Reviewerly/exHarmony huggingface dataset:
Dynamic Multitarget Detection Algorithm of Voxel Point Cloud Fusion Based on PointRCNN

Current 3D target detection methods used in the field of autonomous driving generally have low real-time performance and insufficient target context feature to detect dynamic multi-target accurately. In order to solve these problems, a dynamic multi-target detection algorithm of voxel point cloud fusion based on PointRCNN is proposed, which adopts a two-stage detection structure. The first stage directly processes the point cloud to extract key point features and divides voxel space. A novel submanifold sparse convolution is used to extract voxel features. Then key point features and voxel features of the point cloud are merged to generate pre-selection boxes. In the second stage, reference points are set based on the voxel features. The features of key points around reference points are merged for the second time to achieve optimized detection boxes. Finally, for the problem of inconsistent confidence, a mandatory consistency loss function is proposed to improve the accuracy of the detection box. The proposed algorithm was compared with other algorithms in three different datasets, and further tested on a self-made dataset from an actual vehicle platform. Results showed that the proposed algorithm had higher accuracy, better robustness, stronger generalization ability for dynamic multi-target detection.
A Blockchain-Based Cross-Domain and Autonomous Access Control Scheme for Internet of Things

The volume, variety and value of data generated by Internet of Things (IoT) devices are expected to increase significantly in foreseeable future, hence, reinforcing the importance of secure and efficient access control solutions for these devices and their networks. However, existing access control solutions are not generally lightweight or scalable, particularly for geographically disperse, inexpensive resource constrained IoT devices. To tackle above challenges, we propose a lightweight consortium blockchain based architecture to enable intelligent autonomous access control for IoT devices. In our architecture, intelligent blockchain facilitates the storing of access policies, provision of authentication services for data access control, and trust evaluation for access request nodes through token accumulation mechanism. Specifically, the user's access request is approved only after it is confirmed by the blockchain network. To ensure the reliability of authenticity, a compromised resistant consensus algorithm is adapted and implemented to defend against at most <inline-formula><tex-math notation="LaTeX">$1/3$</tex-math></inline-formula> compromised authenticators. In addition, a cross-domain and flexible access control model is not only used to support data sharing among various users but can also be used for access control for exceptional blockchain situations. We explain how our system meets our design goals of reliability, availability, confidentiality, integrity, lightweight, security and scalability. In addition, we also analyze the proposed system's performance from computational, storage and network overheads (e.g., running cryptographic algorithms on a Raspberry Pi 4B), and the findings suggest that the time to run typical cryptographic algorithms is in the microsecond range.
Blockchain-Based Secure Sharing Mechanism of Online Education Data

The development of online education has broken the limitations of traditional education in region and time, and promoted the reform of education. However, there are some problems in traditional online education, such as data island, lack of data sharing model and so on. This paper constructs an online education data management model based on blockchain technology, which solves the problems of trust authentication of online learning data and curriculum resources; We put forward the trust generation and security sharing mechanism of online education data, analyze its security, and realize the security sharing of online education data on the basis of privacy protection.
Applicability of Intrusion Detection System on Ethereum Attacks: A Comprehensive Review

Ethereum attracts more investors, researchers, and even scammers for many reasons; this is the first platform that enables the new Decentralized Applications (DApps) to run on top of the blockchain network.However, the rich semantics and applications of DApps inevitably introduce many security issues that have grabbed significant attention from industry and academics due to their destructive impact on DApps in recent years.Therefore, there is a vital need to study the applicability of Intrusion Detection System in detecting Ethereum-based attacks.Hence, this paper is among the first comprehensive review that studies the applicability of IDS in detecting Ethereum-based attacks.In addition, this paper lists all the potential attacks on Ethereum passing through the vulnerabilities that cause those attacks and ending with the consequences of each attack.Besides, this paper analyses all the IDS-based related works of Ethereum attacks detection since the Ethereum platform was launched in 2015.Finally, this paper discusses the open issues regarding vulnerabilities and attacks, challenges, and future directions.
An Efficient and Lightweight CNN Model With Soft Quantification for Ship Detection in SAR Images

Convolutional neural networks have been widely used for synthetic aperture radar (SAR) target detection. Typical methods based on convolutional neural network have obtained favorable detection accuracy at the cost of high model complexity, and thus are difficult to be directly applied to real-time satellites on board as well as maritime rescue. To deal with this problem, this paper proposes an efficient and lightweight target detection network incorporating soft quantization. Firstly, to compensate for the lack of accuracy caused by lightweight networks, a feature fusion module called split bidirectional feature pyramid network is proposed to alleviate the interference of complex background on SAR images. Meanwhile, to adapt the lightweight network and the feature fusion module, a linear transformation module is presented to enhance the linear representation of the model via learnable parameters. Eventually, to make the model size smaller, a soft quantization algorithm is proposed to reduce the accuracy degradation caused by quantization errors. We validate the robustness of the model in several publicly available datasets. Experimental results show that our model achieves 97.0% detection accuracy on SAR ship detection dataset, with a 0.9% accuracy improvement compared to mainstream methods using less than 15x the number of parameters and less than 6x the number of flops.
Multi-Stream Dense View Reconstruction Network for Light Field Image Compression

Recently, many view synthesis-based methods are proposed for high-efficiency light field (LF) image compression. However, most existing methods fail to recover more texture details on occlusion regions, which reduces the compression efficiency. In this paper, we propose a multi-stream dense view reconstruction network to further improve LF image compression performance. In our method, only sparsely-sampled LF views are transmitted and the rest of the views are reconstructed at the decoder side. During the reconstruction process, we firstly constitute a multi-disparity geometry (MDG) structure based on the decoded sparse LF views, which can reflect abundant disparity characteristics. Subsequently, a multi-stream view reconstruction network (MSVRNet) is put forward to reconstruct a high-quality dense LF image, which consists of a multi-scale feature fusion sub-network, a fusion reconstruction sub-network, and a detail refinement sub-network. The multi-scale feature fusion sub-network can implicitly lean abundant multiscale geometric structure features from the constituted MDG structure. The fusion reconstruction sub-network and the detail refinement sub-network are respectively utilized to fuse the learned multiscale geometric features and restore more texture details, especially for occlusion regions. Moreover, 3D convolutional operations are adopted in the whole reconstruction process, which allow information propagation among the learned multiscale geometric features. Comprehensive experimental results demonstrate the effectiveness of the proposed method. The perceptual quality of reconstructed views and application on depth estimation also demonstrate that the proposed method can keep structural consistency of the reconstructed LF image and recover more texture details.
Supine Infant Pose Estimation via Single Depth Image

objective: The infant pose estimation is very important for the evaluation of infants&#x2019; brain development and motor quality. Here, we propose a new method to quickly build a large-scale depth image pose dataset, and on this basis, establish the XJTU-IDP dataset, then we also propose a new method based on joint feature coding to estimate the infants&#x2019; posture from depth image. Methods: First, we use Kinect V2 to collect depth and color images synchronously, and use the proposed framework to build a large dataset. Then we encode the offset vector in the confidence region of each joint of the infant, and export the joint positioning and connection encoding through our convolutional neural network (CNN) model. The proposed method is tested from two datasets (including 16 and 27 clinical actual infants, respectively). Results: When applied to pose estimation, the median root mean square distance (RMSD) calculated among all limbs is 4.12 pixels, which is much lower than the baseline (9.06 pixels) on babyPose dataset and RMSD in XJTU-IDP dataset is 3.93 pixels, which achieves the best in comparison with the state-of-the-arts. Conclusion: The proposed framework of the building dataset in our work is effective, and the joint feature coding can enrich the network information. Significance: Our work not only provides a larger infant pose dataset to make up for the deficiencies of babyPose, but also proposes a new framework for quickly establishing high-quality dataset. The proposed pose estimation method significantly improves the state of art in automatic measurement of infants&#x2019; health status.
SmartVM: A Smart Contract Virtual Machine for Fast On-Chain DNN Computations

Blockchain-based artificial intelligence (BC-AI) has been applied for protecting deep neural network (DNN) data from being tampered with, which is expected to further boost trusted distributed AI applications in many fields. However, due to smart contract execution environment architectural defects, it is challenging for previous BC-AI systems to support computing-intensive tasks on-chain performing such as DNN convolution operations. They have to offload computations and a large amount of data from blockchain to off-chain platforms to execute smart contracts as native code. This failure to take advantage of data locality has become one of the major critical performance bottlenecks in BC-AI system. To this end, in this article, we propose SmartVM with optimization methods to support on-chain DNN inference for BC-AI system. The key idea is to design and optimize the computing mechanism and storage structure of smart contract execution environment according to the characteristics of DNN such as high computational parallelism and large data volume. We decompose SmartVM into three components: 1) a compact DNN-oriented instruction set to describe computations in a short number of instructions to reduce interpretation time. 2) a memory management mechanism to make SmartVM memory dynamic free/allocated according to the size of DNN feature maps. 3) a block-based weight prefetching and parallel computing method to organize each layer's computing and weights prefetching in a pipelined manner. We perform the typical image classification in a private Ethereum blockchain testbed to evaluate SmartVM performance. Experimental results highlight that SmartVM can support DNN inference on-chain with roughly the same efficiency against the native code execution. Compared with the traditional off-chain computing, SmartVM can speed up the overall execution by <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">70×</b> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16×</b> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11×</b> , and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12×</b> over LeNet5, AlexNet, ResNet18, and MobileNet, respectively. The memory footprint can be reduced by <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">84%</b> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">90.8%</b> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">94.3%</b> , and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">93.7%</b> over the above four models, while offering the same level model accuracy. This article sheds light on the design space of the smart contract virtual machine for DNN computation and is promising to further boost BC-AI applications.
From the Knowable and Transparent Individual to the Secret Thought of Individuation: An Anti-Capitalist Postdigital Ecopedagogy

This chapter builds from Alexander Means’ identification of the domination of ‘solutionism’ over our pedagogical landscape, in which social problems are approached through technological fixes. Following Means, we shift the political terrain to capitalist postdigital ecopedagogies, demonstrating, through examples, how contemporary postdigital educational processes limit subjective and political potentials by dictating and naturalizing individuality as a finished product and starting point of sociality and facilitating capital’s demand for transparent knowledge. We then identify the antagonism between different postdigital ecologies as running between the demand for knowledge and the immersion in thought and non-knowledge. Addressing the general inadequacy of educational and political responses to solutionism, we advance an anti-capitalist postdigital ecopedagoy that opens us up to new registers through presenting the unfinished processes of individuation and disindividuation and mobilizing the contagion of thought as a stay against transparency and calculable knowledge.
Strengthening Gradient Descent by Sequential Motion Optimization for Deep Neural Networks

In this article, we explore the advantages of heuristic mechanisms and devise a new optimization framework named sequential motion optimization (SMO) to strengthen gradient-based methods. The key idea of SMO is inspired from a movement mechanism in a recent metaheuristic method called balancing composite motion optimization (BCMO). Specifically, SMO establishes a sequential motion chain of two gradient-guided individuals, including a leader and a follower to enhance the effectiveness of parameter updates in each iteration. A surrogate gradient model with low computation cost is theoretically established to estimate the gradient of the follower by that of the leader through chain rule during the training process. Experimental results in terms of training quality on both fully connected multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) with respect to three popular benchmark datasets, including MNIST, Fashion-MNIST, and CIFAR-10 demonstrate the superior performance of the proposed framework in comparison with the vanilla stochastic gradient descent (SGD) implemented via backpropagation (BP) algorithm. Although this study only introduces the vanilla gradient descent (GD) as a main gradient-guided factor in SMO for deep neural network (DNN) training application, it is great potential to combine with other gradient-based variants to improve its effectiveness and solve other large-scale optimization problems in practice.
Anonymous, Robust Post-quantum Public Key Encryption

A core goal of the NIST PQC competition is to produce PKE schemes which, even if attacked with a large-scale quantum computer, maintain the security guarantees needed by applications. The main security focus in the NIST PQC context has been IND-CCA security, but other applications demand that PKE schemes provide anonymity (Bellare et al., ASIACRYPT 2001), and robustness (Abdalla et al., TCC 2010). Examples of such applications include anonymous cryptocurrencies, searchable encryption, and auction protocols. However, almost nothing is known about how to build post-quantum PKE schemes offering these security properties. In particular, the status of the NIST PQC candidates with respect to anonymity and robustness is unknown. This paper initiates a systematic study of anonymity and robustness for post-quantum PKE schemes. Firstly, we identify implicit rejection as a crucial design choice shared by most post-quantum KEMs, show that implicit rejection renders prior results on anonymity and robustness for KEM-DEM PKEs inapplicable, and transfer prior results to the implicit-rejection setting where possible. Secondly, since they are widely used to build post-quantum PKEs, we examine how the Fujisaki-Okamoto (FO) transforms (Fujisaki and Okamoto, Journal of Cryptology 2013) confer robustness and enhance weak anonymity of a base PKE. We then leverage our theoretical results to study the anonymity and robustness of three NIST KEM finalists—Saber, Kyber, and Classic McEliece—and one alternate, FrodoKEM. Overall, our findings for robustness are definitive: we provide positive robustness results for Saber, Kyber, and FrodoKEM, and a negative result for Classic McEliece. Our negative result stems from a striking property of KEM-DEM PKE schemes built with the Classic McEliece KEM: for any message m, we can construct a single hybrid ciphertext c which decrypts to the chosen m under any Classic McEliece private key. Our findings for anonymity are more mixed: we identify barriers to proving anonymity for Saber, Kyber, and Classic McEliece. We also found that in the case of Saber and Kyber, these barriers lead to issues with their IND-CCA security claims. We have worked with the Saber and Kyber teams to fix these issues, but they remain unresolved. On the positive side, we were able to prove anonymity for FrodoKEM and a variant of Saber introduced by D'Anvers et al. (AFRICACRYPT 2018). Our analyses of these two schemes also identified technical gaps in their IND-CCA security claims, but we were able to fix them.
DDRM: A Continual Frequency Estimation Mechanism with Local Differential Privacy

Many applications rely on continual data collection to provide real-time information services, e.g., real-time road traffic forecasts. However, the collection of original data brings risks to user privacy. Recently, local differential privacy (LDP) has emerged as a private data collection framework for mass population. However, for continual data collection, existing LDP schemes, e.g., those employing the memoization technique, are known to have privacy leakage on data change points over time. In this paper, we propose a new scheme with stronger privacy guarantee for continual frequency estimation under LDP, namely, Dynamic Difference Report Mechanism (DDRM). In DDRM, we introduce difference trees to capture the data changes over time, which well addresses possible privacy leakage on data change points. As for the utility enhancement, DDRM exploits the common case of no data change in time series and thereby suppresses the consumption of privacy budget in such cases. Meanwhile, an optimal privacy budget allocation scheme is proposed to encourage users to report more data for better estimation accuracy. By both theoretical analysis and experimental evaluations, we show DDRM achieves highly accurate frequency estimation in real time.
Low-Light Image Enhancement for UAVs With Multi-Feature Fusion Deep Neural Networks

Object Detection in low-light aerial images is a challenging problem due to considerable variation in brightness and varying contrast. Deep Learning-based approaches have recently demonstrated great promise in image enhancement. Many existing neural networks used for image quality enhancement first encode the input into low-resolution representations and then decode these representations back to a higher resolution for the contextual information. However, this method leads to the loss of semantic content. Recent research has demonstrated the advantage of maintaining high-resolution information along with lower resolution representations, which maintains image features throughout the network. In this paper, we propose a novel architecture named RNet for low-light image enhancement of aerial images. The proposed network contains multi-resolution branches for better understanding of different levels of local and global context through different streams. The performance of RNet is evaluated on a recent synthetic dataset. We also present a comprehensive evaluation with a representative set of state-of-the-art enhancement techniques and neural net architectures.
Indistinguishability-Enhanced Entanglement Recovery by Spatially Localized Operations and Classical Communication

We extend a procedure exploiting spatial indistinguishability of identical particles to recover the spoiled entanglement between two qubits interacting with Markovian noisy environments. Here, the spatially localized operations and classical communication (sLOCC) operational framework is used to activate the entanglement restoration from the indistinguishable constituents. We consider the realistic scenario where noise acts for the whole duration of the process. Three standard types of noises are considered: a phase damping, a depolarizing, and an amplitude damping channel. Within this general scenario, we find the entanglement to be restored in an amount proportional to the degree of spatial indistinguishability. These results elevate sLOCC to a practical framework for accessing and utilizing quantum state protection within a quantum network of spatially indistinguishable subsystems.
Small Object Detection Based on Modified FSSD and Model Compression

Small objects have relatively low resolution, the unobvious visual features which are difficult to be extracted, so the existing object detection methods cannot effectively detect small objects, and the detection speed and stability are poor. Thus, this paper proposes a small object detection algorithm based on FSSD, meanwhile, in order to reduce the computational cost and storage space, pruning is carried out to achieve model compression. Firstly, the semantic information contained in the features of different layers can be used to detect different scale objects, and the feature fusion method is improved to obtain more information beneficial to small objects; secondly, batch normalization layer is introduced to accelerate the training of neural network and make the model sparse; finally, the model is pruned by scaling factor to get the corresponding compressed model. The experimental results show that the average accuracy (mAP) of the algorithm can reach 80.4% on PASCAL VOC and the speed is 59.5 FPS on GTX1080ti. After pruning, the compressed model can reach 79.9% mAP, and 79.5 FPS in detection speed. On MS COCO, the best detection accuracy (APs) is 12.1%, and the overall detection accuracy is 49.8% AP when IoU is 0.5. The algorithm can not only improve the detection accuracy of small objects, but also greatly improves the detection speed, which reaches a balance between speed and accuracy.