Reviewerly/exHarmony huggingface dataset:
Improving rare relation inferring for scene graph generation using bipartite graph network

Scene graph generation (SGG) task is plagued by insufficient and long-tailed training samples. Thus external text knowledge has been introduced into SGG task, which augments the training dataset. However, rare relations or low-frequency events, such as <dog, standing on, surfboard>, are still hard to be discovered since they also rarely appear in external texts. Addressed these issues, this paper proposes a model-agnostic Bipartite Graph Network with Dual-Group Message Passing (DG-BGN). It extends the relation space of each object pair in training dataset based on the similarity of different object pairs, which cooperates external text knowledge and internal visual information. Specifically, the main framework of DG-BGN consists of three parts, that is, bipartite graph construction (BGC), dual-group message propagation (DG-MP), and pseudo label generation (PLG). For BGC, alignment between external texts and visual dataset is performed to mine information from multimodal resources. Taking object pair as the index of the grouping, dual bipartite graph containing intra/inter-group graph is established to represent predicate label probability distribution of object pair instances. Among them, inter-group bipartite graph is built based on the similarity between object pair instance. For DG-MP, efficient message passing is running on both kinds of bipartite graph through Graph Convolution Neural Networks to refine the graphs, in order to let each object pair instance learn from its similar instances. Finally in PLG, pseudo labels, which are used to train SGG models in fully supervised way, are obtained by aggregating these refined graphs. Systematic experiments on Visual Genome dataset and Conceptual Captions show that our method performs better on discovering rare relations.
Quantum sub-phase sensitivity of a Mach–Zehnder interferometer with the superposition of Schrödinger’s cat-like state with vacuum state as an input under product detection scheme

We study the phase sensitivity of a Mach–Zehnder interferometer (MZI) with product detection (PD) scheme by using the superposition of Schrödinger’s cat-like state with the vacuum state (SCVS) and the coherent state as the inputs. We compare the results derived from PD with the parity detection scheme’s results. We found that PD performs better than parity detection and approaches the single parameter quantum Cramér-Rao bound in some cases. Therefore, we expect that SCVS may play as an alternative quantum resource with PD for the improvement in the phase sensitivity of an MZI having potential application in quantum metrology and quantum sensing.
Predicting new hackers’ criminal careers: A group-based trajectory approach

The current study employs group-based trajectory modeling to assess the longitudinal attack patterns of new hackers involved in website defacement. Specifically, we track the activity of 241 emergent hackers for one year following their first verified website defacement. In doing so, we find four distinct criminal trajectories: low threat (29.0%), naturally desisting (26.5%), increasingly prolific (22.3%), and persistent threat (22.1%). Hackers classified as low threats engage in few defacements, whereas persistent threats engage in high-frequency attacks. Those labeled as naturally desisting begin their careers with velocity but become less prolific with time. Conversely, those classified as increasingly prolific engage in more attacks as they advance in their criminal careers. Using a series of regression models, we find that digital artifacts and open-source intelligence are predictive of group involvement. The findings presented in this study contribute to our theoretical understanding of the developmental trajectories of hackers, while providing valuable insights in fostering targeted intervention strategies aimed at effectively mitigating and preventing cyber-attacks.
Quantum simulation for partial differential equations with physical boundary or interface conditions

This paper explores the feasibility of quantum simulation for partial differential equations (PDEs) with physical boundary or interface conditions. Semi-discretization of such problems does not necessarily yield Hamiltonian dynamics and even alters the Hamiltonian structure of the dynamics when boundary and interface conditions are included. This seemingly intractable issue can be resolved by using a recently introduced Schrödingerisation method [1], [2] – it converts any linear PDEs and ODEs with non-Hermitian dynamics to a system of Schrödinger equations, via the so-called warped phase transformation that maps the equation into one higher dimension. We implement this method for several typical problems, including the linear convection equation with inflow boundary conditions and the heat equation with Dirichlet and Neumann boundary conditions. For interface problems we study the (parabolic) Stefan problem, linear convection, and linear Liouville equations with discontinuous and even measure-valued coefficients. We perform numerical experiments to demonstrate the validity of this approach, which helps to bridge the gap between available quantum algorithms and computational models for classical and quantum dynamics with boundary and interface conditions.
An executable domain-specific modeling language for simulating organizational auction-based coordination strategies for crisis response

Emergency response environments in which the crisis occurs include many homogenous and heterogeneous entities, e.g., organizations and persons. These entities need to collaborate to address a crisis. However, most of the time, the organizations that are involved in a crisis suffer from communication and coordination problems. This may cause unfair resource distribution. To mitigate the problem, there is a pressing need to construct models of emergency response environments and simulate the coordination strategies proactively, before the onset of any crisis. This paper highlights the complexity of the system formed by interacting organizations during a crisis and the difficulty in programming for the simulation of such complex systems. To cope with this issue, this paper presents an executable domain-specific modeling language called CoorERE. The language facilitates modeling emergency response environments for the simulation of one of the most common coordination strategies, i.e., auction-based coordination strategies in crisis response. CoorERE is used for the graphical modeling of emergency response environments and simulation of both inter and intra-organizational auction-based coordination strategies. The paper presents two case studies with different complexities to show the applicability of CoorERE. An empirical study is conducted to compare CoorERE with MDD4ABMS, which is one of the most recent tools for modeling and simulation of multi-agent systems. The evaluation includes both objective and subjective assessments. The objective assessment considers the development effort required for modeling a complete case study, while the subjective assessment compares the tools in terms of satisfying modeling method requirements and quality metrics such as usability, productivity, and expressiveness. The results of the evaluation indicate that CoorERE reduces the development effort by about 47%. In terms of quality metrics, CoorERE obtained higher scores than MDD4ABMS for all the measurements considered, indicating its superior usability, productivity, and expressiveness.
A comprehensive knowledge map for AI improving security management of cyber-physical system enabled smart manufacturing

The enhancement of security management for smart manufacturing has garnered considerable attention, prompting extensive research efforts that explore AI-based secure Cyber-Physical System (CPS) methods. While previous studies have made valuable contributions to this field, the current state of literature reviews still requires a comprehensive examination to construct the knowledge map for future research. This study employed scientometric analyses, including collaboration network analysis, co-occurrence network analysis, and co-citation network analysis, to examine 1133 previous articles conducted between 2015 and 2023 concerning the research topic of AI application in secure CPS-enabled smart manufacturing. The findings reveal that the academic communities in this field comprise influential authors affiliated with leading academic institutions in China, India, and the USA. The key research topics of AI application in secure CPS-enabled smart manufacturing show evolutionary trends during different research periods. Existing research on AI application in secure CPS-enabled smart manufacturing is supported by classical references, which can be divided into four significant co-citation clusters, including the theory of security and privacy in CPS, DL-based IDS, DDoS attacks on CPS by botnet, and FL method for privacy-preserved data sharing. Finally, this study predicted four potential future research directions for the research field of AI application in secure CPS-enabled smart manufacturing: the well-structured and representative dataset for model training, methods for resisting PUF attacks against ML techniques, DL-based methods for detecting and resisting botnets, and lightweight FL-based methods for privacy-preserved data sharing. The findings of this study can guide researchers in this field for future collaboration and work.
SABDTM: Security-first architecture-based dynamic trusted measurement scheme for operating system of the virtual computing node

In the cloud environment, the virtual computing node has become the dominant form of cloud services used by users. Hence, it is increasingly critical to guarantee the trusted operation of the virtual computing node's operating system (VCNOS). However, previous schemes suffer a lot, such as insufficient consideration of the comprehensive of the measured objects, ignoring the dynamic of trusted, and the trusted measurement mechanisms rarely consider their security. Thus, a three-dimensional dynamic trusted measurement model SABDTM, which integrates the integrity measurement of kernel static data, trusted evaluation of operating system behavior (OSB), and the feedback trust of interacting nodes, is proposed. First, SABDTM divided OSB into multiple atomic behaviors and introduced the Bayesian decision theory to predict trusted expectations of OSBs. Second, the feedback trust of interacting nodes is considered to improve the comprehensiveness of the trusted measurement and evaluate its value based on the Euclidean distance function to reduce the impact of inaccurate feedback from malicious nodes. Subsequently, we set the appropriate weight for trusted measurement values of different moments based on Induced Ordered Weighted Averaging to accurately portray the actual state of VCNOS. Moreover, we designed a lightweight and independent subsystem to perform the trusted measurement, which guarantees the security of the measurement service. The security of our model is proved rigorously based on the non-interference theory. Finally, the experiments and comparative analysis demonstrated our model has better functionality and superiority.
Quantum computing enhanced distance-minimizing data-driven computational mechanics

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

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

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

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

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

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

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

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

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.