Solving constrained multi-objective optimization problems (CMOPs) is a challenging task, because they need to optimize multiple conflicting objectives and satisfy several constraints. In the past several years, though many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed to deal with CMOPs, they still have some limitations. For instance, different CMOEAs may be required for CMOPs with different characteristics. To tackle this issue, this paper proposes a Pareto fronts (PFs) guided co-evolutionary algorithm (called PFCEA) for CMOPs. Inspired by competitive multitasking (CMT), PFCEA employs two populations with specialized roles. The first population aims to use the unconstrained PF (UPF) and the most useful single-constrained PF (SPF) to find the constrained PF (CPF). To choose the most useful SPF, a novel single constraint priority (SCP) method is designed. The second population considers total constraint violation and objective values by using an improved fuzzy constraint handling technique. During the search, a reward accumulation method is used for the co-evolution of two populations. To verify the performance of PFCEA, four popular benchmark sets and six real-world CMOPs are tested. The performance of PFCEA is compared with eleven other state-of-the-art CMOEAs. Experimental results demonstrate that the proposed PFCEA is competitive in solving CMOPs with different properties.

Introduction: Epilepsy, a complex neurological disorder characterised by recurrent seizures and significant genetic heterogeneity, presents considerable challenges form accurate diagnosis and drug target identification. While traditional genomewide association studies (GWAS) and sequencing technologies have advanced our understanding of epilepsy-related gene targets, they often struggle to identify novel and rare variants crucial for precise diagnosis and targeted drug development. The increasing availability of large-scale genomic data, coupled with the power of deep learning, offers a promising avenue for progress.Method: In this work, we introduce GraphTransNet, a novel hybrid neural network model designed for predicting epilepsy-associated gene targets, with direct implications for improved disease diagnosis and therapeutic target identification. GraphTransNet leverages protein language models (specifically ESM) to generate numerical embeddings from gene sequences. These embeddings are then processed by a novel architecture integrating transformer and convolutional neural network (CNN)components to predict epilepsy-related gene targets.Results: Our results demonstrate that GraphTransNet achieves high accuracy in identifying epilepsy targets, outperforming existing predictive tools in terms of both recall and precision metrics for reliable disease diagnosis and effective drug target identification. Rigorous comparisons with established machine learning methods and other deep learning architectures further underscore the efficacy of GraphTransNet.Discussion: This approach represents a valuable computational tool for advancing epilepsy genetics research, with the potential to contribute to more accurate diagnostic strategies and the discovery of novel drug targets for improved treatment outcomes.

The performance of evolutionary algorithms using reference vectors to guide the evolution process mainly depends on the adaptive reference vector update strategy. In order to solve the challenging many-objective optimization problems with irregular Pareto fronts, this paper proposes an adaptive reference vector update strategy based on the Pareto front density estimation, which estimates the true Pareto front by finding sparse regions while ensuring the uniform distribution of reference vectors. In addition, an improved environmental selection strategy using the angle-based neighborhood density estimation has been proposed for estimating the neighborhood density to effectively guide the population evolution. On this basis, this paper proposes an adaptive reference vector guided many-objective optimization algorithm based on Pareto front density estimation (MaOEA-PDE). Experimental results on a large number of benchmark problems show MaOEA-PDE achieves better performance compared with some state-of-the-art algorithms in the literature.

Multi-set membership query is a fundamental issue for network functions such as packet processing and state machines monitoring. Given the rigid query speed and memory requirements, it would be promising if a multi-set query algorithm can be designed based on Bloom filter (BF), a space-efficient probabilistic data structure. However, existing efforts on multi-set query based on BF suffer from at least one of the following drawbacks: low query speed, low query accuracy, limitation in only supporting insertion and query operations, or limitation in the set size. To address the issues, we design a novel B h sequence-based Bloom filter (B h BF) for multi-set query, which supports four operations: insertion, query, deletion, and update. In B h BF, the set ID is encoded as a code in a B h sequence. Exploiting good properties of B h sequences, we can correctly decode the BF cells to obtain the set IDs even when the number of hash collisions is high, which brings high query accuracy. In B h BF, we propose two strategies to further speed up the query speed and increase the query accuracy. On the theoretical side, we analyze the false positive and classification failure rate of our B h BF. Our results from extensive experiments over two real datasets demonstrate that B h BF significantly advances state-of-the-art multi-set query algorithms.

Using the NS2 network simulation tool to teach the “wireless network” course can enhance students’ understanding of wireless network protocols through visualization and can also improve students’ ability to analyze problems hands on. We built a multimedia wireless network modularized English teaching platform based on NS2 simulation software; made full use of the rich components and network resources provided by NS2 to achieve a high degree of code reuse and reduce the time for developing simulation codes; designed the overall framework of this multimedia wireless network modular English teaching platform based on the B/S structure; and solved the weight coefficient and importance value of the learning input evaluation index, based on the feedback elite teaching optimization algorithm, the improved teaching formula, the increase in the number of exchanges, and the improvement in the learner’s performance. The experimental results show that the LEACH and LEACH-C protocols of the clustered NS2 simulation software have more surviving nodes most of the time, which indicates that the clustering routing protocol has better performance.

针对铝锂合金焊后易产生气孔、抗拉强度低的缺点,提出"∞"形激光扫描填丝焊接工艺方法,以2 mm厚2060铝锂合金为研究对象开展对接焊接试验研究,探究激光扫描填丝焊接方法对铝锂合金焊接缺陷抑制作用.借助高速相机摄像系统,探究了激光扫描填丝焊接工艺下熔池的动态演变过程,同时探究了扫描参数对焊缝气孔的影响规律及扫描填丝工艺对气孔的抑制机理.采用曲面响应统计方法探究工艺参数对抗拉强度的影响,并给出工艺参数组合与抗拉强度的定量关系及最优参数组合,焊接接头最大抗拉强度可达382 MPa,为母材的76.4％.结果表明,"∞"形激光扫描填丝焊接工艺下熔池流动平稳,小孔喷发强度较弱且呈现出周期性;"∞"形激光扫描填丝焊接工艺可以有效抑制焊缝气孔,提高铝锂合金焊接质量.

Hot data identification techniques for flash memory have attracted a plethora of research interests because of its crucial impact on performance and life span of SSD. Our proposed technique, Data Temperature-Aware Bloom Filters (DTABF), is suitable for the requirements of both the Buffer Management and the Flash Translation Layer in SSD. DTABF divides the access period of LPAs into n cycles. Thus, DTABF can record the access frequency of LPAs with a round-robin fashion in each cycle by combining one Counting Bloom filter and n Bloom filters. The access pattern of each LPA, indicated by the data access frequency, recency, and access frequency changes over time, is presented with n different data temperatures in n cycles. Specifically, we employ a bijective function to characterize the data temperature of each LPA. Based on the data temperature identified by DTABF, the data stored in LPAs, which presenting the similar access pattern, can be gathered in the same flash block in SSD. Meanwhile, those data occasionally becoming cold in the buffer can be effectively identified to avoid being meaninglessly replaced from the buffer. Analytical and experimental results show that DTABF can help to improve hit rate, write performance and alleviate write amplification, while achieving lower memory cost and computational complexity.

To provide quick name lookup technique, the paper designs a Hash bloom filter (HBF). The HBF consists of g on-chip counter bloom filters (CBFs), g on-chip counters and g off-chip Hash tables. Each Hash table is associated with a CBF and a counter. To reduce the false positive rate introduced by unbalanced name insertion in to CBFs, we propose two-Hash-choice algorithm which evenly disperses the FIB/CS/PIT entries into g Hash tables and CBFs. Moreover, HBF has a good feature of parallel processing of data packet forwarding because HBF adopts multiple Hash tables and CBFs. Theoretical and simulated results demonstrate that HBF can achieve very efficient name lookup by well utilizing the on-chip memory through localization and filtering function of CBF. Therefore, the proposed HBF improves data packet forwarding rate and effectively avoids flooding attacks.

The popularity of smart phones fosters the growth of Proximity-based Mobile Social Networking (PMSN). Although some profile matching approaches have been proposed to facilitate a user to find another user that shares his/her interest in the proximity, these approaches usually model the matching problem as a Private Set Intersection problem or a Private Set Intersection Cardinality problem and require high complexity of computation. Different from current studies, to facilitate more effective building of PMSNs, we propose a novel similarity metric to evaluate the common interests of mobile users by considering the time-dependent features of their interests. To calculate the metric in a low cost and privacy-protection way, we propose a novel time-dependent bloom filter to encode the time-dependent interest and a novel probabilistic algorithm to estimate the time-dependent similarity metric based on the bloom filter. Based on the proposed BF-based profile matching approach, we further propose InterestMatch, a novel distributed mobile communication system to facilitate more efficient social networking among strangers in the physical proximity. We have done extensive experiments on real-world phones, our experiment results demonstrate that our approach is promising for facilitating mobile social interactions in the physical proximity due to its low complexity and consequently low power consumption.

Bloom filters are space-efficient data structures for fast set membership queries. Counting Bloom Filters (CBFs) extend Bloom filters by allowing insertions and deletions to support dynamic sets. The performance of CBFs is critical for various applications and systems. This paper presents a novel approach to building a fast and accurate data structure called Multiple-Partitioned Counting Bloom Filter (MPCBF) that addresses large-scale data processing challenges. MPCBF is based on two ideas: reducing the number of memory accesses from k (for k hash functions) in the standard CBF to only one memory access in the basic MPCBF-1 case, and a hierarchical structure to improve the false positive rate. We also generalize MPCBF-1 to MPCBF-g to accommodate up to g memory accesses. Our simulation and implementation in MapReduce show that MPCBF outperforms the standard CBF in terms of speed and accuracy. Compared to CBF, at the same memory consumption, MPCBF significantly reduces the false positive rate by an order of magnitude, with a reduction of processing overhead by up to 85.9%.

Bloom filters are space-efficient randomized data structures for fast membership queries, allowing false positives. Counting Bloom Filters (CBFs) perform the same operations on dynamic sets that can be updated via insertions and deletions. CBFs have been extensively used in MapReduce to accelerate large-scale data processing on large clusters by reducing the volume of datasets. The false positive probability of CBF should be made as low as possible for filtering out more redundant datasets. In this paper, we propose a multilevel optimization approach to building an Accurate Counting Bloom Filter (ACBF) for reducing the false positive probability. ACBF is constructed by partitioning the counter vector into multiple levels. We propose an optimized ACBF by maximizing the first level size, in order to minimize the false positive probability while maintaining the same functionality as CBF. Simulation results show that the optimized ACBF reduces the false positive probability by up to 98.4% at the same memory consumption compared to CBF. We also implement ACBFs in Map Reduce to speed up the reduce-side join. Experiments on realistic datasets show that ACBF reduces the false positive probability by 72.3% as well as the map outputs by 33.9% and improves the join execution times by 20% compared to CBF.