Correntropy-Induced Weighted GAN for Bridge Anomaly Detection

Abstract: Bridges play an essential role in transportation and economics. Since the loss is significant in the event of bridge damage, the monitoring of bridge structure health is important. One of the important tasks of bridge health monitoring is the bridge anomaly detection. The bridge anomaly detection is a challenging problem since the real-world bridge data collected by the sensors is usually contaminated by large non-Gaussian noises. To address this issue, we propose a correntropy-induced weighted generative adversarial network (CWGAN) for bridge anomaly detection. Specifically, we first propose a correntropy-based generator, which has an encoder-decoder structure with a memory structure, and optimize it based on the maximum correntropy criterion. Then, derived from the correntropy of the reconstruction errors, we specially design a weight module to obtain a weight variable that measures the contamination-free degree of the corresponding sample. Afterwards, we take advantage of the weight module and transform the Generator and Discriminator modules of CWGAN into weighted versions to achieve robustness against contamination and obtain smaller reconstruction errors for non-contaminated normal data. We integrate and iteratively perform the reconstruction and weighting process to achieve better anomaly detection performance. After the model optimization, we detect the anomalies with reconstruction errors. We conduct experiments on a model bridge to demonstrate the effectiveness of the proposed CWGAN.


3-D Object Detection With Balanced Prediction Based on Contrastive Point Loss

Abstract: Object detection on point clouds is widely used in autonomous driving technology. Recent researches have demonstrated that good feature representation is the key to 3D object detection, especially for point-based methods. Meanwhile, contrastive learning has shown its effectiveness to learn the general visual representation of images as a pre-training paradigm. Motivated by this, the contrastive learning is extended to the task of object detection for better distinguishing different types of objects. In this paper, a simple and effective single-stage detector, named 3D Object Detection with Balanced Prediction based on Contrastive Point Loss (BP-CPL), is proposed with a Contrastive Point Loss and a Re-balanced branch. Through the Contrastive Point Loss, similar representations among points of the same category and discriminative information among points of different categories can be learned. In order to keep the contrastive points consistency, a point filter is proposed. In addition, compared to the original point-wise feature, the self-supervised learned embedding is more robust to few-sample categories. Therefore, a re-balanced branch combined with the origin classification branch in a cumulative learning manner is proposed to re-balance the prediction results during the training phase. Extensive experiments show the effectiveness of the proposed method, especially for few-sample objects. The code will be available at https://github.com/Tongjiaxun/BP-CPL.


Using easy coefficients conjecture for rotation symmetric Boolean functions

Abstract: A Boolean function in n variables is rotation symmetric (RS) if it is invariant under powers of ρ(x1,…,xn)=(x2,…,xn,x1). An RS function is called monomial rotation symmetric (MRS) if it is generated by applying powers of ρ to a single monomial. Completing earlier research on special cases, the author showed in 2018 that for any RS function fn in n variables, the sequence of Hamming weights wt(fn) for all values of n satisfies a linear recurrence relation. It was also proved that the associated recursion polynomial could be explicitly calculated as the minimal polynomial of a rules matrix and an algorithm for computing the rules matrix was explained. Examples showed that the usual formula which gives the values of wt(fn)−2n−1 as a linear combination of powers of the irrational roots of the minimal polynomial has simple coefficients which are all 1/2 if the multiset made up of the roots of the characteristic polynomial of the rules matrix is used instead, no matter what the degree of the Boolean function is. The conjecture that this is always true is called the Easy Coefficients Conjecture (ECC). The special case of MRS quadratics was proved in an earlier paper. The present paper gives some ECC applications valid for any function for which the ECC is true, even though there is a proof only for the quadratic MRS case so far.


Stochastic first-order learning for large-scale flexibly tied Gaussian mixture models

Abstract: Gaussian Mixture Models (GMMs) are one of the most potent parametric density models used extensively in many applications. Flexibly-tied factorization of the covariance matrices in GMMs is a powerful approach for coping with the challenges of common GMMs when faced with high-dimensional data and complex densities which often demand a large number of Gaussian components. However, the expectation–maximization algorithm for fitting flexibly-tied GMMs still encounters difficulties with streaming and very large dimensional data. To overcome these challenges, this paper suggests the use of first-order stochastic optimization algorithms. Specifically, we propose a new stochastic optimization algorithm on the manifold of orthogonal matrices. Through numerous empirical results on both synthetic and real datasets, we observe that stochastic optimization methods can outperform the expectation–maximization algorithm in terms of attaining better likelihood, needing fewer epochs for convergence, and consuming less time per each epoch.


The area-efficient gate level information flow tracking schemes of digital circuit with multi-level security lattice

Abstract: The Gate Level Information Flow Tracking (GLIFT) is an effective method to uphold the information security for high-assurance digital circuits. The GLIFT method associates a security label with each data bit and monitors these labels to expose the illegal information flow in the circuit under tracking. However, the label propagation usually consumes a significant area overhead, especially for the multi-level security lattices. This work aims to reduce the area cost of the GLIFT logic to enhance its applicability. A new implementation technique of GLIFT logic of basic gates is proposed by introducing the logic operation 'minus'. A decoupling-encoding technique of the security labels is also proposed to simplify the GLIFT logic. Furthermore, three improved GLIFT schemes are introduced for different scenarios based on these two techniques. These improved schemes are applied to some ISCAS'85 benchmarks. The evaluation results show that the improved schemes only consume 38.90%, 39.84% and 29.16% area of that of the original GLIFT scheme on average, respectively.


Accelerated Structured Matrix Factorization

Abstract: Matrix factorization exploits the idea that, in complex high-dimensional data, the actual signal typically lies in lower-dimensional structures. These lower dimensional objects provide useful insights, with interpretation favored by sparse structures. Sparsity, in addition, is beneficial in terms of regularization and, thus, to avoid over-fitting. By exploiting Bayesian shrinkage priors, we devise a computationally convenient approach for high-dimensional matrix factorization. The dependence between row and column entities is modeled by inducing flexible sparse patterns within factors. The availability of external information is accounted for in such a way that structures are allowed while not imposed. Inspired by boosting algorithms, we pair the proposed approach with a numerical strategy relying on a sequential inclusion and estimation of low-rank contributions, with a data-driven stopping rule. Practical advantages of the proposed approach are demonstrated by means of a simulation study and the analysis of soccer heatmaps obtained from new generation tracking data. Supplementary materials for this article are available online.


Subdomain alignment based open-set domain adaptation image classification

Abstract: Domain adaptation has achieved great success in using labeled source domain samples to identify unlabeled target domain samples. Here, we aim to solve the open-set domain adaptation, which is different from the closed-set domain adaptation in that it contains categories in target domain that do not appear in source domain. To solve this problem, this paper proposes open-set domain adaptation model based on subdomain alignment, which uses variable weights for discriminative training of unknown samples in target domain. Aiming at the distribution differences between domains, the model aligns the distributions of the category subspaces of source and target domains, enhancing the distribution similarity within the subspaces of the same category. Through experiments on different domain adaptation datasets, the results show that the model proposed in this paper effectively improves the accuracy of open-set domain adaptation classification.


A cell-based association data integration method for locating potential drilling targets: A case study at the Dalli Cu–Au porphyry deposit in Iran

Abstract: The presence of various spatial exploration proxies (predictor patterns) as vectors to distal porphyry mineralization in surrounding areas of a known outcropping mineralized porphyry center at the Dalli Cu–Au deposit in Iran provided evidence for the occurrence of similar porphyry mineralization in the subsurface. Factor analysis was applied to soil geochemical data from the Dalli South Hill area to define favorable geochemical variables and to create surface multi-element anomaly maps reflecting the presence of porphyry Cu–Au mineralization in the subsurface. Reduction-to-pole and analytical signal filters were applied to ground total magnetic field data to define high magnetic signatures that could be associated with potassic alteration and porphyry mineralization in the subsurface. Then, a data-driven cell based association (CBA) data integration method was applied to the favorable spatial proxies to define their association with Cu–Au mineralization and locate subtle high potential targets (cells) for drilling. The conventional CBA algorithms did not consider weights for the spatial proxies, and by using the ascendant hierarchical clustering (AHC) algorithm the results were classified into a binary map to show favorable and unfavorable cells with scores of 1 and 0, respectively. Predicting high potential targets for high-priced drilling, just based on using the binary map, could be very risky. Therefore, an innovative CBA data integration approach was employed to assign proper weights between 0 and 1 to the spatial proxies by calculating the importance of the presence of an exploration proxy in a cell using training (labeled) trenching geochemical data. A final continuous predictor map was generated and it successfully mapped the known outcropping porphyry center and its subsurface extension to the northeast as a high prospectivity class. This map also identified a narrow NE-trending high potential target (a few cells) of 200 m long in the southern part of the known porphyry center for drilling.


A dynamic similarity weighted evolving fuzzy system for concept drift of data streams

Abstract: Financial markets and weather prediction are generating streaming data at a rapid rate. The frequent concept drifts in these data streams pose significant challenges to learners during training and prediction. Concept drift means the distribution of data in the data stream changes dramatically at any time, and it can lead to decreased effectiveness in many data-driven systems. So, obtaining online models that adapt to concept drift is essential. In the face of concept drifts in data streams, most proposed evolving fuzzy systems (EFSs) suffer from two problems. First, it is difficult to quickly adapt to a drastic change in a short period, such as sudden drift. Second, most ensemble EFSs adjust the weights according to the error, which will easily lead to the risk of under-training and repeated training of the base model. To solve the above problems, we propose a new type of ensemble EFS called dynamic similarity weighted evolving fuzzy system (DSW-EFS). Unlike existing ensemble EFSs, DSW-EFS assigns weights to base learners based on the similarity between data distributions, and each base learner describes only one data distribution in the data stream. This ensemble method enables DSW-EFS to describe multiple data distributions in the data stream and quickly adapt to multiple concept drifts. To ensure the accuracy of drift detection, we propose a Gaussian mixture model (GMM)-based concept drift detection algorithm that can obtain the similarity between data distributions. This detection method can achieve high accuracy based on a smaller sliding window size. The DSW-EFS model is tested from concept drifts and prediction accuracy in the experimental part. The results show that DSW-EFS can quickly adapt to concept drift by assigning weights to base learners based on similarity when concept drifts occur. Moreover, DWS-EFS achieves high prediction accuracy on most datasets.


Decentralized federated learning based on blockchain: concepts, framework, and challenges

Abstract: Decentralized federated learning integrates advanced technologies, including distributed computing and secure encryption methodologies, to facilitate a robust and efficient mechanism for safeguarding data privacy and security during collaborative model training endeavors. The incorporation of blockchain technology into Federated Learning provides a transformative framework characterized by its inherent decentralization and data immutability, making it a focal point of contemporary research inquiry. The literature on the integration of blockchain technology with federated learning frameworks is presently deficient in comprehensive summary works. Such summaries are essential for advancing understanding of the implementation challenges and for guiding future research efforts in this domain. Therefore, in this work, we first summarize a typical decentralized federated learning framework based on blockchain and describe its operational workflow and its applications in the fields of the Internet of Things, the Internet of Vehicles, etc. A systematic summary of the challenges confronting this framework and an analysis of the solutions proposed to address these challenges are provided. Finally, this work provides insight into the possible future research directions.


Deep clustering framework review using multicriteria evaluation

Abstract: The application of clustering has always been an important method for problem-solving. In the era of big data, most classical clustering methods suffer from the curse of dimensionality and scalability issues. Recently, deep clustering models have garnered more attention due to their capabilities in dealing with complex, high-dimensional, and large-scale datasets. They offer intriguing perspectives owing to their outstanding representative capacity and fast inference speed. The remaining major problem in clustering scenarios with high-dimensional data revolves around determining an appropriately compressed representation that semantically preserves cluster structures. Without labels, defining an objective function to encourage a suitable representation becomes a critical question. After several years of stagnation, impressive results have been achieved in the last two years. This paper proposes a comprehensive and up-to-date review of deep clustering methods. We first introduce the basic concepts shared by several deep clustering algorithms, available network architectures, and optimization strategies. Then, a detailed review is presented for each family by analyzing their most representative algorithms. These algorithms are then assessed based on their classification accuracy and from a multi-criteria perspective to aid investigators in selecting the most appropriate solution. Finally, an overview of the diversity of tasks and application domains is provided, and current issues and challenges are discussed.


Multi-agent cooperative operation based on cross-domain zero-shot learning

Abstract: Multi-agent systems (MAS) based on reinforcement learning have shown impressive performance in complex tasks. However, they are still weak when it comes to addressing tasks without any samples. In this paper, we design a 2D ground and a 3D underwater cooperative task, which serve as the source and target tasks, respectively. The MAS requires learning prior knowledge that can be generalized to the target task from the source task. To solve this problem, we propose cross-domain zero-shot learning (CDZSL) for MAS cooperative operation. Specifically, we first construct a state transition graph by decomposing the source tasks into multiple state phases. Subsequently, we describe the task description document for MAS and construct the task-related semantic embedding space, which effectively combats the semantic gap. Then, we propose zero-shot learning based on feature synthesis mapping (FSM), which synthesizes the feature centers of the unseen classes by the features of seen classes. During knowledge transfer, FSM is employed to assist MAS's decisions-making in the target task. Finally, extensive experiments show that CDZSL, compared with MAX-Q, improves the adaptability of MAS, where the cost of physical time for the first time to complete the new task is reduced by eight times.


Satellite image recognition using ensemble neural networks and difference gradient positive-negative momentum

Abstract: The modern machine learning theory finds application in many areas of human activity. One of the most dispersed tasks is pattern recognition on satellite images. It is difficult for a person to recognize a large number of images in a short time. It made the researchers develop the automation process, such as neural network engagement. The loss function minimization and ensemble learning raise the pattern recognition accuracy. We propose the robust difference gradient positive-negative momentum optimization algorithm that achieves the global minimum of the loss function with higher accuracy and fewer iterations than known analogs. Such an optimization algorithm contains the generalized average moving estimation approach and more effective learning rate control by additional parameters. The proposed optimizer has the regret-bound rate estimation, belonging to OT, and converges to the global minimum. However, the main problems in optimization theory are vanishing and blowing gradient values, where the standard gradient-based algorithms fail to achieve the required objective function value. The vanishing and blowing gradient problems meet in Rastrigin and Rosebrock test functions, where the proposed optimization algorithm attains the global extreme in the shortest number of iterations and has a more stable convergence process than state-of-the-art methods. Afterward, there are trained deep convolutional neural networks with different optimizers on satellite images from the University of California merced dataset containing 21 object classes, where the proposed algorithm gives the highest accuracy. There is a suggested ensemble-learning model consisting of 4 networks with different optimizers. The prediction results receive weight coefficients distributed according to the majority voting and ensemble neural network retrains with the higher pattern recognition accuracy. The suggested ensemble-learning model with the developed optimizer raised the accuracy by 1 %–4 % percentage points.


Deep learning-based spectral reconstruction in camouflaged target detection

Abstract: Camouflaged target detection aims to detect targets that blend into their surroundings, but RGB has difficulty distinguishing between targets and backgrounds. While methods using multispectral image (MSI) can distinguish targets from background via spectral information, they are limited by imaging speed, resolution, and high cost for camouflaged target detection. Here, we propose a novel camouflaged target detection workflow based on reconstructed MSI from RGB image. Specifically, we propose a spectral reconstruction model, S2HFormer, which utilizes the deep neural network to fit the mapping of RGB image to MSI without additional information. And the reconstructed MSI based on S2HFormer achieves higher accuracy in both reconstruction and target detection, outperforming existing methods. Furthermore, we integrate a spectral band selection algorithm to optimize the number of bands used for improving detection efficiency. Experimental results show that the proposed method acquires MSI at 55 frames per second (FPS) and achieves an F-score of 0.925, achieving real-time (24 FPS) MSI acquisition. The evaluation indicates the effectiveness and efficiency of our method for camouflaged target detection.


FusionPlanner: A multi-task motion planner for mining trucks via multi-sensor fusion

Abstract: In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportation in open-pit mines is proposed in this research. Firstly, we propose a multi-task motion planning algorithm, called FusionPlanner, for autonomous mining trucks by the multi-sensor fusion method to adapt both lateral and longitudinal control tasks for unmanned transportation. Then, we develop a novel benchmark called MiningNav, which offers three validation approaches to evaluate the trustworthiness and robustness of well-trained algorithms in transportation roads of open-pit mines. Finally, we introduce the Parallel Mining Simulator (PMS), a new high-fidelity simulator specifically designed for open-pit mining scenarios. PMS enables the users to manage and control open-pit mine transportation from both the single-truck control and multi-truck scheduling perspectives. The performance of FusionPlanner is tested by MiningNav in PMS, and the empirical results demonstrate a significant reduction in the number of collisions and takeovers of our planner. We anticipate our unmanned transportation paradigm will bring mining trucks one step closer to trustworthiness and robustness in continuous round-the-clock unmanned transportation.


Improving conversational recommender systems via multi-preference modelling and knowledge-enhanced

Abstract: Conversational recommender systems (CRS) aim to capture user preferences and provide high-quality recommendations through conversations. Existing CRSs use items in conversations to represent user preferences, and these items usually need to match corresponding entities in external knowledge graphs (KGs). However, current general approaches extract subgraphs from large KGs (e.g., DBpedia) and thus may contain noise from nonrelevant domains and do not cover items in the CRS domain well. In addition, existing CRSs do not focus on hidden semantic preferences in user conversations, nor do they emphasize item attributes. Moreover, the system-generated responses lack diversity and descriptiveness. To address these issues, we propose a novel approach for Improving Conversation Recommendation Systems via Multi-Preference Modelling and Knowledge-Enhanced (MPKE). Specifically, we first customize a higher-quality knowledge graph LMKG for CRS to help the model fully exploit external knowledge to improve its performance. Second, we design a semantic fusion module to augment the semantic connections between external knowledge and dialogue text. Then, we propose a two-stage recommendation mechanism to explore more item attributes and fuse entity and text semantics to enhance user representation. For response generation, we use a word-level KG (i.e., ConceptNet) to enhance the representation of conversation keywords and construct explanation templates with slots to enhance the diversity of system responses. extensive experiments show that our approach outperforms past state-of-the-art methods. the source code will be published at https://github.com/zcy-cqut/MPKE.


Joint Detection, Tracking, and Classification of Multiple Maneuvering Star-Convex Extended Targets

Abstract: The extended target joint detection, tracking and classification (JDTC) algorithms based on elliptical shape utilize rough target size information for classification, which is difficult to effectively solve the problem of accurate classification of similarly sized targets. Therefore, this paper proposes a multiple maneuvering star-convex extended target JDTC algorithm called JDTC-MM-CBMeMBer filter. First, the target extent state is modeled as a star-convex shape via the star-convex random hypersurface model (RHM). By modeling the target class-related prior information with vector form, we construct its relationship with the simultaneous extent state, and integrate it into the Bayesian filter framework for joint processing. Second, to solve the implementation difficulty due to the high-dimensional target state and strong nonlinear observation model in the star-convex RHM, we model the target kinematic by two separate vectors, and use the particle filter to update target class probability. Next, we use the multiple model cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter to derive the JDTC recursion process of the multiple maneuvering star-convex extended targets. At last, the Gamma-Gaussian-Gaussian mixture implementation is present. The simulation results show that: (1) compared with the extended target JDTC algorithm based on an elliptical shape, the proposed filter can accurately classify targets with similar sizes but different shapes (2) compared with the multiple extended target tracking algorithm based on star-convex RHM, the proposed filter can significantly improve the target state estimation results without significantly increasing the running time.


A Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing

Abstract: Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills.The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ∼10 cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors.This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the "IMAV 2022 Nanocopter AI Challenge."We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal.Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments.Our system ranked 1 st among seven competing teams at the competition.In our best attempt, we scored 115 m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles.Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field.


SSE4Rec: Sequential recommendation with subsequence extraction

Abstract: Sequential recommendation mines the sequential patterns in user behavior data to recommend items to users. Recent studies have mainly followed the language modeling paradigm, on the premise that the next item depends on the sequence of previous items. Notably, differences exist between user behavior and textual data. One key difference is that behavioral sequences can encompass multiple intentions, unlike sentences that typically express a single intention. Furthermore, behavioral sequences emerge freely from users, whereas sentences conform to grammatical rules. This study highlights the risk of treating behavior sequences as a unified sequence, and the resultant potential for overfitting the observed transitions. We mitigated this risk by using subsequence extraction for recommendation (SSE4Rec). This model employs a subsequence extraction module that disperses items into distinct subsequences and groups of related items. Each subsequence is then processed by an independent downstream sequence model, which discourages the memorization of inconsequential transitions. Both the training and inference strategies are inherently integrated into the model. The proposed method was evaluated on four public datasets, whereby it was demonstrated to outperform publicly available alternatives or deliver competitive results. The properties of the model were also explored, further visualizing the output of the subsequence extraction module.


Sentence-level sentiment classification based on multi-attention bidirectional gated spiking neural P systems

Abstract: The GSNP model is a new recurrent-like network inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems. In this study, a novel sentiment classification model MA-BiGSNP is established by using bidirectional GSNP model combined with multi-attention mechanism. BiGSNP, which is created by two GSNP models with opposite directions, is used to capture semantic correlations between word contexts in a sentence. The multi-attention mechanism simulates the variety of relationships between sentences as well as the significance of words in sentences. To evaluate the effectiveness of the proposed MA-BiGSNP model, we perform comparative experiments and ablation experiments on five real datasets and twelve baseline models. The experimental results show that the proposed MA-BiGSNP model is effective for sentiment classification task.


Blockchain for the digital twin-driven autonomous optical network

Abstract: In recent years, optical networks have become increasingly large-scale and complex, resulting in mounting pressures on operation and maintenance management. The techniques of the artificial intelligence (AI)-driven digital twin (DT) have been applied to optical networks to pave the way for creating the self-optimizing and self-controlling autonomous optical network (AON). However, the AON is confronted with many security and reliability issues: the centralized architecture is prone to data loss during system disconnection and active/standby switchover, and the massive data, DT models, and control instructions in the AON all face the risk of being tampered with, directly affecting the effectiveness of network regulation. In addition, the reliability of complex autonomous processes should be ensured. To address these challenges, in this paper, we applied blockchain to a DT-driven AON with the designed architecture and deployment scheme to realize data loss prevention, anti-tamper storage, secure autonomous operations, access control, and network element controller decisions. A blockchain-based solution was studied by considering the quality of transmission (QoT) estimation and link optimization as examples. Four chaincodes were designed and developed to perform the model storage, service definition, telemetry subscription, data collection, model implementation, and instruction delivery in the AON. Furthermore, the proposed solution was developed and deployed using Hyperledger Fabric. The Hyperledger Caliper was used to test the performance of the proposed blockchain system. The distributed environment is used for performance testing to simulate the real scenario. The test results are analyzed to indicate the future development direction.


Style Elimination and Information Restitution for generalizable person re-identification

Abstract: Domain generalizable person re-identification (DG ReID) aims to obtain a model that can be applied directly to unseen domains once trained on a set of source domains (datasets collected from different camera networks). Among current DG ReID methods, instance normalization is a promising solution to reduce the effect of domain bias, but it inevitably filters out some discriminative information. Besides, since most pioneering approaches cannot effectively address the loss of discriminative information, this paper proposes a Style Elimination and Information Restitution (SEIR) module and constructs a generalizable framework. Specifically, we utilize instance normalization to reinforce the generalization capability on unseen domains. Then, the instance-specific mean and variance are employed to construct a feature vector that includes discriminative and style information. Besides, an encoder–decoder structure is designed to restitute the discriminative information to ensure a high recognition rate. Finally, a dual optimization strategy is devised to update the proposed module by simulating a real train-test process to enhance the generalization robustness further and prevent SEIR from overfitting to the source domain. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by a large margin on the public DG ReID benchmarks.


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Abstract: Over the past decade, incorporating information from the objective function into the constraint-handling process has garnered considerable attention in evolutionary algorithm research. Stemming from this, multiobjective optimization has emerged as a promising approach that simultaneously optimizes the objective function and constraints. However, the challenges associated with optimizing objective functions and satisfying constraints exhibit significant variability. Some constraints and/or objective functions can be exceptionally challenging, necessitating specific methods to identify the optimal solution within a limited feasible region. This study proposes an adaptive gradient descent-based repair method to enhance the search capability for both objective function optimization and constraint satisfaction. This method leverages objective function information to rectify infeasible solutions using gradient descent, thereby reducing the limitations of a purely constraint-based approach and automating the application of the repair method. Furthermore, an enhanced variant of the ɛ-constrained multiobjective differential evolution algorithm is developed for solving constrained optimization problems. The efficacy of the proposed approach is assessed using 57 benchmark test functions derived from real-world applications. Empirical results demonstrate that our approach is capable of locating high-quality solutions, outperforming several selected state-of-the-art algorithms.


SDSDet: A real-time object detector for small, dense, multi-scale remote sensing objects

Abstract: Object detection in remote sensing images (RSIs) plays a crucial role in aerial and satellite image analysis. Existing methods lack the capability to effectively detect small and multi-scale objects in RSIs. Consequently, achieving an optimal trade-off between speed and accuracy remains unattainable. Extensive investigation reveals that state-of-the-art detectors have largely overlooked two critical aspects: Spatial artifacts from convolution operations and gradient confusion caused by neighboring levels in the Feature Pyramid Network. To address the first problem, we propose adopting a non-reorganized patch-embedding layer in the downsampling stage and a dual-path learning network (DPLNet) as the backbone, which can effectively mitigate the adverse effects of the edge pixel feature bias in feature maps. Additionally, using DPLNet as the backbone network can minimize costs while learning the intrinsic feature information of objects in RSI. For the second aspect, we propose a neighbor-erasing module with only one gradient flow (OGF-NEM). This module utilizes deep features to erase large objects to highlight small objects in shallow features and changes the backpropagation path to prevent the backflow of unreasonable gradients and the erosion of information from neighbor scales. Thus, a novel detector, called SDSDet, is proposed, which achieves excellent performance for small, dense, and multi-scale objects in RSIs. We have conducted exhaustive experiments on DOTA and MS COCO datasets. Specifically, the SDSDet achieves 42.8% AP on DOTA and 33.3% AP on MS COCO, together with nearly 4.87 M model size and 95 FPS.


Vendor capabilities development in blockchain sourcing: A parallel play approach

Abstract: Vendor capabilities development has been an important research stream in the information systems (IS) sourcing literature. The existing research, which was based on the context of implementing centralized enterprise systems, focused on the adoption of deliberate learning and implicit learning, with the assumption that effective selection between these two learning approaches is sufficient for service providers to acquire vendor capabilities. However, this assumption may not hold in implementing a decentralized system based on enterprise blockchain. Because an enterprise blockchain system affords a new way of governing transactions and demands a new governance mode, it creates an uncertain and complex landscape in which the mere selection between deliberate learning and implicit learning becomes insufficient. Addressing this gap is crucial because many enterprise blockchain projects fail due to the incompetence of service providers. By conducting an in-depth case study of a successful blockchain service provider, our study reveals a new mechanism for developing vendor capabilities: parallel play. As with parallel play by preschoolers, the service provider combines a set of learning approaches featuring borrowing from peers and learning through experimentation. Our study extends the IS sourcing literature on vendor capabilities development from the context of implementing centralized enterprise systems to the context of implementing decentralized enterprise blockchain systems. Our findings also provide guidelines for blockchain service providers to follow when acquiring vendor capabilities and for clients to use when selecting service providers.


A Sketch-texture Retrieval Framework using Perceptual Similarity

Abstract: Sketch-based image retrieval is an important research topic in the field of image processing. Hand-drawn sketches consist only of contour lines, and lack detailed information such as color and textons. As a result, they differ significantly from color images in terms of image feature distribution, making sketch-based image retrieval a typical cross-domain retrieval problem. To solve this problem, we constructed a perceptual space consistent with both textures and sketches, and using perceptual similarity for sketch-based texture retrieval. To implement this approach, we first conduct a set of psychological experiments to analyze the similarity of visual perception of the textures, then we create a dataset of over a thousand hand-drawn sketches according to the textures. We proposed a layer-wise perceptual similarity learning method that integrates perceptual similarity, with which we trained a similarity prediction network to learn the perceptual similarity between hand-drawn sketches and natural texture images. The trained network can be used for perceptual similarity prediction and efficient retrieval. Our experimental results demonstrate the effectiveness of sketch-based texture retrieval using perceptual similarity.


A detailed occupant activity classification model in a residential environment using building monitoring data: Considering occupant characteristics

Abstract: In this study, we developed a detailed occupant activity classification model to detect occupant activities using various non-intrusive monitoring data in buildings (i.e., indoor environmental data, energy consumption data, motion·door sensor data, and state of indoor climate control devices). To collect building monitoring data, we conducted experiments with five subjects (Person A to E) in an experimental testbed. In total, ten occupant activities (i.e., Sleeping, Eating, Personal hygiene and care, Working, Cooking, Cleaning up after meals, Indoor cleaning, Exercising, Resting, and Away) were selected to predict occupant activity experimentally. Three classification algorithms, including random forest (RF), K-nearest neighbor, and support vector machine, were employed to develop the detailed occupancy activity classification models. In this study, we found that the key variables influencing each occupant activity classification varied significantly for each individual. The lifestyle and characteristics of each occupant were reflected in the building monitoring data. Accordingly, the performance of the model varied depending on each occupant's behavior. In the case of Person A, all classification algorithms displayed a high performance when only environmental datasets were considered; the accuracy was 0.98, and the f1-score was also above 0.98. Person B's classification model required various building monitoring data to ensure optimal performance; the highest accuracy and f1-score was 0.86 using RF. In contrast, Person D's classification model showed a low performance when all monitoring datasets were used; the accuracy was 0.43, and the f1-score was 0.41 using RF. These results support that occupants' characteristics affect the feature variables in an activity classification model, and these feature variables determine the performance of the model of each occupant.


A survey on just noticeable distortion estimation and its applications in video coding

Abstract: With the developing explosion in video data delivery, perceptual video coding (PVC) plays an increasingly significant role in video compression. The just noticeable distortion (JND) reflects the tolerance limit of human visual system (HVS) to coding distortion directly, resulting in that JND-based PVC is the most important branch of video coding. This paper provides an extensive overview of JND estimation and JND-based PVC, so as to make interested readers aware of the status quo. The main contribution of this article can be briefly outlined as follows. Firstly, the general description of JND concepts and most existing computation models for JND are to be reviewed systematically. Secondly, most related works about JND-based perceptual image and video coding schemes are introduced, including JND-based coding preprocessing and JND-based codec embedding. Thirdly, in addition to a thorough summary of JND estimation and JND-based PVC, possible future directions and opportunities are analyzed and discussed.


Comment on “An efficient identity-based signature scheme with provable security”

Abstract: In this comment paper, we reveal a security issue in an efficient identity-based signature (IBS) scheme that has been proven secure in the standard model. More specifically, we perform a key-only attack on the IBS scheme to break its universal unforgeability. Since the security notion of universal unforgeability under key-only attack (uuf-koa) is a weaker notion than the existential unforgeability under chosen message attack (euf-cma), this invalidates the euf-cma security claimed by the IBS scheme. Subsequently, we propose a fix to achieve a stronger seuf-cma security without a random oracle.


Echo state network structure optimization algorithm based on correlation analysis

Abstract: Echo State Network (ESN) is an effective variant of Recurrent Neural Network (RNN). However, it is difficult for traditional ESN to determine the reservoir size that matches a given task. In this paper, an ESN structure optimization pruning algorithm based on correlation analysis, called PCESN, is proposed to design the size of the reservoir automatically. First, a characteristic matrix is constructed utilizing probability theory and information theory to measure the correlation between each neuron in the reservoir and the output neuron. On this basis, a pruning criterion is proposed to achieve a sparse reservoir structure by dynamically removing neurons with low correlation. Second, in order to retain the sample information of the neurons in the removed reservoir during the network reduction, the input weights of the retained reservoir are updated by means of the average transverse propagation of the weights. Finally, the performance of PCESN is tested on multiple time series. Simulation results show that the proposed PCESN outperforms some fixed size ESN and other dynamic ESN in terms of prediction accuracy, generalization performance and model complexity.


Influence of the operating regime of the magnonic active ring oscillator on the performance of the magnonic reservoir computer based on it

Abstract: A magnonic active ring oscillator based on an yttrium iron garnet (YIG) is a promising hardware platform for physical reservoir computing. It meets the required conditions for the availability of fading memory and nonlinearity. However, its performance still needs further improvement. Two methods can be employed to improve the performance. One is increasing the time of establishing steady self-oscillations in the auto-oscillator. The second one is increasing the amplitude of spin waves propagating in the YIG film. In this work, we achieve an increase of the amplitude of the reservoir response by broadening the available range for the ring gain coefficient. We use a metallized YIG film as a waveguide in which spin waves propagate. This shifts up the amplitude threshold for the development of spin wave modulation instability in the ring. The threshold defines the upper limit of the gain range for the ring. Binary pulse sequence is entered into the ring by switching its gain between upper and lower levels. The modulation instability threshold then sets the upper limit for the higher gain level. In addition, in this work, we set the lower level of the gain to negative values. This is one more improvement with respect to earlier works. Switching the gain between a negative and a positive value increases the amplitude of the reservoir response and the depth of its fading memory. We employ the standard short-term memory and parity-check tests to quantify the performance of the reservoir computer. Both tests demonstrate an increase in the computational efficiency of the magnonic reservoir.


Mission-Oriented Gaussian Process Motion Planning for UUVs Over Complex Seafloor Terrain and Current Flows

Abstract: We present a novel motion planning framework for unmanned underwater vehicles (UUVs) - the first framework that applies Gaussian process motion planning to solve a 3D path planning problem for a 6-DoF robot in underwater environments. We address missions requiring UUVs to remain in close proximity to seafloor terrain, which must be achieved alongside collision avoidance. Our framework also considers the influence of current flows as part of the cost function, allowing for more accurate planning. To evaluate the performance of our proposed framework, we compare it with the widely used RRT* and STOMP algorithms over a range of underwater environments. Our experimental results demonstrate the stability and time efficiency of our framework.


Abstract: In this paper, we construct a class of Hermitian self-dual 2-quasi-abelian codes over a finite field. Based on counting the number of such codes and estimating the number of the codes in this class whose relative minimum weights are small, we prove that the class of Hermitian self-dual 2-quasi-abelian codes over any finite field is asymptotically good. The existence of such codes is unconditional, which is different from the case of Euclidean self-dual 2-quasi-abelian codes over a special finite field.


Beyond geofencing: Behavior detection using AIS

Abstract: This research paper proposes two different methods for removing biases from geo-spatial trajectories obtained from AIS, enabling the creation of machine learning models that generalize well to areas with no training data. We focus here on the task of behavior detection such as moored, underway or drifting from AIS. The first method utilizes data augmentation techniques specifically designed for augmenting geo-spatial trajectories that include angled information such as direction, while the second method is based on engineering features that eliminate these geographical biases. These two methods are compared using different types of machine learning models, including random forests, hidden Markov models, LSTMs, and transformer networks.


Learning Technologies

Abstract: The aim of the chapter is to present the learning technologies helping students to get high-quality personalised assistance in the learning process. Digital transformation in education makes a huge impact on the quality of education; however, authors are focusing on the chatbots that can be used for providing personalised support and guidance to students in education. Moreover, chatbots can help to improve academic performance and reduce the number of drop out students. The authors will discuss the chatbot aspects based on the 6 main chatbot parameters that determine design and development presented by Kumar: (1) knowledge, (2) services, (3) goals, (4) input processing, (5) human aid, and (6) building open-source or closed platforms. These parameters convey that a chatbot can fulfil numerous communication and interaction functionalities based on needs, platforms, and technologies.


A Compact and Robust 6-DoF Pose Tracking System Using Magnetic-Inertial Sensors and a Single Uniaxial Electromagnetic Coil

Abstract: Magnetic pose tracking, which is non-contacting and occlusion-free, is increasingly used in virtual/augmented reality, rehabilitation, and computer-assisted medical interventions to track the motion of human body and various tools and devices. Most existing magnetic tracking systems employ multiple magnetic sources and multiple magnetic sensors, making the system bulky and energy consuming. Complex signal processing circuits and algorithms are needed to distinguish the magnetic signal from each magnetic source. In this paper, a compact 6-DoF magnetic-inertial tracking system is developed using only a single stationary uniaxial electromagnetic coil and a single magnetic-inertial sensor board attached to the moving object. Magnetic measurement of the electromagnetic source, geomagnetic measurement, and inertial measurements are fused using an extended Kalman filter framework to provide accurate, drift-free, and robust pose estimates. An on-off switching strategy for electromagnet control and a difference magnetic measurement technique are used to cancel out potential ambient magnetic disturbances. Magnetic measurement of the electromagnetic source is mainly responsible for 3-DoF position estimation while geomagnetic field measurement is used to correct the yaw angle drift in inertial orientation estimation. Since geomagnetic measurement is susceptible to magnetic disturbances, it is switched off adaptively once detecting disturbances to maintain the robustness of the pose tracking system. Experimental results show that the RMS Euclidean position error is 2.84 mm and the average RMS orientation angle error is 1.22° even in presence of magnetic disturbance of a permanent magnet.


Firearm-related action recognition and object detection dataset for video surveillance systems

Abstract: The proposed dataset is comprised of 398 videos, each featuring an individual engaged in specific video surveillance actions. The ground truth for this dataset was expertly curated and is presented in JSON format (standard COCO), offering vital information about the dataset, video frames, and annotations, including precise bounding boxes outlining detected objects. The dataset encompasses three distinct categories for object detection: "Handgun", "Machine_Gun", and "No_Gun", dependent on the video's content. This dataset serves as a resource for research in firearm-related action recognition, firearm detection, security, and surveillance applications, enabling researchers and practitioners to develop and evaluate machine learning models for the detection of handguns and rifles across various scenarios. The meticulous ground truth annotations facilitate precise model evaluation and performance analysis, making this dataset an asset in the field of computer vision and public safety.


Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

Abstract: As a fundamental aspect of human life, two-person interactions contain meaningful information about people’s activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this article, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches in most standard evaluation settings.


Unified Model Based on Reinforced Feature Reconstruction for Metro Track Anomaly Detection

Abstract: Metro track anomaly detection can prevent accidents, thus avoiding severe life safety and property losses. Unsupervised methods that rely on one model per category or scene are unsuitable for complex and diverse track environments and unified detection, exhibiting poor stability. For most feature-based methods, the multi-stage features extracted by the pre-trained model contain the redundant information and noise, which interferes the feature reconstruction and anomaly detection. Additionally, the presence of abnormal information in the reconstructed feature further degrades the performance of anomaly detection. To address the aforementioned issues, a unified anomaly detection model based on feature reconstruction, named Reinforced Feature Reconstruction-based Anomaly Detection Network (RFReconAD), is proposed. The proposed efficient channel feature reinforcement module cooperated with the designed loss function weakens the interference of redundant information and noise on feature reconstruction task. The layer-wise learnable queries embedded in the decoder alleviate the problem of anomaly reconstruction. Moreover, the proposed detection scheme achieves more accurate anomaly detection. Under unified training and inference, our method achieves 99.8% and 98.2% Image-level AUROC, as well as 99.2% and 97.2% Pixel-level AUROC, on the Track Foreign Object Detection dataset and MVTec-AD dataset, respectively; And its inference speed reaches 37 frames/s, outperforming the state-of-the-art methods.


GTEA: Guided Taylor Expansion Approximation Network for Optical Flow Estimation

Abstract: Deep neural network (DNN) has proven its powerful ability in feature representation and has been widely applied in computer vision tasks, especially optical flow estimation (OFE). However, these DNN-based OFE approaches have a black-box nature, resulting in lacking a clear interpretation for their success. More unfortunately, they usually suffer from the loss of objects’ shape, which is not beneficial for performance improvement. To address these issues, we develop a novel guided Taylor expansion approximation (GTEA) network, including the following key procedures: First, we present the detail analysis of the relationship between Taylor expansion and optical flow. Then, a convolutional guided filter (ConvGF) network is developed to refine the shape of objects. Finally, the final output is obtained by employing basic network, ConvGF, and detail network to approximate the Taylor expansion. Quantitatively and qualitatively experimental results on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sintel and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KITTI datasets demonstrate that the proposed GTEA is effective and even performs better than the state-of-the-art optical flow estimation methods on performance improvement and shape preservation.


RCS Information Aided Poisson Multi-Bernoulli Mixture Filter in Clutter Background

Abstract: For modern radar systems, the measurement of the target’s radar cross section (RCS) is a standard output besides kinematic measurements. It is straightforward to incorporate RCS information into tracking algorithms for performance improvements in more realistic and difficult scenarios. However, in practice, the proper integration of RCS recursion and Bayesian filter is promising but challenging. To address this issue, an RCS information aided Poisson multi-Bernoulli mixture (RCSI-PMBM) filter in clutter background is proposed in this paper. Firstly the Bayesian RCS estimation strategy is presented, and then the RCSI-PMBM filter is analytically developed. Moreover, based on the Gamma Gaussian mixture (GGM) form, an effective and efficient implementation of the proposed RCSI-PMBM filter is developed. Finally, the validity of the proposed algorithm is verified by simulation tests with challenging scenarios.


Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge Graph

Abstract: The cross-domain recommender systems aim to alleviate the data sparsity problem in the target domain by transferring knowledge from the auxiliary domain. However, existing works ignore the fact that the data sparsity problem may also exist in the single auxiliary domain, and sharing user behavior data is restricted by the privacy policy. In addition, their cross-domain models lack interpretability. To address these concerns, we propose a novel multi-source cross-domain model based on knowledge graph. Specifically, to avoid the insufficiency of single auxiliary domain, we construct a knowledge graph comprehensively leveraging items from multiple auxiliary domains. To avoid the leakage of user privacy when user information is transferred to multiple domains, we construct graph for information transfer between items to effectively avoid the propagation of users’ private information between different domains. We implicitly integrate the user–item interaction by transferring the learned item embeddings. To improve the interpretability of cross-domain knowledge transfer, we propose a knowledge graph-based retrieval and fusion method to transfer knowledge derived from multiple auxiliary domains. An attention-based fusion network is designed to enhance the representation of the targeted user and items with the transferred item embedding. We perform extensive experiments on three real-world datasets, demonstrating that our model outperforms the states of the art.


Neighbor2Global: Self-supervised image denoising for Poisson-Gaussian noise

Abstract: Sub-sampled pairs generated from noisy images solve the lack of noisy-clean pairs in denoising model training. However, real sub-sampled image pairs with signal-dependent Poisson-Gaussian noise and the approximation of neighboring pixels lead to denoising performance degradation. For this reason, a novel self-supervised Neighbor2Global is proposed to train an efficient denoising model for real-world images denoising. Firstly, the problems faced by model training are analyzed theoretically, and a GAT-based image generation strategy is introduced to make the sub-sampled pair from a single noisy image approximately independent. Secondly, a complementary training strategy is presented to train the denoising network on sub-sampled GAT image pairs, GAT images and PD-transformed GAT images, with improved reconstruction loss and optimized regulation for better performance. Extensive experiments have been conducted on both synthetic and real-world datasets to validate the superior performance of the proposed method in balancing detailed texture and global information, as well as weakening over-smoothing.


Optimizing solar power plant efficiency through advanced analytical framework and comparative analysis

Abstract: As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience.


A hybrid multi-criteria meta-learner based classifier for imbalanced data

Abstract: Numerous imbalanced datasets exist in modern machine learning dilemmas. Challenges of generalization and fairness stem from the existence of underrepresented classes with sensitive characteristics. Additionally, defining an optimal criterion is crucial when dealing with imbalanced data. To address this, it is common in practice to employ composite metrics and loss functions that evenly distribute the cost across each class. However, relying solely on a single metric or loss function, frequently yields sub-par outcomes due to the tendency of the optimization criteria to be overly responsive to imbalanced data, resulting in excessive adaptation and flawed generalization. In order to overcome these limitations, various heuristics can be applied to optimize the criterion. To this end, we aim at addressing the problem of imbalanced data classification by leveraging a multicriteria ensemble procedure, the Hybrid Multi-criteria Meta-learner (HML). The suggested approach focuses on optimizing precision, recall together with balanced accuracy in the Multi-Objective optimization phase and brings forth optimal ensembles, from which the concluding Meta-learner can be chosen after a clustering step, based on a multicriteria decision-making aspect contingent on the selected metrics. Comprehensive experiments portrayed the effectiveness of HML, offering both quality assurance and addresses a vast array of imbalanced data problems.


Appearance flow estimation for online virtual clothing warping via optimal feature linear assignment

Abstract: Clothing warping to spatially align source garments with the corresponding body parts is crucial in clothing media tasks such as virtual try-on and pose-guided person generation. Recent pioneering work has utilized flow fields with additional dimensions of freedom to simulate clothing warping flexibly. However, current methods for estimating appearance flow typically rely on calculating just the local cost volume which contains multiple noisy matching points, potentially leading to a mismatch between clothing and body parts. To address this issue, we propose a novel appearance flow estimation network(Warping-Flow) for clothing warping based on optimal features linear assignment. Specially, two remarkable contributions are made to improve feature matching precision. First, a local context feature aggregation module is proposed to enhance the semantic feature distinction of the source cloth and target pose. Second, Warping-Flow estimates a hard attention mask through the cost volume to filter irrelevant features, followed by the optimal linear assignment algorithm to normalize the cost volume to a discrete permutation matrix that explicitly models the most contributing bipartite matches. Experiments conducted on the VITON and VITON-HD datasets demonstrate that Warping-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex clothing deformation. Furthermore, Warping-Flow can serve as a plug-in to improve existing garment media technologies.


Delta robot control by learning systems: Harnessing the power of deep reinforcement learning algorithms

Abstract: The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot’s end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper.


Pyramid hybrid pooling quantization for efficient fine-grained image retrieval

Abstract: Deep hashing approaches, including deep quantization and deep binary hashing, have become a common solution to large-scale image retrieval due to their high computation and storage efficiency. Most existing hashing methods cannot produce satisfactory results for fine-grained retrieval, because they usually adopt the outputs of the last CNN layer to generate binary codes. Since deeper layers tend to summarize visual clues, e.g., texture, into abstract semantics, e.g., dogs and cats, the feature produced by the last CNN layer is less effective in capturing subtle but discriminative visual details that mostly exist in shallow layers. To improve fine-grained image hashing, we propose Pyramid Hybrid Pooling Quantization (PHPQ). Specifically, we propose a Pyramid Hybrid Pooling (PHP) module to capture and preserve fine-grained semantic information from multi-level features, which emphasizes the subtle discrimination of different sub-categories. Besides, we propose a learnable quantization module with a partial codebook attention mechanism, which helps to optimize the most relevant codewords and improves the quantization. Comprehensive experiments on two widely-used public benchmarks, i.e., CUB-200-2011 and Stanford Dogs, demonstrate that PHPQ outperforms state-of-the-art methods.


Music curriculum integration and reconstruction model based on advanced music waveform iterative reconstruction algorithm

Abstract: To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms.


Bayesian network structure learning based on HC-PSO algorithm

Abstract: Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy.


GLOCAL: A self-supervised learning framework for global and local motion estimation

Abstract: Motions in videos are typically a mixture of local dynamic object motions and global camera motion, which are inconsistent in some cases, and even interfere with each other, causing difficulties in various downstream applications, such as video stabilization that requires the global motion, and action recognition that consumes local motions. Therefore, it is crucial to estimate them separately. Existing methods separate two motions from the mixed motion fields, such as optical flow. However, the quality of mixed motion determines the higher bounds of the performance. In this work, we propose a framework, GLOCAL, to directly estimate global and local motions simultaneously from adjacent frames in a self-supervised manner. Our GLOCAL consists of a Global Motion Estimation (GME) module and a Local Motion Estimation (LME) module. The GME module involves a mixed motion estimation backbone, an implicit bottleneck structure for feature dimension reduction, and an explicit bottleneck for global motion recovery based on the global motion bases with foreground mask under the training guidance of proposed global reconstruction loss. An attention U-Net is adopted for LME which produces local motions while excluding motion of irrelevant regions under the guidance of proposed local reconstruction loss. Our method can achieve better performance than the existing methods on the homography estimation dataset DHE and the action recognition dataset NCAA and UCF-101.


Structural Discovery with Partial Ordering Information for Time-Dependent Data with Convergence Guarantees

Abstract: Structural discovery among a set of variables is of interest in both static and dynamic settings. In the presence of lead-lag dependencies in the data, the dynamics of the system can be represented through a structural equation model (SEM) that simultaneously captures the contemporaneous and temporal relationships amongst the variables, with the former encoded through a directed acyclic graph (DAG) for model identification. In many real applications, a partial ordering amongst the nodes of the DAG is available, which makes it either beneficial or imperative to incorporate it as a constraint in the problem formulation. This article develops an algorithm that can seamlessly incorporate a priori partial ordering information for solving a linear SEM (also known as Structural Vector Autoregression) under a high-dimensional setting. The proposed algorithm is provably convergent to a stationary point, and exhibits competitive performance on both synthetic and real datasets. Supplementary materials for this article are available online.


Dilated convolution for enhanced extractive summarization: A GAN-based approach with BERT word embedding

Abstract: Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance.


Transliteration Characteristics in Romanized Assamese Language Social Media Text and Machine Transliteration

Abstract: This article aims to understand different transliteration behaviors of Romanized Assamese text on social media. Assamese, a language that belongs to the Indo-Aryan language family, is also among the 22 scheduled languages in India. With the increasing popularity of social media in India and also the common use of the English Qwerty keyboard, Indian users on social media express themselves in their native languages, but using the Roman/Latin script. Unlike some other popular South Asian languages (say Pinyin for Chinese), Indian languages do not have a common standard romanization convention for writing on social media platforms. Assamese and English are two very different orthographical languages. Thus, considering both orthographic and phonemic characteristics of the language, this study tries to explain how Assamese vowels, vowel diacritics, and consonants are represented in Roman transliterated form. From a dataset of romanized Assamese social media texts collected from three popular social media sites: (Facebook, YouTube, and X (formerly known as Twitter)), 1 we have manually labeled them with their native Assamese script. A comparison analysis is also carried out between the transliterated Assamese social media texts with six different Assamese romanization schemes that reflect how Assamese users on social media do not adhere to any fixed romanization scheme. We have built three separate character-level transliteration models from our dataset. One using a traditional phrase-based statistical machine transliteration model, (1) PBSMT model and two separate neural transliteration models, (2) BiLSTM neural seq2seq model with attention, and (3) Neural transformer model. A thorough error analysis has been performed on the transliteration result obtained from the three state-of-the-art models mentioned above. This may help to build a more robust machine transliteration system for the Assamese social media domain in the future. Finally, an attention analysis experiment is also carried out with the help of attention weight scores taken from the character-level BiLSTM neural seq2seq transliteration model built from our dataset.


Bridging Software-Hardware for CXL Memory Disaggregation in Billion-Scale Nearest Neighbor Search

Abstract: We propose CXL-ANNS , a software-hardware collaborative approach to enable scalable approximate nearest neighbor search (ANNS) services. To this end, we first disaggregate DRAM from the host via compute express link (CXL) and place all essential datasets into its memory pool. While this CXL memory pool allows ANNS to handle billion-point graphs without an accuracy loss, we observe that the search performance significantly degrades because of CXL’s far-memory-like characteristics. To address this, CXL-ANNS considers the node-level relationship and caches the neighbors in local memory, which are expected to visit most frequently. For the uncached nodes, CXL-ANNS prefetches a set of nodes most likely to visit soon by understanding the graph traversing behaviors of ANNS. CXL-ANNS is also aware of the architectural structures of the CXL interconnect network and lets different hardware components collaborate with each other for the search. Furthermore, it relaxes the execution dependency of neighbor search tasks and allows ANNS to utilize all hardware in the CXL network in parallel. Our evaluation shows that CXL-ANNS exhibits 93.3% lower query latency than state-of-the-art ANNS platforms that we tested. CXL-ANNS also outperforms an oracle ANNS system that has unlimited local DRAM capacity by 68.0%, in terms of latency.


Quantum speed limits of quantum system in colored environments

Abstract: In this paper, we investigate quantum speed limits (QSLs) of a two-level system in the colored environments which are described by stochastic processes. We present QSL can be affected by the correlation time, the partitions of the constituent elements of the two-level system in different colored environments: the Ornstein–Uhlenbeck environment, simple fluctuating bottleneck environment, and the Dichotomic (two-state) environment. The investigations provide us a potential way to manipulate the quantum speed limit time (QSLT) of the quantum system in the colored environments. Our results could provide different insights on quantum control technologies of QSL and decoherent dynamics for the quantum computation.


Assessing the Impact of Body Location on the Accuracy of Detecting Daily Activities through Accelerometer/Gyroscope Data Using Deep Learning

Abstract: Recent advancements in digital biomarkers have highlighted the importance of accelerometer and gyroscope data for monitoring activities, identifying motion-related diseases, and assessing disease severity. Prior studies predominantly limit sensor placement to one or two locations. Here, we conducted a trial focusing on the impact of sensor placement in predicting 21 common activities using convolutional neural networks (CNN) and long short-term memory networks (LSTM). Our research found that the optimal locations for activity detection are the right and left upper arms, right wrist, and lower back. These locations yielded an average AUC of 0.76-0.77 using both accelerometer and gyroscope data. Combining data from all locations improved AUC to 0.796 for accelerometer and 0.811 for gyroscope data. We also noted specific activity-body part sensitivity relationships. This study provides a valuable reference for selecting appropriate sensor locations in future digital biomarker studies focused on specific activities.


Chaotic neural network quantization and its robustness against adversarial attacks

Abstract: Achieving robustness against adversarial attacks while maintaining high accuracy remains a critical challenge in neural networks. Parameter quantization is one of the main approaches used to compress deep neural networks to have less inference time and less storage memory size. However, quantization causes severe degradation in accuracy and consequently in model robustness. This work investigates the efficacy of stochastic quantization to enhance robustness and accuracy. Noise injection during quantization is explored to understand the impact of noise types and magnitudes on model performance. A comprehensive comparison between different applying scenarios for stochastic quantization and different noise types and magnitudes was implemented in this paper. Compared to the baseline deterministic quantization, chaotic quantization achieves a comparable accuracy, however, it achieves up to a 43% increase in accuracy against various attack scenarios. This highlights stochastic quantization as a promising defense mechanism. In addition, there is a crucial role played by the choice of noise type and magnitude in stochastic quantization. Lorenz and Henon noise distributions in stochastic quantization outperform traditional uniform and Gaussian noise in defending against attacks. A transferability analysis was discussed to understand the generalizability and effectiveness of the proposed stochastic quantization techniques. A cross-validation definition was newly evaluated in this scope to analyse the model's stability and robustness against attacks. The study outperformed a quantization network technique and improved the model's robustness and stability against adversarial attacks using chaotic quantization instead of deterministic quantization or even instead of stochastic quantization using traditional noise.


A provably secure and practical end-to-end authentication scheme for tactile Industrial Internet of Things

Abstract: In the Industrial Internet of Things (IIoT), haptic control of machines or robots can be managed remotely. However, with the emergence of Tactile Industrial Internet of Things (TIIoT), the transmission of haptic data over public channels has raised security and privacy concerns. In such an environment, mutual authentication between haptic users and remotely controlled entities is crucial to prevent illegal control by adversaries. Therefore, we propose an end-to-end authentication scheme, SecTIIoT, to establish secure communication between haptic users and remote IoT devices. The scheme addresses security issues by using lightweight hash cryptographic primitives and employs a useful piggyback strategy to improve authentication efficiency. We demonstrate that SecTIIoT is resilient to various known attacks with formal security proofs and informal security analysis. Furthermore, our detailed performance analysis shows that SecTIIoT outperforms existing lightweight authentication schemes as it provides more security features while reducing computation and communication costs.


AIPT: Adaptive information perception for online multi-object tracking

Abstract: Information perception is crucial in MOT tasks. Recent approaches use positional, motion, and appearance information to model object states. However, in scenes involving camera motion, tracking tasks suffer from image distortion, trajectory loss, and mismatching issues. In this paper, we propose Adaptive Information Perception for Online Multi-Object Tracking, abbreviated as AIPT. AIPT consists of an Adaptive Motion Perception Module (AMPM) and an Asymmetric Information Suppression Module (AISM). In AMPM, we design an Adaptive Image Distortion Recovery Module (AIDRM) to perceive distortions in unknown scenes, allowing the tracker to autonomously recover distorted images as the scene changes. By designing the Information-Guided Trajectory Restoration Module (IGTRM), the tracker learns object motion states from prior information and constructs accurate reconstruction information during trajectory loss. Furthermore, our AISM module utilizes masking information to suppress potential relationships between asymmetric objects, thereby enhancing the ability of tracker to handle mismatches. Both AMPM and AISM exhibit excellent scalability, seamlessly integrating with most advanced tracking methods. Ultimately, our AIPT achieves leading performance on multiple benchmark platforms, including MOT17, MOT20, and KITTI.


PRIMIS: Privacy-preserving medical image sharing via deep sparsifying transform learning with obfuscation

Abstract: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images. In tandem, we aim to provide authorized users with compact private keys that could be used to reconstruct the corresponding images.Our approach involves utilizing a neural auto-encoder. The convolutional filter outputs are passed through sparsifying transformations to produce multiple compact codes. Each code is responsible for reconstructing different attributes of the image. The key privacy-preserving element in this process is obfuscation through the use of specific pseudo-random noise. When applied to the codes, it becomes computationally infeasible for an attacker to guess the correct representation for all the codes, thereby preserving the privacy of the images.The proposed framework was implemented and evaluated using chest X-ray images for different medical image analysis tasks, including classification, segmentation, and texture analysis. Additionally, we thoroughly assessed the robustness of our method against various attacks using both supervised and unsupervised algorithms.This study provides a novel, optimized, and privacy-assured data-sharing mechanism for medical images, enabling multi-party sharing in a secure manner. While we have demonstrated its effectiveness with chest X-ray images, the mechanism can be utilized in other medical images modalities as well.


A multi-aspect framework for explainable sentiment analysis

Abstract: The demand for explainable sentiment analysis has intensified, emphasizing the need for models that are both accurate and interpretable. This research introduces the Multi-Aspect Framework for Explainable Sentiment Analysis (MAFESA), a groundbreaking model that seamlessly integrates aspect extraction, sentiment prediction, and explainability. By harnessing the power of Latent Dirichlet Allocation (LDA) for aspect extraction and leveraging hierarchical neural networks for sentiment prediction, MAFESA achieves remarkable performance metrics. Notably, our framework outperforms state-of-the-art baseline models across benchmark datasets such as IMDB Movie Reviews, Amazon Product Reviews, and Twitter Sentiment Analysis. The inclusion of an explainability module, built around techniques like LIME, offers unparalleled insights into the model's decision-making, ensuring predictions are transparent and trustworthy. Our performance evaluations, underscored by a thorough ablation study, cross-validation, and rigorous statistical tests, attest to MAFESA's robustness, generalizability, and superiority. A detailed qualitative analysis further showcases the model's adeptness at discerning aspect-level nuances and delivering clear explanations for sentiment predictions. This research not only sets a new benchmark in explainable sentiment analysis but also provides a holistic framework that balances prediction precision with interpretability.


Entanglement versus Bell non-locality via solving the fractional Schrödinger equation using the twisting model

Abstract: The memory fractional effects of a one-axis twisting model on the dynamics of two-qubit entanglement and non-locality are discussed. It consists of solving the time-dependent fractional Schrödinger equation by extending any integration into non-integer orders using Riemann–Liouville integration. The obtained results present the possibility of controlling the fractional order of memory, varying the parameters to significantly generate concurrency and Bell's non-locality. Under the current investigation setup, it is noticeable that the behaviors of the proposed quantifiers are similar to each other, but with a small difference in the amplitude of non-locality with respect to entanglement. Importantly, we show that the most intriguing aspect of this paper is to detect that pair-qubit entanglement and non-locality can be preserved for an indefinite time, which still holds significance in quantum information processing.


Intent-Oriented Dynamic Interest Modeling for Personalized Web Search

Abstract: Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.


Data-Reuse Adaptive Algorithms for Graph Signal Estimation Over Sensor Network

Abstract: The least mean square (LMS) algorithm of the graph signal processing (GSP) can be used for sensor signal processing due to its simplicity and low computational complexity, and the recursive least squares (RLS) algorithm of GSP can be applied to sensor signal processing since it has fast convergence rate. However, the GSP LMS algorithm has poor convergence performance, the tracking performance of the GSP RLS algorithm turns weak after signal mutation. To solve the mentioned problems, we focus on the data-reuse strategy, aiming to improve the convergence/tracking performance of the related algorithms by reusing the same set of data several times, thus the graph signal processing data-reuse least mean square (GSP-DR-LMS) algorithm and the graph signal processing data-reuse recursive least squares (GSP-DR-RLS) algorithm are proposed. Moreover, to make the GSP-DR-LMS algorithm achieve a better coordination between steady state error and convergence rate, we propose the variable step size (VSS) strategy applicable to the GSP-DR-LMS algorithm, thus the GSP VSS-DR LMS algorithm is proposed. In addition, the performance analysis of the related algorithms is performed. Ultimately, we verify the superiority of the proposed algorithms in terms of convergence/tracking performance by performing computer simulations.


Discovering Predictive Relational Object Symbols With Symbolic Attentive Layers

Abstract: In this paper, we propose and realize a new deep learning architecture for discovering symbolic representations for objects and their relations based on the self-supervised continuous interaction of a manipulator robot with multiple objects in a tabletop environment. The key feature of the model is that it can take a changing number of objects as input and map the object-object relations into symbolic domain explicitly. In the model, we employ a self-attention layer that computes discrete attention weights from object features, which are treated as relational symbols between objects. These relational symbols are then used to aggregate the learned object symbols and predict the effects of executed actions on each object. The result is a pipeline that allows the formation of object symbols and relational symbols from a dataset of object features, actions, and effects in an end-to-end manner. We compare the performance of our proposed architecture with state-of-the-art symbol discovery methods in a simulated tabletop environment where the robot needs to discover symbols related to the relative positions of objects to predict the action's result. Our experiments show that the proposed architecture performs better than other baselines in effect prediction while forming not only object symbols but also relational symbols.


Bayesian mixed-effect higher-order hidden Markov models with applications to predictive healthcare using electronic health records

Abstract: The disease progression dynamics observed in electronic health records often reflect patients' health condition evolution, holding the promise of enabling the development of clinical predictive models. These dynamics, however, generally display significant variability among patients, due to some critical factors (e.g., gender and age) and patient-level heterogeneity. Moreover, future health state may not only depend on the current state, but also more distant history states due to the complicated disease progression. To capture this complex transition behavior and address mixed effects in clinical prediction problems, we propose a novel and flexible Bayesian Mixed-Effect Higher-Order Hidden Markov Model (MHOHMM), and develop a classifier based on MHOHMMs. A range of MHOHMMs are designed to capture different data structures and the optimal one is identified by using the k-fold cross-validation approach. An effective two-stage Markov chain Monte Carlo (MCMC) sampling algorithm is designed for model inference. A simulation study is conducted to evaluate the performance of the proposed sampling algorithm and the MHOHMM-based classification method. The practical utility of the proposed framework is demonstrated by a case study on the acute hypotensive episode prediction for intensive care unit patients. Our results show that the MHOHMM-based framework provides good prediction performance.


Faster region based convolution neural network with context iterative refinement for object detection

Abstract: In this paper, proposed a novel method to improve the localisation precision of identified objects. We present a framework for iteratively enhancing image region recommendations to meet ground truth values in this research. The Faster R–CNN (FR-CNN) seems to be an object recognition deep convolutional network. It gives the user the impression that the network is cohesive and single. The network can provide accurate and timely predictions about the whereabouts of a range of objects. We first build a unified model based on rapid predictions to relocate inaccurate area recommendations. Because the emphasis is on object detection, it may be utilized with a wide range of datasets and is compatible with various FR-CNN architectures. Second, we focus on the application of the joint score function to a variety of picture features. This joint score function depicts the location of the concealed object concerning other objects. The picture data and an updated structured production loss function are the only two inputs that influence the parameters of the joint scoring function. The join-score function and iterative context refinement (CIR) are used to generate our final unified model, which is then classified using Smooth Support Vector Machine (SSVM). We measured accuracy using the mean average precision after training FR-CNN + CIR and SSVM on a low-cost GPU using the PASCAL VOC 2012 dataset. Our results are 3.6 % more exact than rival deep learning algorithms on average.