Quantum two-block group algebra codes

Abstract: We consider quantum two-block group algebra (2BGA) codes, a family of smallest lifted-product (LP) codes. These codes are related to generalized-bicycle codes, except a cyclic group is replaced with an arbitrary finite group, generally non-Abelian. As special cases, 2BGA codes include a subset of square-matrix LP codes over Abelian groups, including quasicyclic codes, and all square-matrix hypergraph-product codes constructed from a pair of classical group codes. We establish criteria for permutation equivalence of 2BGA codes and give bounds for their parameters, both explicit and in relation to other quantum and classical codes. We also enumerate the optimal parameters of all inequivalent binary connected 2BGA codes with stabilizer generator weights $W\ensuremath{\le}8$, of length $n\ensuremath{\le}100$ for Abelian groups, and $n\ensuremath{\le}200$ for non-Abelian groups.


Pyrite geochemistry reveals the key controlling factors of large gold deposit formation in Jiaodong Peninsula: A comparative study

Abstract: With an estimated gold reserve of more than 5000 t, the Jiaodong Peninsula is incontestably the largest gold ore belt in China. Dozens of gold deposits developed in the area with variable deposit types, grades, and reserves, and the key controlling factors for these differences are not clear. In this study, the representative Jiuqu (JQ), Canzhuang (CZ), and Sanshandao Northern Sea (SNS) gold deposits located in the NW portion of the Jiaodong district have been comparatively investigated using pyrite geochemistry, to constrain the mineralization sources and grade differences of each deposit. The pyrite from ore samples in JQ, CZ, and SNS deposits display euhedral to sub-euhedral geometries and do not show obvious zonation. The SNS pyrites present higher values for most trace elements (e.g., Cu, As, Ni) than the JQ and CZ pyrites. Moreover, the SNS deposit has the highest δ34S value (+11.70 ‰), followed by the CZ deposit (+10.43 ‰) and the JQ deposit (+7.67 ‰). This is positively correlated to their ore grade in order. The presence of relatively high contents of Cu, As, Ni as well as δ34S values in the SNS deposit pyrites may suggest that more mantle materials have been contributed to the gold ores. It can be argued that these high values are associated with high oxygen fugacity in the subducting and stagnant slabs related to the lithospheric mantle metasomatism, which is responsible for the δ34S enrichment and formation of Au and S reservoirs, serving as the key controlling factor for the formation of large gold deposits in Jiaodong. This study enables us to better describe the evolution of the mineralizing fluids and understand the mechanisms of ore genesis in gold mineralization systems by using pyrite geochemistry.


Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain

Abstract: Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific climatic conditions, but predictions are less accurate on a regional scale. Since radiometric weather stations are relatively dispersed, even in the most developed countries, it is interesting to develop indirect models based on measurements that are common in secondary network stations. This paper develops a monthly global solar radiation model based on a simple neural network structure, using temperature, geographical, and topographical data from 105 meteorological stations, representative of the whole of peninsular Spain. A hierarchical clustering procedure was employed to select the data used to train and validate the model. To avoid functional dependencies between parameters and variables, which hinder the generality of the model, all input and output variables are dimensionless. The estimates fit the 1260 monthly data with RRMSE values of about 6%, which improves results obtained previously, using regression models, and proves that simplicity is compatible with the generality and accuracy of a model, even in large regions with very varied characteristics.


Dissolved oxygen prediction using regularized extreme learning machine with clustering mechanism in a black bass aquaculture pond

Abstract: Dissolved oxygen (DO) is an important indicator of aquaculture water quality. The prediction accuracy of DO content is the key role in managing and controlling aquaculture water quality. However, potential trends of DO under various conditions (such as weather) are always overlooked. This study aims to develop a novel DO forecasting model using the optimized regularized extreme learning machine (RELM) with factor extraction operation and K-medoids clustering strategy in a black bass aquaculture pond. We adopt the leave-one-out cross (LOO) error validation to obtain the optimal regularization parameter of RELM and enhance the forecasting accuracy. We further adjust the activation function to accelerate the RELM. Next, we divide the time series into day and night segments, and construct the clustering mechanism with the K-medoids method to extract the different patterns of data streams under various weather conditions. The experiments on 14 days’ data from a real-world aquaculture pond demonstrate the efficiency and accuracy of our proposed DO prediction model. We believe that our research will facilitate the development of a forecasting tool for warning hypoxia in the near future, which combines intelligent prediction models and real-time data.


Scattering expansion for localization in one dimension

Abstract: We present a perturbative approach to disordered systems in one spatial dimension that accesses the full range of phase disorder and clarifies the connection between localization and phase information. We consider a long chain of identically disordered scatterers and expand in the reflection strength of any individual scatterer. As an example application, we show analytically that in a discrete-time quantum walk, the localization length can depend nonmonotonically on the strength of phase disorder (whereas expanding in weak disorder yields monotonic decrease). More generally, we obtain to all orders in the expansion a particular nonseparable form for the joint probability distribution of the transmission coefficient logarithm and reflection phase. Furthermore, we show that for weak local reflection strength, a version of the scaling theory of localization holds: the joint distribution is determined by just three parameters.


Integrability Breaking and Bound States in Google’s Decorated XXZ Circuits

Abstract: Recent quantum simulation by Google [Nature 612, 240 (2022)] has demonstrated the formation of bound states of interacting photons in a quantum-circuit version of the XXZ spin chain. While such bound states are protected by integrability in a one-dimensional chain, the experiment found the bound states to be unexpectedly robust when integrability was broken by decorating the circuit with additional qubits, at least for small numbers of qubits (≤24) within the experimental capability. Here we scrutinize this result by state-of-the-art classical simulations, which greatly exceed the experimental system sizes and provide a benchmark for future studies in larger circuits. We find that the bound states consisting of a finite number of photons are indeed robust in the nonintegrable regime, even after scaling to the infinite-time and infinite-system size limit. Moreover, we show that such systems possess unusual spectral properties, with level statistics that deviates from the random matrix theory expectation. On the other hand, for low but finite density of photons, we find a much faster onset of thermalization and significantly weaker signatures of bound states, suggesting that anomalous dynamics may only be a property of dilute systems with zero density of photons in the thermodynamic limit. The robustness of the bound states is also influenced by the number of decoration qubits and, to a lesser degree, by the regularity of their spatial arrangement.9 MoreReceived 6 July 2023Accepted 20 December 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010316Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEigenstate thermalizationQuantum circuitsQuantum simulationPhysical SystemsFloquet systemsQuantum spin modelsTechniquesExact diagonalizationMatrix product statesRandom matrix theoryQuantum Information, Science & TechnologyStatistical Physics & Thermodynamics


It's Not a Feature, It's a Bug: Fault-Tolerant Model Mining from Noisy Data

Abstract: The mining of models from data finds widespread use in industry. There exists a variety of model inference methods for perfectly deterministic behaviour, however, in practice, the provided data often contains noise due to faults such as message loss or environmental factors that many of the inference algorithms have problems dealing with. We present a novel model mining approach using Partial Max-SAT solving to infer the best possible automaton from a set of noisy execution traces. This approach enables us to ignore the minimal number of presumably faulty observations to allow the construction of a deterministic automaton. No pre-processing of the data is required. The method's performance as well as a number of considerations for practical use are evaluated, including three industrial use cases, for which we inferred the correct models.


Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?

Abstract: The growth of the decentralized finance (DeFi) ecosystem built on blockchain technology and smart contracts has led to an increased demand for secure and reliable smart contract development. However, attacks targeting smart contracts are increasing, causing an estimated $6.45 billion in financial losses. Researchers have proposed various automated security tools to detect vulnerabilities, but their real-world impact remains uncertain.


Research on Privacy Protection in Federated Learning Combining Distillation Defense and Blockchain

Abstract: Traditional federated learning addresses the data security issues arising from the need to centralize client datasets on a central server for model training. However, this approach still poses privacy protection risks. For instance, central servers cannot verify privacy leaks resulting from poisoning attacks by malicious clients. Additionally, adversarial sample attacks can infer specific samples from the original data by testing the local models on client devices. This paper proposes a federated learning privacy protection method combining distillation defense technology with blockchain architecture. The method utilizes distillation defense technology to reduce the sensitivity of client devices participating in federated learning to perturbations and enhance their ability to resist adversarial sample attacks locally. This not only reduces communication overhead and improves learning efficiency but also enhances the model’s generalization ability. Furthermore, the method leverages the “decentralized” nature of blockchain architecture as a trusted record-keeping mechanism to audit information interactions among clients and shared model parameters. This addresses privacy leakage issues resulting from poisoning attacks by some clients during the model construction process. Simulation experiment results demonstrate that the proposed method, compared with traditional federated learning, ensures model convergence, detects malicious clients, and improves the participation level of highly reputable clients. Moreover, by reducing the sensitivity of local clients to perturbations, it enhances their ability to effectively resist adversarial sample attacks.


Whole Slide Image Understanding in Pathology: What Is the Salient Scale of Analysis?

Abstract: Background: In recent years, there has been increasing research in the applications of Artificial Intelligence in the medical industry. Digital pathology has seen great success in introducing the use of technology in the digitisation and analysis of pathology slides to ease the burden of work on pathologists. Digitised pathology slides, otherwise known as whole slide images, can be analysed by pathologists with the same methods used to analyse traditional glass slides. Methods: The digitisation of pathology slides has also led to the possibility of using these whole slide images to train machine learning models to detect tumours. Patch-based methods are common in the analysis of whole slide images as these images are too large to be processed using normal machine learning methods. However, there is little work exploring the effect that the size of the patches has on the analysis. A patch-based whole slide image analysis method was implemented and then used to evaluate and compare the accuracy of the analysis using patches of different sizes. In addition, two different patch sampling methods are used to test if the optimal patch size is the same for both methods, as well as a downsampling method where whole slide images of low resolution images are used to train an analysis model. Results: It was discovered that the most successful method uses a patch size of 256 × 256 pixels with the informed sampling method, using the location of tumour regions to sample a balanced dataset. Conclusion: Future work on batch-based analysis of whole slide images in pathology should take into account our findings when designing new models.


Learning Output Relevant Features by Joint Autoencoder

Abstract: Classical regression is a supervised task that uses target output to guide the modeling. Generally, the original input contains both output relevant and irrelevant information, whereas the latter may decrease the predictive performance to a certain extent. Without prior information about what information contributes to the prediction, the predictive performance of regression models can be improved based on such output relevant information as input. Thus, a joint autoencoder (JAE) combining the supervised and unsupervised mechanisms is proposed to learn output relevant features from the original input. In this way, both predictive and reconstructive performance are considered to learn the essential characteristics and avoid poor generalized performance on untrained instances. Meanwhile, hierarchy representations can be learned by successive JAEs with a local parameter embedding strategy, which is presented to preserve the predictive performance in this structure. In the experiments, the predictive performance and robustness of the proposal are verified on nine datasets with different sample sizes.


EDSUCh: A robust ensemble data summarization method for effective medical diagnosis

Abstract: Identifying rare patterns for medical diagnosis is a challenging task due to heterogeneity and the volume of data. Data summarization can create a concise version of the original data that can be used for effective diagnosis. In this paper, we propose an ensemble summarization method that combines clustering and sampling to create a summary of the original data to ensure the inclusion of rare patterns. To the best of our knowledge, there has been no such technique available to augment the performance of anomaly detection techniques and simultaneously increase the efficiency of medical diagnosis. The performance of popular anomaly detection algorithms increases significantly in terms of accuracy and computational complexity when the summaries are used. Therefore, the medical diagnosis becomes more effective, and our experimental results reflect that the combination of the proposed summarization scheme and all underlying algorithms used in this paper outperforms the most popular anomaly detection techniques.


SHT-based public auditing protocol with error tolerance in FDL-empowered IoVs

Abstract: With the intelligentization of the Internet of Vehicles (IoVs), Artificial Intelligence (AI) technology is becoming more and more essential, especially deep learning. Federated Deep Learning (FDL) is a novel distributed machine learning technology and is able to address the challenges like data security, privacy risks, and huge communication overheads from big raw data sets. However, FDL can only guarantee data security and privacy among multiple clients during data training. If the data sets stored locally in clients are corrupted, including being tampered with and lost, the training results of the FDL in intelligent IoVs must be negatively affected. In this paper, we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs. Specifically, the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance, including efficient corrupted data locating and recovery. In addition, a novel data structure, Skip Hash Table (SHT) is designed to optimize data dynamics. Finally, we illustrate the security of the scheme with the Computational Diffie-Hellman (CDH) assumption on bilinear groups. Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.


A Recognizable Expression Line Portrait Synthesis Method in Portrait Rendering Robot

Abstract: An artistic line portrait robot can generate, process, and draw line portraits. Compared to real face images, line portraits lose some recognizable information. Maintaining recognizability during the process of expression edition of line portraits is an important challenge for artistic portrait robots. A recognizable expression line portrait synthesis method based on a triangle coordinate system (TCS) is proposed. First, based on public facial expression databases JAFFE, Oulu CASIA, RaFD, and Cohn-Kanade (CK), by studying the feature deviations between different expressions of the same person, an expression deformation constraint criterion (EDCC) that is conducive to maintaining recognizable features is proposed. Then, by comparing features between the source line portrait and reference expression portrait, the expression features are calculated. Finally, under the EDCC, based on expression features, a recognizable expression line portrait is generated through image topological deformation based on TCS. In addition, we can synthesize different degrees of expression line portraits. On the public face datasets (FHHQ, CelebA-HQ, and CK), we implemented qualitative and quantitative contrast experiments. Experimental results demonstrate that this method can automatically synthesize an expression line portrait with reference expression, where the expression degree of the reference expression is controllable, and the generated expression portrait still has high recognizability. The expression samples generated by the proposed method are used for face authentication on the CK dataset, and only 0.22% of the samples fail to pass the authentication.


Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection

Abstract: Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we propose HateMAML, a model-agnostic meta-learning (MAML)-based framework that effectively performs hate speech detection in low-resource languages. HateMAML utilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show that HateMAML outperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics of HateMAML.


Underwater Color Disparities: Cues for Enhancing Underwater Images Toward Natural Color Consistencies

Abstract: We observe that a natural image tends to exhibit similar histograms for color channels in the RGB color space and consistent statistical estimates for color channels in the Lab color space. We refer to these observations as natural color consistencies. In contrast, we discover that an underwater image does not always follow the natural color consistencies. Different color channels in an underwater image tend to give rise to very different distributions, regardless of whether the channels are in the RGB color space or Lab color space. We refer to these observations as underwater color disparities. To enhance an underwater image to make it appear more natural, it is necessary to correct its underwater color disparities to align with the natural color consistencies. To this end, we develop an adaptive attenuated channel compensation method based on optimal channel precorrection and a salient absorption map-guided fusion method for eliminating the color deviation in the RGB color space. We then develop a method to enhance the contrast of channel L and an adaptive color distribution specification method for improving the contrast and matching the color distribution in the Lab color space. Additionally, we develop an edge-enhanced mask fusion method for correcting blurry details. Our method is not a deep learning method but can effectively be applied to a single underwater image. The qualitative and quantitative empirical results validate that our method outperforms state-of-the-art underwater image enhancement methods. We release the reproducible code at https://gitee.com/wanghaoupc/Underwater Color Disparities for public evaluation.


Secure Federated Learning With Fully Homomorphic Encryption for IoT Communications

Abstract: The emergence of the Internet of Things (IoT) has revolutionized people’s daily lives, providing superior quality services in cognitive cities, healthcare, and smart buildings. However, smart buildings use heterogeneous networks. The massive number of interconnected IoT devices increases the possibility of IoT attacks, emphasizing the necessity of secure and privacy-preserving solutions. Federated Learning (FL) has recently emerged as a promising Machine Learning (ML) paradigm for IoT networks to address these concerns. In FL, multiple devices collaborate to learn a global model without sharing their raw data. However, FL still faces privacy and security concerns due to the transmission of sensitive data (i.e., model parameters) over insecure communication channels. These concerns can be addressed using Fully Homomorphic Encryption (FHE), a powerful cryptographic technique that enables computations on encrypted data without requiring them to be decrypted first. In this study, we propose a secure FL approach in IoT-enabled smart cities that combines FHE and FL to provide secure data and maintain privacy in distributed environments. We present four different FL-based FHE approaches in which data are encrypted and transmitted over a secure medium. The proposed approaches achieved high accuracy, recall, precision, and F-scores, in addition to providing strong privacy and security safeguards. Furthermore, the proposed approaches effectively reduced communication overhead and latency compared to the baseline approach. These approaches yielded improvements ranging from 80.15% to 89.98% in minimizing communication overhead. Additionally, one of the approaches achieved a remarkable latency reduction of 70.38%. The implementation of these security models is non-trivial, and the code is publicly available at https://github.com/Artifitialleap-MBZUAI/Secure-Federated-Learning-with-Fully-Homomorphic-Encryption-for-IoT-Communications.


Newton-Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems

Abstract: The Newton-Raphson-Based Optimizer (NRBO), a new metaheuristic algorithm, is suggested and developed in this paper. The NRBO is inspired by Newton-Raphson's approach, and it explores the entire search process using two rules: the Newton-Raphson Search Rule (NRSR) and the Trap Avoidance Operator (TAO) and a few groups of matrices to explore the best results further. The NRSR uses a Newton-Raphson method to improve the exploration ability of NRBO and increase the convergence rate to reach improved search space positions. The TAO helps the NRBO to avoid the local optima trap. The performance of NRBO was assessed using 64 benchmark numerical functions, including 23 standard benchmarks, 29 CEC2017 constrained benchmarks, and 12 CEC2022 benchmarks. In addition, the NRBO was employed to optimize 12 CEC2020 real-world constrained engineering optimization problems. The proposed NRBO was compared to seven state-of-the-art optimization algorithms, and the findings showed that the NRBO produced promising results due to its features, such as high exploration and exploitation balance, high convergence rate, and effective avoidance of local optima capabilities. In addition, the NRBO also validated on challenging wireless communication problem called the internet of vehicle problem, and the NRBO was able to find the optimal path for data transmission. Also, the performance of NRBO in training the deep reinforcement learning agents is also studied by considering the mountain car problem. The obtained results also proved the NRBO's excellent performance in handling challenging real-world engineering problems.


Efficient Quantum Image Classification Using Single Qubit Encoding

Abstract: The domain of image classification has been seen to be dominated by high-performing deep-learning (DL) architectures. However, the success of this field, as seen over the past decade, has resulted in the complexity of modern methodologies scaling exponentially, commonly requiring millions of parameters. Quantum computing (QC) is an active area of research aimed toward greatly reducing problems of complexity faced in classical computing. With growing interest toward quantum machine learning (QML) for applications of image classification, many proposed algorithms require usage of numerous qubits. In the noisy intermediate-scale quantum (NISQ) era, these circuits may not always be feasible to execute effectively; therefore, we should aim to use each qubit as effectively and efficiently as possible, before adding additional qubits. This article proposes a new single-qubit-based deep quantum neural network for image classification that mimics traditional convolutional neural network (CNN) techniques, resulting in a reduced number of parameters compared with previous works. Our aim is to prove the concept of the initial proposal by demonstrating classification performance of the single-qubit-based architecture, as well as to provide a tested foundation for further development. To demonstrate this, our experiments were conducted using various datasets including MNIST, Fashion-MNIST, and ORL face datasets. To further our proposal in the context of the NISQ era, our experiments were intentionally conducted in noisy simulation environments. Initial test results appear promising, with classification accuracies of 94.6%, 89.5%, and 82.5% achieved on the subsets of MNIST, FMNIST, and ORL face datasets, respectively. In addition, proposals for further investigation and development were considered, where it is hoped that these initial results can be improved.


Potential sources of sensor data anomalies for autonomous vehicles: An overview from road vehicle safety perspective

Abstract: Outstanding steps towards intelligent transportation systems with autonomous vehicles have been taken in the past few years. Nevertheless, the safety issue in autonomous vehicles is critical and remains to be fully solved. Sensor data provide information about the internal status of the system and the impact of its external environment, where the occurrence of sensor data anomalies indicates the existence of potential safety risks. Therefore, in this work, a taxonomy for potential sensor data anomaly sources from the perspective of road vehicle safety is proposed, motivated by the lack of a unified comprehensive taxonomy of sensor data anomaly identification for autonomous vehicles. In this context, sources are divided into; 1) fault or failure of the components or subsystems; 2) failure of the adaptability to the external environment; 3) cyber-attacks; and 4) faults or design deficiencies of sensors. Based on the taxonomy proposed, related works, and in particular, countermeasures for the four potential sources of sensor data anomalies in autonomous vehicles are then reviewed. In the context of providing a comprehensive discussion, other taxonomies of potential sources causing sensor data anomalies for autonomous vehicles and the issue of interpretability of sensor data anomalies are also discussed, providing insight into the strengths of the proposed taxonomy.


RoFormer: Enhanced transformer with Rotary Position Embedding

Abstract: Position encoding has recently been shown to be effective in transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding (RoPE) to effectively leverage the positional information. Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the explicit relative position dependency in the self-attention formulation. Notably, RoPE enables valuable properties, including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and the capability of equipping linear self-attention with relative position encoding. Finally, we evaluate the enhanced transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark datasets. Our experiments show that it consistently overcomes its alternatives. Furthermore, we provide a theoretical analysis to explain some experimental results. RoFormer is already integrated into Huggingface: https://huggingface.co/docs/transformers/model_doc/roformer.


Light Field Depth Estimation for Non-Lambertian Objects via Adaptive Cross Operator

Abstract: Light field (LF) depth estimation is a crucial basis for LF-related applications. Most existing methods are based on the Lambertian assumption and cannot deal with non-Lambertian surfaces represented by transparent objects and mirrors. In this paper, we propose a novel Adaptive-Cross-Operator-based(ACO) depth estimation algorithm for non-Lambertian LF. By analyzing the imaging characteristics of non-Lambertian regions, it is found that the difficulty of depth estimation lies in the photo inconsistency of the center view. Combining with the two-branch structure, we propose ACO with an inter-branch cooperation strategy to adaptively separate depth information with different reflectance coefficients. We discover that the bimodal distribution feature of the operator filtering results can assist in the separation of multi-layer scene information. The first detection branch filters the EPI and implicitly records the severity of multi-layer scene aliasing. According to the identification of bimodal distribution features, the non-Lambertian regions are marked out and the depth of the foreground is estimated. The second branch receives guidance from the first to dynamically adjust the inner weight and infer the background’s depth after weakening the interference from the foreground. Finally, the depth information separation of multi-layer scenes is achieved by extracting the unique X-shaped linear structure. Without the reflection coefficients of the non-Lambertian object, the proposed method can produce high-quality depth estimation under the transparency of 90% to 20%. Experimental results show that the proposed ACO outperforms state-of-the-art LF depth estimation methods in terms of accuracy and robustness.


Tailoring Gradient Methods for Differentially Private Distributed Optimization

Abstract: Decentralized optimization is gaining increased traction due to its widespread applications in large-scale machine learning and multi-agent systems. The same mechanism that enables its success, i.e., information sharing among participating agents, however, also leads to the disclosure of individual agents' private information, which is unacceptable when sensitive data are involved. As differential privacy is becoming a de facto standard for privacy preservation, recently results have emerged integrating differential privacy with distributed optimization. However, directly incorporating differential privacy design in existing distributed optimization approaches significantly compromises optimization accuracy. In this paper, we propose to redesign and tailor gradient methods for differentially-private distributed optimization, and propose two differential-privacy oriented gradient methods that can ensure both rigorous <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -differential privacy and optimality. The first algorithm is based on static-consensus based gradient methods, and the second algorithm is based on dynamic-consensus (gradient-tracking) based distributed optimization methods and, hence, is applicable to general directed interaction graph topologies. Both algorithms can simultaneously ensure almost sure convergence to an optimal solution and a finite privacy budget, even when the number of iterations goes to infinity. To our knowledge, this is the first time that both goals are achieved simultaneously. Numerical simulations using a distributed estimation problem and experimental results on a benchmark dataset confirm the effectiveness of the proposed approaches.


Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems

Abstract: The Dung beetle optimization algorithm is a kind of group intelligence optimization algorithm proposed by Jiankai Xue in 2022, which has the characteristics of strong optimization-seeking ability and fast convergence but suffers from the defect of easily falling into local optimum at the late stage of optimization-seeking as other group intelligence optimization algorithms. To address this problem, this paper proposes a dung beetle search algorithm (QHDBO) based on quantum computing and a multi-strategy hybrid. The good point set strategy is used to initialize the initial population of dung beetles . That makes the initial population more evenly distributed, and reduces the likelihood of the algorithm falling into a local optimum solution. The convergence factor and dynamic balance between the number of Spawning and foraging dung beetles is proposed. That allows the algorithm to focus on the global search in the early stages and local exploration in the later stages. The quantum computing based t-distribution variation strategy is used to variate the optimal global solution, that prevents the algorithm from falling into a local optimum. To verify the performance of the QHDBO algorithm, this paper compares QHDBO with six other swarm intelligence algorithms through 37 test functions and practical engineering application problems. The experimental results show that the improved dung beetle optimization algorithm significantly improves convergence speed and optimization accuracy and has good robustness.


SDIGRU: Spatial and Deep Features Integration Using Multilayer Gated Recurrent Unit for Human Activity Recognition

Abstract: Smart video surveillance plays a significant role in public security via storing a huge amount of continual stream data, evaluates them, and generates warns where undesirable human activities are performed. Recognition of human activities in video surveillance has faced many challenges such as optimal evaluation of human activities under growing volumes of streaming data with complex computation and huge time processing complexity. To tackle these challenges we introduce a lightweighted spatial-deep features integration using multilayer GRU (SDIGRU). First, we extract spatial and deep features from frames sequence of realistic human activity videos via utilizing a lightweight MobileNetV2 model and then integrate those spatial-deep features. Although deep features can be used for human activity recognition, they contain only the high-level appearance, which is insufficient to correctly identify the particular activity of human. Thus, we jointly apply deep information with spatial appearance to produce detailed level information. Furthermore, we select rich informative features from spatial-deep appearances. Then, we train multilayer gated recurrent unit (GRU) and feed informative features to learn the temporal dynamics of human activity frames sequence at each time step of GRU. We conduct our experiments on benchmark YouTube11, HMDB51, and UCF101 datasets of human activity recognition. The empirical results show that our method achieved significant recognition performance with low computational complexity and quick response. Finally, we compare the results with existing state-of-the-art techniques, which show the effectiveness of our method.


CVANet: Cascaded visual attention network for single image super-resolution

Abstract: Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction and detail reconstruction capabilities for single image super-resolution (SISR). Nevertheless, most previous DCNN-based methods do not fully utilize the complementary strengths between feature maps, channels, and pixels. Therefore, it hinders the ability of DCNNs to represent abundant features. To tackle the aforementioned issues, we present a Cascaded Visual Attention Network for SISR called CVANet, which simulates the visual attention mechanism of the human eyes to focus on the reconstruction process of details. Specifically, we first designed a trainable feature attention module (FAM) for feature-level attention learning. Afterward, we introduce a channel attention module (CAM) to reinforce feature maps under channel-level attention learning. Meanwhile, we propose a pixel attention module (PAM) that adaptively selects representative features from the previous layers, which are utilized to generate a high-resolution image. Satisfactory, our CVANet can effectively improve the resolution of images by exploring the feature representation capabilities of different modules and the visual perception properties of the human eyes. Extensive experiments with different methods on four benchmarks demonstrate that our CVANet outperforms the state-of-the-art (SOTA) methods in subjective visual perception, PSNR, and SSIM.The code will be made available https://github.com/WilyZhao8/CVANet.


A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

Abstract: The application of artificial intelligence technology in Internet of Vehicles (IoV) has attracted great research interests with the goal of enabling smart transportation and traffic management. Meanwhile, concerns have been raised over the security and privacy of the tons of traffic and vehicle data. In this regard, Federated Learning (FL) with privacy protection features is considered a highly promising solution. However, in the FL process, the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering, which can enable the verifiability of the local models while achieving privacy-preservation. Additionally, we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty. The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.


Segment anything model for medical images?

Abstract: The Segment Anything Model (SAM) is the first foundation model for general image segmentation. It has achieved impressive results on various natural image segmentation tasks. However, medical image segmentation (MIS) is more challenging because of the complex modalities, fine anatomical structures, uncertain and complex object boundaries, and wide-range object scales. To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks. We comprehensively analyzed different models and strategies on the so-called COSMOS 1050K dataset. Our findings mainly include the following: (1) SAM showed remarkable performance in some specific objects but was unstable, imperfect, or even totally failed in other situations. (2) SAM with the large ViT-H showed better overall performance than that with the small ViT-B. (3) SAM performed better with manual hints, especially box, than the Everything mode. (4) SAM could help human annotation with high labeling quality and less time. (5) SAM was sensitive to the randomness in the center point and tight box prompts, and may suffer from a serious performance drop. (6) SAM performed better than interactive methods with one or a few points, but will be outpaced as the number of points increases. (7) SAM's performance correlated to different factors, including boundary complexity, intensity differences, etc. (8) Finetuning the SAM on specific medical tasks could improve its average DICE performance by 4.39% and 6.68% for ViT-B and ViT-H, respectively. Codes and models are available at: https://github.com/yuhoo0302/Segment-Anything-Model-for-Medical-Images. We hope that this comprehensive report can help researchers explore the potential of SAM applications in MIS, and guide how to appropriately use and develop SAM.


Bachelor Degree Program “Software Engineering” in the Higher School of Economics: Background and Perspectives

Abstract: The Higher School of Economics (HSE),


Self-Supervised Pretraining Based on Noise-Free Motion Reconstruction and Semantic-Aware Contrastive Learning for Human Motion Prediction

Abstract: Human motion prediction is to forecast future human motions based on the observed ones. Currently, a prediction model always includes an encoder to extract the features of the observed motions and a decoder for motion prediction with the extracted features. However, existing encoders have limited abilities of anti-interference and semantic attribute learning; thus, the prediction results are not fully satisfactory. To solve this problem, we propose two novel pretext tasks, i.e., noise-free motion reconstruction and semantic-aware contrastive learning, to implement self-supervised pretraining on the encoder. The former extracts the features of a noise-added motion to reconstruct a noise-free motion. This pretext task can improve the ability of anti-interference of the encoder. The latter shortens the distance between two motions with the same motion category while widening the distance between two motions from different motion categories. This pretext task enables the encoder to capture the semantic commonality of the motions with the same motion category, thereby enhancing the encoder's ability of semantic attribute learning. After implementing the self-supervised pretraining, the pretrained encoder is connected with a decoder to build a prediction model called ReLe-GCN, which is fine-tuned for human motion prediction. The results on public datasets show the high accuracies of ReLe-GCN in both short-term and long-term motion predictions.


$\log$-Sigmoid Activation-Based Long Short-Term Memory for Time-Series Data Classification

Abstract: With the enhanced usage of Artificial Intelligence (AI) driven applications, the researchers often face challenges in improving the accuracy of the data classification models, while trading off the complexity. In this paper, we address the classification of time series data using the Long Short-Term Memory (LSTM) network while focusing on the activation functions. While the existing activation functions such as sigmoid and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tanh$</tex-math></inline-formula> are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> -sigmoid, inside the LSTM cell for time series data classification, and analyze its properties. We also present the use of a linear transformation (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log \tanh$</tex-math></inline-formula> ) of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> -sigmoid activation as a replacement of the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\tanh$</tex-math></inline-formula> function in the LSTM cell. Both the cell activation as well as recurrent activation functions inside the LSTM cell are modified with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> -sigmoid activation family while tuning the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\log$</tex-math></inline-formula> bases. Further, we report a comparative performance analysis of the LSTM model using the proposed and the state-of-the-art activation functions on multiple public time-series databases.


A Radial Basis Function-Based Graph Attention Network With Squeeze Loss Optimization for Link Prediction

Abstract: Graph attention networks is a popular method to deal with link prediction tasks, but the weight assigned to each sample is not focusing on the sample's own performance in training. Moreover, since the number of links is much larger than nodes in a graph, mapping functions are usually used to map the learned node features to link features, whose expression of node similarity determines the quality of link feature learning. To tackle the above issues, a new model graph attention networks based on Radial Basis Function (RBF) with squeeze loss is proposed, including two improvements. Firstly, RBF function with extended parameters is used to transform the node features output by attention layer into link features. The result of link feature embedding can be improved by shortening the distance of nodes with links and enlarging the distance of nodes without links in vector space. Secondly, squeeze loss is designed to adjust the loss according to the performance of samples in training and change the proportion of sample loss in the loss function to allocate training resources reasonably. The link prediction task performed on datasets shows that the performance of proposed method is better than baselines.


A progressive framework for rotary motion deblurring

Abstract: The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles. Traditional rotary motion deblurring methods suffer from ringing artifacts and noise, especially for large blur extents. To solve the above problems, we propose a progressive rotary motion deblurring framework consisting of a coarse deblurring stage and a refinement stage. In the first stage, we design an adaptive blur extents factor (BE factor) to balance noise suppression and details reconstruction. And a novel deconvolution model is proposed based on BE factor. In the second stage, a triple-scale deformable module CNN(TDM-CNN) is designed to reduce the ringing artifacts, which can exploit the 2D information of an image and adaptively adjust spatial sampling locations. To establish a standard evaluation benchmark, a real-world rotary motion blur dataset is proposed and released, which includes rotary blurred images and corresponding ground truth images with different blur angles. Experimental results demonstrate that the proposed method outperforms the state-of-the-art models on synthetic and real-world rotary motion blur datasets. The code and dataset are available at https://github.com/Jinhui-Qin/RotaryDeblurring.


A Phishing Account Detection Model via Network Embedding for Ethereum

Abstract: As the first blockchain platform to support smart contracts, Ethereum has gained popularity and breeds various cybercrimes. Many phishing accounts on Ethereum take advantage of the blockchain’s anonymity to participate in illegal acts. To this end, to solve the transaction security problem caused by phishing accounts on Ethereum, this brief proposes a network embedding-based phishing account detection model. Firstly, we crawl the history of transactions from both labeled phishing and non-phishing accounts and use a new method to build these transactions as a transaction network, where each transaction edge is accompanied by additional transaction information for all source nodes of the target node. Then we propose a new random walk-based network embedding algorithm named bias2vec to obtain the embeddings of nodes as the feature inputs. Finally, we classify the accounts into phishing and non-phishing ones by different classifiers such as lightGBM and XGBoost. Experiments show that our proposed phishing account detection model achieves effective detection performance under different classifiers.


Neural Temporal Difference and Q Learning Provably Converge to Global Optima

Abstract: Temporal difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning. However, because of the nonlinearity in value function approximation, such a coupling leads to nonconvexity and even divergence in optimization. As a result, the global convergence of neural TD remains unclear. In this paper, we prove for the first time that neural TD converges at a sublinear rate to the global optimum of the mean-squared projected Bellman error for policy evaluation. In particular, we show how such global convergence is enabled by the overparameterization of neural networks, which also plays a vital role in the empirical success of neural TD. We establish the theory for two-layer neural networks in the main paper and extend them to multilayer neural networks in the appendix. Beyond policy evaluation, we establish the global convergence of neural (soft) Q learning. Funding: Z. Yang acknowledges the Theory of Reinforceement Learning program at Simons Institute. J. D. Lee acknowledges support of the ARO under MURI Award W911NF-11-1-0304, the Sloan Research Fellowship, NSF CCF 2002272, NSF IIS 2107304, ONR Young Investigator Award, and NSF-CAREER under award #2144994. Z. Wang acknowledges National Science Foundation [Awards 2048075, 2008827, 2015568, 1934931], Simons Institute (Theory of Reinforcement Learning), Amazon, J.P. Morgan, and Two Sigma for their supports.


Vehicle Detection in High Density Traffic Surveillance Data using YOLO.v5

Abstract: Abstract: Computer vision is one of the prime domains that enable to derive meaningful and crisp information from digital media, such as images, videos, and other visual inputs. Background: Detection and correctly tracking the moving objects in a video streaming is still a challenging problem in India. Due to the high density of vehicles, it is difficult to identify the correct objects on the roads. Methods: In this work, we have used a YOLO.v5 (You Only Look Once) algorithm to identify the different objects on road, such as trucks, cars, trams, and vans. YOLO.v5 is the latest algorithm in the family of YOLO. To train the YOLO.v5, KITTY dataset was used having 11682 images having different objects in a traffic surveillance system. After training and validating the dataset, three different models have been constructed setting various parameters. To further validate the proposed approach, results have also been evaluated on the Indian traffic dataset DATS_2022. Results: All the models have been evaluated using three performance metrics, such as precision, recall, and mean average precision (MAP). The final model has attained the best performance on KITTY dataset as 93.5% precision, 90.7% recall, and 0.67 MAP for different objects. The results attained on the Indian traffic dataset DATS_2022 included 0.65 precision, 0.78 recall value, and 0.74 MAP for different objects. Conclusion: The results depict the proposed model to have improved results as compared to stateof-the-art approaches in terms of performance and also reduce the computation time and object loss.


Probabilistic Model-Based Reinforcement Learning Unmanned Surface Vehicles Using Local Update Sparse Spectrum Approximation

Abstract: This article focuses on the computational efficiency of probabilistic model-based reinforcement learning (MBRL) in unmanned surface vehicles (USV) under unforeseeable and unobservable external disturbances. A novel MBRL approach, local update spectrum probabilistic model predictive control (LUSPMPC) is proposed to fully release the superiority of the probabilistic model approximated in the frequency domain in computational efficiency while mitigating its risk of overfitting during the learning procedure. It employs a local update strategy to relieve the violation of Bochner's theory, and a frequency clipping trick to encourage the approximated model to focus on the features in the low-frequency domain. Evaluated by the position-keeping task in a real USV data-driven simulation, LUSPMPC shows its significant advantages in computational efficiency while achieving better learning capability, generalization capability and control performances in a wide range of sparse scales compared with the baseline MBRL approaches that approximate their models in sample space and frequency domain, and therefore becomes an appealing solution for MBRL USV system defending against rapidly changing ocean disturbances.


Intelligent Technique for Moving Object Detection from Problematic Video Captured through Camera Sensor

Abstract: Aim: The significant aim of the proposed work is to develop an adaptive method to compute the threshold during run-time and update it adaptively for each pixel in the testing phase. It classifies motion-oriented pixels from the scene for moving objects using background subtraction and enhances using post-processing. Background: According to the huge demand for surveillance system, society is looking towards an intelligent video surveillance system that detect and track moving objects from video captured through a surveillance camera. So, it is very crucial and highly recommended throughout the globe in numerous domains such as video-based surveillance, healthcare, transportation, and many more. Practically, this research area faces lots of challenging issues such as illumination variation, cluttered background, camouflage, etc. So, this paper has developed an adaptive background subtraction method to handle such challenging problems. Objective: To focus and study the problematic video data captured through the camera sensor. To handle challenging issues available in real-time video scenes. To develop a background subtraction method and update the background model adaptively for moving object detection. Method: The proposed method has been accomplished using the following sections: Background model construction Automatic generation of threshold Background subtraction Maintenance of background model Results: The qualitative analysis of the proposed work is experimented with publicly available datasets and compared with considered state-of-the-art methods. In this work, library sequence (thermal data) of CDNET and other color video frame sequences Foreground aperture, Waving Tree and Camouflage are considered from Microsoft’s Wallflower. The quantitative values depicted in Table- 1. This work demonstrate the better performance of the proposed method as compared to state-ofthe- art methods. It also generates better outcomes and handles the problem of a dynamic environment and illumination variation. Conclusion: Currently, the world is demanding computer vision-based security and surveillancebased applications for society. This work has provided a method for the detection of moving information using an adaptive method of background subtraction approach for moving object detection in video scenes. The performance evaluation depicts better average results as compared to considered peer methods.


Identifying serpentine minerals by their chemical compositions with machine learning

Abstract: Abstract The three main serpentine minerals, chrysotile, lizardite, and antigorite, form in various geological settings and have different chemical compositions and rheological properties. The accurate identification of serpentine minerals is thus of fundamental importance to understanding global geochemical cycles and the tectonic evolution of serpentine-bearing rocks. However, it is challenging to distinguish specific serpentine species solely based on geochemical data obtained by traditional analytical techniques. Here, we apply machine learning approaches to classify serpentine minerals based on their chemical compositions alone. Using the Extreme Gradient Boosting (XGBoost) algorithm, we trained a classifier model (overall accuracy of 87.2%) that is capable of distinguishing between low-temperature (chrysotile and lizardite) and high-temperature (antigorite) serpentines mainly based on their SiO2, NiO, and Al2O3 contents. We also utilized a k-means model to demonstrate that the tectonic environment in which serpentine minerals form correlates with their chemical compositions. Our results obtained by combining these classification and clustering models imply the increase of Al2O3 and SiO2 contents and the decrease of NiO content during the transformation from low-to high-temperature serpentine (i.e., lizardite and chrysotile to antigorite) under greenschist–blueschist conditions. These correlations can be used to constrain mass transfer and the surrounding environments during the subduction of hydrated oceanic crust.


Multikeyword-Ranked Search Scheme Supporting Extreme Environments for Internet of Vehicles

Abstract: In recent years, the cloud infrastructure has been developed as a promising sharing system for the Internet of Vehicles (IoV) communication. During the information exchange, search service over ciphertext called searchable encryption (SE) is an extraordinary method to prevent data breaches. However, two open problems still need to be solved for ranked search, which hinders the application practicality in IoV. First, each data owner must store the extra information to distribute weight values dynamically. Second, ranking in the cloud has not been supported by most existing schemes. In this paper, to address the above problems and fit the characteristics of real-time data exchange in IoV, we present a multi-keyword ranked search scheme supporting extreme environments for the IoV. Specifically, our system designs a unique encrypted index tree structure to realize the multi-keyword ranked retrieval, the weight value dynamic adaptive calculation, and dynamic updating in IoV. Moreover, we use a master-slave dual server model to cope with extreme environments and propose a “Greedy Breadth-First Search” algorithm to achieve an effective sub-linear search. Finally, comprehensive security analysis and experimental simulation for the proposed system prove that our system can guarantee user privacy and acceptable efficiency.


A Multi-Objective Evolutionary Approach to Discover Explainability Tradeoffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behaviour of Electrical Machines

Abstract: Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic features. The main strength of our approach is that it provides decision makers with a clear overview of the optimal tradeoffs between data collection costs, the expected modelling errors and the overall explainability of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller deployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise regularisation technique that can be applied to outline domain-relevant mappings between LR variables and thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic thermal models when training on data associated with a limited set of load points within the safe operating area of the electrical machine under study.


Generative Augmentation-Driven Prediction of Diverse Visual Scanpaths in Images

Abstract: Visual scanpaths of multiple humans on an image represent the process by which they capture the information in it. State-of-the-art models to predict visual scanpaths on images learn directly from recorded human visual scanpaths. However, the generation of multiple visual scanpaths on an image having diversity like human visual scanpaths has not been explicitly considered. In this paper, we propose a deep network for predicting multiple diverse visual scanpaths on an image. Image-specific hidden Markov model based generative data augmentation is performed in the beginning to increase the number of image-visual scanpath training pairs. Considering a similarity between our generative data augmentation process and the use of long short-term memory (LSTM) for prediction, we propose an LSTM based visual scanpath predictor. A network to predict a single visual scanpath on an image is designed first. The network is then modified to predict multiple diverse visual scanpaths representing different viewer varieties by using a parameter indicating the uniqueness of a viewer. A random vector is also employed for subtle variations within scanpaths of the same viewer variety. Our models are evaluated on three standard datasets using multiple performance measures, which demonstrate the superiority of the proposed approach over the state-of-the-art. Empirical studies are also given indicating the significance of our generative data augmentation method and our multiple scanpath prediction strategy producing diverse visual scanpaths. Code link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ashishverma03.github.io/Diverse-Visual-Scanpath</uri>


A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry

Abstract: Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of Things (IoT), and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing "predict-and-prevent" maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This limits the AI employment in predictive maintenance, where it is vital to understand and evaluate the model before deployment. In addition, it's also important to comprehend the maintenance system's decisions. This paper presents a type-2 fuzzy-based Explainable AI (XAI) system for predictive maintenance within the water pumping industry. The proposed system is optimised via Big-Bang Big-Crunch (BB-BC), which maximises the model accuracy for predicting faults while maximising model interpretability. We evaluated the proposed system on water pumps using real-time data obtained by our hardware placed at real-world locations around the United Kingdom and compared our model with Type-1 Fuzzy Logic System (T1FLS), a Multi-Layer Perceptron (MLP) Neural Network, an effective deep learning method known as stacked autoencoders (SAEs) and an interpretable model like decision trees (DT). The proposed system predicted water pumping equipment failures with good accuracy (outperforming the T1FLS accuracy by 8.9% and DT by 529.2% while providing comparable results to SAEs and MLPs) and interpretability. The system predictions comprehend why a specific problem may occur, which leads to better and more informed customer visits to reduce equipment failure disturbances. It will be shown that 80.3% of water industry specialists strongly agree with the model's explanation, determining its acceptance.


Virtual, augmented, and mixed reality: Potential clinical and training applications in pediatrics

Abstract: Background: COVID-19 pandemic has significantly impacted the field of medical training, necessitating innovative approaches to education and practice. During this period, the use of novel technologies like virtual reality (VR), augmented reality (AR), and mixed reality (MR) has become increasingly vital. These technologies oﬀer the advantage of transcending the limitations of time and space, thus enabling medical professionals to access various personalized programs for both education and service delivery. This shift is particularly relevant in the realm of pediatric medicine, where traditional training and clinical methods face unique challenges.Purpose: The primary aim of this study is to explore the application of VR, AR, and MR technologies in pediatric medical settings, with a focus on both clinical applications and the training of pediatric medical professionals. We aim to comprehensively search and review studies that have utilized these technologies in the treatment of pediatric patients and the education of healthcare providers in this field.Methods: Peer-reviewed articles published in PubMed, the Cochrane Library, ScienceDirect, Google Scholar, and Scopus from January 1, 2018, to March 1, 2023, were comprehensively searched. The review was conducted according to the PRISMA (Preferred Reporting Items for Systematic review and Meta-Analyses) guidelines. Among the 89 studies, 63 investigated the clinical applications of VR (n=60) or AR (n=3) in pediatric patients, and 25 investigated the applications of VR (n=19), AR (n=5), or MR (n=1) for training medical professionals.Results: A total of 36 randomized controlled trials (RCTs) for clinical application (n=31) and medical training (n=5) were retrieved. Among the RCTs, 21 reported significant improvements in clinical applications (n=17) and medical training (n=4).Conclusion: Despite a few limitations in conducting research on innovative technology, such research has rapidly expanded, indicating that an increasing number of researchers are involved in pediatric research using these technologies.


STAGED: A Spatial-Temporal Aware Graph Encoder–Decoder for Fault Diagnosis in Industrial Processes

Abstract: Data-driven fault diagnosis for critical industrial processes has exhibited promising potential with massive operating data from the supervisory control and data acquisition system. However, automatically extracting the complicated interactions between measurements and subtly integrating it with temporal evolutions have not been fully considered. Besides, with the increasing complexity of industrial processes, accurately locating fault roots is of tremendous significance. In this article, we propose an unsupervised spatial-temporal aware graph encoder-decoder (STAGED) model for industrial fault diagnosis. Firstly, the high-dimensional measurements are constructed as a weighted graph to depict the complicated interactions. Then, the graph convolutional network, long short-term memory network and attention mechanism are applied to learn a comprehensive representation for multi-series. To enforce the model to better capture the temporal evolution, the dual decoder that performs reconstruction and prediction tasks simultaneously is adopted with a well-designed comprehensive loss function. By learning the spatial-temporal evolutions of datasets, faults can be diagnosed and located at a fine-grained level based on reconstruction deviations. To verify the performance of STAGED, experiments on Cranfield three-phase flow facility and secure water treatment datasets are implemented and the results indicate that it can provide insight into fault evolution and accurately diagnose faults.


ICOE: A Lightweight Group-Consensus-Based Off-Chain Execution Model for Smart Contract-Based Industrial Applications

Abstract: Programmable smart contracts facilitate the application of blockchain to many areas of industry, e.g., supply chains, energy, and healthcare. The off-chain execution model for blockchain smart contracts is a promising approach to achieve scalability for industrial applications, which executes each smart contract in one of the small-scale consensus groups. However, previous work based on such a model cannot efficiently handle cross-group contract transactions that involve multiple cross-group invocations between smart contracts, because of the heavyweight state synchronization and repeated executions. In this paper, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE , a lightweight group-consensus based off-chain execution model for cross-group contract transactions without the need of state synchronization and duplicate executions, and also prove its correctness. The key novelty of our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE is to let the invoked smart contract only executed in its residing group and only return the execution result to the invoking smart contract, while leveraging the two-phase commit(2PC) protocol guarantees consistent commits across multiple groups within the same invoking smart contract transaction. We implement <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE and extensive experimental results show that our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ICOE achieves a 14x improvement of throughput on average compared to the state-of-the-art model.


Robotic Inserting a Moving Object Using Visual-Based Control With Time-Delay Compensator

Abstract: Tracking-and-inserting a moving peg using a robot manipulator is a challenging task in manufacturing. In the past decades, various visual-based methods have been proposed for robotic manipulating static targets, which usually ignore the time delay in robot command transmission and image processing. However, for tracking and inserting a moving peg, time delays cannot be overlooked because they can reduce the tracking performance and even cause manipulations to fail. In this paper, a robot visual-based control with a time-delay compensator is presented to solve the problem of inserting a moving peg. The time-delay compensator was designed using RBFNNs, a feedback compensator aimed at eliminating the tracking errors caused by the time delays. Thus, we could manipulate a moving object using a commercial industry robot, even with the time-variant delays in the control loop. Furthermore, the visual-based controller with the pseudo-inverse image Jacobian matrix was designed using a linearization model. Thus, the matrix could be efficiently updated using the model. In the experiment, we inserted a peg into a moving hole using an eye-in-hand robot with precision.


The effect of individual stress on the signature verification system using muscle synergy

Abstract: Biometric authentication systems, in terms of using the biometric characteristic, and because these indicators are resistant to the passage of time, and environmental, and physical changes of people, in most different situations from the environment or changes in emotional factors of person, perform identity verification with optimal accuracy. In the signature verification systems, people may experience stress caused by environmental or internal factors when registering a signature, this stress can affect the performance of the signature verification system in terms of accuracy and stability. We try to study the performance of a signature verification system based on a biometric characteristic (muscle synergy patterns) for people who are stressed while signing. For this purpose, electromyography (EMG) signals were recorded from hand and arm muscles of people while signing. Then, using Non-Negative Matrix Factorization (NMF) method to extracted muscle synergies from EMG signals after pre-processing. The extracted synergy patterns are classified into genuine and forgery classes by Support Vector Machine (SVM) classifier. Furthermore, the confirmation of the authentication of stressed people was studied using this method. Finally, the results obtained were compared to the results obtained from the signature verification system for unstressed people and using K-means classifier method.


PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification

Abstract: Automatic nuclei segmentation and classification play a vital role in digital pathology.However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks.In this paper, we aim to build a reliable and robust method capable of dealing with data from the 'the clinical wild'.Specifically, we study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin (H&E) stained histopathology data, and evaluate our approach using the recent largest dataset: PanNuke.We address the detection and classification of each nuclei as a novel semantic keypoint estimation problem to determine the center point of each nuclei.Next, the corresponding class-agnostic masks for nuclei center points are obtained using dynamic instance segmentation.Meanwhile, we proposed a novel Joint Pyramid Fusion Module (JPFM) to model the cross-scale dependencies, thus enhancing the local feature for better nuclei detection and classification.By decoupling two simultaneous challenging tasks and taking advantage of JPFM, our method can benefit from class-aware detection and class-agnostic segmentation, thus leading to a significant performance boost.We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types, delivering new benchmark results.Code available: https://github.com/Kaiseem/PointNu-Net


Dynamic Programming Principle for Automatic Negotiations

Abstract: In automatic negotiation, an automated agent is trained to negotiate on behalf of a human negotiator. We consider that the domain of negotiations is known to both the agent and its opponents. In this setting, a greedy concession algorithm (GCA) is employed to find an optimal policy for the agent when the agent's belief about the opponent is known. However, the GCA is computationally expensive to certain class of policies. In this article, we propose a reverse greedy concession algorithm that is computationally less expensive for such class of policies. We also provide an alternate proof to establish that the GCA is a dynamic programming principle.


An Ensemble Semi-Supervised Adaptive Resonance Theory Model With Explanation Capability for Pattern Classification

Abstract: Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this paper proposes a new interpretable SSL model using the supervised and unsupervised Adaptive Resonance Theory (ART) family of networks, which is denoted as SSL-ART. Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy ARTMAP structure to map the established prototype nodes to the target classes using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is devised to associate a prototype node with more than one class label. The main advantages of SSL-ART include the capability of: (i) performing online learning, (ii) reducing the number of redundant prototype nodes through the OtM mapping scheme and minimizing the effects of noisy samples, and (iii) providing an explanation facility for users to interpret the predicted outcomes. In addition, a weighted voting strategy is introduced to form an ensemble SSL-ART model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART, assigns {\color{black}a different weight} to each class based on its performance pertaining to the corresponding class. The aim is to mitigate the effects of training data sequences on all SSL-ART members and improve the overall performance of WESSL-ART. The experimental results on eighteen benchmark data sets, three artificially generated data sets, and a real-world case study indicate the benefits of the proposed SSL-ART and WESSL-ART models for tackling pattern classification problems.


Joint Dataset Reconstruction and Power Control for Distributed Training in D2D Edge Network

Abstract: The intrinsic nature of non-independent and identically distributed datasets on heterogeneous devices slows down the distributed model training process and reduces the training accuracy. To settle this problem, we propose a dataset reconstruction scheme to transform the data distribution of training device’s dataset into independent and identically distributed dataset via data exchange among trusted devices. For energy efficiency, we further consider power control for the devices. We then formulate an optimization problem, which is a mixed integer non-linear programming problem, to minimize the total energy consumption for each round of distributed training. Due to the NP-hardness and coupling property of the optimization problem, we decompose it into two subproblems for dataset reconstruction and power control, respectively. An approximation algorithm is designed to obtain a near-optimal auxiliary devices set for dataset reconstruction with minimum energy consumption, while meeting the variance constraint of the optimization problem. We prove that approximation algorithm has a worst-case approximation ratio of 1+ln|Ωi(t)|, where |Ωi(t)| is the required data samples for dataset reconstruction of each training device. For power control, we design a dynamic programming algorithm to further reduce the energy consumption. For comparison, we propose three benchmark schemes that adopt either one of the algorithms or neither. We also customize three baseline algorithms based on the state-of-the-arts to compare with our proposed algorithm. Numerical results show that, our proposed algorithm outperforms three benchmarks on the average energy consumption for one round for different cases. When varying the labels that each device owns, our proposed algorithm outperforms the other three baseline algorithms on training accuracy. Besides, when setting a target accuracy, our proposed algorithm always has the lowest energy consumption.


Q-DDCA: Decentralized Dynamic Congestion Avoid Routing in Large-Scale Quantum Networks

Abstract: The quantum network that allows users to communicate in a quantum way will be available in the foreseeable future. The network capable of distributing Bell state entangled pairs faces many challenges due to entanglement decoherence and limited network performance, especially when the network scale is enormous. Many entanglement distribution protocols have been proposed so far, and most of them are in a centralized and synchronized manner, which may be infeasible in large-scale networks. As such, in this paper, we propose a full spontaneous version of quantum networks in which the quantum nodes autonomously manage multiple entanglement distribution requests. However, one major issue is that quantum nodes have little knowledge about the network, especially the congestion (e.g., some nodes may have no usable quantum memories). We present a routing algorithm to adaptive evaluate the congestion on the neighbor nodes to avoid potential congestion. We use SimQN, the new network layer simulation platform built by our research team, to evaluate our proposed design. The result demonstrates that it can adapt to changes in network resources and reduce the drop rate that eventually leads to a higher entanglement distribution rate but remains fair for multiple requests to use the network resources fairly and achieve a more balanced throughput.


Learning Semantic Alignment Using Global Features and Multi-Scale Confidence

Abstract: Semantic alignment aims to establish pixel correspondences between images based on semantic consistency. It can serve as a fundamental component for various downstream computer vision tasks, such as style transfer and exemplar-based colorization, etc. Many existing methods use local features and their cosine similarities to infer semantic alignment. However, they struggle with significant intra-class variation of objects, such as appearance, size, etc. In other words, contents with the same semantics tend to be significantly different in vision. To address this issue, we propose a novel deep neural network of which the core lies in global feature enhancement and adaptive multi-scale inference. Specifically, two modules are proposed: an enhancement transformer for enhancing semantic features with global awareness; a probabilistic correlation module for adaptively fusing multi-scale information based on the learned confidence scores. We use the unified network architecture to achieve two types of semantic alignment, namely, cross-object semantic alignment and cross-domain semantic alignment. Experimental results demonstrate that our method achieves competitive performance on five standard cross-object semantic alignment benchmarks, and outperforms the state of the arts in cross-domain semantic alignment.


Privacy-Preserving and Lightweight Verification of Deep Packet Inspection in Clouds

Abstract: In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10-20 microseconds.


Imperceptible Attacks on Fault Detection and Diagnosis Systems in Smart Buildings

Abstract: Automated Fault Detection and Diagnosis (AFDD) systems are critical to safe and efficient operation of smart buildings. A significant amount of building data can be collected and analyzed to detect building compo-nent failures. Attacks against such data that are contami-nated with small additive disturbances (i.e., adversarial per-turbation attacks) could dreadfully impact the performance of such systems while maintaining a high level of imper-ceptibility. The vulnerability studies of such data attacks is lacking. Specifically, most existing detection and clas-sification models have flat structures, regarded as Single-Stage Classifiers (SSCs), are prone to adversarial data perturbation attacks. In this paper, we present a coarse-to-fine Hierarchical Fault Detection and multi-level Diagnosis (HFDD) model, and formulate a mathematical program to derive targeted attacks on the model with respect to a pre-specified target diagnosis level. Two algorithms are developed based on convex relaxations of the formulated program for non-targeted attacks. An Alternating Direction Method of Multipliers (ADMM)-based solver is developed for the convex programs. Extensive experiments are con-ducted using two real-world datasets of measurements from air handling units and chillers, demonstrating the fea-sibility of the proposed attacks with regard to misclassifi-cation rate and imperceptibility of the attack. We also show that the HFDD is more robust to disturbances than SSC-based fault detection and multi-level diagnosis systems.


TMANomaly: Time-Series Mutual Adversarial Networks for Industrial Anomaly Detection

Abstract: Large-scale sewage treatment plants are one of the typical Industrial Internet of Things systems, where the presence of a large number of sensors generates massive dynamic time series data, and such multivariate time series data are usually time-dependent and random. Therefore, there is a certain risk when fitting the potential anomalies of real-world data, which will bring great challenges to anomaly detection. In this article, we propose a time-series mutual adversarial network (TMAN), a novel reconstruction model for anomaly detection on multivariate time series. It is based on the idea of adversarial learning and consists of two identical subnetworks. During the training process, two subnetworks can independently complete the learning of the time distribution of normal samples of industrial time series data for mutual adversarial. In the process of detecting, we obtain the residual values of TMAN reconstructed for different time series samples to discriminate anomalies. We combine TMAN and anomaly determination mechanisms to build a new industrial time series anomaly detection framework named TMANomaly. In addition, we select the dataset features with a grey correlation algorithm to achieve very high performance with a small number of features. Experimental results show that our proposed TMANomaly outperforms five popular anomaly detection methods and effectively improves the accuracy of industrial multivariate time series anomaly detection.


A Streaming Graph Partitioning Method to Achieve High Cohesion and Equilibrium via Multiplayer Repeated Game

Abstract: With the advent of 5G era, Internet-of-Things will become possible, and a large amount of correlative data, namely the streaming graph (SG), is continuously generated in various application fields, which poses a great challenge to analyze and make good use of such graph data. The balanced partition of a big graph, especially a dynamic SG, has always been basic research in graph theory, and there are many classical methods. However, this article explores a novel method, that is, an SG <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -way partitioning via a multiplayer repeated game so as to realize the equilibrium of the total number of nodes in different partitions and achieve high cohesion or low edge-cut in each partition. We regard the SG partitioning process during each time window as a multiplayer game process, treat <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> target partitions as game players, and use all possible actions that players select new nodes as the strategy set. Some effective constraints are given to reduce the strategy space and accelerate the game process. Moreover, we define equilibrium degree (ED) and modularity degree (MD), which are used to design the utility function of game players. All game players finally reach the Nash equilibrium via multiplayer repeated games. At this time, each player corresponds to a strategy. Under the guidance of these strategies, the new nodes are added to each target partition, and then the goal of optimal partitioning is achieved. The above operations are carried out in each time window. The subsequent time windows can reuse the partitioning results of the previous time window in order to realize dynamic incremental partitioning for an SG. Extensive experiments indicate that while ensuring the partition quality of an SG, our proposed algorithm also greatly improves the speed of online partitioning. Even compared with the state-of-the-art SG partitioning algorithm PLDG, our algorithm still obtains better performance.


DAG-BLOCK: A Novel Architecture for Scaling Blockchain-Enabled Cryptocurrencies

Abstract: With the rapid development of blockchain technology and industries, scalability has been widely realized as one of the primary and urgent concerns for the large-scale adoption of blockchain, especially for cryptocurrencies. In this respect, directed acyclic graph (DAG) proves to be an elegant solution to scaling blockchain but suffers from weak consistency and security issues. In this article, we designed a novel DAG-BLOCK architecture for blockchain-enabled cryptocurrency markets in order to improve the scalability. In our work, DAG is used to replace the Merkel-tree-based transaction structure within the block, and a novel design of open blocks is proposed to enable user nodes to participate in verifying the transactions in blockchain systems. On this basis, we designed a new segmented market structure, in which each miner serves only a group of users instead of all users, so as to reduce miners’ workload and thus scale transaction processing capabilities. Our work can help improve the scalability of cryptocurrencies via evolving the underlying blockchain systems to graph-based distributed ledgers and is expected to shed new light on designing blockchain-based decentralized markets.


DeepInsight: Topology Changes Assisting Detection of Adversarial Samples on Graphs

Abstract: With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax. It is found that different attack methods have their specific attack preferences on changing the target network structures. Such attack patterns are further verified by experimental results on some real-world networks, revealing that, generally, the top-4 most important network attributes on detecting adversarial samples suffice to explain the preference of an attack method. Based on these findings, the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.


Learning Embedding for Signed Network in Social Media With Global Information

Abstract: Signed networks contain nodes connected by positive and negative signed links. Signed network representation learning concentrates on learning the low-dimensional representations of these nodes, which facilitates downstream tasks such as link prediction using general data mining framework. However, most signed network embedding (SiNE) approaches neglect global information in the networks, limiting the capacity to learn genuine signed graph topology. To overcome this limitation, a deep graph neural network (GNN) framework SiG to learn SiNE with global information is proposed. To be more explicit, a hierarchical pooling mechanism is developed to encode the high-level features of the networks. Moreover, a graph convolution layer is introduced to aggregate both positive and negative information from neighbor nodes, and the concatenation of two parts generates the final embedding of nodes. Extensive experiments on four large real-world signed network datasets demonstrate the effectiveness and excellence of the proposed method.


Distributed Differentially Private Ranking Aggregation

Abstract: A key step in cooperative decision-making is for all participants to achieve a consensus that avoids individual favoritism. To reach a consensus, a quantitative systematic mechanism is sometimes preferred. An example of such a mechanism is ranking aggregation, where the task is to rank elements in a certain order. While participating in ranking activities, it is also critical under certain circumstances to protect each decision maker’s preference. A promising privacy-preserving technique that is suitable for such a need is differential privacy (DP), which ensures plausible deniability of the protected information with rigorous mathematical guarantee and adjustable privacy level. A concern of the standard DP model is its assumption of letting a curator collect and analyze sensitive information, where in practical situations such a trusted independent curator may not exist. This article proposed a mechanism to solve the above issue using the distributed DP (DDP) framework. The proposed mechanism collects locally differential private rankings from individuals, and then randomly permutes pairwise rankings using a shuffle model to further amplify privacy protection. The final representative is produced by hierarchical rank aggregation (HRA). The mechanism was theoretically analyzed and experimentally compared against the existing methods and demonstrated competitive results in both output accuracy and privacy protection.


Sparse Graph Transformer With Contrastive Learning

Abstract: Information aggregation and propagation over networks via graph neural networks (GNNs) plays an important role in node or graph representation learning, which currently depend on the calculation with a fixed adjacency matrix, facing over-smoothing problem, and difficulty to stack multiple layers for high-level representations. In contrast, Transformer calculates an importance score for each node to learn its embedding via the attention mechanism and has achieved great successes in many natural language processing (NLP) and computer vision (CV) tasks. However, Transformer is inflexible to extend to graphs, as its input and output must have the same dimension. It will also become intractable to allocate attention over a large-scale graph due to distractions. Moreover, most graph Transformers are trained in supervised ways, which consume additional resources to annotate samples with potentially wrong labels and have limited generalization of representations. Therefore, this article attempts to build a new Sparse Graph Transformer with Contrastive learning for graph representation learning, called SGTC. Specifically, we first employ centrality measures to remove the redundant topological information from input graph according to the influences of nodes and edges, then disturb the pruned graph to get two different augmentation views, and learn node representations in a contrastive manner. Besides, a novel sparse attention mechanism is also proposed to capture structural features of graphs, which effectively save memory and training time. SGTC can produce low-dimensional and high-order node representations, which have better generalization for multiple tasks. The proposed model is evaluated on three downstream tasks over six networks, and experimental results confirm its superior performance against the state-of-the-art baselines.