Reviewerly/exHarmony huggingface dataset:
Improving SHADE with a Linear Reduction P Value and a Random Jumping Strategy

DE/current-to-pbest/1 is a novel mutation scheme introduced in the JADE algorithm, and is inherited by the famous JADE variant, SHADE. Thereafter, it has continued to be used in various JADE and SHADE variants. Considering the parameter ‘p’ in mutation operator DE/current-to-pbest/1 controls the greediness of mutation directly, this paper adopts a linear reduction strategy of ‘p’ during the evolution process under the framework of SHADE algorithm (LRP-SHADE), and then helps to further trade off the exploration and exploitation abilities. Meanwhile, a random jumping strategy is also adopted, assisting the population to jump out the local optima and keep evolving. Finally, to verify the effectiveness of the proposed LRP-SHADE algorithm, groups of experiments have been conducted on benchmark CEC2014, including both a step-by-step validation experiment for strategies and performance comparisons between LRP-SHADE and other peer algorithms. According to the experimental results, the efficiency and effectiveness of the LRP-SHADE algorithm have been confirmed.
An Energy-Conscious Task Scheduling Algorithm for Minimizing Energy Consumption and Makespan in Heterogeneous Distributed Systems

Heterogeneous distributed systems have been widely used in the industrial field to address the demand for scalability and performance. However, with the increase in the number of computing nodes, the energy consumption of the system has sharply risen. Therefore, reducing energy consumption has become an important objective in the field of sustainable computing. To address this challenge, this paper proposes an energy-conscious scheduling algorithm based on heterogeneous computing systems for allocating and scheduling tasks with different priorities. The algorithm initializes the population based on the Earliest Finish Time (EFT) and processor allocation strategy and adopts the superior individual selection strategy to reduce the influence of inferior solutions. Additionally, the MECMA algorithm introduces a novel adaptive mutation operator to enhance the diversity of the population and accelerate the convergence speed. Simulation experiments have been conducted on a set of randomly generated tasks, and the experimental results have demonstrated the efficiency of the proposed algorithm. The proposed MECMA algorithm has significantly reduced energy consumption and shortened task completion time. The research results have great potential to contribute to the development of sustainable computing and the optimization of energy utilization in industrial fields.
CL-BOSIC: A Distributed Agent-Oriented Scheme for Remote Data Integrity Check and Forensics in Public Cloud

As electronic evidence, electronic data is easily tampered with and forged, so it is very important to ensure the authenticity, reliability and integrity of electronic data in forensics. In this paper, we propose a certificateless distributed agent-oriented data outsourcing and integrity checking scheme in forensics scenarios — CL-BOSIC, which utilizes smart contracts deployed on the blockchain to check the integrity of outsourced data periodically. Besides, smart contracts guarantee the fair payment between the user and the cloud server. During forensics, the judiciary verify the validity of electronic data according to the records on the blockchain, which solves the judgment of legal force on electronic evidence. CL-BOSIC embeds the non-interactive framework in both verification process and the data retrieval process during forensics to reduce the communication overhead and data leakage. In addition, CL-BOSIC also realizes data privacy protection and user identity anonymity. Security analysis is presented in the context of completeness, soundness, and privacy protection. We implement a prototype of CL-BOSIC based on Fabric, and evaluate the performance of the prototype from different aspects, the evaluation results indicate that our proposal is feasible and incurs tolerable overheads for each party.
An Improved UAV Detection Method Based on YOLOv5

Because unmanned aerial vehicles(UAVs) have the characteristics of low flight trajectory, slow motion speed, and small volume, they are difficult to identify using current vision technologies. To meet the requirements for detection speed and accuracy in the field of UAV detection, we propose an improved You Only Look Once version 5(YOLOv5) UAV detection method. It uses visible light and infrared imaging datasets for daytime detection and nighttime detection respectively. Based on these two datasets, our model can improve detection accuracy in challenging environments such as dim light or nighttime. We choose a model pruning method based on the Batch Normalization(BN) layers to prune the YOLOv5 detection model to improve the detection speed of the model. To solve the problem of misjudging birds as UAVs, EfficientNet is added to re-classify the detection results of YOLOv5. We constructed a dataset containing over 10000 visible light images and over 10000 infrared imaging images to evaluate the performance of the proposed algorithm. Qualitative and quantitative experimental results show that the proposed algorithm has greatly improved the accuracy and speed of UAV detection.
A Deep Transfer Fusion Model for Recognition of Acute Lymphoblastic Leukemia with Few Samples

Distinguishing between different sub-classes of Acute Lymphoblastic leukemia (ALL) based on morphological differences in blood smear images is challenging. Deep learning methods have been successful for morphological classification. This paper aims to develop a deep transfer fusion model (TFDNet) to predict ALL sub-classes using a few blood cell images. TFDNet is a customized Convolutional Neural Network (CNN) that consists of two transfer learning modules, Xception and Dense, working in parallel for feature extraction. TFDNet then utilizes a two-branch feature extraction layer to fuse the multi-scale features for the diagnosis of ALL sub-classes. To evaluate the effectiveness and generalizability of TFDNet, we compare it with seven state-of-the-art methods on five different datasets, including three small-sample ALL types, as well as skin cancer and brain cancer data. The experimental results demonstrate that TFDNet outperforms the seven state-of-the-art methods on all five datasets.
Zeroth-Order Gradient Approximation Based DaST for Black-Box Adversarial Attacks

In recent years, adversarial attacks have arisen widespread attention in the domain of deep learning. Compared to white-box attacks, black-box attacks can be launched only with output information, which is more realistic under real-world attack conditions. An important means of black-box attacks is to train a substitute model of the target model. Yet, obtaining the training data of target model is difficult. Previous works use GAN to accomplish data-free training of the generator and the substitute model. However, the gradient of generator has no relationship with the target model, which causes the requirement of massive iterations and the limitation of Attack Success Rate (ASR). To address this issue, we propose zeroth-order gradient approximation based Data-free Substitute Training (DaST) for black-box adversarial attacks. It estimates the gradient of target model using forward difference and back propagates the gradient to generator, which improves ASR and training efficiency. Four popular auxiliary white-box attack algorithms are used to compare our method with previous works. Experiment results on MNIST and CIFAR-10 demonstrate higher ASR, less training time and memory of our method. Specifically, on MNIST, our method reaches an ASR of 99.87% consuming only 10 min and 1092.56 MB memory, with the increase of 3.51% ASR, the time reduction of 38 h and the memory decrease of 929.72 MB compared to the state-of-the-art method. Moreover, our method remains effective even when the structure of substitute model differs from that of target model.
An Experimental Study on MRI Denoising with Existing Image Denoising Methods

In this paper, we perform a systematical study on existing 2D denoising methods for reducing the noise in magnetic resonance imaging (MRI). We conduct experiments on six MRI images with the following denoising methods: wiener2, wavelet-based denoising, bivariate shrinkage (BivShrink), SURELET, Non-local Means (NLM), block matching and 3D filtering (BM3D), denoising convolutional neural networks (DnCNN) and weighted nuclear norm minimization (WNNM). Based on our experiments, the BM3D and the WNNM are the best two methods for MRI image denoising. Nevertheless, the WNNM is the slowest in term of CPU computational time. As a result, it is preferable to choose the BM3D for MRI denoising.
CharCaps: Character-Level Text Classification Using Capsule Networks

Text classification is a hot topic in the field of natural language processing and has achieved great success. Existing character-level text classification methods mainly use convolutional neural networks to extract character-level local features, making them ineffective in modeling the hierarchical spatial relationship information on the character-level features, reducing the classification performance. This paper proposes a new character-level text classification framework based on the capsule network called CharCaps to solve the above problem. The proposed CharCaps framework first extracts character-level text features using seven convolutional layers and then reconstructs them based on the capsule vector representation to obtain the hierarchical spatial relationship information between character-level features effectively and achieve a significant classification without pre-trained models. Experimental results on five challenging benchmark datasets demonstrate that our proposed method outperforms state-of-the-art character-level text classification models, especially convolutional neural network-based models.
A Quantum Simulation Method with Repeatable Steady-State Output Using Massive Inferior Solutions

In evolutionary computation, inferior solutions are often discarded to expedite convergence, potentially bypassing valuable optimization insights. To rectify this, we have developed a novel method, drawing inspiration from ground state evolution (GSE) and quantum annealing (QA). These processes retain numerous positions with non-minimum potential energy, enabling the construction of the lowest possible energy wave function. Our method’s theoretical foundation is meticulously explained through the quantum path integral. We have realized this theory via numerical simulation, utilizing population-based evolution driven by multi-scale Gaussian sampling with a decreasing scale, mimicking QA with multi-scale diffusion Monte Carlo (DMC). A series of rigorous experiments highlight the unique attributes and effectiveness of this method. Importantly, our approach generates a vast array of inferior solutions consistently. Their distribution indicates regions of lower function values within the solution space, presenting a new perspective on the utilization of inferior solutions. The implications of this research promise enhancements in solving optimization problems, potentially improving efficiency in evolutionary computation and beyond.
A High-Performance Steganography Algorithm Based on HEVC Standard

In today's digital age, digital video has the characteristics of wide sources, large capacity and strong camouflage, making it an excellent carrier for steganography. At the same time, HEVC video coding standard has good application prospects. This paper presents a novel and high-performance steganography algorithm based on HEVC standard. The secret message is embedded into the multi-coefficients of the selected 4 × 4 luminance QDST blocks to avert the intra-frame distortion drift. And a novel embedding scheme is used to reduce the modification of these embedded blocks. The experimental results show that the proposed algorithm can effectively increase the visual quality and get good embedding capacity.
Q-Learning Based Particle Swarm Optimization with Multi-exemplar and Elite Learning

The particle swarm optimization (PSO) is widely adopted for solving global optimization problems. However, most existing enhanced PSO versions use fixed learning exemplars for particles’ movements, which may reduce the population diversity. Additionally, there is a lack of learning strategies for well-performing particles, which may restrict their potential. To address the abovementioned issues, this paper proposes a Q-learning based particle swarm optimization with multi-exemplar and elite learning (QPSOEL). In QPSOEL, reinforcement learning method is applied to adaptively choose the optimal multi-exemplar selection strategy from a set of optional actions. Under the control of the Q-learning method with the trial-and-error mechanism, each particle aims to maximize the long-term reward as an agent. Moreover, an elite learning strategy is designed for recorded particles with ideal positions to achieve high-quality local search and balance exploration and exploitation. The comprehensive performance of the proposed QPSOEL algorithm is evaluated on 12 benchmark functions, including unimodal, simple multimodal, and rotated multimodal problems. Statistical analysis demonstrates that QPSOEL significantly outperforms five compared PSO variants in terms of solution accuracy and robustness.
A Blockchain-Based Network Alignment System for Power Equipment Data Inconsistency

This paper resolves the problem of data inconsistencies and redundancies in the current equipment management system of State Grid Corporation of China (SGCC). We propose a blockchain-based network alignment system (BNAS). Our system offers three key features: fine-grained access control, efficient equipment data synchronization, and retrieval of similar equipment data, improving data management and accuracy during data updates. Specifically, the proposed system utilizes blockchain to connect multiple equipment data sources and employs representation learning techniques to match similar items. An efficient compression algorithm that utilizes composite hashing is proposed to improve the synchronization speed and reduce the storage burden of blockchain by approximately 43%. Moreover, we introduce HAC (Hierarchy Access Control) Tree, a novel data structure that mimics SGCC’s affiliations to manage staff’s permission to update equipment data. The evaluation results demonstrate BNAS’s efficiency in dealing with power equipment data inconsistencies.
STASIS: Reinforcement Learning Simulators for Human-Centric Real-World Environments

We present on-going work toward building Stasis, a suite of reinforcement learning (RL) environments that aim to maintain realism for human-centric agents operating in real-world settings. Through representation learning and alignment with real-world offline data, Stasis allows for the evaluation of RL algorithms in offline environments with adjustable characteristics, such as observability, heterogeneity and levels of missing data. We aim to introduce environments the encourage training RL agents that are capable of maintaining a level of performance and robustness comparable to agents trained in real-world online environments, while avoiding the high cost and risks associated with making mistakes during online training. We provide examples of two environments that will be part of Stasis and discuss its implications for the deployment of RL-based systems in sensitive and high-risk areas of application.
Dynamic Attention Filter Capsule Network for Medical Images Segmentation

In the existing works, Capsule Networks (CapsNets) have been proven to be promising alternatives to Convolutional Neural Networks (CNNs). However, CapsNets perform poorly on complex datasets with RGB backgrounds and cannot handle images with large input sizes. We propose a Dynamic Attention Filter (DAF) method to improve the performance of CapsNets. DAF is a filter unit between low-level capsule layers and voting layers, which can effectively filter the invalid background capsules and improve the classification performance of CapsNet. Besides, we propose DAF-CapsUNet for inputting medical images of large sizes, combining the advantages of the U-shaped encoder-decoder structure and DAF-CapsNet. Specifically, it contains three fundamental operations: an encoder is responsible for extracting shallow feature information, a capsule module is responsible for capturing the detailed feature information lost due to the pooling layer of the CNNs, and a decoder is responsible for fusing the feature information extracted from the two stages. Extensive experiments demonstrate that DAF can improve the performance of CapsNets on complex datasets and reduce the number of parameters, GPU memory cost, and running time of CapsNets. Benefiting from DAF-CapsNet, our model can achieve more useful information for precise localization. The medical experiments show that DAF-CapsUNet achieves SOTA performance compared to other segmentation models.
Graph-Based Short Text Clustering via Contrastive Learning with Graph Embedding

Clustering is an unsupervised learning technique that helps us quickly classify short texts. It works by effectively capturing the semantic themes of texts and assigning the similar texts into the same cluster. Due to the excellent ability of contrastive learning to learn representations, using contrastive learning to extract semantic features for clustering tasks has become a new trend for short text clustering. However, the existing short text clustering methods pay more attention to the global information, and lead to wrong classification for samples with ambiguous clusters. Therefore, we propose graph-based short text clustering via contrastive learning with graph embedding (GCCL) - a novel framework that leverages the affinity between samples and neighbors to impose constraints on the low-dimensional representation space. To verify the effectiveness of our method, we evaluate GCCL on short text benchmark datasets. The experimental results show that GCCL outperforms the baseline method in terms of accuracy (ACC) and normalized mutual information (NMI). In addition, our approach achieves impressive results in terms of convergence speed, demonstrating the guidance of graph embeddings for short text clustering tasks.
Surface Target Saliency Detection in Complex Environments

Water surface images are susceptible to interference from complex environments, resulting in low contrast of the acquired images and increased noise interference, which seriously affects the accuracy of extracting saliency regions. To address the above problems, a pyramidal feature fusion network for water surface target saliency detection method is proposed. First, a perceptual field enhancement module is designed to enrich the initial feature information extracted from the backbone network; subsequently, the adjacent features outputted in the previous step are fused layer by layer in a pyramidal manner by improving the cross-feature module; finally, the model is supervised and trained with pixel location-aware loss to output the water surface target saliency map. The experimental results show that the comprehensive performance of the method proposed in this paper is superior, and the S-measure, E-measure and F-measure metrics are improved by 1.3%, 2.3% and 2.6%, respectively, and the MAE is reduced by 16.7% compared with the F3Net method.
Multi-student Collaborative Self-supervised Distillation

Knowledge distillation is a widely used technique for transferring knowledge from a teacher network to a student network. We leverage the spatial distribution properties of distinct model output values and the similarities of homologous targets, explore the decay and update mechanisms of multi-domain weights, and propose a multi-student collaborative self-supervised distillation approach to achieve a self-improvement strategy for multi-domain collaborative models. The distillation process comprises two parts: Distillation learning from teachers to students, where distillation loss is computed for the logical values divided into targets and non-targets. And mutual learning between students, where KL-Divergence (Kullback Leibler Divergence) is employed as the mutual learning loss to explore additional convergence space. We conduct target classification and text detection experiments on CIFAR100 and ICDAR2015. The results demonstrate that our method can effectively reduce the number of parameters and computation required for the model after distillation while maintaining only a slight decrease in model accuracy. Moreover, multi-student inference at the same scale requires fewer resources than single-student inference for higher accuracy. In the distillation comparison of classification models using ResNet and VGG architectures, our method achieved an average improvement of 0.4%, while multi-student collaborative inference led to an average improvement of 1.4%. In the text detection distillation experiments, our method outperformed the DKD distillation methods with a 5.12% improvement in F1 scores.
Attributed Multi-relational Graph Embedding Based on GCN

Learning low-dimensional representations of graphs is crucial for network analysis and various downstream tasks. Existing methods typically assume that only single type of relation between nodes, but, graphs often contain multiple relationship types. While recent researches have attempted to consider the multiple relations in graph, they ignore the problem of topological redundancy. This paper proposes a simple yet effective method called Attributed Multi-relational Graph embedding based on GCN (AMGGCN), which aims to solve the problem of topological redundancy and information aggregation in each relation. Specifically, AMGGCN enhances the effectiveness of node embedding by differential operations, and can learn the weights of different relations adaptively by fusing information in multiple relations using an attention mechanism. The effectiveness of AMGGCN is evaluated on two downstream tasks, unsupervised clustering and supervised classification. The experimental results show that our approach achieves state-of-the-art performance.
Collaborative Encoder for Accurate Inversion of Real Face Image

In recent times, there has been notable progress in the effectiveness of Generative Adversarial Networks (GANs) for synthesizing images. Consequently, numerous studies have started utilizing GANs for image editing purposes. To enable editing of real images, it is crucial to embed a real image into the latent space of GANs. This involves obtaining the latent code of the real image and subsequently modifying the image by altering the latent code. However, accurately reconstructing the real image using the obtained latent code remains a challenge. This paper introduces a novel inversion scheme that achieves high accuracy. In contrast to conventional approaches that employ a single encoder for image inversion, our method utilizes collaborative encoders to accomplish the inversion task. Specifically, two encoders are employed to invert distinct regions in the image, namely the face and background regions. By distributing the inversion task between these encoders, the burden on a single encoder is reduced. Furthermore, to optimize efficiency, this paper adopts a lightweight network structure, resulting in faster inference speed. Experimental results demonstrate that our proposed method significantly enhances visual quality and improves the speed of inference. By leveraging collaborative encoders and a lightweight network structure, we achieve notable improvements in image inversion, thus enabling more effective image editing capabilities.