Reviewerly/exHarmony huggingface dataset:
LABANet: A Lightweight Asymmetrical Bottleneck and Attention-Based Network for Cloud Detection

Authors:
raw_author_name: Ximing Yu
raw_affiliation_string: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Wenyi Shao
raw_affiliation_string: School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
raw_author_name: Liansheng Liu
raw_affiliation_string: School of Electronics and Information Engineering and the Zhengzhou Research Institute, Harbin Institute of Technology, Harbin, China
raw_author_name: Kaipeng Sun
raw_affiliation_string: Shanghai Institute of Satellite Engineering, Shanghai, China

Abstract: Recently, on-board satellite cloud detection has attracted extensive interest due to its ability to reduce invalid data, saving satellite storage and downlink resources. As the demand for accuracy has increased, deep neural networks with encoder-decoder architectures have been widely utilized for cloud detection. Most networks mainly focus on extracting contextual features and fusing more information derived from multilevel features to achieve high accuracy. However, convolutional layers with contextual feature extraction abilities in networks always contain many computations and parameters, making them difficult to deploy in processors with limited computing and memory resources. Additionally, fusing features acquired from different levels easily introduces redundant noise and decreases accuracy. To address these issues, a novel lightweight asymmetrical bottleneck and attention-based network (LABANet) is proposed. In the encoder, a multiscale context-based asymmetrical bottleneck (MCAB) is designed to extract more contextual features in an efficient manner. The numbers of computations and parameters required by the bottleneck can be effectively reduced via asymmetric channel compression and grouping for different pointwise convolutions. Multiscale contextual features can be extracted by dilated depthwise asymmetric convolution. In the decoder, a spatial-channel cascade attention (SCCA) module is proposed to fuse multilevel features with less redundant noise by highlighting spatial and channel correlation information. Experimental results obtained on public datasets demonstrate that the proposed LABANet can exceed 95% overall accuracy (OA) with a computational cost of 1.20 giga-floating-point operations (GFLOPs) and a parameter count of 1.27×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> ; thus, it achieves competitive accuracy with fewer computations and parameters than other baseline cloud detection networks.
Differential private knowledge trading in vehicular federated learning using contract theory

Authors:
raw_author_name: Jiancong Zhang
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, PR China
raw_author_name: Shining Li
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, PR China

Abstract: In vehicular differentially private federated learning, the personalized differential privacy mechanism allows vehicles to customize their local privacy budget. However, it leads to uneven utility across datasets. Therefore, model owners must consider the unevenness in utility within the incentive mechanisms to enhance learning performance. Due to limitations in system resources, servers in the Internet of Vehicles should prioritize the selection of high-utility datasets before initiating the learning process. Since existing utility evaluation schemes are costly and inefficient, we propose a knowledge trading framework based on prior utility evaluation and contract theory. Specifically, we first derive a utility evaluation function that captures the functional relationship between utility and privacy elements, such as privacy cost, sensitivity, and learning rounds. These elements are shared as hyperparameters among vehicles and servers for utility evaluation. In the presence of information asymmetry, we utilize the self-revealing property of contract theory to mitigate dishonest behavior. The learning process is modeled as an optimization function that incorporates utility and privacy costs. The complexity is further reduced by utilizing the monotonicity of the utility function. Experimental results based on multiple models and datasets illustrate that our scheme outperforms existing schemes in terms of convergence and accuracy.
Hybrid cross-modal interaction learning for multimodal sentiment analysis

Authors:
raw_author_name: Yanping Fu
raw_affiliation_string: School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China
raw_author_name: Zhiyuan Zhang
raw_affiliation_string: School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China
raw_author_name: Ruidi Yang
raw_affiliation_string: School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China
raw_author_name: Cuiyou Yao
raw_affiliation_string: School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China

Abstract: Multimodal sentiment analysis (MSA) predicts the sentiment polarity of an unlabeled utterance that carries multiple modalities, such as text, vision and audio, by analyzing labeled utterances. Existing fusion methods mainly focus on establishing the relationship of characteristics among different modalities to enhance their emotion recognition abilities. However, they always ignore the all-round interaction between different modalities, especially the cross-modal interaction, which is critical to the sentiment decision of multimodal data. To address these issues, we propose a novel hybrid cross-modal interaction learning (HCIL) framework for hybrid learning of intra-modal, inter-modal, interactive-modal and cross-modal interactions, with which the model can fully utilize the sentiment information of multimodalities and enhance the sentiment assistance between modalities. Specifically, we propose two core substructures to learn discriminative multimodal features. One is the comparative learning interaction structure that can track the class dynamics in the intra-modal, reduce the modal gap in the inter-modal and establish emotional communication in the interactive-modal; the other is the cross-modal prediction structure, which can build the sentiment relationship between cross-modal pairs, especially exploring the auxiliary sentiment effect of audio on the vision and text. Furthermore, we adopt a hierarchical feature fusion structure to generate the multimodal feature for the final sentiment prediction. Extensive experiments on three benchmark datasets showed that our HCIL approach can obtain significant performance on the MSA task and that the design of a cross-modal interaction structure can directly promote the improvement of sentiment classification performance.
Towards Unsupervised Referring Expression Comprehension with Visual Semantic Parsing

Authors:
raw_author_name: Yaodong Wang
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
raw_author_name: Zhong Ji
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
raw_author_name: Di Wang
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
raw_author_name: Yanwei Pang
raw_affiliation_string: School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
raw_author_name: Xuelong Li
raw_affiliation_string: School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, 710072, China

Abstract: Referring Expression Comprehension (REC) is a task that involves grounding a specific object in an image based on a given referring query in the form of bounding boxes. Existing fully-supervised or weakly-supervised REC methods rely on either manually annotated regional coordinates or query texts. In this paper, we propose an unsupervised training paradigm for the REC task that does not require any manual annotated data. Specifically, we introduce a Visual-Semantic-Parsing-based Unsupervised Referring Expression Comprehension framework (VUREC), which leverages a Visual Semantic Parser (VSP) as its core module to recognize the rich semantic information in images and construct pseudo-region-query pairs as the training supervision for REC. The VSP comprises a Scene Graph Parser (SGP) and a Visual Concept Detector (VCD) that can detect the locations, categories, attributes of objects, and visual relationships among them in images. Furthermore, we present a Referring Expression Reasoning (RER) model that utilizes a Multi-Modal Cascade Attention Decoder (MCAD) for fine-grained multi-modality fusion and regresses the four coordinates of the referential object directly. The experimental results on three benchmark datasets of Refcoco, Refcoco+ and Refcocog demonstrate the effectiveness of our proposed method.
PU-Detector: A PU Learning-based Framework for Real Money Trading Detection in MMORPG

Authors:
raw_author_name: Yilin Wang
raw_affiliation_string: Department of Computer Science, Zhejiang University, and State Key Laboratory of Brain-machine Intelligence, China
raw_author_name: Sha Zhao
raw_affiliation_string: Department of Computer Science, Zhejiang University, and State Key Laboratory of Brain-machine Intelligence, China
raw_author_name: Shiwei Zhao
raw_affiliation_string: Fuxi AI Lab, NetEase Games, China
raw_author_name: Runze Wu
raw_affiliation_string: Fuxi AI Lab, NetEase Games, China
raw_author_name: Yuhong Xu
raw_affiliation_string: Fuxi AI Lab, NetEase Games, China
raw_author_name: Jianrong Tao
raw_affiliation_string: Fuxi AI Lab, NetEase Games, China
raw_author_name: Tangjie Lv
raw_affiliation_string: Fuxi AI Lab, NetEase Games, China
raw_author_name: Shijian Li
raw_affiliation_string: Department of Computer Science, Zhejiang University, and State Key Laboratory of Brain-machine Intelligence, China
raw_author_name: Zhipeng Hu
raw_affiliation_string: Fuxi AI Lab, NetEase Games, China
raw_author_name: Gang Pan
raw_affiliation_string: Department of Computer Science, Zhejiang University, and State Key Laboratory of Brain-machine Intelligence, China

Abstract: Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples.
Explanation Guided Knowledge Distillation for Pre-trained Language Model Compression

Authors:
raw_author_name: Zhao Yang
raw_affiliation_string: School of Artificial Intelligence, University of Chinese Academy of Sciences, China and The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Yuanzhe Zhang
raw_affiliation_string: School of Artificial Intelligence, University of Chinese Academy of Sciences, China and The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Dianbo Sui
raw_affiliation_string: School of Computer Science, Harbin Institute of Technology at Weihai, China
raw_author_name: Yiming Ju
raw_affiliation_string: School of Artificial Intelligence, University of Chinese Academy of Sciences, China and The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Jun Zhao
raw_affiliation_string: School of Artificial Intelligence, University of Chinese Academy of Sciences, China and The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Kang Liu
raw_affiliation_string: School of Artificial Intelligence, University of Chinese Academy of Sciences, China and The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Abstract: Knowledge distillation is widely used in pre-trained language model compression, which can transfer knowledge from a cumbersome model to a lightweight one. Though knowledge distillation based model compression has achieved promising performance, we observe that explanations between the teacher model and the student model are not consistent. We argue that the student model should study not only the predictions of the teacher model but also the internal reasoning process. To this end, we propose Explanation Guided Knowledge Distillation (EGKD) in this article, which utilizes explanations to represent the thinking process and improve knowledge distillation. To obtain explanations in our distillation framework, we select three typical explanation methods rooted in different mechanisms, namely gradient-based , perturbation-based , and feature selection methods. Then, to improve computational efficiency, we propose different optimization strategies to utilize the explanations obtained by these three different explanation methods, which could provide the student model with better learning guidance. Experimental results on GLUE demonstrate that leveraging explanations can improve the performance of the student model. Moreover, our EGKD could also be applied to model compression with different architectures.
DSISA: A New Neural Machine Translation Combining Dependency Weight and Neighbors

Authors:
raw_author_name: Ming-Xing Luo
raw_affiliation_string: School of Information Science and Technology, Southwest Jiaotong University, China

Abstract: Most of the previous neural machine translations (NMT) rely on parallel corpus. Integrating explicitly prior syntactic structure information can improve the neural machine translation. In this article, we propose a Syntax Induced Self-Attention (SISA) which explores the influence of dependence relation between words through the attention mechanism and fine-tunes the attention allocation of the sentence through the obtained dependency weight. We present a new model, Double Syntax Induced Self-Attention (DSISA), which fuses the features extracted by SISA and a compact convolution neural network (CNN). SISA can alleviate long dependency in sentence, while CNN captures the limited context based on neighbors. DSISA utilizes two different neural networks to extract different features for richer semantic representation and replaces the first layer of Transformer encoder. DSISA not only makes use of the global feature of tokens in sentences but also the local feature formed with adjacent tokens. Finally, we perform simulation experiments that verify the performance of the new model on standard corpora.
Active contrastive coding reducing label effort for sensor-based human activity recognition

Authors:
raw_author_name: Zhixin Li
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China
raw_author_name: Hao Liu
raw_affiliation_string: School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China
raw_author_name: Zhan Huan
raw_affiliation_string: School of Microelectronics and Control Engineering, Changzhou University, Changzhou, China

Abstract: Human activity recognition (HAR) plays a crucial role in remotely monitoring the health of the elderly. Human annotation is time-consuming and expensive, especially for abstract sensor data. Contrastive learning can extract robust features from weakly annotated data to promote the development of sensor-based HAR. However, current research mainly focuses on the exploration of data augmentation methods and pre-trained models, disregarding the impact of data quality on label effort for fine-tuning. This paper proposes a novel active contrastive coding model that focuses on using an active query strategy to evenly select small, high-quality samples in downstream tasks to complete the update of the pre-trained model. The proposed uncertainty-based balanced query strategy mines the most indistinguishable hard samples according to the data posterior probability in the unlabeled sample pool, and imposes class balance constraints to ensure equilibrium in the labeled sample pool. Extensive experiments have shown that the proposed method consistently outperforms several state-of-the-art baselines on four mainstream HAR benchmark datasets (UCI, WISDM, MotionSense, and USCHAD). With approximately only 10% labeled samples, our method achieves impressive F1-scores of 98.54%, 99.34%, 98.46%, and 87.74%, respectively.
Evolving masked low-rank transformer for long text understanding

Authors:
raw_author_name: Xiangru Chen
raw_affiliation_string: College of Computer Science, Sichuan University, 610065, Chengdu, China; Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore
raw_author_name: Jie Lin
raw_affiliation_string: Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore
raw_author_name: Peng Hu
raw_affiliation_string: College of Computer Science, Sichuan University, 610065, Chengdu, China; Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore
raw_author_name: Junfeng Wang
raw_affiliation_string: College of Computer Science, Sichuan University, 610065, Chengdu, China
raw_author_name: Xue Geng
raw_affiliation_string: Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore

Abstract: Long sequence text processing is time-consuming owing to the ultra-large-scale self-attention computing. Recent advances demonstrate the attention in transformer can be accelerated by redundancy removal, and there are various sparse variants for attention in large sequences are proposed, which leads to state-of-the-art performance on language and vision task. Low-rank method achieve outstanding success in the field of efficient transformer. The dynamic token sparsification is efficiently time-saving and cost-saving, which can be easily extended to prune redundant spans and to yield semantic features. Evolutionary algorithm is attractive for selecting hyperparameter which is of significant importance in effectiveness. Motivated by these works, we propose an efficient transformers model, termed EMLT, to alleviate time and cost without sacrificing the accuracy. EMLT effectively combines strengths of Low-rank transformers, dynamic token sparsification and evolutionary algorithm to ulteriorly cut redundant token and meanwhile maintains the original precision, which can achieve a linear memory and time complexity. We compress transformer in three stages. Firstly, sliding window is validated as local attention to capture fine-grained dependency semantics. After that, low-rank approximation of attention matrix is applied as global attention to store long-range dependency semantics, and aggregated with local attention. On this basis, we consistently prune redundant token in accordance with importance score to further sparse the attention operation. Finally, Evolutionary algorithm is utilized to optimize the hyper-parameters of every layer. The results of comprehensive experiments and analysis show that our method can rival others on accuracy, and outperforms others on efficiency by a large margin in terms of the computational complexity.
Strengthening Cooperative Consensus in Multi-Robot Confrontation

Authors:
raw_author_name: Meng Xu
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Xinhong Chen
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Yechao She
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Yang Jin
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Guanyi Zhao
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China
raw_author_name: Jianping Wang
raw_affiliation_string: Department of Computer Science, City University of Hong Kong, Hong Kong, China

Abstract: Multi-agent reinforcement learning (MARL) has proven effective in training multi-robot confrontation, such as StarCraft and robot soccer games. However, the current joint action policies utilized in MARL have been unsuccessful in recognizing and preventing actions that often lead to failures on our side. This exacerbates the cooperation dilemma, ultimately resulting in our agents acting independently and being defeated individually by their opponents. To tackle this challenge, we propose a novel joint action policy, referred to as the consensus action policy (CAP). Specifically, CAP records the number of times each joint action has caused our side to fail in the past and computes a cooperation tendency, which is integrated with each agent’s Q -value and Nash bargaining solution to determine a joint action. The cooperation tendency promotes team cooperation by selecting joint actions that have a high tendency of cooperation and avoiding actions that may lead to team failure. Moreover, the proposed CAP policy can be extended to partially observable scenarios by combining it with Deep Q network or actor-critic–based methods. We conducted extensive experiments to compare the proposed method with seven existing joint action policies, including four commonly used methods and three state-of-the-art methods, in terms of episode rewards, winning rates, and other metrics. Our results demonstrate that this approach holds great promise for multi-robot confrontation scenarios.