Reviewerly/exHarmony huggingface dataset:
Variational quantum amplitude estimation

We propose to perform amplitude estimation with the help of constant-depth quantum circuits that variationally approximate states during amplitude amplification. In the context of Monte Carlo (MC) integration, we numerically show that shallow circuits can accurately approximate many amplitude amplification steps. We combine the variational approach with maximum likelihood amplitude estimation [Y. Suzuki et al., Quantum Inf. Process. 19, 75 (2020)] in variational quantum amplitude estimation (VQAE). VQAE typically has larger computational requirements than classical MC sampling. To reduce the variational cost, we propose adaptive VQAE and numerically show in 6 to 12 qubit simulations that it can outperform classical MC sampling.
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution

With the recently massive development in convolution neural networks, numerous lightweight CNN-based image super-resolution methods have been proposed for practical deployments on edge devices. However, most existing methods focus on one specific aspect: network or loss design, which leads to the difficulty of minimizing the model size. To address the issue, we conclude block devising, architecture searching, and loss design to obtain a more efficient SR structure. In this paper, we proposed an edge-enhanced feature distillation network, named EFDN, to preserve the high-frequency information under constrained resources. In detail, we build an edge-enhanced convolution block based on the existing reparameterization methods. Meanwhile, we propose edge-enhanced gradient loss to calibrate the reparameterized path training. Experimental results show that our edge-enhanced strategies preserve the edge and significantly improve the final restoration quality. Code is available at https://github.com/icandle/EFDN.
CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net

Convolutional Neural Networks(CNNs) are a floating area in Deep Learning. Now a days CNNs are used inside the more note worthy some portion of the Object Recognition tasks. It is used in stand-out utility regions like Speech Recognition, Pattern Acknowledgment, Computer Vision, Object Detection and extraordinary photograph handling programs. CNN orders the realities in light of an opportunity regard. Right now, inside and out assessment of CNN shape and projects are built up. A relative examine of different assortments of CNN are too portrayed on this work.
Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model

With the introduction of concepts such as ubiquitous mapping, mapping-related technologies are gradually applied in autonomous driving and target recognition. There are many problems in vision measurement and remote sensing, such as difficulty in automatic vehicle discrimination, high missing rates under multiple vehicle targets, and sensitivity to the external environment. This paper proposes an improved RES-YOLO detection algorithm to solve these problems and applies it to the automatic detection of vehicle targets. Specifically, this paper improves the detection effect of the traditional YOLO algorithm by selecting optimized feature networks and constructing adaptive loss functions. The BDD100K data set was used for training and verification. Additionally, the optimized YOLO deep learning vehicle detection model is obtained and compared with recent advanced target recognition algorithms. Experimental results show that the proposed algorithm can automatically identify multiple vehicle targets effectively and can significantly reduce missing and false rates, with the local optimal accuracy of up to 95% and the average accuracy above 86% under large data volume detection. The average accuracy of our algorithm is higher than all five other algorithms including the latest SSD and Faster-RCNN. In average accuracy, the RES-YOLO algorithm for small data volume and large data volume is 1.0% and 1.7% higher than the original YOLO. In addition, the training time is shortened by 7.3% compared with the original algorithm. The network is then tested with five types of local measured vehicle data sets and shows satisfactory recognition accuracy under different interference backgrounds. In short, the method in this paper can complete the task of vehicle target detection under different environmental interferences.
A flash(bot) in the pan

The rise of Ethereum has lead to a flourishing decentralized marketplace that has, unfortunately, fallen victim to frontrunning and Maximal Extractable Value (MEV) activities, where savvy participants game transaction orderings within a block for profit. One popular solution to address such behavior is Flashbots, a private pool with infrastructure and design goals aimed at eliminating the negative externalities associated with MEV. While Flashbots has established laudable goals to address MEV behavior, no evidence has been provided to show that these goals are achieved in practice.
Chimp optimization algorithm in multilevel image thresholding and image clustering

Multilevel image thresholding and image clustering, two extensively used image processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach of yielding multiple threshold values for each color channel to generate clustered and segmented images appears to be quite efficient and it provides significant performance, although this method is computationally heavy. To ease this complicated process, nature inspired optimization algorithms are quite handy tools. In this paper, the performance of Chimp Optimization Algorithm (ChOA) in image clustering and segmentation has been analyzed, based on multilevel thresholding for each color channel. To evaluate the performance of ChOA in this regard, several performance metrics have been used, namely, Segment evolution function, peak signal-to-noise ratio, Variation of information, Probability Rand Index, global consistency error, Feature Similarity Index and Structural Similarity Index, Blind/Referenceless Image Spatial Quality Evaluatoe, Perception based Image Quality Evaluator, Naturalness Image Quality Evaluator. This performance has been compared with eight other well known metaheuristic algorithms: Particle Swarm Optimization Algorithm, Whale Optimization Algorithm, Salp Swarm Algorithm, Harris Hawks Optimization Algorithm, Moth Flame Optimization Algorithm, Grey Wolf Optimization Algorithm, Archimedes Optimization Algorithm, African Vulture Optimization Algorithm using two popular thresholding techniques-Kapur’s entropy method and Otsu’s class variance method. The results demonstrate the effectiveness and competitive performance of Chimp Optimization Algorithm.
Differential Game Model of Information Sharing among Supply Chain Finance Based on Blockchain Technology

In the development of traditional supply-chain finance, the information asymmetry of all parties in the supply chain has become the primary problem hindering its development. Blockchain technology is an effective method to solve the problem of information silos. Based on differential game theory, this paper constructs a game model of supply-chain financial information-sharing behavior based on blockchain technology. Three scenarios of independent decision, the cost-subsidy mechanism of financial institutions, and dealers and collaborative decisions are studied, and the theoretical model is verified through a simulation algorithm. The results show that information sharing in supply-chain finance based on blockchain technology is much higher than that of traditional supply-chain finance, and the use of blockchain technology can promote more sustainable development of supply chains. Blockchain technology can effectively solve the information-silo effect, and the information sharing cost-subsidy mechanism can effectively relieve the cost pressure of information sharing and optimize the supply-chain structure. In addition, the amount of information and benefits shared among the three parties of supply-chain finance based on blockchain technology and the overall benefits show an increasing and stabilized trend over time. This study provides a reference for supply-chain finance members to reasonably choose the optimal strategic behavior.
Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning

Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities.
Unsupervised Underwater Image Restoration: From a Homology Perspective

Underwater images suffer from degradation due to light scattering and absorption. It remains challenging to restore such degraded images using deep neural networks since real-world paired data is scarcely available while synthetic paired data cannot approximate real-world data perfectly. In this paper, we propose an UnSupervised Underwater Image Restoration method (USUIR) by leveraging the homology property between a raw underwater image and a re-degraded image. Specifically, USUIR first estimates three latent components of the raw underwater image, i.e., the global background light, the transmission map, and the scene radiance (the clean image). Then, a re-degraded image is generated by randomly mixing up the estimated scene radiance and the raw underwater image. We demonstrate that imposing a homology constraint between the raw underwater image and the re-degraded image is equivalent to minimizing the restoration error and hence can be used for the unsupervised restoration. Extensive experiments show that USUIR achieves promising performance in both inference time and restoration quality.
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They are popular for their simple principles and strong performance on unstructured, feature-based data that is commonplace in many practical applications. However, they cannot learn to adapt for a particular set of data due to their lack of trainable parameters. In this paper, we begin by unifying local outlier methods by showing that they are particular cases of the more general message passing framework used in graph neural networks. This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method. LUNAR learns to use information from the nearest neighbours of each node in a trainable way to find anomalies. We show that our method performs significantly better than existing local outlier methods, as well as state-of-the-art deep baselines. We also show that the performance of our method is much more robust to different settings of the local neighbourhood size.
Comprehensive Regularization in a Bi-directional Predictive Network for Video Anomaly Detection

Video anomaly detection aims to automatically identify unusual objects or behaviours by learning from normal videos. Previous methods tend to use simplistic reconstruction or prediction constraints, which leads to the insufficiency of learned representations for normal data. As such, we propose a novel bi-directional architecture with three consistency constraints to comprehensively regularize the prediction task from pixel-wise, cross-modal, and temporal-sequence levels. First, predictive consistency is proposed to consider the symmetry property of motion and appearance in forwards and backwards time, which ensures the highly realistic appearance and motion predictions at the pixel-wise level. Second, association consistency considers the relevance between different modalities and uses one modality to regularize the prediction of another one. Finally, temporal consistency utilizes the relationship of the video sequence and ensures that the predictive network generates temporally consistent frames. During inference, the pattern of abnormal frames is unpredictable and will therefore cause higher prediction errors. Experiments show that our method outperforms advanced anomaly detectors and achieves state-of-the-art results on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.
Uncertainty-Driven Dehazing Network

Deep learning has made remarkable achievements for single image haze removal. However, existing deep dehazing models only give deterministic results without discussing the uncertainty of them. There exist two types of uncertainty in the dehazing models: aleatoric uncertainty that comes from noise inherent in the observations and epistemic uncertainty that accounts for uncertainty in the model. In this paper, we propose a novel uncertainty-driven dehazing network (UDN) that improves the dehazing results by exploiting the relationship between the uncertain and confident representations. We first introduce an Uncertainty Estimation Block (UEB) to predict the aleatoric and epistemic uncertainty together. Then, we propose an Uncertainty-aware Feature Modulation (UFM) block to adaptively enhance the learned features. UFM predicts a convolution kernel and channel-wise modulation cofficients conitioned on the uncertainty weighted representation. Moreover, we develop an uncertainty-driven self-distillation loss to improve the uncertain representation by transferring the knowledge from the confident one. Extensive experimental results on synthetic datasets and real-world images show that UDN achieves significant quantitative and qualitative improvements, outperforming the state-of-the-arts.
High-order conditional mutual information maximization for dealing with high-order dependencies in feature selection

This paper presents a novel feature selection method based on the conditional mutual information (CMI). The proposed High Order Conditional Mutual Information Maximization (HOCMIM) method incorporates high order dependencies into the feature selection procedure and has a straightforward interpretation due to its bottom-up derivation. The HOCMIM is derived from the CMI’s chain expansion and expressed as a maximization optimization problem. The maximization problem is solved using a greedy search procedure, which speeds up the entire feature selection process. The experiments are run on a set of benchmark datasets (20 in total). The HOCMIM is compared with eighteen state-of-the-art feature selection algorithms, from the results of two supervised learning classifiers (Support Vector Machine and K-Nearest Neighbor). The HOCMIM achieves the best results in terms of accuracy and shows to be faster than high order feature selection counterparts.
Attribute-Based Hierarchical Access Control With Extendable Policy

Attribute-based encryption scheme is a promising mechanism to realize one-to-many fine-grained access control which strengthens the security in cloud computing. However, massive amounts of data and various data sharing requirements bring great challenges to the complex but isolated and fixed access structures in most of the existing attribute-based encryption schemes. In this paper, we propose an attribute-based hierarchical encryption scheme with extendable policy, called Extendable Hierarchical Ciphertext-Policy Attribute-Based Encryption (EH-CP-ABE), to improve the data sharing efficiency and security simultaneously. The scheme realizes the function of hierarchical encryption, in which, data with hierarchical access control relationships could be encrypted together flexibly to improve the efficiency. The scheme also achieves external and internal extension of the access structure to further encrypt newly added hierarchical data without updating the original ciphertexts or with only a minor update depending on the data sharing requirements, which simplifies the encryption process and greatly reduces the computation overhead. We formally prove the security of the scheme is IND-CCA secure in the random oracle model based on bilinear Diffie-Hellman assumption, and we also implement our scheme to demonstrate its efficiency and practicality.
Prototypical Verbalizer for Prompt-based Few-shot Tuning

Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning.Typically, prompt-based tuning wraps the input text into a cloze question.To make predictions, the model maps the output words to labels via a verbalizer, which is either manually designed or automatically built.However, manual verbalizers heavily depend on domain-specific prior knowledge and human efforts, while finding appropriate label words automatically still remains challenging.In this work, we propose the prototypical verbalizer (ProtoVerb) which is built directly from training data.Specifically, Pro-toVerb learns prototype vectors as verbalizers by contrastive learning.In this way, the prototypes summarize training instances and are able to enclose rich class-level semantics.We conduct experiments on both topic classification and entity typing tasks, and the results demonstrate that ProtoVerb significantly outperforms current automatic verbalizers, especially when training data is extremely scarce.More surprisingly, ProtoVerb consistently boosts promptbased tuning even on untuned PLMs, indicating an elegant non-tuning way to utilize PLMs.Our codes are avaliable at https: //github.com/thunlp/OpenPrompt.
AraT5: Text-to-Text Transformers for Arabic Language Generation

Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach.Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data.To investigate this question, we apply mT5 on a language with a wide variety of dialects-Arabic.For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks.For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN.Although pre-trained with ∼ 49% less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs.Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021).Our models are publicly available.We also link to individual ARGEN datasets through our public repository. 1
Integrating Machine Learning Algorithms With Quantum Annealing Solvers for Online Fraud Detection

Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems can only detect the incidents after they have already occurred, not at or near real time. As spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection even more challenging. This study has proposed a detection framework and implemented it using Support Vector Machine (SVM) enhanced with quantum annealing solvers. To evaluate its detection performance and examine the impact of feature selection, we have applied this quantum machine learning (QML) system along with systems built with twelve traditional machine learning methods on two datasets: Israel credit card transactions (non-time series) which is moderately imbalanced and a bank loan dataset (time series) that is highly imbalanced. The result shows that, the quantum enhanced SVM has categorically outperformed the rest in both speed and accuracy with the bank loan dataset. However, it&#x2019;s detection accuracy is similar to others with Israel credit card transactions data. Furthermore, for both datasets, feature selection has been shown to significantly improve the detection speed, although the improvement on accuracy is marginal. These findings have demonstrated the potential of QML applications on time series based, highly imbalanced data, and the merit of traditional machine learning approaches in non-time series data. This study provides insight on selecting appropriate approach with different types of datasets while taking the tradeoffs of speed, accuracy, and cost into consideration.
An Attribute-Based Access Control Model for Internet of Things Using Hyperledger Fabric Blockchain

The Internet of Things (IoT) is emerging from its infancy and establishing itself as a component of the future Internet. However, the ability to manage a huge number of IoT devices is one of the IoT technical challenges. To implement access control, traditional schemes typically rely on a trusted central organization. Traditional centralized access control systems of the IoT lead to the shortcomings of a single point of failure, low overall system efficiency, and ethical and privacy issues. To overcome such challenges, an attribute-based access control model using Hyperledger Fabric blockchain (ABAC-HLFBC) is proposed in this paper. By adopting ABAC, it is no longer to create access control lists (ACLs) or assign roles to all system users. Instead, ABAC grants access based on the attributes presented by the target. No one is permitted access unless he/she possesses sufficient attributes that correspond to the access policy. The Hyperledger Fabric Raft consensus mechanism has been used to verify the transaction on the proposed model because it has a demanding feature for faster and less complicated consensus in comparison to the Kafka ordering service. To evaluate the proposed model, it has been tested against the most recent previous work, called fabric-iot model. The proposed model has been tested and evaluated in two parts. The first part tests the cost time using a client test program written in Golang. The second part tests the latency and throughput using the Hyperledger Caliper benchmark tool. Results show that the proposed model efficiently outperforms the previous work in terms of the performance metrics mentioned above.