Reviewerly/exHarmony huggingface dataset:
Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking

3D single object tracking with LiDAR points is an important task in the computer vision field. Previous methods usually adopt the matching-based or motion-centric paradigms to estimate the current target status. However, the former is sensitive to the similar distractors and the sparseness of point clouds due to relying on appearance matching, while the latter usually focuses on short-term motion clues (eg. two frames) and ignores the long-term motion pattern of target. To address these issues, we propose a mixed paradigm with two stages, named MTM-Tracker, which combines motion modeling with feature matching into a single network. Specifically, in the first stage, we exploit the continuous historical boxes as motion prior and propose an encoder-decoder structure to locate target coarsely. Then, in the second stage, we introduce a feature interaction module to extract motion-aware features from consecutive point clouds and match them to refine target movement as well as regress other target states. Extensive experiments validate that our paradigm achieves competitive performance on large-scale datasets (70.9% in KITTI and 51.70% in NuScenes). The code will be open soon at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LeoZhiheng/MTM-Tracker.git</uri> .
Dual-Branch Multitask Fusion Network for Offline Chinese Writer Identification

Chinese characters are complex and contain discriminative information, meaning that their writers have the potential to be recognized using less text. In this study, offline Chinese writer identification based on a single character was investigated. To extract comprehensive features to model Chinese characters, explicit and implicit information as well as global and local features are of interest. A dual-branch multitask fusion network is proposed that contains two branches for global and local feature extraction simultaneously, and introduces auxiliary tasks to help the main task. Content recognition, stroke number estimation, and stroke recognition are considered as three auxiliary tasks for explicit information. The main task extracts implicit information of writer identity. The experimental results validated the positive influences of auxiliary tasks on the writer identification task, with the stroke number estimation task being most helpful. In-depth research was conducted to investigate the influencing factors in Chinese writer identification, with respect to character complexity, stroke importance, and character number, which provides a systematic reference for the actual application of neural networks in Chinese writer identification.
High-performance montgomery modular multiplier with NTT and negative wrapped convolution

Modular multiplication plays a crucial role in modern cryptography. Montgomery modular multiplication(MMM), one of the most classic and practical modular multiplication algorithms, has been widely used in cryptographic algorithms such as RSA, Diffie–Hellman algorithm, and Elliptic Curve Cryptography. In this paper, we incorporate negative wrapped convolution (NWC) into the FFT-based Montgomery modular multiplication to avoid the issue of zero-padding and use carry-save arithmetics for parallel computation. By utilizing coefficient pairs (pos_part and neg_part), we reconstruct the final result and eliminate the restrictions imposed by nega-cyclic parts. Moreover, Karatsuba-like algorithm is introduced for building fine-grained large integer multipliers. We have modified the parameter specifications for our design to meet requirements from diverse application scenarios. We implement the design on Xilinx Virtex-7 FPGA under different conditions and compare the results with the state-of-the-art MMM designs. The comparisons confirm that our design has the following characteristics: low latency for process, competitive area-latency-product(ALP), efficient DSP usage, and constant delay, which enhances security against timing attacks.
Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation

Sequential recommendation aims to predict the next item of interest to users based on their historical behavior data. Usually, users’ global and local preferences jointly affect the final recommendation result in different ways. Most existing works use transformers to globally model sequences, which makes them face the dilemma of quadratic computational complexity when dealing with long sequences. Moreover, the scope setting of the user’s local preference is usually static and single, and cannot cover richer multi-level local semantics. To this end, we proposed a parallel architecture for capturing global representation and M ulti-granularity L ocal dependencies with M LP for sequential Rec ommendation ( MLM4Rec ). For global representation, we utilize modified MLP-Mixer to capture global information of user sequences due to its simplicity and efficiency. For local representation, we incorporate convolution into MLP and propose a multi-granularity local awareness mechanism for capturing richer local semantic information. Moreover, we introduced a weight pooling method to adaptively fuse local-global representations instead of directly concatenation. Our model has the advantages of low complexity and high efficiency thanks to its simple MLP structure. Experimental results on three public datasets demonstrate the effectiveness of our proposed model. Our code is available here 1 .
SCTrans: Self-align and cross-align transformer for few-shot segmentation

Few-shot Semantic Segmentation (FSS) refers to train a segmentation model that can be generalized to novel categories with limited labeled images. One challenge of FSS is spatial inconsistency between support and query images, e.g., appearance and texture. Most existing methods are only committed to utilizing the semantic-level prototypes of support images to guide mask predictions. These methods, nevertheless, only focus on the most discriminate regions of the object rather than holonomic feature representations. Besides, another question exists that the lack of interaction between paired support and query images. In this paper, we propose a self-align and cross-align transformer (SCTrans) to remedy the above limitations. Specifically, we design a feature fusion module (FFM) to incorporate low-level information from the query branch into mid-level semantic features, boosting the semantic representations of query images. In addition, a feature alignment module is designed to bidirectionally propagate semantic information from support to query images conditioned on more representative support and query features, increasing both intra-class similarities and inter-class differences. Extensive experiments on PASCAL-5i and COCO-20i show that our SCTrans significantly advances the state-of-the-art methods.
Sequential auction for cloud manufacturing resource trading: A deep reinforcement learning approach to the lot-sizing problem

Cloud manufacturing is a rapidly growing trend in modern manufacturing, which has transformed the traditional operations and value chain structure of enterprises. It is crucial to develop a rational and effective trading mechanism of cloud manufacturing resources to meet the ever-increasing demands in this new environment. This paper proposes a sequential auction-based paradigm for the trading of manufacturing resources. The main challenge of the design of the paradigm is to determine the dynamic lot size of resources allocated to each auction considering the uncertainty of arriving demands. To achieve this, we first develop a competitive game model to identify optimal bidding strategies of arrived customers and estimate the expected revenue for each round with the given lot size. Secondly, we construct a Markov decision process (MDP) model to characterize the dynamics of the arrival of stochastic demand and the inventory transition of the manufacturing resources in sequential auctions. Lastly, we leverage a data-driven approach by integrating machine learning with an offline deep reinforcement learning (RL) approach. Specifically, we employ a long short-term memory (LSTM) model to predict forthcoming demands in the environment and develop the deep Q-network (DQN) learning algorithm to optimize lot-sizing policy by interacting with the well-learned LSTM environment model. Our simulation experiments validate the effectiveness of our approach and some management insights are given.
EFDCNet: Encoding fusion and decoding correction network for RGB-D indoor semantic segmentation

Semantic segmentation is a crucial task in vision measurement systems that involves understanding and segmenting different objects and regions within an image. Over the years, numerous RGB-D semantic segmentation methods have been developed, leveraging the encoder-decoder architecture to achieve outstanding performance. However, existing methods have two main problems that constrain further performance improvement. Firstly, in the encoding stage, existing methods have a weak ability to fuse cross-modal information, and low-quality depth maps can easily lead to poor feature representation. Secondly, in the decoding stage, the upsampling of high-level semantic information may cause the loss of contextual information, and low-level features from the encoder may bring noises to the decoder through skip connections. To solve these issues, we propose a novel Encoding Fusion and Decoding Correction Network (EFDCNet) for RGB-D indoor semantic segmentation. First, in the encoding stage of EFDCNet, we focus on extracting valuable information from low-quality depth maps, and employ a channel-wise filter to select informative depth features. Additionally, we establish the global dependencies between RGB and depth features via the self-attention mechanism to enhance the cross-modal feature interactions, extracting discriminant and powerful features. Then, in the decoding stage of EFDCNet, we use the highest-level information as semantic guidance to compensate for the upsampling information and filter out noise from the low-level encoder features propagated through the skip connections to the decoder. Extensive experiments conducted on two widely-used RGB-D indoor semantic segmentation datasets demonstrate that the proposed EFDCNet surpasses the performance of relevant state-of-the-art methods. The code is available at https://github.com/ Mark9010/EFDCNet
The Computational Method for Supporting Thai VerbNet Construction

VerbNet is a lexical resource for verbs that has many applications in natural language processing tasks, especially ones that require information about both the syntactic behavior and the semantics of verbs. This article presents an attempt to construct the first version of a Thai VerbNet corpus via data enrichment of the existing lexical resource. This corpus contains the annotation at both the syntactic and semantic levels, where verbs are tagged with frames within the verb class hierarchy and their arguments are labeled with the semantic role. We discuss the technical aspect of the construction process of Thai VerbNet and survey different semantic role labeling methods to make this process fully automatic. We also investigate the linguistic aspect of the computed verb classes and the results show the potential in assisting semantic classification and analysis. At the current stage, we have built the verb class hierarchy consisting of 28 verb classes from 112 unique concept frames over 490 unique verbs using our association rule learning method on Thai verbs.
Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges

The advent of pervasive computing and ambient intelligence has opened up new possibilities for the development of intelligent systems that can adapt to the needs of their users. This paper provides a comprehensive survey of recent advancements in this field, focusing on the different techniques, tools, and applications that have been developed to leverage the benefits of pervasive computing for ambient intelligence. We discuss the various challenges and opportunities that arise when designing such systems and provide a critical evaluation of the current state-of-the-art. In particular, we examine the role of machine learning, data analytics, and context awareness in the development of intelligent systems, and we investigate the impact of these technologies on the quality of life of their users. The paper also presents a discussion on the open challenges in this field, such as ensuring user privacy, security, and trust, designing effective user interfaces, and dealing with the complexity of large-scale systems. Finally, we conclude with an outlook on the future of ambient intelligence and its potential impact on society, and we highlight some of the emerging research directions that are likely to shape the field in the coming years.
Autoregressive Feature Extraction with Topic Modeling for Aspect-based Sentiment Analysis of Arabic as a Low-resource Language

This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, Bidirectional Encoder Representations from Transformers (BERT) and XLNet, along with topic modeling algorithms; namely, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), for aspect-based sentiment analysis of online Arabic text. In addition, Bidirectional Long Short Term Memory (Bi-LSTM) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.
Memory backtracking strategy: An evolutionary updating mechanism for meta-heuristic algorithms

The search domain of meta-heuristic algorithms is always constantly changing, which make it difficult to adapt the diverse optimization issues. To overcome above issue, an evolutionary updating mechanism called Memory Backtracking Strategy (MBS) is proposed, which contains thinking stage, recall stage, and memory stage. Overall, the adoption of the MBS enhances the efficiency of MHSs by incorporating group memory, clue recall, and memory forgetting mechanisms. These strategies improve the algorithm's ability to explore the search space, optimize the search process, and escape local optima. MBS will be applied to three different types of MHS algorithms: evolutionary based (LSHADE_SPACMA), physical based (Stochastic Fractal Search, SFS), and biological based (Marine Predators Algorithmnm, MPA) to demonstrate the universality of MBS. In the experimental section including 57 engineering problems, algorithm complexity analysis, CEC2020 Friedman ranking, convergence curve, Wilcoxon statistical, and box plot. Among them, 21 algorithms participated in the Friedman experiment, including MBS_LSHADE_SPACMA ranked first, LSHADE_SPACMA ranked second, MBS_MPA ranked 6th, MPA ranked 8th, MBS_SFS ranked 9th and SFS ranked 12th. Combined with the analysis of "MBS testing analysis" and the experimental results of engineering problems, it has proven that MBS has universality and good ability to improve optimization algorithm performance. The source codes of the proposed MBS (MBS_MPA) can be accessed by https://github.com/luchenghao2022/Memory-Backtracking-Strategy
The synchronization of K-valued Fuzzy cognitive maps

Fuzzy cognitive maps (FCMs) with the abilities of knowledge representation and causal reasoning have been widely applied in the control-related fields of multi-agent systems (MAS), such as the mobile robots and unmanned aerial vehicles (UAVs). From the perspective of system applications, the cooperative control of group behaviors is a meaningful topic in this community, where the synchronization of agents is one of fundamental issues. However, how to achieve the synchronization of FCM-based control mechanism is still an open problem. In this article, two finite state fuzzy cognitive map systems with unidirectional coupling in the drive-response configuration are introduced. By utilizing matrix semi-tensor product (STP), the coupling dynamical systems are transformed into algebraic forms, based on which the necessary and sufficient conditions for complete synchronization are presented. The corresponding theoretical proofs are given and quantitative analysis are implemented. Finally, several experiments are given to prove the effectiveness of the proposed methods.
GradDiff: Gradient-based membership inference attacks against federated distillation with differential comparison

Membership inference attacks (MIAs) has demonstrated a great threat to federated learning (FL) and its extension federated distillation (FD). However, existing research on MIAs against FD is insufficient. In this paper, we propose a novel membership inference attack named GradDiff, which is a passive gradient-based MIA employing differential comparison. Additionally, to make full use of the federated training process, we also design the gradient drift attack (GradDrift), an active version of GradDiff, in which the attacker modifies the target model by gradient tuning and is able to obtain more information about membership privacy. We conduct extensive experiments on three real-world datasets to evaluate the effectiveness of the proposed attacks. The results show that our proposed attacks can outperform the existing baseline methods in terms of precision and recall. Besides, we perform a thorough investigation of the factors that may influence the performance of MIAs against FD.
Enhancing deep feature representation in self-knowledge distillation via pyramid feature refinement

In recent years, various self-knowledge distillation approaches have been proposed to reduce the cost of training teacher networks. However, these methods often overlook the significance of deep features. To address this limitation and strengthen the capability of deep features while preserving the ability of shallow features, we propose performing Self-Knowledge Distillation via Pyramid Feature Refinement (PR-SKD). Inspired by the representation learning characteristics of deep neural networks, PR-SKD builds a cohort of sub-networks with a pyramid architecture to hierarchically transfer refined information to the target network. According to the different contributions and functions between deep and shallow feature maps, our PR-SKD fully utilizes feature information to improve deep feature representation ability without compromising the capability of shallow feature maps. Extensive experiments on various image classification datasets demonstrate the superiority of our proposed method over widely used state-of-the-art knowledge distillation methods. The code is available at: https://github.com/wo16pao/PR-SKD.
LiDAR video object segmentation with dynamic kernel refinement

In this paper, we formalize memory- and tracking-based methods to perform the LiDAR-based Video Object Segmentation (VOS) task, which segments points of the specific 3D target (given in the first frame) in a LiDAR sequence. LiDAR-based VOS can directly provide target-aware geometric information for practical application scenarios like behavior analysis and anticipating danger. We first construct a LiDAR-based VOS dataset named KITTI-VOS based on SemanticKITTI, which acts as a testbed and facilitates comprehensive evaluations of algorithm performance. Next, we provide two types of baselines, i.e., memory-based and tracking-based baselines, to explore this task. Specifically, the first memory-based pipeline is built on a space–time memory network equipped with the non-local spatiotemporal attention-based memory bank. We further design a more potent variant to introduce the locality into the spatiotemporal attention module by local self-attention and cross-attention modules. For the second tracking-based baseline, we modify two representative 3D object tracking methods to adapt to LiDAR-based VOS tasks. Finally, we propose a refine module that takes mask priors and generates object-aware kernels, which could boost all the baselines’ performance. We evaluate the proposed methods on the dataset and demonstrate their effectiveness.
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning

Deep reinforcement learning (DRL) faces significant challenges in addressing hard-exploration tasks with sparse or deceptive rewards and large state spaces. These challenges severely limit the practical application of DRL. Most previous exploration methods relied on complex architectures to estimate state novelty or introduced sensitive hyperparameters, resulting in instability. To mitigate these issues, we propose an efficient adaptive trajectory-constrained exploration strategy for DRL. The proposed method guides the agent’s policy away from suboptimal solutions by regarding previous offline demonstrations as references. Specifically, this approach gradually expands the exploration scope of the agent and strives for optimality in a constrained optimization manner. Additionally, we introduce a novel policy-gradient-based optimization algorithm that utilizes adaptive clipped trajectory-distance rewards for both single- and multi-agent reinforcement learning. We provide a theoretical analysis of our method, including a deduction of the worst-case approximation error bounds, highlighting the validity of our approach for enhancing exploration. To evaluate the effectiveness of the proposed method, we conducted experiments on two large 2D grid world mazes and several MuJoCo tasks. The extensive experimental results demonstrated the significant advantages of our method in achieving temporally extended exploration and avoiding myopic and suboptimal behaviors in both single- and multi-agent settings. Notably, the specific metrics and quantifiable results further support these findings. The code used in the study is available at https://github.com/buaawgj/TACE.
Unsupervised feature selection with high-order similarity learning

Graph-based unsupervised feature selection methods have successfully processed high-dimensional data since they can effectively preserve data structure information. However, most existing methods are facing two challenges: (1) it is unreliable to exploit the intrinsic spatial structure from raw data because of the existence of redundant information and noise; (2) they only consider the first-order similar information but ignore the high-order similar information (i.e., samples with similar neighborhood network structure should be similar to each other). This study proposes an unsupervised feature selection with high-order similarity learning (HSL) to tackle the above problems. The projection matrix, first-order similar information, and high-order similar information are simultaneously learned in a unified framework, such that the intrinsic structure information can be exploited in the clean data that come from a latent low-dimensional embedding subspace. Specifically, we project raw data into a low-dimensional embedding subspace to effectively eliminate redundant information and noise. Moreover, we simultaneously consider the first-order similarity and the high-order similarity in the low-dimensional embedding space so that a better graph Laplacian matrix can be learned to fully preserve the intrinsic structure information of raw data and simultaneously utilize the label information. Furthermore, we design an effective optimization algorithm to tackle the proposed HSL. Comprehensive experiments on real-world datasets verify the superiority and effectiveness of HSL.