Reviewerly/exHarmony huggingface dataset:
AAKD-Net: Attention-Based Adversarial Knowledge Distillation Network for Image Classification

Deep neural networks have achieved remarkable success in various research fields, but they face limitations. Firstly, complex models are often required to handle challenging scenarios. Secondly, limited storage and processing power on mobile devices hinder model training and deployment. To address these challenges, we propose a novel approach using a compact and efficient student model to learn from a cumbersome teacher model. To enhance feature map information extraction, we introduce an attention structure that leverages the rich features in the teacher model's feature maps. Adversarial training is incorporated by treating the student model as a generator and employing a discriminator to differentiate between teacher and student feature maps. Through an iterative process, the student model's feature map gradually approximates that of the teacher while improving the discriminator's discrimination abilities. By leveraging the knowledge of the teacher model and incorporating attention mechanisms and adversarial training, our approach provides a compelling solution to the challenges of complex model architectures and limited hardware resources. It achieves impressive performance enhancements with the student model.
Graph Multi-dimensional Feature Network

Graph Neural Networks (GNNs) have attracted extensive interest in the world because of its superior performance in the field of graph representation learning. Most GNNs have a message passing mechanism to update node representations by aggregating and transforming input from node neighbors. The current methods use the same strategy to aggregate information from each feature dimension. However, according to current papers, the model will be more practical if the feature information of each dimension can be treated differently throughout the aggregating process. In this paper, we introduces a novel Graph Neural Network-Graph Multi-Dimensional Feature Network (GMDFN). The method is accomplished by mining feature information from diverse dimensions and aggregating information using various strategies. Furthermore, a self-supervised learning module is built to keep the node feature information from being destroyed too much in the aggregation process to avoid over-smoothing. A large number of experiments on different real-world datasets have shown that the model outperforms various current GNN models and is more robust.
CBDN: A Chinese Short-Text Classification Model Based on Chinese BERT and Fused Deep Neural Networks

To address the common issues in Chinese short-text classification caused by the lack of contextual information, ambiguity, and sparsity of semantic features due to the short length of the text, a feature fusion-based Chinese short-text classification model CBDN is proposed. Firstly, the Chinese-BERT-wwm pre-trained model, improved by the full-word masking technique, is selected as the embedding layer to output the vector representation of the short text. Secondly, to fully extract the limited semantic features of the short text, the model employs a multi-head self-attention module and a long connected bidirectional LSTM (LC-BiLSTM) network to further learn the semantic features, and then fuses the hidden layer output vector with the feature vector further processed by these two methods. Finally, to improve the classification performance, the fused features are input into an improved "pyramid CNN" (PCNN) layer, and the short-text classification result is obtained through the classifier. The CBDN model is experimentally compared with various baseline models on the THUCNews dataset. The experimental results show that the proposed model achieves an accuracy and precision of 94.38% and 94.37%, respectively, outperforming other baseline models, indicating that the model better extracts the semantic information of short text and effectively improves the classification performance of Chinese short text.
Efficient Hierarchical Reinforcement Learning via Mutual Information Constrained Subgoal Discovery

Goal-conditioned hierarchical reinforcement learning has demonstrated impressive capabilities in addressing complex and long-horizon tasks. However, the extensive subgoal space often results in low sample efficiency and challenging exploration. To address this issue, we extract informative subgoals by constraining their generation range in mutual information distance space. Specifically, we impose two constraints on the high-level policy during off-policy training: the generated subgoals should be reached with less effort by the low-level policy, and the realization of these subgoals can facilitate achieving the desired goals. These two constraints enable subgoals to act as critical links between the current states and the desired goals, providing more effective guidance to the low-level policy. The empirical results on continuous control tasks demonstrate that our proposed method significantly enhances the training efficiency, regardless of the dimensions of the state and action spaces, while ensuring comparable performance to state-of-the-art methods.
Leveraging Two-Scale Features to Enhance Fine-Grained Object Retrieval

Constructing a discriminative embedding for an image based on the features extracted by a convolutional neural network (CNN) has become a common solution for fine-grained object retrieval (FGOR). However, existing methods construct the embedding based solely on features extracted by the last layer of CNN, neglecting the potential benefits of leveraging features from other layers. Based on the fact that features extracted by different layers of CNN represent different abstraction and semantic information on those levels, we believe that leveraging features from multiple layers of CNN can construct a more discriminative embedding. Upon this, we propose a simple yet efficient end-to-end model named TSF-Enhance, which leverages two-scale features extracted by the CNN to construct the discriminative embedding. Specifically, we extract features from the third and fourth layers of Resnet50 and construct an embedding based on features from these two layers respectively. When testing, we concatenate these two embeddings to get a more discriminative embedding for retrieval. Additionally, we design a Feature Enhancement Module (FEM) that consists of several common operations, such as layer normalization, to process the features. Finally, we achieve competitive results on three FGOR datasets, specifically exceeding the current state-of-the-art performance on the most challenging dataset CUB200. Furthermore, our model also demonstrates strong scalability compared to localization-based methods, achieving the best performance on two general-purpose image retrieval datasets. The source code is available at https://github.com/jingyj203/TSF-Enhance .
Microwave-to-optical conversion in a room-temperature 87Rb vapor for frequency-division multiplexing control

Abstract Coherent microwave-to-optical conversion is crucial for transferring quantum information generated in the microwave domain to optical frequencies, where propagation losses can be minimized. Coherent, atom-based transducers have shown rapid progress in recent years. This paper reports an experimental demonstration of coherent microwave-to-optical conversion that maps a microwave signal to a large, tunable 550(30) MHz range of optical frequencies using room-temperature 87 Rb atoms. The inhomogeneous Doppler broadening of the atomic vapor advantageously supports the tunability of an input microwave channel to any optical frequency channel within the Doppler width, along with the simultaneous conversion of a multi-channel input microwave field to corresponding optical channels. In addition, we demonstrate phase-correlated amplitude control of select channels, providing an analog to a frequency domain beam splitter across five orders of magnitude in frequency. With these capabilities, neutral atomic systems may also be effective quantum processors for quantum information encoded in frequency-bin qubits.
Unsupervised Feature Selection Using Both Similar and Dissimilar Structures

Unsupervised feature selection method is widely used to handle the rapid increasing complex and high-dimensional sparse data without labels. Many good methods have been proposed in which the relationships between the similar data points are mainly considered. The graph embedding theory is used which occupies a large proportion. Despite their achievements, the existing methods neglect the information from the most dissimilar data. In this paper, we follow the research line of graph embedding and present a novel method for unsupervised feature selection. Two different viewpoints in the positive and negative are used to keep the data structure after feature selection. Besides a Laplacian matrix by which the most similar data structure is kept, we build an additional Laplacian matrix to keep the least similar data structure. Furthermore, an efficient algorithm is designed by virtue of the existing generalized powered iteration method. Extensive experiments on six benchmark data sets are conducted to verify the state-of-the-art effectiveness and superiority of the proposed method.
Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification

When classifiers face imbalanced class distributions, they often misclassify minority class samples, consequently diminishing the predictive performance of machine learning models. Existing oversampling techniques predominantly rely on the selection of neighboring data via interpolation, with less emphasis on uncovering the intrinsic patterns and relationships within the data. In this research, we present the usefulness of an algorithm named RuLer to deal with the problem of classification with imbalanced data. RuLer is a learning algorithm initially designed to recognize new sound patterns within the context of the performative artistic practice known as live coding. This paper demonstrates that this algorithm, once adapted (Ad-RuLer), has great potential to address the problem of oversampling imbalanced data. An extensive comparison with other mainstream oversampling algorithms (SMOTE, ADASYN, Tomek-links, Borderline-SMOTE, and KmeansSMOTE), using different classifiers (logistic regression, random forest, and XGBoost) is performed on several real-world datasets with different degrees of data imbalance. The experiment results indicate that Ad-RuLer serves as an effective oversampling technique with extensive applicability.
Belief bias and censorship of religious extremism on digital media in 15 EU states: exploring individual and country-level moderators, a cross-country multilevel analysis

The debate over freedom of expression is critical because of the long-term social and political consequences of potentially harmful expressions, such as religiously or politically offensive speeches that invite violent deeds and seek justification. This research note attempted to theorize that individuals’ belief bias is associated with their support for the censorship of religious extremist views. It also explores the conditional roles of educational attainment on an individual level and the quality of democracy on the country level. Relying on cross-sectional survey data from 15 EU states, the cross-country multilevel analysis found a significant positive relationship between belief bias and support for censorship of religious extremist views on digital media – the Internet and social media. The association between belief bias and support for censorship of religious extremist views was greater among people with a low level of education and in healthier democracies.
Blockchain and OR Based Data Sharing Solution for Internet of Things

Communication between nodes in the Internet of Things (IoT) is still in its infancy, and several contemporary security dangers and issues are impeding data transmission in this process. In this research, we initially use a communication control gateway on the edge server to distinguish malicious and non-malicious message requests from machines and forward them to the onion routing (OR) network to overcome the aforementioned security vulnerabilities. Next, by integrating the authentication token field in the initial message request, we connected the standard OR network with blockchain technology to improve its security and dependability. The suggested blockchain-based onion network beats the conventional network in terms of throughput and decryption speed, according to experimental results (with low computational cost).
A LSTM and GRU-Based Hybrid Model in the Cryptocurrency Price Prediction

Cryptocurrency is a new type of digital currency that utilizes blockchain technology and cryptography to achieve transparency, decentralization, and immutability. Bitcoin became the world’s first decentralized cryptocurrency in 2009. With increasing attention given to cryptocurrency, predicting its price has become a popular research topic. Many machine learning and deep learning algorithms, such as Gated Recurrent Unit (GRU), Neural Network (NN), and Long Short-Term Memory (LSTM), have been studied for cryptocurrency price prediction. In this paper, we propose a hybrid cryptocurrency price prediction model based on LSTM and GRU. The model achieves better results than the LSTM model in cryptocurrency price prediction and performs the best among existing hybrid models based on LSTM and GRU.
ePoW Energy-Efficient Blockchain Consensus Algorithm for Decentralize Federated Learning System in Resource-Constrained UAV Swarm

With the proliferation of Unmanned Aerial Vehicles (UAV) and UAV swarms, there has been growing interest in using them for collaborative computing tasks. Blockchain-based Federated learning (BFL) is an excellent approach for training artificial intelligence models in UAV swarms, providing benefits such as privacy protection, trusted computing, node autonomy, and low communication overhead. However, due to limited battery life and processing power, UAVs require energy-efficient solutions that can handle blockchain consensuses in decentralized BFL manners. To address such challenges, we propose an energy-efficient Proof of Work (ePoW) consensus algorithm for the resource-constrained UAV swarms, which to achieve fault tolerance and data integrity while minimizing energy consumption. At the commencement of the ePoW protocol, the UAV nodes perform computation and dissemination of their engagement metrics to facilitate the selection of participating nodes in the ePoW competition. Following a successful BFL global generation event, the dynamic difficulty adjustment mechanism collaborates with the engagement metrics to identify the most reliable node and mitigate resource consumption during the consensus process. Our ePow algorithm is designed to work with BFL systems in a UAV swarm, where each UAV device acts as a node in the BFL system. We evaluate the algorithm’s performance in various scenarios and found that it achieves high accuracy while consuming significantly less energy compared to existing consensus algorithms. Our proposed approach presents a promising solution for energy-efficient decentralized BFL systems in UAV swarms.
A Hierarchical Blockchain Framework with Selectable Consensus Scope for Data Sharing in Internet of Vehicles

With the development of Internet of Vehicles (IoVs), data sharing has become a crucial means to improve driving safety and travel efficiency. In the process of data sharing, ensuring the security of the sharing process is of paramount importance. Using blockchain technology to ensure the security of data sharing in vehicular networking scenarios is a promising approach. However, there are some issues in applying blockchain to data sharing in vehicular networking scenarios. On the one hand, the consensus of blockchain requires multiple full network broadcasts, and as the number of nodes in the network increases, system overhead will grow rapidly. On the other hand, malicious nodes in blockchain may affect system security. To address these problems, we propose a hierarchical blockchain framework with selectable consensus scope. We dynamically select the consensus scope of data according to its impact range, avoiding consensus on the entire network. In addition, we propose a trust model to compute the reliability of Roadside units (RSUs) in the area, assigning different permissions to different RSUs in the system. We also improve the PBFT algorithm based on this reputation model, further reducing the cost of consensus. Simulation results show that the proposed approach can reduce consensus consumption and identify malicious nodes more quickly, ensuring the efficiency and security of the data sharing process.
Who Needs the Most Research Effort? Investigating the Importance of Smart Contract Weaknesses

Smart contracts have witnessed widespread adoption across various industries since the introduction of Ethereum. Also, smart contracts’ vulnerabilities have gradually attracted academic attention, e.g., Oyente was developed to detect security bugs in contracts. However, it remains unclear which smart contract vulnerabilities require more research attention. To fill the gap, we recruit eight researchers to collect 281 Ethereum-related security events from SlowMist and Rekt, and analyze the root causes (e.g., the code weakness) behind each occurrence based on SWC Registry. Our results reveal that only 10 SWC weaknesses lead to security events, causing a total loss of USD 74 Billion. Moreover, Reentrancy exhibits the highest frequency. Our results provide guidance for research efforts allocated in the academic community.
An Improved PBFT Consensus Algorithm for Supply Chain Finance

Supply chain finance is a financing scheme to provide financial services for the enterprises of supply chain. Blockchain technology, with distributed, traceable and non-tamperable characteristics, offers opportunities to solve problems in the development of supply chain finance. Consensus algorithms are the key to ensuring the efficient cooperation of the blockchain, among which the Practical Byzantine Fault Tolerance (PBFT) algorithm has a wide range of applications in consortium chains which solves the core issue of how to make nodes keep their data consistent. However, the PBFT algorithm has a higher communication overhead and does not support the nodes join or exit. It is difficult to meet the requirement of supply chain finance. Therefore, this paper proposes an improved PBFT algorithm for Supply Chain Finance, named PBFT-SCF. Firstly, a dynamic node entry and exit mechanism be designed for dynamically changing the number of nodes at runtime. Secondly, the scoring model is established to build an appropriate consensus node group, which replaces the original that all nodes participate in consensus so that the resource consumption can be reduced. Finally, the consistency protocol of the PBFT algorithm is optimized to achieve higher efficiency. Results indicate that the PBFT-SCF algorithm has better characteristics in terms of consensus efficiency and communication overhead. Specifically, when the number of node is 46, the transaction latency is reduced to 3.5% and the number of communication times is 2.8% of the PBFT algorithm.
Ouroboros Taktikos: Regularizing Proof-of-Stake via Dynamic Difficulty

In any Nakamoto-style distributed ledger technology, participants must eventually come to a deterministic ordering of proposed extensions as a necessary precondition to consensus. To prevent Sybil attacks, this process encodes a bias toward selecting proposers who commit a limited resource. In proof-of-work (PoW) schemes, block proposals are secured using a hashing mechanism that preferences miners with greater computational power. In Nakamoto-style proof-of-stake (PoS) paradigms, proposers’ eligibilities derive from their staked holdings, relying on a thresholding mechanism to fairly select the next block proposer by favoring those who have committed more stake. This eligibility threshold controls which parties may propose a block in a given slot, with easier thresholds increasing block density. However, higher density also increases forking – periods of uncertainty where consensus remains (temporarily) unsettled. Therefore, the selection of the PoS eligibility threshold critically affects both security and throughput in the associated chain. Previous work relies on static threshold values, which simplifies security analysis. In this work, we extend the static eligibility threshold to a dynamic one, and introduce the concept of a local dynamic difficulty mechanism in which thresholds follow a non-monotonic difficulty curve. We implement this mechanism in a novel PoS protocol, Ouroboros Taktikos, finding that the dynamic regime regularizes slot intervals and improves block throughput. The pseudo-predictable nature of the protocol also penalizes covert attacks, simultaneously increasing security. We compare Ouroboros Taktikos to Ouroboros Praos and show that the addition of local dynamic difficulty improves throughput by $$\sim $$ 2.9 $$\times $$ and reduces 99 $$^\mathrm{{th}}$$ percentile block latency by $$\sim $$ 5.7 $$\times $$ compared to the state of the art.
Smart Contract Vulnerability Detection Methods: A Survey

Blockchain technology has gained widespread attention due to its decentralized nature. With the combination of smart contracts, the application of blockchain technology has expanded into more complex business scenarios, including digital collectibles, financial trades, and metaverses. However, the security vulnerabilities in smart contracts not only pose significant risks to their applications but also undermine the credibility of the blockchain system. As a result, research on smart contract vulnerability detection methods has attracted global attention. This paper introduces common smart contract vulnerabilities and summarizes the research on smart contract vulnerability detection methods and commonly used datasets. The progress of smart contract vulnerability detection research is reviewed from the perspectives of static analysis methods and dynamic analysis methods. A comparison and analysis are conducted on the input code categories, vulnerability types, and classification output formats of each method. Finally, based on the summary of existing research on smart contract vulnerability detection, the challenges in the field of smart contract vulnerability detection are discussed, and future research directions are proposed.
An Optimized Scheme of Federated Learning Based on Differential Privacy

With the arrival of the Web3 era, data has seen an explosive growth. The use of differential privacy mechanisms in federated learning has been proposed to protect user privacy and avoid security threats from data sharing. The core idea of this approach is that multiple clients train their local data, add noise to their client parameters, and then transmit them to a central server for parameter aggregation. However, there are still defects that need to be addressed. First, it is difficult to resist attacks from malicious clients, which means that user privacy is not fully protected. Second, it is challenging to add an appropriate amount of noise to achieve high model accuracy. Therefore, this paper proposes a bidirectional adaptive noise addition federated learning scheme, which adds adaptive noise satisfying the differential privacy mechanism to both the central server and clients to improve model accuracy. Considering the heterogeneity of client hardware, this paper samples gradients and samples separately to reduce communication costs and uses RMSprop to accelerate model training on both clients and central servers. Experimental results show that the proposed scheme enhances user privacy protection while maintaining high efficiency.