The rapid adoption of electric vehicles (EVs) has created new opportunities for decentralized energy trading, where EVs can act as mobile energy providers in peer-to-peer markets. Blockchain provides a secure foundation for such systems, ensuring trust and accountability. However, its inherent transparency creates privacy risks, as it enables the tracking of trading activities. Existing privacy-preserving mechanisms typically focus on concealing payment transactions but often expose other critical interactions, such as matching coordination. To address these challenges, we proposePriVET, a privacy-preserving framework for vehicular energy trading. PriVET leverages smart contracts for trade matching and uses an enhanced Paillier encryption scheme to support encrypted comparisons, ensuring secure coordination without revealing sensitive data. Additionally, a Bloom-filter– based Geohash encoding is used to protect location privacy during spatial matching. We evaluate PriVET through both theoretical analysis and practical experiments. In a simulation environment, the transaction computation time for 100 vehicles is shown to be under 30ms, with communication overhead kept below 20KB. These results demonstrate that PriVET provides robust privacy protection while maintaining minimal overhead, making it a practical solution for real-world blockchain-based energy trading scenarios.

Cloud service providers use geo-distributed datacenters to provide resources and services to clients located in different regions. However, uneven population density leads to unbalanced development of geo-distributed datacenters and cloud service providers face a shortage of land resources to further develop datacenters in densely populated regions. Thus, it is a real challenge for cloud service providers to meet the increasing demand from clients in affluent regions with saturated resources and to better utilize underutilized data centers in other regions. To address this challenge, we study an online resource allocation problem in geo-distributed clouds, whose goal is to assign each user request upon arrival to an appropriate geographic cloud region to minimize the resulting peak utilization of resource pools with different cost coefficients. To this end, we formulate the problem as a dynamic bin packing problem with heterogeneous dependent bins where user requests correspond to items to be packed and heterogeneous cloud resources are bins. To solve this online problem with high uncertainty, we propose a simulation based memetic algorithm to generate robust offline proactive policies based on historical data, which enable fast decision making for online packing. Our experiments based on realistic data show that the proposed approach leads to a reduction in total costs of up to 15% compared to the current practice, while being much faster for decision making compared to a popular online method.

Dynamic searchable symmetric encryption (DSSE) enables updates and keyword searches on outsourced encrypted data while minimizing the information revealed to the server. However, existing DSSE schemes that support conjunctive keyword searches disclose added documents or fail to filter deleted ones in certain circumstances, thus violating forward and backward privacy. Besides, the size of their search tokens increases with the number of documents, which incurs a heavy communication cost. In this paper, we develop a conjunctive DSSE scheme that has a search token size only related to the conjunction size and fully supports forward and backward privacy. Our scheme is based on a new three-dimensional chain structure called CUBE. We also rethink the leakage function of conjunctive queries and prove that our scheme satisfies the refined security definition. Experimental results demonstrate that compared with the state-of-the-art schemes, our scheme increases the computational cost by at most 9.62% but reduces the communication cost by 99.78% when searching six conjunctive keywords.

BACKGROUND: Epigenetic modifications established in mammalian gametes are largely reprogrammed during early development, however, are partly inherited by the embryo to support its development. In this study, we examine CpG island (CGI) sequences to predict whether a mouse blastocyst CGI inherits oocyte-derived DNA methylation from the maternal genome. Recurrent neural networks (RNNs), including that based on gated recurrent units (GRUs), have recently been employed for variable-length inputs in classification and regression analyses. One advantage of this strategy is the ability of RNNs to automatically learn latent features embedded in inputs by learning their model parameters. However, the available CGI dataset applied for the prediction of oocyte-derived DNA methylation inheritance are not large enough to train the neural networks. RESULTS: We propose a GRU-based model called CMIC (CGI Methylation Inheritance Classifier) to augment CGI sequence by converting it into variable-length k-mers, where the length k is randomly selected from the range [Formula: see text] to [Formula: see text], N times, which were then used as neural network input. N was set to 1000 in the default setting. In addition, we proposed a new embedding vector generator for k-mers called splitDNA2vec. The randomness of this procedure was higher than the previous work, dna2vec. CONCLUSIONS: We found that CMIC can predict the inheritance of oocyte-derived DNA methylation at CGIs in the maternal genome of blastocysts with a high F-measure (0.93). We also show that the F-measure can be improved by increasing the parameter N, that is, the number of sequences of variable-length k-mers derived from a single CGI sequence. This implies the effectiveness of augmenting input data by converting a DNA sequence to N sequences of variable-length k-mers. This approach can be applied to different DNA sequence classification and regression analyses, particularly those involving a small amount of data.

Adverse event (AE) analysis is one of the most important missions in French social and medico-social centers, which enables targeted prevention measures to avoid recurrences. However, social and medico-social centers are struggling to conduct an efficient analysis of AEs due to the abysmal quality of event reports. In this paper, we propose an automated classification solution to predict the fact of an AE from the event description to ease the decision-making task of center managers. This solution explores different text similarity methods based on a dedicated terminology of synonyms for the social and medico-social sector in being able to propose the most appropriate facts. The approach has the advantage of being fast with an accurate prediction since it achieved an accuracy up to 88%, when it is tested on 388 real AE reports.

Stochastic Kronecker Graph model is widely used to generate synthetic graphs that simulate real-world graphs. In this model, the initiator matrix decides the degree to which the synthetic graph approximates the real-world graph. The computing of initiator matrix, however, requires that the number of the nodes of input graph is the power of the dimension of the initiator matrix. In order to fulfill such requirement, some methods (e.g., KronFit and Moment) add isolated nodes to the input graph, which damages the input graph's properties. Other method (e.g., KronEM) predicts the links between the nodes after adding isolated nodes. Unfortunately, the prediction dramatically increases the complexity of computing. In this paper, we propose a method named as MomentSA to solve the problems. Our method completes the input graphs by leveraging the law of property changes of graphs, which does not need to predict the links as method KronEM. Simultaneously, our method uses the moment-based estimation and ADAM (Adaptive Moment Estimation) method to compute the initiator matrix, which can compute the initiator matrix quickly. Experiment results on our prototype implementation suggest that the initiator matrix computed from our method is more accurate than existing state-of-art systems, and the speed of computing is about three to four orders of magnitude faster than state-of-art systems.