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Relation Reconstructive Binarization of word embeddings

Frontiers of Computer Science 2022年第2期16卷 47-54页

作者： Feiyang PAN Shuokai LI Xiang AO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Word-embedding acts as one of the backbones of modern natural language processing(NLP).Recently,with the need for deploying NLP models to low-resource devices,there has been a surge of interest to compress word embeddings into hash codes or binary vectors so as to save the storage and memory ***,existing work learns to encode an embedding into a compressed representation from which the original embedding can be *** these methods aim to preserve most information of every individual word,they often fail to retain the relation between words,thus can yield large loss on certain *** this end,this paper presents Relation Reconstructive Binarization(R2B)to transform word embeddings into binary codes that can preserve the relation between *** its heart,R2B trains an auto-encoder to generate binary codes that allow reconstructing the wordby-word relations in the original embedding *** showed that our method achieved significant improvements over previous methods on a number of tasks along with a space-saving of up to 98.4%.Specifically,our method reached even better results on word similarity evaluation than the uncompressed pre-trained embeddings,and was significantly better than previous compression methods that do not consider word relations.

关键词： embedding compression variational auto-encoder binary word embedding

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Density peaks clustering algorithm based on multi-cluster merge and its application in the extraction of typical load patterns of users

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Journal of Ambient Intelligence and Humanized computing 2024年第8期15卷 3193-3209页

作者： Zhao, Jia Yao, Zhanfeng Qiu, Liujun Fan, Tanghuai Lee, Ivan School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City Nanchang Institute of Technology Nanchang330099 China UniSA STEM University of South Australia AdelaideSA5000 Australia

The density peaks clustering (DPC) algorithm is simple in principle, efficient in operation, and has good clustering effects on various types of datasets. However, this algorithm still has some defects: (1) due to the definition limitations of local density and relative distance of samples, it is difficult for the algorithm to find correct density peaks;(2) the allocation strategy of the algorithm has poor robustness and is prone to cause other problems. In response to solve the above shortcomings, we proposed a density peaks clustering algorithm based on multi-cluster merge (DPC-MM). In view of the difficulty in selecting density peaks of the DPC algorithm, a new method of calculating relative distance of samples was defined to make the density peaks found more accurate. The allocation strategy of multi-cluster merge was proposed to alleviate or avoid problems caused by allocation errors. Experimental results revealed that the DPC-MM algorithm can efficiently perform clustering on datasets of any shape and scale. The DPC-MM algorithm was applied in extraction of typical load patterns of users, and can more accurately perform clustering on user loads. The extraction results can better reflect electricity consumption habits of users. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Clustering algorithms

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Improving Fake News Video Detection by Correlating with Neighbors 61

Improving Fake News Video Detection by Correlating with Neig...

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Findings of the Association for Computational Linguistics, ACL 2023

作者： Qi, Peng Zhao, Yuyang Shen, Yufeng Ji, Wei Cao, Juan Chua, Tat-Seng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China National University of Singapore Singapore

ISBN: (纸本)9781959429623

The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are available in https://***/ICTMCG/NEED. © 2023 Association for Computational Linguistics.

关键词： Benchmarking

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Repetitive Path Planning with Experience-Based Bidirectional RRT 1

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International Conference on Robots for Space Applications in Orbital Stations, TORVEASTRO 2023

作者： Zuo, Guoyu Wu, Chun Huang, Gao Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (数字)9783031284472

ISBN: (纸本)9783031284465

Repetitive motion planning in semi-structured environments is involved in many robotics applications, such as manufacturing and in-orbit service of spatial robotic arms. In these scenarios, the environment is mainly static, but it may contain some movable obstacles that the robot needs to avoid, and the planning problems that the robot will encounter are very similar. However, most current sampling-based motion planning methods ignore the correlation among tasks, and instead plan each task instance from scratch, or assume that the environment is highly structured. In order to overcome this problem, this paper proposes an experience-based bidirectional rapidly-exploring random trees* (EBRRT*) for repetitive path planning in semi-structured environments. First, an adaptive sampler learned from the demonstration paths by a Gaussian mixture model (GMM) is constructed, which can guide the planner to generate samples in task-related regions. Second, an experience graph is proposed to capture the exploration of configuration space by previous similar tasks. Subsequent tasks can retrieve locally valuable information from the graph to avoid exploring the configuration space repeatedly. In addition, the validity of edges contained in the experience graph is lazily verified during the planning process so that the experience graph can adapt to small environmental changes. Finally, the proposed method is verified in a 2D simulation environment and a 7-DOF manipulator scene. Experimental results show that the proposed method can return a high-quality path in less time than other state-of-the-art sample-based motion planning for repetitive path planning in semi-structured environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motion planning

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A graph reasoning method for multi-object unordered stacking scenarios

A graph reasoning method for multi-object unordered stacking...

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2023 IEEE International Conference on Real-Time computing and Robotics, RCAR 2023

作者： Zuo, Guoyu Wang, Zihao Gong, Daoxiong Huang, Gao Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9798350327182

In order to enable the robot to safely pick the target object from it in the face of a scene with multiple objects stacked in a disorderly manner. It is of great importance for intelligent robot grasping to get the operation order by obtaining the position relationship between the target object and other objects in space. In this paper, an end-to-end detection inference network is proposed, in which features are extracted from the input RGB information by EfficientNet-B0 combined with BiFPN in terms of feature extraction, and in terms of operation relationships, object pairs without up-down position relationships are first eliminated to reduce the amount of operations, and graph attention networks (GAT) are later used to reason about the positions between objects. The network model is trained and tested on the VMRD dataset, and the experiments show that this model has good results in inference of object spatial position relationships for multi-object unordered stacked scenes. © 2023 IEEE.

关键词： intelligent robots

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Heralded path-entangled NOON states generation from a reconfigurable photonic chip

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Chinese Physics B 2022年第6期31卷 358-362页

作者： Xinyao Yu Pingyu Zhu Yang Wang Miaomiao Yu Chao Wu Shichuan Xue Qilin Zheng Yingwen Liu Junjie Wu Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China

Maximal multi-photon entangled states,known as NOON states,play an essential role in quantum *** the number of photons growing,NOON states are becoming increasingly powerful and advantageous for obtaining supersensitive and super-resolved *** this paper,we propose a universal scheme for generating three-and four-photon path-entangled NOON states on a reconfigurable photonic chip via photons subtracted from pairs and detected by heralding *** method is postselection free,enabling phase supersensitive measurements and sensing at the Heisenberg *** NOON-state generator allows for integration of quantum light sources as well as practical and portable precision phase-related measurements.

关键词： path-entangled NOON state phase measurement

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DragEntity:Trajectory Guided Video Generation using Entity and Positional Relationships 24

DragEntity:Trajectory Guided Video Generation using Entity a...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wan, Zhang Tang, Sheng Wei, Jiawei Zhang, Ruize Cao, Juan Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Zhengzhou University Zhengzhou China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798400706868

In recent years, diffusion models have achieved tremendous success in the field of video generation, with controllable video generation receiving significant attention. However, existing control methods still face two limitations: Firstly, control conditions (such as depth maps, 3D Mesh) are difficult for ordinary users to obtain directly. Secondly, it's challenging to drive multiple objects through complex motions with multiple trajectories simultaneously. In this paper, we introduce DragEntity, a video generation model that utilizes entity representation for controlling the motion of multiple objects. Compared to previous methods, DragEntity offers two main advantages: 1) Our method is more user-friendly for interaction because it allows users to drag entities within the image rather than individual pixels. 2) We use entity representation to represent any object in the image, and multiple objects can maintain relative spatial relationships. Therefore, we allow multiple trajectories to control multiple objects in the image with different levels of complexity simultaneously. Our experiments validate the effectiveness of DragEntity, demonstrating its excellent performance in fine-grained control in video generation. © 2024 ACM.

关键词： controllable video generation

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Combined CNN LSTM with attention for speech emotion recognition based on feature-level fusion

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Multimedia Tools and Applications 2024年第21期83卷 59839-59859页

作者： Liu, Yanlin Chen, Aibin Zhou, Guoxiong Yi, Jizheng Xiang, Jin Wang, Yaru Institute of Artificial Intelligence Application College of Computer and Information Engineering Central South University of Forestry and Technology Hunan Changsha410004 China Hunan Key Laboratory of Intelligent Logistics Technology Central South University of Forestry and Technology Hunan Changsha410004 China

According to the problem that emotional features cannot be well represented by a single feature and it is difficult to extract in the task of Speech Emotion Recognition (SER), we propose a Feature-Level (FL) fusion method for 9 types of acoustic features and a combined CNN-LSTM network with attention (CNN-A-LSTM). The feature vector set after feature-level fusion is used as the input of CNN-A-LSTM network, which contains prosodic features and spectral features. High-level Statistical Functions (HSFs) are also added to receive global features. The feature extraction network CNN-A-LSTM can more effectively read time-series input and extract speech emotion information. Finally, Softmax is applied as a classifier to obtain the final emotion classification results. The experiment is verified on SAVEE and CASIA datasets. The experimental results show that the method in this paper has the best effect compared with the state-of-the-art, and the accuracy rates of 94.5% and 96.7% are respectively gained on SAVEE and CASIA. The above results prove the effectiveness of the algorithm in this paper with some generalization. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Speech recognition

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PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

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Computers, Materials & Continua 2024年第10期81卷 1491-1509页

作者： Shuai Cao Jianan Liang Yongjun Cao Jinglun Huang Zhishu Yang Institute of Intelligent Manufacturing GDASGuangdong Key Laboratory of Modern Control TechnologyGuangzhou510030China School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhou511442China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen529020China

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)***,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction *** methods commonly use standard convolutions,which increase the convolutional operation cost of the *** this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this ***,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image ***,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature *** by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an *** can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature *** the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

关键词： Deep learning single image super-resolution lightweight network multiscale fusion

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different *** work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： Deep learning Metaheuristics Reinforcement learning Benchmark testing Search problems Robustness Behavioral sciences

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