Reviewerly/exHarmony huggingface dataset:
End-to-End Multi-View Structure-from-Motion with Hypercorrelation Volume

Image-based 3D reconstruction is one of the most important tasks in Computer Vision with many solutions proposed over the last few decades. The objective is to extract metric information i.e. the geometry of scene objects directly from images. These can then be used in a wide range of applications such as film, games, virtual reality, etc. Recently, deep learning techniques have been proposed to tackle this problem. They rely on training on vast amounts of data to learn to associate features between images through deep convolutional neural networks and have been shown to outperform traditional procedural techniques. In this paper, we improve on the state-of-the-art two-view structure-from-motion(SfM) approach of [11] by incorporating 4D correlation volume for more accurate feature matching and reconstruction. Furthermore, we extend it to the general multi- view case and evaluate it on the complex benchmark dataset DTU [5]. Quantitative evaluations and comparisons with state-of-the-art multi-view 3D reconstruction methods demonstrate its superiority in terms of the accuracy of reconstructions.
Research on power entity recognition technology base on BiLSTM-CRF

With the increasing demand for data automation and intelligent management in the field of electric power, knowledge atlas plays a particularly important role in intelligent query, electric power knowledge management and decision support. Entity recognition is the basis of building power knowledge map. In the field of power grid, because of its many types, large numbers and strong professionalism of entities, it is difficult to extract them by rules or manually, and it is also impossible to directly reuse entity recognition methods in other fields. For the field of power, this paper first analyzes the subject and characteristics of entity recognition, defines the task objectives of entity recognition, and then studies the entity recognition methods and technologies, and combines the advantages of BiLSTM and CRF, and proposes a method of power entity recognition based on BiLSTM CRF. Firstly, bi-directional features of text are extracted based on BiLSTM, and then the best sequence mark is obtained based on CRF method as the segmentation result. Based on the corpus of power science and technology papers, a comparative experiment is carried out. The accuracy rate of identifying power entities is about 88%, which is 3%~10% higher than the current common methods, which proves that this method can effectively identify power entities.
Edge Assisted Asymmetric Convolution Network for MR Image Super-Resolution

High-resolution magnetic resonance (MR) imaging is beneficial for accurate disease diagnosis and subsequent analysis. Currently, the single image super-resolution (SR) technique is an effective and less costly alternative technique to improve the spatial resolution of MR images. Structural information in MR images is crucial during clinical diagnosis, but it is often ignored by existing deep learning MR image SR technique. Consequently, we propose edge assisted feature extraction block (EAFEB), which can efficiently extract the content and edge features from low-resolution (LR) images, allowing the network to focus on both content and geometric structure. To fully utilize the features extracted by EAFEB, an asymmetric convolutional group (ACG) is proposed, which can balance structural feature preservation and content feature extraction. Moreover, we design a novel contextual spatial attention (CSA) method to facilitate the network focus on critical information. Experiment results in various MR image sequences, including T1, T2, and PD, show that our Edge Assisted Asymmetric Convolution Network (EAACN) has superior results relative to recent leading SR models.
Feature extraction and analysis of speech signal based on fractional Fourier transform

As an analog signal carrying specific information, voice has become an important means of obtaining and disseminating information in people’s social life. The purpose of speech signal processing is to extract effective speech information in complex speech environment. Speech signal has a strong time-varying characteristics. In a short time interval, the characteristics of speech signal can be regarded as basically unchanged, which is an important starting point of speech signal processing. The rate of speech recognition also depends on the accuracy and robustness of Speech signal feature extraction. Therefore, Speech signal feature extraction plays an important role in speech signal processing applications. By comparing the time-frequency resolution of Fourier transform and fractional Fourier transform in noisy background, the unique advantages of fractional Fourier transform to frequency-varying signals are further clarified.
Blockchain-Based Certificate Authentication System with Enabling Correction

Blockchain has proven to be an emerging technology in the digital world, changing the way everyone thinks about data security and bringing efficiency to several industries.It has already been applied to a wide range of applications, from financial services and supply chain management to voting systems and identity verification.An organization must verify its candidates before selecting them.Choosing an unqualified candidate can ruin an organization's reputation.In this paper, a blockchain-based academic certificate authentication system will be used to ensure authenticity and make the assertion of the decentralized system secure.However, the system will generate, authenticate and make corrections on academic certificates.Ultimately, some blockchain-based authentication systems already exist, they can't correct any errors that occur during generation.A blockchain-based certificate authentication system was built using blockchain technology.Where admin could generate, authenticate and correct the certificate if necessary.The admin can also check how many times a certificate has been modified.Other users can only check the authenticity of the certificates.We're using two blockchains to enable corrections.Blockchain technology can successfully implement a certificate authentication system.This system will eliminate doubts about the authenticity of certificates, provide fast responses, and ensure reliable and secure storage.The proposed system will help in many ways, such as providing a user-friendly university admission, and smooth job hiring process, etc.In conclusion, our proposed system can permanently eradicate certificate forgeries and create and promote trust in society.
PBFT optimization algorithm based on community contributions

Community governance is the basic unit of social governance, and it is also an important direction for building a social governance pattern of co-construction, co-governance and sharing. Previous studies have solved the problems of data security, information traceability and participant enthusiasm in the process of community digital governance by building a community governance system based on blockchain technology and incentive mechanisms. The application of blockchain technology can solve the problems of low data security, difficulty in sharing and tracing and low enthusiasm on the part of multiple subjects regarding participation in community governance. The process of community governance involves the cooperation of multiple government departments and multiple social subjects. Under the blockchain architecture, the number of alliance chain nodes will reach 1000 with the expansion of community governance. The existing consensus algorithms for coalition chains are difficult to meet the high concurrent processing requirements under such large-scale nodes. An optimization algorithm has improved the consensus performance to a certain extent, but the existing systems still cannot meet the data needs of the community and are not suitable for community governance scenarios. Since the community governance process only involves the participation of relevant departments in users, all nodes in the network are not required to participate in the consensus under the blockchain architecture. Therefore, a practical Byzantine fault tolerance (PBFT) optimization algorithm based on community contribution (CSPBFT) is proposed here. First, consensus nodes are set according to different roles of participants in community activities, and participants are given different consensus permissions. Second, the consensus process is divided into different stages, and the amount of data processed by each consensus step is reduced. Finally, a two-level consensus network is designed to perform different consensus tasks, and reduce unnecessary communication between nodes to reduce the communication complexity of consensus among nodes. Compared with the PBFT algorithm, CSPBFT reduces the communication complexity from O(N2) to O(N2/C3). Finally, the simulation results show that, through rights management, network level setting and consensus phase division, when the number of nodes in the CSPBFT network is 100-400, the consensus throughput can reach 2000 TPS. When the node in the network is 1000, the instantaneous concurrency is guaranteed to be above 1000 TPS, which can meet the concurrent needs of the community governance scenario.
Multimedia Datasets: Challenges and Future Possibilities

Public multimedia datasets can enhance knowledge discovery and model development as more researchers have the opportunity to contribute to exploring them. However, as these datasets become larger and more multimodal, besides analysis, efficient storage and sharing can become a challenge. Furthermore, there are inherent privacy risks when publishing any data containing sensitive information about the participants, especially when combining different data sources leading to unknown discoveries. Proposed solutions include standard methods for anonymization and new approaches that use generative models to produce fake data that can be used in place of real data. However, there are many open questions regarding whether these generative models hold information about the data used to train them and if this information could be retrieved, making them not as privacy-preserving as one may think. This paper reviews some important milestones that the research community has reached so far in important challenges in multimedia data analysis. In addition, we discuss the long-term and short-term challenges associated with publishing open multimedia datasets, including questions regarding efficient sharing, data modeling, and ensuring that the data is appropriately anonymized.
Solar PV Power Forecasting with a Hybrid LSTM-AdaBoost Ensemble

Smart grids aim at achieving unprecedented flexibility in energy management and a resilient quality of supply. However, the inclusion of variable renewable energy (RE) generation units makes the realization of a resilient supply a challenging one. One of the solutions to the achievement of the goal is the ability to predict or forecast power generation as accurately as possible from the various RE in the grid. Despite extensive research on the subject, RE generation forecasting still remains a challenge, and research is ongoing to achieve a near-perfect and efficient prediction. Deep Neural Network (DNN) algorithms have performed efficiently in areas like speech recognition, image classification, as well as forecasting tasks, such as economic time series, but have been sparsely applied in renewable energy power forecasting. This paper proposes a hybrid long short term memory (LSTM)-Adaboost ensemble method for solar power generation forecasting. It also does a comparative study of different LSTM configurations tested on solar PV data from Germany. In particular, the work in this paper looks at how well the LSTM-AdaBoost ensemble model predicts solar power compared to machine learning methods that do not use ensembles.
Knowledge-Aided Federated Learning for Energy-Limited Wireless Networks

The conventional model aggregation-based federated learning (FL) approach requires all local models to have the same architecture, which fails to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over a million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. This framework allows devices to design their machine-learning models independently and reduces the communication overhead in the training process. We then theoretically analyze the convergence bound of the proposed framework under a non-convex loss function setting, revealing that scheduling more data volume in each round helps to improve the learning performance. In addition, large data volume should be scheduled in early rounds if the total scheduled data volume during the entire learning course is fixed. Inspired by this, we define a new objective function, i.e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation, and power control. To deal with unknown time-varying wireless channels, we transform the considered problem into a deterministic problem for each round with the assistance of the Lyapunov optimization framework. Then, we derive the optimal bandwidth allocation and power control solution by convex optimization techniques. We also develop an efficient online device scheduling algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on two typical datasets (i.e., MNIST and CIFAR-10) under highly heterogeneous local data distributions show that the proposed KFL is capable of reducing over 99% communication overhead while achieving better learning performance than the conventional model aggregation-based algorithms. In addition, the proposed device scheduling algorithm converges faster than the benchmark scheduling schemes.
Convolutional Neural Network-Based Robot Control for an Eye-in-Hand Camera

In past decades, much progress has been obtained in vision-based robot control theories with traditional image processing methods.With the advances in deep learning based methods, Convolutional Neural Network (CNN) has now replaced the traditional image processing methods for object detection and recognition.However, it is not clear how the CNN-based methods can be integrated into robot control theories in a stable and predictable manner for object detection and tracking, especially when the aspect ratio of the object is unknown and also varies during manipulation.In this paper, we develop a vision-based control method for robots with an eye-in-hand configuration, which can be directly integrated with existing CNN-based object detectors.The task variables are generated based on parameters of the bounding box from the output of any real-time CNN object detector such as You Only Look Once (Yolo).To address the chattering problem of bounding box, Long Short-Term Memory (LSTM) is used to provide smoothed bounding box information.A vision-based controller is then proposed following task-space motion control design formulation in order to keep the object of unknown aspect ratio in the center of field of view of the camera.The stability of the overall closed-loop control system is analyzed rigorously using Lyapunov-like approach.Experimental results are presented to illustrate the performance of the proposed CNNbased robot controller.
Accurately Identify and Localize Commodity Devices from Encrypted Smart Home Traffic

Nowadays, Internet of Things (IoT) based smart home system is equipped with a large number of smart devices, such as smart speakers and cameras, which can greatly facilitate users to control and automate their home environment. However, recent studies have shown that smart home system is at great risk of privacy leakage. Especially, external attackers can infer user privacy information by passively sniffing encrypted smart home network traffic. Traditional methods mainly focus on sniffing WiFi devices but pay less attention to other commodity devices such as Zigbee and Bluetooth (BLE). In this paper, we focus on inferring fine-grained sensitive details about users using diverse commodity devices. We apply deep learning techniques to infer users' behaviors through identifying and localizing smart home devices being used due to the excellent performance of deep learning in many fields. Specifically, we first pre-process encrypted device traffic, select valid features, and use Convolutional Neural Networks (CNN) for device identification. In addition, we extract the Received Signal Strength Indicator (RSSI) from the frame information of traffic packets and employ Sparse Autoencoder (SAE) to extract stable and distinguishable high-dimensional features for RSSI measurement. Features are fed into a Multilayer Perceptron (MLP) to predict the device's localization. In this way, we can infer human activity by identifying and localizing the devices being used. Extensive experiment results show that our work can achieve a mean position estimation error of 1.34m even in an unseen environment, outperforming other common- used localization algorithms based on RSSI fingerprints.
Low-power Robustness Learning Framework for Adversarial Attack on Edges

Recent works on adversarial attacks uncover the intrinsic vulnerability of neural networks, which reveal a critical issue that the neural networks are easily misled by adversarial attacks. As the development of edge computing, more and more real-time tasks are deployed on edge devices. The safety of these neural network-based applications is threatened by adversarial attack. Therefore, the defense technique against adversarial attack has very important application value for edges. Especially, the defense technique should consider the deployment condition on edges, such as low power and low time consumption. Unfortunately, until now, very limited research considers the security problem under adversarial attack on edges. In this paper, we propose a low-power robust learning framework to deal with the adversarial attacks at resource-constrained edge devices. In this framework, we make a rough categorization of approaches on defending against adversarial attacks, and reveal how this edge device-based framework can be used to resist adversarial attacks. Furthermore, we propose a staged ensemble defense strategy in the framework, which achieves better defensive performance than a single defense algorithm. To verify our framework on real application, we build a Drone Search and Rescue System (DSRS) which is employed to examine the performance of the proposed framework. The results indicate that our framework achieves outstanding performance in all aspects, such as robustness, time and power consumption. Multiple evaluations of the low-power robust learning framework provide the advice that help to choose the optimal security configuration on power-constrained and performance-expected environments.
Research on the Visualization Technology of Diesel Engine Acoustic State Based on Augmented Reality

Aiming at the problems of insufficient visualization and insufficient visual and intuitive monitoring methods in the field of diesel engine acoustic condition monitoring, this paper proposes a method for visual monitoring using augmented reality technology. Through the research on the visualization modeling processing technology of the analysis results of the acoustic state of the diesel engine, the visualization processing methods are explored, the acoustic state parameters are presented through the visualization means, and the presented results are displayed based on the augmented reality glasses, which improves the visualization of the acoustic state monitoring. And it is more intuitive.
Breaking Distributed Backdoor Defenses for Federated Learning in Non-IID Settings

Federated learning (FL) is a privacy-preserving distributed machine learning architecture to solve the problem of data silos. While FL is proposed to protect data security, it still faces security challenges. Backdoor attacks are potential threats in FL and aim to manipulate the model performance on chosen backdoor tasks by injecting adversarial triggers. As a more insidious variant of backdoor attacks, distributed backdoor attacks decompose the same global trigger into multiple local patterns and respectively assign them to different attackers. In this paper, we study deep into the entire training process of current distributed backdoor attack (DBA) and propose a cooperative DBA method for non-IID FL to break through existing defenses. To bypass the cosine similarity detection, we design an update rotation and scaling technique based on two independent training to well disguise malicious updates among benign updates. We conduct an exhaustive experiment to evaluate the performance of our proposed method under the state-of-the-art defenses. The experimental results show that it is much more stealthy than the current DBA method while maintaining the high backdoor attack intensity.
Fast Self-guided Multi-view Subspace Clustering

Multi-view subspace clustering is an important topic in cluster analysis. Its aim is to utilize the complementary information conveyed by multiple views of objects to be clustered. Recently, view-shared anchor learning based multi-view clustering methods have been developed to speed up the learning of common data representation. Although widely applied to large-scale scenarios, most of the existing approaches are still faced with two limitations. First, they do not pay sufficient consideration on the negative impact caused by certain noisy views with unclear clustering structures. Second, many of them only focus on the multi-view consistency, yet are incapable of capturing the cross-view diversity. As a result, the learned complementary features may be inaccurate and adversely affect clustering performance. To solve these two challenging issues, we propose a Fast Self-guided Multi-view Subspace Clustering (FSMSC) algorithm which skillfully integrates the view-shared anchor learning and global-guided-local self-guidance learning into a unified model. Such an integration is inspired by the observation that the view with clean clustering structures will play a more crucial role in grouping the clusters when the features of all views are concatenated. Specifically, we first learn a locally-consistent data representation shared by all views in the local learning module, then we learn a globally-discriminative data representation from multi-view concatenated features in the global learning module. Afterwards, a feature selection matrix constrained by the l2,1 -norm is designed to construct a guidance from global learning to local learning. In this way, the multi-view consistent and diverse information can be simultaneously utilized and the negative impact caused by noisy views can be overcame to some extent. Extensive experiments on different datasets demonstrate the effectiveness of our proposed fast self-guided learning model, and its promising performance compared to both, the state-of-the-art non-deep and deep multi-view clustering algorithms. The code of this paper is available at https://github.com/chenzhe207/FSMSC.