Content-based Music Retrieval (CBMR) is a fundamental task in music information retrieval, encompassing sub-tasks including Audio Identification, Audio Matching, and Version Identification. Traditional methods typically analyze audio signals or spectrograms to extract features related to rhythm, melody, harmony, and timbre. However, with the rapid development of Music Transcription and digital music technologies, MIDI representation has emerged as a powerful alternative fo r music analysis. In this paper, we propose MIDI-Zero, a novel self-supervisedlearning framework for CBMR that operates entirely on MIDI representations. Unlike existing approaches, MIDI-Zero requires no external training data; all training data is automatically generated based on predefined task rules, eliminating the need for labeled datasets or external music collections. MIDI-Zero is designed to handle both symbolic music data and audio-based tasks by leveraging Music Transcription models. Its strong robustness ensures effectiveness even with low-quality transcriptions. Extensive experiments demonstrate that MIDI-Zero achieves competitive performance across various CBMR sub-tasks, particularly excelling in Audio Matching. Our approach simplifies the feature extraction process, bridges the gap between audio and symbolic music representations, and offers a versatile and scalable solution for music retrieval.

This article investigates the issue of cooperative real-time trajectory design for multiple unmanned aerial vehicles (UAVs) to support emergency communication in disaster areas. To restore communication links rapidly between mobile users (MUs) and the ground base stations, UAVs equipped with both radio frequency (RF) modules and free space optics (FSO) modules are utilized as relay nodes. Given the challenges of setting up a central controller for the UAVs and the urgency of emergency communication, the trajectory design problem for these UAVs is formulated as a distributed cooperative optimization problem. Based on the enhanced${K}$-mean algorithm and multiagent PPO (MAPPO) algorithm, a cooperative trajectory design method, abbreviated as KMAPPO, is proposed for the UAVs to minimize interaction overhead and optimize deployment efficiency. Compared to the state-of-the-art deep reinforcement learning (DRL) methods, simulations reveal KMAPPO’s superior performance. It converges 32% faster, boosts RF allocation efficiency, and augments FSO communication backhaul capacity.

Asynchronous distributed key generation (ADKG) is a strong-robustness key management technology to bootstrap threshold cryptosystems without a global clock, which can enable decentralized security management for threshold digital wallets in blockchain network. However, the high-computational cost of existing ADKG protocols makes it difficult to remove the “slow” connotation from the word “asynchronous” in a high-threshold security context. In this article, we propose a simpler “two-high” ADKG protocol (H2CT) for blockchain to improve the computational efficiency of asynchronous communications while balancing it with threshold security. Concretely, we first construct a computationally efficient asynchronous complete secret sharing (ACSS) scheme using number theory transformation, reducing the computational complexity of share evaluation from$O(n^{2})$to$O(n\log n)$in the dealing phase. To eliminate the negative impact of up to t biased secrets brought by the implicate messages in the agreement phase, we extend the verifiable ACSS scheme to a publicly verifiable ACSS scheme (pvACSS) using Feldman polynomial commitment. Leveraging this enhanced randomness, the matrix computation cost and message size in the remaining phases are reduced to about half that of the existing works, i.e.,$O(2n^{2})$. Finally, considering the high-scalability requirements, the network size n is increased to up to 1024 nodes and the results show that our pvACSS and the derived H2CT reduce the runtime by approximately 33.96 s and 824.46 s, respectively, over the state-of-the-art. Moreover, we perform simulations on an open-source blockchain testbed, fully demonstrating the efficiency advantages of our H2CT protocol.

Targeted identification of essential proteins is of great significance for species identification, drug manufacturing, and disease treatment. It is a challenge to analyze the binding mechanism between essential proteins and improve the identification speed while ensuring the accuracy of the identification. This paper proposes a novel method called EPCRO for identifying essential proteins, which incorporates the chemical reaction optimization (CRO) algorithm and the naive Bayes model to effectively detect essential proteins. In EPCRO, the naive Bayes model is employed to analyze the homogeneity between proteins. In order to improve the identification rate and speed of essential proteins, the protein homogeneity rate is integrated into the CRO algorithm to balance between local and global searches. EPCRO is experimentally compared with 17 existing methods (including, DC, SC, IC, EC, LAC, NC, PeC, WDC, EPD-RW, RWHN, TEGS, CFMM, BSPM, AFSO-EP, CVIM, RWEP, and EPPSO-DC) based on biological datasets. The results show that EPCRO is superior to the above methods in identification accuracy and speed.

As one of the next generation of network architectures, Software-Defined Networking (SDN) decouples the forwarding and control function of the traditional network device. However, it also faces new threats from network attacks, such as the Low-rate Distributed Denial of Service (L-DDoS) attack. To resist L-DDoS attack in SDN, this work proposes METER, an enseMble discrEte wavelet Transform-based method for idEntifying low-Rate DDoS attack in SDN. The rationale for METER is to identify L-DDoS attacks based on a new metric referred to as the Ensemble Wavelet Energy Entropy set (EWEEs), which is calculated by a novel ensemble DWT method. Firstly, the ensemble wavelet coefficients matrix is attained by METER using the ensemble DWT method. After that, METER combines the related entropy values with wavelet energy of the ensemble wavelet coefficients matrix to obtain EWEEs. Furthermore, to increase the precision of the L-DDoS detection method, machine learning methods are used to mine the correlation between EWEEs and L-DDoS attacks for identifying L-DDoS. The experiments were implemented on the RYU controller and Mininet, which demonstrates that METER outperforms the baseline method, in terms of precision, accuracy, F -score, recall, ROC curve, and P-R curve.