Reviewerly/exHarmony huggingface dataset:
Ultra High-Speed Polynomial Multiplications for Lattice-Based Cryptography on FPGAs

Lattice-based cryptography (LBC) has emerged as the most viable substitutes to the classical cryptographic schemes as 5 out of 7 finalist schemes in the 3rd round of the NIST post-quantum cryptography (PQC) standardization process are lattice based in construction. This work explores novel architectural optimizations in the FPGA-based hardware implementation of polynomial multiplication, which is a bottleneck in every LBC construction. To target ultra-high throughput, both schoolbook polynomial multiplication (SPM) and number theoretic transform (NTT) are explored: a completely parallel architecture of an SPM is undertaken while for NTT, radix-2 and radix- <inline-formula><tex-math notation="LaTeX">$2^2$</tex-math></inline-formula> multi-path delay commutator (MDC) based pipelined architectures are adopted. Our proposed high-speed SPM (HSPM) structure on latest Xilinx UltraScale+ FPGA is 5× faster than the state-of-the-art LBC designs. Whereas, the proposed high-speed NTT (HNTT) structure (i.e., R <inline-formula><tex-math notation="LaTeX">$2^2$</tex-math></inline-formula> MDC) takes only 0.63 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s for the encryption, hence achieving the highest throughput of 408 Mbps. Moreover, all of the proposed designs achieve highest design efficiencies (i.e., throughput per slice (TPS)) in comparison to available LBC designs.
An Optoelectronic Reservoir Computing for Temporal Information Processing

The implementation of 5G increases the demand for data acquisition, thus increasing the pressure of data processing. Although artificial neural network shows great potential in processing big data, efficient neuromorphic visual system is desired due to the waste of computation resources when processing non-structural visual data. Although reservoir computing (RC) has advantages in temporal information processing, the separation of sensors and RC results in addition cost. Here, an optoelectronic RC system is proposed for temporal information processing in sensors. The reservoir is built on photodetectors based on a non-uniform MoS<sub>2</sub> film. The persistent photoconductivity effect of the photodetectors enables mapping different temporal inputs into corresponding reservoir states. The readout layer could be simply trained to identify different reservoir states. As a proof of concept, different classification tasks of numbers are demonstrated. The proposed optoelectronic RC system provides a low training cost strategy for intelligent edge machine visual system to process temporal information.
Deep Image Denoising With Adaptive Priors

Image denoising methods using deep neural networks have achieved a great progress in the image restoration. However, the recovered images restored by these deep denoising methods usually suffer from severe over-smoothness, artifacts, and detail loss. To improve the quality of restored images, we first propose Supplemental Priors (SP) method to adaptively predict depth-directed and sample-directed prior information for the reconstruction (decoder) networks. Furthermore, the over-parameterized deep neural networks and too precise supplemental prior information may cause an over-fitting, restricting the performance promotion. To improve the generalization of denoising networks, we further propose Regularization Priors (RP) method to flexibly learn depth-directed and dataset-directed regularization noise for the retrieving (encoder) networks. By respectively integrating the encoder and decoder with these plug-and-play RP block and SP block, we propose the final Adaptive Prior Denoising Networks, called APD-Nets. APD-Nets is the first attempt to simultaneously regularize and supplement denoising networks from the adaptive priors’ view with drawing learning-based mechanism into producing adaptive regularization noise and supplemental information. Extensive experiment results demonstrate our method significantly improves the generalization of denoising networks and the quality of restored images with greatly outperforming the traditional deep denoising methods both quantitatively and visually. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The code will be released at <uri>https://github.com/JiangBoCS/APD-Nets</uri></b> .
Truthful auction mechanisms for resource allocation in the Internet of Vehicles with public blockchain networks

Recording vehicle driving data into the blockchain can effectively solve problems of data authenticity and security, which is a focus of research on blockchain and the Internet of Vehicles. However, when using blockchain technology, the proof-of-work completed by vehicles may consume substantial energy and computing resources, which limits the application of blockchain technology in the internet of vehicles environment. Therefore, this paper considers deploying edge computing nodes to support blockchain technology and introducing an auction mechanism to encourage users to record vehicle driving data as miners. This paper proposes two auction mechanisms for the blockchain network formed by edge computing service providers and miners to maximize the social welfare. Specifically, one mechanism is used when the resource demands of the miners are the same, and the other is used when the resource demands of the miners are different. For resource allocation, the former uses the maximum cost maximum flow algorithm to achieve optimal allocation, and the latter uses a heuristic algorithm. For price payment, the former uses the Vickrey–Clarke–Groves mechanism, and the latter uses dichotomy. These two price payment algorithms use critical value theory to calculate the payment price. This paper demonstrates that both are truthful and individually rational. Through experiments, this paper evaluates indicators such as social welfare, satisfaction, and resource utilization. The experiments show that the proposed auction mechanism can effectively maximize social welfare in the blockchain network and provide an effective resource allocation strategy for edge computing service providers.
Applications of game theory in deep learning: a survey

This paper provides a comprehensive overview of the applications of game theory in deep learning. Today, deep learning is a fast-evolving area for research in the domain of artificial intelligence. Alternatively, game theory has been showing its multi-dimensional applications in the last few decades. The application of game theory to deep learning includes another dimension in research. Game theory helps to model or solve various deep learning-based problems. Existing research contributions demonstrate that game theory is a potential approach to improve results in deep learning models. The design of deep learning models often involves a game-theoretic approach. Most of the classification problems which popularly employ a deep learning approach can be seen as a Stackelberg game. Generative Adversarial Network (GAN) is a deep learning architecture that has gained popularity in solving complex computer vision problems. GANs have their roots in game theory. The training of the generators and discriminators in GANs is essentially a two-player zero-sum game that allows the model to learn complex functions. This paper will give researchers an extensive account of significant contributions which have taken place in deep learning using game-theoretic concepts thus, giving a clear insight, challenges, and future directions. The current study also details various real-time applications of existing literature, valuable datasets in the field, and the popularity of this research area in recent years of publications and citations.
GANsformer: A Detection Network for Aerial Images with High Performance Combining Convolutional Network and Transformer

There has been substantial progress in small object detection in aerial images in recent years, due to the extensive applications and improved performances of convolutional neural networks (CNNs). Typically, traditional machine learning algorithms tend to prioritize inference speed over accuracy. Insufficient samples can cause problems for convolutional neural networks, such as instability, non-convergence, and overfitting. Additionally, detecting aerial images has inherent challenges, such as varying altitudes and illuminance situations, and blurred and dense objects, resulting in low detection accuracy. As a result, this paper adds a transformer backbone attention mechanism as a branch network, using the region-wide feature information. This paper also employs a generative model to expand the input aerial images ahead of the backbone. The respective advantages of the generative model and transformer network are incorporated. On the dataset presented in this study, the model achieves 96.77% precision, 98.83% recall, and 97.91% mAP by adding the Multi-GANs module to the one-stage detection network. These three indices are enhanced by 13.9%, 20.54%, and 10.27%, respectively, when compared to the other detection networks. Furthermore, this study provides an auto-pruning technique that may achieve 32.2 FPS inference speed with a minor performance loss while responding to the real-time detection task’s usage environment. This research also develops a macOS application for the proposed algorithm using Swift development technology.
Secure Healthcare Record Sharing Mechanism with Blockchain

The transfer of information is a demanding issue, particularly due to the presence of a large number of eavesdroppers on communication channels. Sharing medical service records between different clinical jobs is a basic and testing research topic. The particular characteristics of blockchains have attracted a large amount of attention and resulted in revolutionary changes to various business applications, including medical care. A blockchain is based on a distributed ledger, which tends to improve cyber security. A number of proposals have been made with respect to the sharing of basic medical records using a blockchain without needing earlier information or the trust of patients. Specialist service providers and insurance agencies are not secure against data breaches. The safe sharing of clinical records between different countries, to ensure an incorporated and universal medical service, is also a significant issue for patients who travel. The medical data of patients normally reside on different healthcare units around the world, thus raising many concerns. Firstly, a patient’s history of treatment by different physicians is not accessible to the doctor in a single location. Secondly, it is very difficult to secure widespread data residing in different locations. This study proposed record sharing in a chain-like structure, in which every record is globally connected to the others, based on a blockchain under the suggestions and recommendations of the HL7 standards. This study focused on making medical data available, especially of patients who travel in different countries, for a specific period of time after validating the required authentication. Authorization and authentication are performed on the Shibboleth identity management system with the involvement of patient in the sanction process, thereby revealing the patient data for the specific period of time. The proposed approach improves the performance with respect to other record sharing systems, e.g., it reduces the time to read, write, delete, and revoke a record by a noticeable margin. The proposed system takes around three seconds to upload and 7.5 s to download 250 Mb of data, which can contain up to sixteen documents, over a stable network connection. The system has a latency of 413.76 ms when retrieving 100 records, compared to 447.9 and 459.3 ms in previous systems. Thus, the proposed system improved the performance and ensured seclusion by using a blockchain.
GMDL: Toward precise head pose estimation via Gaussian mixed distribution learning for students’ attention understanding

• A Gaussian mixed distribution label-based model is proposed for students’ head pose estimation. • The model can learn the discriminative facial features in each angle image. • Experimental results show that the GMDL model outperforms other existing models. Students’ head pose estimation is a very difficult task since the training data is insufficient for many head pose angles. In this study, we consider each head pose image as a Gaussian mixed distribution other than the traditional hard label, in which the adjacent head pose images can provide supplementary information for the target image. Specifically, the Gaussian mixed distribution covers the current head pose image and its adjacent 8 head pose images. Each label of head pose image describes the similar degree between the current image and its adjacent head pose images. Then, a novel network architecture is proposed by constructing the Gaussian mixed distribution which learns more discriminative facial features. The extensive evaluations on two public HPE databases show that the proposed GMDL model obtains the better performance compared with the conventional algorithms. In practice, the proposed model can be utilized to estimate learners’ head pose angle for attention understanding in the instruction and learning scenarios.
Diagnosis for multiple faults of chiller using ELM-KNN model enhanced by multi-label learning and specific feature combinations

Existing fault detection and diagnosis methods for chillers are usually very effective on single faults diagnosis, but perform poorly while diagnosing multiple faults, and these fault diagnosis models depend on the range of training data. Such models tend to fail when the system encounters a fault that was not included in training data. This paper presents a novel multiple faults diagnosis method for chiller based on multi-label learning and specific feature combinations enhanced ELM-KNN. Firstly, Extreme Learning Machine (ELM) is used for mapping of features to improve the accuracy of K-Nearest Neighbor (KNN) algorithm for multiple faults. Then, through multi-label learning, a model is established for each single fault, and by merging these models a final multiple faults diagnosis model is formed. Finally, by examining the input features, the specific combinations of input features for each single fault are determined and applied to the actual chiller. Experimental results show that the model proposed in this paper has the highest accuracy among the compared methods. The model proposed has achieved better diagnosis performance for the multiple faults in the absence of several multiple faults training data. The specific feature combinations are beneficial to improve the diagnosis performance of the model. The model can have excellent diagnostic performance for both normal operation and multiple faults by only using a small amount of single fault data in training. In addition, the model proposed can also complete the diagnosis of single faults in the absence of some single faults training data.
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation

Unsupervised representation learning has been proven to be effective for the challenging anomaly detection and segmentation tasks. In this paper, we propose a multi-scale patch-based representation learning method to extract critical and representative information from normal images. By taking the relative feature similarity between patches of different local distances into account, we can achieve better representation learning. Moreover, we propose a refined way to improve the self-supervised learning strategy, thus allowing our model to learn better geometric relationship between neighboring patches. Through sliding patches of different scales all over an image, our model extracts representative features from each patch and compares them with those in the training set of normal images to detect the anomalous regions. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the proposed method achieves the state-of-the-art accuracy for both anomaly detection and segmentation.
Sharded Blockchain for Collaborative Computing in the Internet of Things: Combined of Dynamic Clustering and Deep Reinforcement Learning Approach

Immutability, decentralization, and linear promoted scalability make the sharded blockchain a promising solution, which can effectively address the trust issue in the large-scale Internet of Things (IoT). However, currently, the throughput of sharded blockchains is still limited when it comes to high proportion of cross-shard transactions (CSTs). On the other hand, the assemblage characteristic of the collaborative computing in IoT has not been received attention. Therefore, in this article, we present a clustering-based sharded blockchain strategy for collaborative computing in the IoT, where the sharding of the blockchain system is implemented in two steps: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-means</i> -clustering-based user grouping and the assignment of consensus nodes. In this framework, how to reasonably group the IoT users while simultaneously guaranteeing the system performance is the key point. Specifically, we describe the data transactions among IoT devices by data transaction flow graph (DTFG) based on a dynamic stochastic block model. Then, formed as a Markov decision process (MDP), the optimization of the cluster number (shard number) and the adjustment of consensus parameters are jointly trained by deep reinforcement learning (DRL). Simulation results show that the proposed scheme improves the scalability of the sharded blockchain in the IoT application.
Secret-Key Provisioning With Collaborative Routing in Partially-Trusted-Relay-based Quantum-Key-Distribution-Secured Optical Networks

Quantum Key Distribution (QKD) is a promising technology that provides proven unconditional security based on fundamentals of quantum physics, especially for point-to-point communications. It could be applied in large-scale optical networks for long-distance key provisioning mainly by three relaying methods: quantum-repeater-based QKD, trusted-relay-based QKD, and measurement-device-independent QKD (MDI-QKD). However, quantum-repeater-based QKD is still under study because of the immature technologies such as the preliminary quantum memory. The trusted-relay-based QKD is vulnerable since insecurity of non-ideal single-photon sources and detectors cannot be ignored in practical applications. On the other hand, for MDI-QKD, there exists a limitation on its key rate under long-distance communications. According to these limitations, embedding protocols like MDI-QKD into the existing trusted-relay-based QKD secured optical networks (QKD-ON) with usage of untrusted relays is a promising key-provisioning scheme. In this paper, we study such partially-trusted relay scenarios and focus on its routing of keys in different kinds of typical network topologies. A partially-trusted-relay-based QKD method is described, which can allow a pair of optical nodes sharing secret keys under the coexistence of trusted relays and untrusted relays. The secret-key provisioning with collaborative routing ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SKP-CR</i> ) algorithm is proposed to search for the optimal key-relay routing path. We perform the simulations with different proportion of trusted relays versus untrusted relays, initial secret keys in the quantum key pools (QKPs), and traffic load. The simulations verify that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SKP-CR</i> algorithm can significantly outperform the conventional trusted-relay-based scheme in terms of key-distribution success rate with an improvement of up to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">62%</i> with a mesh topology.
DRL-Based V2V Computation Offloading for Blockchain-Enabled Vehicular Networks

Vehicular edge computing (VEC) is an effective method to increase the computing capability of vehicles, where vehicles share their idle computing resources with each other. However, due to the high mobility of vehicles, it is challenging to design an optimal task allocation policy that adapts to the dynamic vehicular environment. Further, vehicular computation offloading often occurs between unfamiliar vehicles, how to motivate vehicles to share their computing resources while guaranteeing the reliability of resource allocation in task offloading is one main challenge. In this paper, we propose a blockchain-enabled VEC framework to ensure the reliability and efficiency of vehicle-to-vehicle (V2V) task offloading. Specifically, we develop a deep reinforcement learning (DRL)-based computation offloading scheme for the smart contract of blockchain, where task vehicles can offload part of computation-intensive tasks to neighboring vehicles. To ensure the security and reliability in task offloading, we evaluate the reliability of vehicles in resource allocation by blockchain. Moreover, we propose an enhanced consensus algorithm based on practical Byzantine fault tolerance (PBFT), and design a consensus nodes selection algorithm to improve the efficiency of consensus and motivate base stations to improve reliability in task allocation. Simulation results validate the effectiveness of our proposed scheme for blockchain-enabled VEC.
Learning with Noisy Labels via Sparse Regularization

Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer from severe overfitting to noisy labels. Robust loss functions that satisfy the symmetric condition were tailored to remedy this problem, which however encounter the underfitting effect. In this paper, we theoretically prove that any loss can be made robust to noisy labels by restricting the network output to the set of permutations over a fixed vector. When the fixed vector is one-hot, we only need to constrain the output to be one-hot, which however produces zero gradients almost everywhere and thus makes gradient-based optimization difficult. In this work, we introduce the sparse regularization strategy to approximate the one-hot constraint, which is composed of network output sharpening operation that enforces the output distribution of a net-work to be sharp and the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> -norm (p ≤ 1) regularization that promotes the network output to be sparse. This simple approach guarantees the robustness of arbitrary loss functions while not hindering the fitting ability. Experimental results demonstrate that our method can significantly improve the performance of commonly-used loss functions in the presence of noisy labels and class imbalance, and out-perform the state-of-the-art methods. The code is available at https://github.com/hitcszx/lnl_sr.
<scp>FedBERT</scp>: When Federated Learning Meets Pre-training

The fast growth of pre-trained models (PTMs) has brought natural language processing to a new era, which has become a dominant technique for various natural language processing (NLP) applications. Every user can download the weights of PTMs, then fine-tune the weights for a task on the local side. However, the pre-training of a model relies heavily on accessing a large-scale of training data and requires a vast amount of computing resources. These strict requirements make it impossible for any single client to pre-train such a model. To grant clients with limited computing capability to participate in pre-training a large model, we propose a new learning approach, FedBERT , that takes advantage of the federated learning and split learning approaches, resorting to pre-training BERT in a federated way. FedBERT can prevent sharing the raw data information and obtain excellent performance. Extensive experiments on seven GLUE tasks demonstrate that FedBERT can maintain its effectiveness without communicating to the sensitive local data of clients.