With the rapid development of the blockchain industry and the widespread adoption of IoT devices, which are often deployed on different blockchains, the need for cross-chain value and data exchange has become increasingly important. However, existing cross-chain transactions face challenges such as low efficiency, high costs, and insufficient security. To address these issues, this paper proposes a cross-chain transaction scheme based on aggregated zero-knowledge proofs. This scheme optimizes the allocation of computing resources in a distributed environment and employs a multi-branch balanced Merkle tree to construct aggregated zero-knowledge proofs, significantly reducing the verification costs for batch cross-chain transactions.To further enhance data privacy and integrity, this paper introduces the Secure Aggregated Block Verification (SABV) algorithm and improves system consistency and reliability through the Local Merkle Tree Rebalance (LMTR) algorithm. In addition, this paper analyzes the basic security of the proposed scheme when implemented in adversarial environments and provides countermeasures for common threats in distributed systems. Finally, simulations and actual deployment on the Ethereum test network were conducted. The results indicate that our method reduces CPU usage, memory consumption, and time expenditure by 50.10%, 99.03%, and 99.47%, respectively, during the generation of zero-knowledge proofs for batch cross-chain transactions. At the same time, building upon the performance improvements of the existing zero-knowledge proofs, our approach also demonstrates significant enhancements in contract deployment and cross-chain transaction efficiency.

While Federated Learning is intended to safeguard data privacy, it is confronted with the problem of gradient leakage, which empowers attackers to execute gradient inversion attacks and retrieve the original data through the shared gradient information. Existing gradient inversion attack methods can achieve good results when handling small batches of low-resolution images. However, when dealing with large batches of high-resolution images, problems such as gradient ambiguity and model instability will occur, resulting in a significant decrease in the recovery performance. We propose a novel Image-correction-penalty based Over-parameterized Regression Network (IORN). IORN breaks through the limitations of existing methods with its unique design. The Adaptive Over-parameterized Network in IORN can dynamically adjust its structure, thereby enhancing the network’s ability to capture complex data distributions. This enables it to better handle the complexity of large batches of high-resolution images and improves the model’s reconstruction ability for such images. Meanwhile, the designed image correction penalty term restricts the difference between the generated images and the average image. This not only improves the stability of the optimization process but also reduces the convergence deviation. Experimental results demonstrate that IORN significantly improves the resolution and fidelity of reconstructed images during gradient inversion attacks on the MNIST, CIFAR-100, and LFW datasets, especially showing outstanding performance when dealing with large batches of complex images.

In a fully heterogeneous federated learning environment, the client has significant differences in model structure and local data distribution (Non-IID), and the joint learning of the client model is blocked due to the limited communication content available for interaction in a fully heterogeneous scenario. In this context, the global knowledge constructed by the server through the simple aggregation of the client logits is essentially a fuzzy representation containing a lot of noise and information loss, which is difficult to effectively guide the client model update. To solve these problems, this paper proposes a heterogeneous federated learning framework (FedMkd) based on multi-knowledge distillation fusion to cope with multiple challenges in heterogeneous environments. The FedMkd framework uses a class-grained logits interaction architecture (CLIA) and introduces an efficient knowledge sharing mechanism. It innovatively integrates two knowledge distillation methods: 1) Temperature-Adaptive Knowledge Distillation (TAKD), which provides differentiated temperatures for teacher and student models by adaptively adjusting the distillation temperature, maximizing knowledge transfer between them; 2) Class-related Knowledge Distillation (CRKD), which introduces batch-level sample correlation loss to reduce over-reliance on specific samples or classes and improve the model's understanding of overall data features. We conducted a large number of experiments on four public data sets. The results show that in a variety of data and model heterogeneous scenarios, FedMkd still performs better than the comparison method when the communication overhead is reduced by more than one order of magnitude.

Protein solubility plays a crucial role in various biotechnological, industrial, and biomedical applications. With the reduction in sequencing and gene synthesis costs, the adoption of high-throughput experimental screening coupled with tailored bioinformatic prediction has witnessed a rapidly growing trend for the development of novel functional enzymes of interest (EOI). High protein solubility rates are essential in this process and accurate prediction of solubility is a challenging task. As deep learning technology continues to evolve, attention-based protein language models (PLMs) can extract intrinsic information from protein sequences to a greater extent. Leveraging these models along with the increasing availability of protein solubility data inferred from structural database like the Protein Data Bank holds great potential to enhance the prediction of protein solubility. In this study, we curated an Updated Escherichia coli protein Solubility DataSet (UESolDS) and employed a combination of multiple PLMs and classification layers to predict protein solubility. The resulting best-performing model, named Protein Language Model-based protein Solubility prediction model (PLM_Sol), demonstrated significant improvements over previous reported models, achieving a notable 6.4% increase in accuracy, 9.0% increase in F1_score, and 11.1% increase in Matthews correlation coefficient score on the independent test set. Moreover, additional evaluation utilizing our in-house synthesized protein resource as test data, encompassing diverse types of enzymes, also showcased the good performance of PLM_Sol. Overall, PLM_Sol exhibited consistent and promising performance across both independent test set and experimental set, thereby making it well suited for facilitating large-scale EOI studies. PLM_Sol is available as a standalone program and as an easy-to-use model at https://zenodo.org/doi/10.5281/zenodo.10675340.

With the vigorous development of the blockchain industry, cross-chain transactions can effectively solve the problem of “islands of value” caused by the inability to interact between different chains. However, security risks in reputation management caused by cross-chain transactions implemented through notary solutions have always existed. Consequently, this paper proposes a blockchain cross-chain transaction method based on decentralized dynamic reputation value assessment. The notary election phase addresses the issue of the continually changing behaviour of notaries in actual transactions by designing a dynamic evaluation window mechanism based on an RNN. Moreover, a reputation-rating decay mechanism is introduced to avoid the problem of reputation value recovery caused by malicious notaries being inactive for a long time. Relative to alternative reputation assessment models, the proposed method offers a thorough evaluation of user behavior and effectively identifies malicious activities in real-time. Finally, the method was tested by deploying it on the Ethereum blockchain. Our approach offers more dynamic settings for window parameters, adapting to changes in notary behavior and reducing the number of detections within the same timeframe by approximately 59.14%. The weight factor settings are also optimized, allowing for adjustments based on specific situations to achieve accurate reputation values. Overall, this method not only enhances the security of cross-chain transactions but also reduces operational costs by 53.3% compared to traditional technologies.

Massive open online courses (MOOCs) are a technical trend in the field of education. As the number of available MOOCs continues to grow dramatically, the difficulty for learners to find courses that satisfy their personalized learning goals has also increased. Unstructured texts, such as course descriptions and course skills, contain rich course information and are useful for MOOC platforms in constructing personalized services. This paper proposes a novel search ranking method for MOOCs that integrates unstructured course information. We propose a latent Dirichlet allocation-based model to cluster courses into groups based on course descriptions. Courses in the same cluster are considered to share similar educational contents. We then propose the CourseRank algorithm based on the information of course skills to recommend and rank courses when students search for or click on a specific course. Our experiments on the dataset from Coursera indicate that our method is able to cluster courses effectively and produce satisfactory ranking results for courses in MOOC platforms.

Fuel cells are promising power sources for green data centers thanks to its high energy-efficiency, low greenhouse gas emissions and high reliability. However, fuel cells have a unique feature called limited load following, i.e., they are slow in adjusting power supply due to mechanical limitation of fuel delivery. When power demand of data centers suddenly grows, fuel cells would fail to provide sufficient power supply. On the other hand, fuel cells are slow to reduce its power supply when demand decreases, leading to energy waste. In this paper, we study to mitigate the impact of limited load following by associating a set of heterogeneous batteries with fuel cells. These batteries with different characteristics (e.g., capacity, charging and discharging rate) can power data centers when the energy supply of fuel cells is insufficient. They are charged by excessive power supply when demand decreases. Given future power demand, we formulate the energy management problem as a mixed-integer nonlinear programming. An online algorithm is designed to solve the problem without future knowledge. We conduct extensive simulations using real-world traces and results show that our proposed algorithm significantly outperforms existing solutions.

Some sleep disorders, such as sleep apnea, restless legs syndromes (RLS), and periodic limb movement disorder (PLMD), require a full-night sleep monitoring for diagnosis. Conventional sleep monitoring devices are disturbing and inconvenient for daily scene applications. In this poster paper, we propose a sleep monitoring system by embedding RFID tags into bed cloth and realize two main functions: breath monitoring and body movement detection. We apply a finite impulse response low pass filter to get smooth breath signal wave and use a convolutional neural network (CNN) algorithm to identify the movement of person objects. Finally, we conduct experiments to evaluate the breath monitoring in a real world scenario. The experiment results show that our monitoring system can monitor breath with a high accuracy.

Energy Internet (EI) is proposed as the evolution of smart grid, aiming to integrate various forms of energy into a highly flexible and efficient grid that provides energy packing and routing functions, similar to the Internet. As an essential part in EI system, a scalable and interoperable communication infrastructure is critical in system construction and operation. In this article, we survey the recent research efforts on EI communications. The motivation and key concepts of EI are first introduced, followed by the key technologies and standardizations enabling the EI communications as well as security issues. Open challenges in system complexity, efficiency, reliability are explored and recent achievements in these research topics are summarized as well.