Reviewerly/exHarmony huggingface dataset:
Quantum computing enhanced distance-minimizing data-driven computational mechanics

Authors:
raw_author_name: Jie Yang
raw_affiliation_string: School of Civil Engineering, Wuhan University, 8 South Road of East Lake, Wuchang, 430072 Wuhan, PR China
raw_author_name: Wei Huang
raw_affiliation_string: School of Civil Engineering, Wuhan University, 8 South Road of East Lake, Wuchang, 430072 Wuhan, PR China

Abstract: The distance-minimizing data-driven computational mechanics has great potential in engineering applications by eliminating material modeling error and uncertainty. In this computational framework, the solution-seeking procedure relies on minimizing the distance between the constitutive database and the conservation law. However, the distance calculation is time-consuming and often takes up most of the computational time in the case of a huge database. In this paper, we show how to use quantum computing to enhance data-driven computational mechanics by exponentially reducing the computational complexity of distance calculation. The proposed method is not only validated on the quantum computer simulator Qiskit, but also on the real quantum computer from OriginQ. We believe that this work represents a promising step towards integrating quantum computing into data-driven computational mechanics.
Hydra: Hybrid-model federated learning for human activity recognition on heterogeneous devices

Authors:
raw_author_name: Tao Ouyang
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Qiong Wu
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Qianyi Huang
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Jie Gong
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
raw_author_name: Xu Chen
raw_affiliation_string: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

Abstract: Federated Learning (FL) has recently received extensive attention in enabling privacy-preserving edge AI services for Human Activity Recognition (HAR). However, users’ mobile and wearable devices in the HAR scenario usually possess dramatically different computing capability and diverse data distributions, making it very challenging for such heterogeneous HAR devices to conduct effective collaborative training (co-training) with the traditional FL schemes. To address this issue, we present Hydra, a Hybrid-model federated learning mechanism that facilitates the co-training among heterogeneous devices by allowing them to train models that well fit their own computing capability. Specifically, Hydra leverages BranchyNet to design a large-small global hybrid-model and enables heterogeneous devices to train the proper parts of the model tailored to their computing capability. Hydra drives co-training among the devices and clusters them based on model similarity to mitigate the impact of HAR data heterogeneity on model accuracy. In order to deal with the issue that large model may lack sufficient training data due to the limited number of high-performance devices in FL, we introduce a pairing scheme between high and low performance devices for effective co-training, and further propose sample selection approach to select more valuable samples to participate in co-training. We then formulate a constrained co-training problem within a cluster that is proved to be NP-hard and devise a fast greedy-based heuristic algorithm to solve it. In addition, to address the low accuracy of small models, we also propose a Large-to-Small knowledge distillation algorithm for resource-constrained devices to optimize the efficiency of transferring knowledge from large models to small models. We conduct extensive experiments on three HAR datasets and the experimental results demonstrate the superior performance of Hydra for achieving outstanding model accuracy improvement compared with other state-of-the-art schemes.
Robust GEPSVM classifier: An efficient iterative optimization framework

Authors:
raw_author_name: He Yan
raw_affiliation_string: College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210037, PR China; School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, PR China
raw_author_name: Yan Liu
raw_affiliation_string: Department of Computer Science, Yangzhou University, Yangzhou, 225100, PR China
raw_author_name: Yanmeng Li
raw_affiliation_string: School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, PR China
raw_author_name: Qiaolin Ye
raw_affiliation_string: College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210037, PR China
raw_author_name: Dong-Jun Yu
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, PR China
raw_author_name: Yong Qi
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094, PR China

Abstract: The proximal support vector machine via generalized eigenvalues (GEPSVM) is a well-known pattern classification method. GEPSVM, however, is prone to outliers due to its use of the squared L2-norm distance criterion. A robust GEPSVM version is proposed to tackle this problem using L1-norm distance optimization technique (GEPSVML1). As optimizing a GEPSVML1 with L1-norm terms can be challenging, we have developed an iterative algorithm to address the L1-norm ratio problem associated with GEPSVML1. Furthermore, an efficient iterative optimization framework has been developed to conveniently address related optimization problems. The research contribution of this paper lies in providing a theoretical analysis of the algorithm’s convergence. Besides, we find that the L1-norm distance-based methods in real-world applications, especially for handling samples with outliers, sometimes provides an unsatisfactory recognition result. Thus, a generalized version of GEPSVML1 is proposed. The L1-norm distance is replaced with a Lp-norm distance in GEPSVML1 (GEPSVMLp). It is the robust counterpart of GEPSVML1 and GEPSVM. It is worth noting that we fine-tune GEPSVMLp’s parameters to strike a balance between training time and classification accuracy, an especially crucial step for larger datasets. Our experiments indicate that the proposed GEPSVMLp is more efficient and robust than the competitors in numerous experimental settings. Overall, our work demonstrates the importance of developing robust pattern classification methods in the presence of outliers and provides a practical solution for handling such cases in real-world applications.
Advancing financial fraud detection: Self-attention generative adversarial networks for precise and effective identification

Authors:
raw_author_name: Chuanjun Zhao
raw_affiliation_string: School of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, China; Shanxi Key Laboratory of Economic Big Data, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, China
raw_author_name: Xuzhuang Sun
raw_affiliation_string: School of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, China
raw_author_name: Lu Kang
raw_affiliation_string: School of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, China

Abstract: The burgeoning prominence of electronic payment methods has significantly intensified the importance of financial fraud detection, especially concerning credit card fraud. The challenges encompass data imbalance, overfitting, and a limited capacity to realistically emulate the complexity and dynamics of fraudulent activities. This paper introduces self-attention generative adversarial networks (SAGANs) for the detection of credit card fraud. Leveraging self-attention mechanisms, SAGANs can distinguish salient features and patterns within extensive transaction datasets, thereby fostering a more profound understanding and refined identification of credit card fraud. By incorporating generative adversarial networks (GANs), our model is capable of generating data that mirrors actual fraudulent behavior, which consequently enhances and optimizes fraud detection algorithms. Empirical findings from numerous credit card transaction datasets underscore the considerable superiority of our approach in terms of accuracy and recall. Our proposed SAGANs more competently emulate the complexity and diversity inherent in fraudulent behavior, thereby augmenting both the precision and efficacy of fraud detection.
DPAFD-net: A dual-path adaptive fusion dehazing network

Authors:
raw_author_name: Chenyang Zhang
raw_affiliation_string: College of Robotics, Beijing Union University, No.4 Gongti North Road, Chaoyang District, Beijing 10002, PR China
raw_author_name: Hongyuan Jing
raw_affiliation_string: Beijing Key Laboratory of Information Service Engineering, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing 100101, PR China
raw_author_name: Shuang Wei
raw_affiliation_string: College of Robotics, Beijing Union University, No.4 Gongti North Road, Chaoyang District, Beijing 10002, PR China
raw_author_name: Jiaxing Chen
raw_affiliation_string: College of Robotics, Beijing Union University, No.4 Gongti North Road, Chaoyang District, Beijing 10002, PR China
raw_author_name: Xinna Shang
raw_affiliation_string: Multi-Agent System Research Centre, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing, 100101, PR China
raw_author_name: Aidong Chen
raw_affiliation_string: Multi-Agent System Research Centre, Beijing Union University, No.97 Beisihuan East Road, Chao Yang District, Beijing, 100101, PR China

Abstract: Image dehazing is an ill-posed problem that has been extensively studied in recent years. Unfortunately, most existing deep dehazing models have high computational complexity and lack the dynamic adjustment of details, which hinders their application to high-resolution images in computational vision tasks. In this paper, we propose an efficient dual-path adaptive fusion dehazing network (DPAFD-Net) to directly restore a clear image from a hazy input. Moreover, we propose a pure subnetwork with encoder and decoder structures to further extract the structural information and progressively restore the haze-free image. To evaluate the effectiveness of the proposed method, we validate our approach on synthetic and real hazy images, where our method performs favourably against the state-of-the-art dehazing approaches.
Ensemble strategy using particle swarm optimisation variant and enhanced local search capability

Authors:
raw_author_name: Libin Hong
raw_affiliation_string: School of Information Science and Technology, Hangzhou Normal University, No. 2318 Yuhangtang Road, Hangzhou 31121, PR China
raw_author_name: Guodong Wang
raw_affiliation_string: School of Information Science and Technology, Hangzhou Normal University, No. 2318 Yuhangtang Road, Hangzhou 31121, PR China
raw_author_name: Ender Özcan
raw_affiliation_string: School of Computer Science, University of Nottingham, Wollaton Road, Nottingham NG8 1BB, United Kingdom
raw_author_name: John Woodward
raw_affiliation_string: Department of Computer Science, Loughborough University, Epinal Way, Loughborough LE11 3TU, United Kingdom

Abstract: Particle swarm optimisation is a population-based algorithm for evolutionary computation. A notable recent research direction has been to combine different effective mechanisms to enhance both exploration and exploitation capabilities while employing suitable mechanisms at appropriate instances in the evolutionary process. This study entailed the development of an ensemble strategy that uses a variant of the modified particle swarm optimisation algorithm with a covariance matrix adapted to the retreat phase and sequential quadratic programming. The modified particle swarm optimisation algorithm employs nonlinear population size reduction and uses the candidate elite best solution as the stochastic learning strategy and the fitness-distance balance as the terminal updating mechanism. The proposed algorithm was compared with the most recently proposed particle swarm optimisation-based variants through testing on CEC2017 benchmark functions. In the experimental results, the proposed method achieved the best ranking and exhibited excellent performance. Further effectiveness tests demonstrated that the proposed combination of algorithms and mechanisms exhibits tacit cooperation and significantly improves performance.
A control theoretic framework for adaptive gradient optimizers

Authors:
raw_author_name: Kushal Chakrabarti
raw_affiliation_string: Data & Decision Sciences, Tata Consultancy Services Research, Mumbai 400607, India
raw_author_name: Nikhil Chopra
raw_affiliation_string: University of Maryland, College Park, MD 20742, USA

Abstract: Gradient-based optimization and control frameworks have been utilized in several applications. The learning rate parameter is typically chosen following a schedule or using methods such as line search to enhance the convergence rate. Recently, the machine learning community has developed methodologies for automated tuning of the learning rate, known as adaptive gradient methods. This paper develops a control theory-inspired framework for modeling adaptive gradient methods that solve non-convex optimization problems. We first model the adaptive gradient methods in a state–space framework, which allows us to present simpler convergence proofs of prominent adaptive optimizers, such as AdaGrad, Adam, and AdaBelief. The proposed framework is constructive because it allows synthesizing new adaptive optimizers. To illustrate this fact, we then utilize the transfer function paradigm from classical control to propose a new variant of Adam, coined AdamSSM, and prove its convergence. We add an appropriate pole-zero pair in the transfer function from squared gradients to the second moment estimate. Applications on benchmark machine learning tasks of image classification using CNN architectures and language modeling using LSTM architecture demonstrate that the AdamSSM algorithm improves the gap between generalization accuracy and faster convergence than the recent adaptive gradient methods.
Addressing posterior collapse by splitting decoders in variational recurrent autoencoders

Authors:
raw_author_name: Jianyong Sun
raw_affiliation_string: School of Mathematics and Statistics, Xi’an Jiaotong University, 710049, Xi’an, China
raw_author_name: Qiaohong Li
raw_affiliation_string: School of Mathematics and Statistics, Xi’an Jiaotong University, 710049, Xi’an, China

Abstract: Variational recurrent autoencoder model (VRAE) is an appealing technique for capturing the variabilities underlying complex sequential data, which is realized by introducing high-level latent random variables as hidden states. Existing models suffer from the well-known ‘posterior collapse’ problem, meaning that a powerful autoregressive decoder equipped in the model itself could capture all the variabilities and hence leave the latent variables learning nothing from the data. From the perspective of model training, the posterior collapse problem can result in a very low Kullback–Leibler divergence (KL-divergence) value, which means the posteriors of the latent variables tend to be just the priors. In this paper, we address this problem by proposing a Bayesian variational recurrent neural network (BVRNN) model, in which two additional decoders are added into the original VRAE. These extra decoders can force the latent variables to learn meaningful knowledge during the training process. We conduct experiments on MNIST and Fashion-MNIST dataset. The experimental results show that the proposed model outperforms several baseline models. We further adapt the proposed model to a very challenging task in natural language processing, namely Named-Entity Recognition (NER). Experimental results show that our model is competitive to the state-of-the-art models on NER.
Approximate DBSCAN on obfuscated data

Authors:
raw_author_name: Payel Sadhukhan
raw_affiliation_string: TCG CREST, Kolkata, India
raw_author_name: Sarbani Palit
raw_affiliation_string: Indian Statistical Institute, Kolkata, India

Abstract: With the emergence of remote storage, computation facilities, and the availability of high-speed data connectivity — cloud computation has become the call for the day. In this scenario, security and computability of data have emerged as two crucial aspects which often conflict with each other. An efficient solution is to build a trade-off between the two. In this work, we propose a novel Hilbert-Curve-based data encoding scheme (HC encoding) which obfuscates the data points. We use the obfuscated data in DBSCAN clustering and obtain an approximated output with respect to the original clustering (without invoking the plaintext data at any stages of the computation). HC encoding has a provision to modulate the degree of obfuscation. The correctness of the clustering performance decreases with the increase in obfuscation and vice versa. The empirical study is carried out on 8 regular datasets and a large, color image dataset, CIFAR10. Two key applications are explored– (i) clustering of homogeneous images and (ii) clustering of heterogeneous images originating from two different sources. The empirical results show the effectiveness of the HC encoding to provide approximate clustering performances on the obfuscated multimedia data as well as regular datasets. The outcomes also manifest HC encoding’s superiority against the comparing methods which operate under a similar fully private constraint.
Filtered data augmentation approach based on model competence evaluation

Authors:
raw_author_name: Zhiqiang Yu
raw_affiliation_string: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, China
raw_author_name: Hongfei Zhang
raw_affiliation_string: Information Technology Department, Shaanxi Police College, 199 Qiyuan Second Road, Weiyang District, Xi'an, Shaanxi 710021, China

Abstract: The scale of parallel corpus plays an important role in training high-quality neural machine translation models. In order to expand the scale of parallel corpus in low-resource scenarios, researchers have proposed a series of data augmentation approaches, in which the most representative work is the back-translation. The back-translation approach uses a basic translation model to translate the target monolingual corpus into the source language, and then combines it into a pseudo parallel corpus to expand the training data. Due to the simple and efficient data expansion strategies, the back-translation approach becomes the mainstream data augmentation approach of neural machine translation. However, in low-resource scenarios, only low-precision basic translation models can be used. The pseudo-parallel corpus translated by this model contains low-quality sentence pairs, which inevitably introduces noises and leads to negative impacts to translation procedure. To improve the performance of low-resource neural machine translation, we propose a filtered data augmentation method based on the model competence evaluation, which can effectively improve the quality of training data by using both the filtered back-translation and dynamic evaluation of translation model capabilities. Comparative experiments show that the proposed approach can improve the quality of training data and the performance of low-resource neural machine translation.
Trustworthy adaptive adversarial perturbations in social networks

Authors:
raw_author_name: Jiawei Zhang
raw_affiliation_string: School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
raw_author_name: Jinwei Wang
raw_affiliation_string: School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
raw_author_name: Hao Wang
raw_affiliation_string: Nanjing University of Science and Technology, Nanjing, 210094, China
raw_author_name: Xiangyang Luo
raw_affiliation_string: State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhenzhou, 450001, China
raw_author_name: Bin Ma
raw_affiliation_string: Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology, Jinan, 250353, China

Abstract: Deep neural networks have achieved excellent performance in various research and applications, but they have proven to be susceptible to adversarial examples. Generating adversarial examples can help identify the vulnerability of the deep neural networks and further enhance the robustness and reliability of these models. However, the existing adversarial attacks can hardly achieve the balance between robustness and imperceptibility, which is not trustworthy in social networks. To solve these problems, we propose adaptive adversarial perturbation (AAP) to improve the universal robustness of the adversarial examples while ensuring imperceptibility. To optimize the imperceptibility of the perturbation, we design a noise visibility function (NVF) to reflect the features of the original images based on the human visual system (HVS). By further calculating a coefficient matrix based on the NVF, the perturbation intensity of different pixels can be adjusted dynamically to improve the robustness. The experimental results prove that the proposed method alleviates the trade-off between robustness and imperceptibility, and outperforms existing attack methods in both one-step and iterative ways. Our method makes the adversarial attack more reliable and applicable in social networks.
An interaction model applied to optimize photovoltaic farm location: A case study of China

Authors:
raw_author_name: Xinyu Dong
raw_affiliation_string: School of Public Affairs, Zhejiang University, Hangzhou 310058, China

Abstract: Given the escalating environmental challenges and the looming carbon peaking and carbon neutrality goals, developing renewable energies becomes a global imperative. The pivotal initial stage involves site selection; however, this process has been plagued by the ambiguity of subjective decision-making and the inconsistency of multi-factor decisions. In this regard, this study proposes a new evaluation system which combines Geodetector and Interaction Linear Model to minimize subjective interventions and reveal the mechanisms by which interaction terms influence siting. China is selected to unveil the model's capability through solar PV site selection. The results indicate that compared with the Basic Linear Model, the Interaction Linear Model increased the estimation accuracy by 8%, with some interactive terms showing higher determinant power than single factors. Based on this interaction model, there is a total of 13% of China's land is developable for solar PV plants, consisting of 46% highly suitable areas (581,576 km2), 39% moderately suitable areas (492,615 km2), and 15% marginally suitable areas (198,088 km2). Finally, Xinjiang was identified as the most suitable place in China. Above all, the new siting system proposed here provides a new perspective on renewables' site selection, promoting the high-quality energy transition globally.