Reviewerly/exHarmony huggingface dataset:
3-D Object Detection With Balanced Prediction Based on Contrastive Point Loss

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.