding to each type of encrypted traffic flow. Inspired by this observation, we propose a novel deep learning framework called EC-GCN to classify encrypted traffic flows based on multi-scale graph convolutional neural networks. We first provide a novel lightweight layer that only relies on the metadata and encodes each encrypted traffic flow into graph representations. So that our framework can be independent of different encryption protocols. Then we design a novel graph pooling and structure learning layer to dynamically extract the multi-graph representations and improve the capabilities to adapt to complex network environments. EC-GCN is an end-to-end classification model that learns representative spatial–temporal traffic features hidden in a traffic time series and then classifies them in a unified framework. Our comprehensive experiments on three real-world datasets indicate that EC-GCN can achieve up to 5%–20% accuracy improvement and outperforms state-of-the-art methods.
FedDCS: A distributed client selection framework for cross device federated learning

In cross-device Federated Learning (FL) the existing client selection approaches are centralized, making the assumption that the participatory clients are resource-full and restrict their type to mobile phones. These assumptions limit the applications of FL and make it difficult for the global server to monitor the client (s) resources; as a consequence of which a high client drop-out rate is observed. Thus, a distributed cross-device FL framework with the provision of resource & time-aware client selection is required. This paper proposes a distributed cross-device FL client selection framework (FedDCS) which employs a novel data-type, resource, and time-aware protocol for FL client selection. FedDCS is designed to offload the client selection and their resource monitoring processes to the local edge servers with the objective to minimize client(s) drop-outs, model convergence time, number of message exchanges, and time to achieve a certain level of accuracy. Extensive experiments on two benchmark data sets: MNIST and FashionMNIST, are performed on the PyTorch-based FL platform to evaluate the performance of FedDCS on such metrics. The results show 100% client retention in FedDCS during FL model training for both MNIST and FashionMNIST datasets using IID and Non-IID settings. For the MNIST dataset, FedDCS shows 82% and 36% improvement in the time taken to reach the desired testing accuracy, and 18% and 10% improvement in model convergence time, for IID and Non-IID setting respectively as compared to the best performing state-of-the-art, i.e., FedMCCS. For the FashionMNIST dataset, FedDCS shows 17% and 6% improvement in the time taken to reach the desired testing accuracy, and 4% and 1% improvement in model convergence time, for IID and Non-IID setting respectively as compared to the best performing state-of-the-art, i.e., FedMCCS. In addition, FedDCS also significantly reduces the number of message exchanges during FL.
An Emotion Speech Synthesis Method Based on VITS

People and things can be connected through the Internet of Things (IoT), and speech synthesis is one of the key technologies. At this stage, end-to-end speech synthesis systems are capable of synthesizing relatively realistic human voices, but the current commonly used parallel text-to-speech suffers from loss of useful information during the two-stage delivery process, and the control features of the synthesized speech are monotonous, with insufficient expression of features, including emotion, leading to emotional speech synthesis becoming a challenging task. In this paper, we propose a new system named Emo-VITS, which is based on the highly expressive speech synthesis module VITS, to realize the emotion control of text-to-speech synthesis. We designed the emotion network to extract the global and local features of the reference audio, and then fused the global and local features through the emotion feature fusion module based on the attention mechanism, so as to achieve more accurate and comprehensive emotion speech synthesis. The experimental results show that the Emo-VITS system’s error rate went up a little bit compared with the network without emotionality and does not affect the semantic understanding. However, this system is superior to other networks in naturalness, sound quality, and emotional similarity.
Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists

Numerous cancer histopathology specimens have been collected and digitized over the past few decades. A comprehensive evaluation of the distribution of various cells in tumor tissue sections can provide valuable information for understanding cancer. Deep learning is suitable for achieving these goals; however, the collection of extensive, unbiased training data is hindered, thus limiting the production of accurate segmentation models. This study presents SegPath-the largest annotation dataset (>10 times larger than publicly available annotations)-for the segmentation of hematoxylin and eosin (H&E)-stained sections for eight major cell types in cancer tissue. The SegPath generating pipeline used H&E-stained sections that were destained and subsequently immunofluorescence-stained with carefully selected antibodies. We found that SegPath is comparable with, or outperforms, pathologist annotations. Moreover, annotations by pathologists are biased toward typical morphologies. However, the model trained on SegPath can overcome this limitation. Our results provide foundational datasets for machine-learning research in histopathology.
Part-Based Representation Enhancement for Occluded Person Re-Identification

Retrieving an occluded pedestrian remains a challenging problem in person re-identification (re-id). Most existing methods utilize external detectors to disentangle the visible body parts. However, these methods are unstable due to domain bias and consume numerous computing resources. In this paper, we propose a novel and lightweight Part-based Representation Enhancement (PRE) network for occluded re-id that takes full advantages of the local correlations to aggregate distinctive information for local features without relying on auxiliary detectors. First, according to the information qualities of different body parts, we design a reasonable partition strategy to obtain the local features. Next, a Partial Relationship Aggregation (PRA) module is developed to self-mine the visibility of the body and construct a correlation matrix for collecting the information related to pre-defined classes. Following this, we propose an Inter-part Omnibearing Fusion (IOF) module that leverages the occlusion-suppressed class features to enhance the distinctiveness of the local features via feature completion and reverse fusion strategies. During the testing phase, the global and reconstructed local features are concatenated together for re-id without a complex visible region matching algorithm. Extensive experiments on occluded, partial, and holistic re-id benchmarks demonstrate the superiority of PRE over state-of-the-art methods in terms of accuracy and model complexity.
Optimal Graph Transformer Viterbi knowledge inference network for more successful visual navigation

Visual navigation recently incorporates priors in reinforcement learning (RL), which endows agent's associative ability in searching object task and has made promising results. However, there is the problem of poor exploration in end-to-end learning to new scenes. Besides, most of the priors only serve as the spatial features of scene layout without fully and directly providing guidance for RL's policy, thus making the navigation inefficient. To address these issues, we design a Graph Transformer Viterbi inference network (GTV), which finds new object relations and explores potential graph-based optimal actions in policy. Our method decomposes the RL's action to learn the adaptive priors in state representations and GTV based actions in policy, which generates a dense end-to-end learning signal and new graph edges in novel scenes. This, in turn, enables object-centric relational RL agents to learn policies faster and improve generalization ability. The results demonstrate our framework outperforms the baseline by 25.85% relatively on SPL (Success weighted by Path Length) and 30.64% on success rate, which also requires only 1/5 number of iterations to converge, compared with the baselines. Our code will be made publicly available in the scientific community.
Multiparty Secure Broad Learning System for Privacy Preserving

Multiparty learning is an indispensable technique to improve the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML), a key research task in multiparty learning. Despite this, the existing PPML methods generally cannot simultaneously meet multiple requirements, such as security, accuracy, efficiency, and application scope. To deal with the aforementioned problems, in this article, we present a new PPML method based on the secure multiparty interactive protocol, namely, the multiparty secure broad learning system (MSBLS) and derive its security analysis. To be specific, the proposed method employs the interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train the neural network classifier. To the best of our knowledge, this is the first attempt for privacy computing method that jointly combines secure multiparty computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. Three classical datasets are adopted to verify our conclusion.
Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis

Historical Quality-of-Service (QoS) data describing existing user-service invocations are vital to understanding user behaviors and cloud service conditions. Collaborative Filtering (CF) models based on Matrix Factorization (MF) have proven to be highly efficient in performing representation learning in QoS data. However, its performance is hindered by its linear inherence and implicit encoding of collaborative QoS signal. To address this critical issue, we present a novel approach in this article, dubbed as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>T</u></b> wo-stream <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>G</u></b> raph convolutional network-incorporated <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>L</u></b> atent <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>F</u></b> eature <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <u>A</u></b> nalysis (TGLFA). The proposed TGLFA is significantly different from previous approaches to representation learning in QoS data in the following three aspects. First, it constructs a multilayer fully-connected network to capture the attribute characteristics representing the nonlinear latent features of service. Second, TGLFA constructs a biparty graph to represent the user-service interactions, where the light graph convolutional network is adopted to acquire the high-order connectivity in QoS data. Last, Aiming to improve computational efficiency, the proposed approach considers the mechanism for data density-oriented modeling when building the input and output layers. Detailed experimental results on eight large-scale cases constructed on two real QoS datasets demonstrate that the proposed TGLFA significantly outperforms its state-of-the-art peers in both estimation accuracy for missing QoS data and computational efficiency. The notable results show that TGLFA is a novel and effective approach to QoS data representation learning.