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Found 256 papers, 89 papers with code
Model-Free Information Extraction in Enriched Nonlinear Phase-Space
We benchmark the astonishing accuracy of our method on three diverse and challenging problems in: biology, medicine, and engineering.
A multi-branch convolutional neural network for detecting double JPEG compression
In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input.
Stochastic Patching Process
Due to constraints of partition strategy, existing models may cause unnecessary dissections in sparse regions when fitting data in dense regions.
Improving Efficiency of SVM k-fold Cross-validation by Alpha Seeding
The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters.
Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In UAV Communication Networks
This allows the system to achieve a smoother and more robust performance by optimizing in an alternate space.
Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space
There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction.
Customer Sharing in Economic Networks with Costs
To tackle this problem, we develop a novel mechanism called customer sharing mechanism (CSM) which incentivizes all sellers to share each other's sale information to their private customer groups.
A Study of Deep Feature Fusion based Methods for Classifying Multi-lead ECG
An automatic classification method has been studied to effectively detect and recognize Electrocardiogram (ECG).
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer.
Transition-Based Chinese AMR Parsing
This paper presents the first AMR parser built on the Chinese AMR bank.
High-dimensional Metric Combining for Non-coherent Molecular Signal Detection
If the channel is unknown, we cannot easily achieve traditional coherent channel estimation and cancellation, and the impact of ISI will be more severe.
Sequential Bayesian Detection of Spike Activities from Fluorescence Observations
Extracting and detecting spike activities from the fluorescence observations is an important step in understanding how neuron systems work.
Learn a Prior for RHEA for Better Online Planning
In this paper, we propose to learn a prior for RHEA in an offline manner by training a value network and a policy network.
Selling Multiple Items via Social Networks
We consider a market where a seller sells multiple units of a commodity in a social network.
Binary Space Partitioning Forests
The Binary Space Partitioning~(BSP)-Tree process is proposed to produce flexible 2-D partition structures which are originally used as a Bayesian nonparametric prior for relational modelling.
The Binary Space Partitioning-Tree Process
The Mondrian process represents an elegant and powerful approach for space partition modelling.
Text Guided Person Image Synthesis
This paper presents a novel method to manipulate the visual appearance (pose and attribute) of a person image according to natural language descriptions.
Question Guided Modular Routing Networks for Visual Question Answering
Particularly, The QGMRN is composed of visual, textual and routing network.
Diffusion and Auction on Graphs
Auction is the common paradigm for resource allocation which is a fundamental problem in human society.
Scalable Deep Generative Relational Models with High-Order Node Dependence
We propose a probabilistic framework for modelling and exploring the latent structure of relational data.
Scalable Deep Generative Relational Model with High-Order Node Dependence
In this work, we propose a probabilistic framework for relational data modelling and latent structure exploring.
Reinforcement learning for bandwidth estimation and congestion control in real-time communications
Bandwidth estimation and congestion control for real-time communications (i. e., audio and video conferencing) remains a difficult problem, despite many years of research.
Waiting but not Aging: Optimizing Information Freshness Under the Pull Model
Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different values of the AoI across the servers (due to the random updating processes) and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side.
The Power of Waiting for More than One Response in Minimizing the Age-of-Information
Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side.
Networking and Internet Architecture
Outlier Detection Ensemble with Embedded Feature Selection
Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance.
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling
In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.
Bayesian Nonparametric Space Partitions: A Survey
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a $D$-dimensional space into a set of blocks.
Smoothing Graphons for Modelling Exchangeable Relational Data
We initially propose the Integrated Smoothing Graphon (ISG) which introduces one smoothing parameter to the SBM graphon to generate continuous relational intensity values.
Online Binary Space Partitioning Forests
The Binary Space Partitioning-Tree~(BSP-Tree) process was recently proposed as an efficient strategy for space partitioning tasks.
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning
This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs.
Spatiotemporal graph routing for skeleton-based action recognition
With the representation effectiveness, skeleton-based human action recognition has received considerable research attention, and has a wide range of real applications.
Integration of Automatic Sentence Segmentation and Lexical Analysis of Ancient Chinese based on BiLSTM-CRF Model
The basic tasks of ancient Chinese information processing include automatic sentence segmentation, word segmentation, part-of-speech tagging and named entity recognition.
Construct a Sense-Frame Aligned Predicate Lexicon for Chinese AMR Corpus
The existing lexicons blur senses and frames of predicates, which needs to be refined to meet the tasks like word sense disambiguation and event extraction.
Dual-stream Maximum Self-attention Multi-instance Learning
Recently, MIL models fully parameterized by neural networks have become popular due to the high flexibility and superior performance.
W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network
To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation.
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation
Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution and drug discovery, etc., by now, the inner process of GANs is far from been understood.
Multitask Non-Autoregressive Model for Human Motion Prediction
Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem.
An Adaptive Successive Cancellation List Decoder for Polar Codes with Cyclic Redundancy Check
In this letter, we propose an adaptive SC (Successive Cancellation)-List decoder for polar codes with CRC.
Information Theory Information Theory
Mechanism Design in Social Networks
The challenge to the seller is to design a mechanism to incentivize the buyers, who are aware of the auction, to further propagate the information to their neighbors so that more buyers will participate in the auction and hence, the seller will be able to make a higher revenue.
Computer Science and Game Theory
POND: Pessimistic-Optimistic oNline Dispatching
This paper considers constrained online dispatching with unknown arrival, reward and constraint distributions.
Performance Analysis and Optimization for the MAC Protocol in UAV-based IoT Network
A modified three-dimensional Markov chain model adopting the quitting probability and cluster division is developed for the performance analysis.
Information Theory Information Theory
Ultra-Fast Accurate AoA Estimation via Automotive Massive-MIMO Radar
Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV).
On Performance of Multiscale Sparse Fast Fourier Transform Algorithm
The sFFT algorithms decrease the runtime and sampling complexity by taking advantage of the signal inherent characteristics that a large number of signals are sparse in the frequency domain(e. g., sensors, video data, audio, medical image, etc.).
On Performance of Sparse Fast Fourier Transform Algorithms Using the Aliasing Filter
In the second part, we make two categories of experiments for computing the signals of different SNR, different N, different K by a standard testing platform and record the run time, percentage of the signal sampled and L0, L1, L2 error both in the exactly sparse case and general sparse case.
Model-free Neural Counterfactual Regret Minimization with Bootstrap Learning
Experimental results show that Neural ReCFR-B is competitive with the state-of-the-art neural CFR algorithms at a much lower training cost.
Incentive Mechanism Design for ROI-constrained Auto-bidding
Auto-bidding plays an important role in online advertising and has become a crucial tool for advertisers and advertising platforms to meet their performance objectives and optimize the efficiency of ad delivery.
Computer Science and Game Theory
DR 21 South Filament: a Parsec-sized Dense Gas Accretion Flow onto the DR 21 Massive Young Cluster
DR21 south filament (DR21SF) is a unique component of the giant network of filamentary molecular clouds in the north region of Cygnus X complex.
Astrophysics of Galaxies
Empirical Evaluation of Typical Sparse Fast Fourier Transform Algorithms
Computing the Sparse Fast Fourier Transform(sFFT) of a K-sparse signal of size N has emerged as a critical topic for a long time.
Efficient Learning-based Scheduling for Information Freshness in Wireless Networks
Here, UCB estimates balance the tradeoff between exploration and exploitation in learning and are critical for yielding a small cumulative regret.
Self-supervised Visual-LiDAR Odometry with Flip Consistency
To this end, we design a self-supervised visual-lidar odometry (Self-VLO) framework.
Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image
In this paper, we present a novel steganography scheme denoted as ITE-SYN (based on ITEratively adversarial perturbations onto a SYNchronized-directions sub-image), by which security data is embedded with synchronizing modification directions to enhance security and then iteratively increased perturbations are added onto a sub-image to reduce loss with cover class label of the target CNN classifier.
Infant Cry Classification with Graph Convolutional Networks
We propose an approach of graph convolutional networks for robust infant cry classification.
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints
Thus, the overall computational complexity of our algorithm is similar to that of the linear UCB for unconstrained stochastic linear bandits.
A Generic Object Re-identification System for Short Videos
Short video applications like TikTok and Kwai have been a great hit recently.
Serial-parallel Multi-Scale Feature Fusion for Anatomy-Oriented Hand Joint Detection
Accurate hand joints detection from images is a fundamental topic which is essential for many applications in computer vision and human computer interaction.
Non-contact PPG Signal and Heart Rate Estimation with Multi-hierarchical Convolutional Network
Heartbeat rhythm and heart rate (HR) are important physiological parameters of the human body.
Graph Layer Security: Encrypting Information via Common Networked Physics
Leveraging this, we propose for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption.
Sparse online relative similarity learning
This is clearly inefficient for high dimensional tasks due to its high memory and computational complexity.
Adjustable robust optimization in enabling optimal day-ahead economic dispatch of CCHP-MG considering uncertainties of wind-solar power and electric vehicle
At present, electric vehicles (EVs), small-scale wind power, and solar power have been increasingly integrated into modern power system via the combined cooling heating and power based microgrid (CCHP-MG).
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge
To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure.
MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework.
BezierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images
Pixel-wise classification is a popular approach to segmenting the region of interest.
More but Correct: Generating Diversified and Entity-revised Medical Response
Medical Dialogue Generation (MDG) is intended to build a medical dialogue system for intelligent consultation, which can communicate with patients in real-time, thereby improving the efficiency of clinical diagnosis with broad application prospects.
DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19
Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage.
Stereo Dense Scene Reconstruction and Accurate Localization for Learning-Based Navigation of Laparoscope in Minimally Invasive Surgery
Then, we develop a dense visual reconstruction algorithm to represent the scene by surfels, estimate the laparoscope poses and fuse the depth maps into a unified reference coordinate for tissue reconstruction.
Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
Equivalence Analysis between Counterfactual Regret Minimization and Online Mirror Descent
Follow-the-Regularized-Lead (FTRL) and Online Mirror Descent (OMD) are regret minimization algorithms for Online Convex Optimization (OCO), they are mathematically elegant but less practical in solving Extensive-Form Games (EFGs).
RelationRS: Relationship Representation Network for Object Detection in Aerial Images
To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): 1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution.
中文连动句语义关系识别研究(Research on Semantic Relation Recognition of Chinese Serial-verb Sentences)
“连动句是形如“NP+VP1+VP2”的句子, 句中含有两个或两个以上的动词(或动词结构)且动词的施事为同一对象。相同结构的连动句可以表示多种不同的语义关系。本文基于前人对连动句中VP1和VP2之间的语义关系分类, 标注了连动句语义关系数据集, 基于神经网络完成了对连动句语义关系的识别。该方法将连动句语义识别任务进行分解, 基于BERT进行编码, 利用BiLSTM-CRF先识别出连动句中连动词(VP)及其主语(NP), 再基于融合连动词信息的编码, 利用BiLSTM-Attention对连动词进行关系判别, 实验结果验证了所提方法的有效性。”
中文词语离合现象识别研究(Research on Recognition of the Separation and Reunion Phenomena of Words in Chinese)
“汉语词语的离合现象是汉语中一种词语可分可合的特殊现象。本文采用字符级序列标注方法解决二字动词离合现象的自动识别问题, 以避免中文分词及词性标注的错误传递, 节省制定匹配规则与特征模板的人工开支。在训练过程中微调BERT中文预训练模型, 获取面向目标任务的字符向量表示, 并引入掩码机制对模型隐藏离用法中分离的词语, 减轻词语本身对识别结果的影响, 强化中间插入成分的学习, 并对前后语素采用不同的掩码以强调其出现顺序, 进而使模型具备了识别复杂及偶发性离用法的能力。为获得含有上下文信息的句子表达, 将原始的句子表达与采用掩码的句子表达分别输入两个不同参数的BiLSTM层进行训练, 最后采用CRF算法捕捉句子标签序列的依赖关系。本文提出的BERT MASK + 2BiLSTMs + CRF模型比现有最优的离合词识别模型提高了2. 85%的F1值。”
先秦词网构建及梵汉对比研究(The Construction of Pre-Qin Ancient Chinese WordNet and Cross Language Comparative Study between Ancient Sanskrit WordNet and Pre-Qin Ancient Chinese WordNet)
“先秦汉语在汉语史研究上具有重要地位, 然而以往的研究始终没有形成结构化的先秦词汇资源, 难以满足古汉语信息处理和跨语言对比的研究需要。国际上以英文词网(WordNet)的义类架构为基础, 已经建立了数十种语言的词网, 已经成为多语言自然语言处理和跨语言对比的基础资源。本文综述了国内外各种词网的构建情况, 特别是古代语言的词网和汉语词网, 然后详细介绍了先秦词网的构建和校正过程, 构建起了涵盖43591个词语、61227个义项、17975个义类的先秦汉语词网。本文还通过与古梵语词网的跨语言对比, 尝试分析这两种古老语言在词汇上的共性和差异, 初步验证先秦词网的有效性。”
基于大规模语料库的《古籍汉字分级字表》研究(The Formulation of The graded Chinese character list of ancient books Based on Large-scale Corpus)
"《古籍汉字分级字表》是基于大规模古籍文本语料库、为辅助学习者古籍文献阅读而研制的分级字表。该字表填补了古籍字表研究成果的空缺, 依据各汉字学习优先级别的不同, 实现了古籍汉字的等级划分, 目前收录一级字105个, 二级字340个, 三级字555个。本文介绍了该字表研制的主要依据和基本步骤, 并将其与传统识字教材“三百千”及《现代汉语常用字表》进行比较, 验证了其收字的合理性。该字表有助于学习者优先掌握古籍文本常用字, 提升古籍阅读能力, 从而促进中华优秀传统文化的继承与发展。”
Building a Chinese AMR Bank with Concept and Relation Alignments
One significant change we have made to the AMR annotation methodology is the inclusion of the alignment between word tokens in the sentence and the concepts/relations in the CAMR annotation to make it easier for automatic parsers to model the correspondence between a sentence and its meaning representation.
多轮对话的篇章级抽象语义表示标注体系研究(Research on Discourse-level Abstract Meaning Representation Annotation framework in Multi-round Dialogue)
对话分析是智能客服、聊天机器人等自然语言对话应用的基础课题, 而对话语料与常规书面语料有较大差异, 存在大量的称谓、情感短语、省略、语序颠倒、冗余等复杂现象, 对句法和语义分析器的影响较大, 对话自动分析的准确率相对书面语料一直不高。其主要原因在于对多轮对话缺乏严整的形式化描写方式, 不利于后续的分析计算。因此, 本文在梳理国内外针对对话的标注体系和语料库的基础上, 提出了基于抽象语义表示的篇章级多轮对话标注体系。具体探讨了了篇章级别的语义结构标注方法, 给出了词语和概念关系的对齐方案, 针对称谓语和情感短语增加了相应的语义关系和概念, 调整了表示主观情感词语的论元结构, 并对对话中一些特殊现象进行了规定, 设计了人工标注平台, 为大规模的多轮对话语料库标注与计算研究奠定基础。
基于抽象语义表示的汉语疑问句的标注与分析(Chinese Interrogative Sentences Annotation and Analysis Based on the Abstract Meaning Representation)
疑问句的句法语义分析在搜索引擎、信息抽取和问答系统等领域有着广泛的应用。计算语言学多采取问句分类和句法分析相结合的方式来处理疑问句, 精度和效率还不理想。而疑问句的语言学研究成果丰富, 比如疑问句的结构类型、疑问焦点和疑问代词的非疑问用法等, 但缺乏系统的形式化表示。本文致力于解决这一难题, 采用基于图结构的汉语句子语义的整体表示方法—中文抽象语义表示(CAMR)来标注疑问句的语义结构, 将疑问焦点和整句语义一体化表示出来。然后选取了宾州中文树库CTB8. 0网络媒体语料、小学语文教材以及《小王子》中文译本的2万句语料中共计2071句疑问句, 统计了疑问句的主要特点。统计表明, 各种疑问代词都可以通过疑问概念amr-unknown和语义关系的组合来表示, 能够完整地表示出疑问句的关键信息、疑问焦点和语义结构。最后, 根据疑问代词所关联的语义关系, 统计了疑问焦点的概率分布, 其中原因、修饰语和受事的占比最高, 分别占26. 53%、16. 73%以及16. 44%。基于抽象语义表示的疑问句标注与分析可以为汉语疑问句研究提供基础理论与资源。
基于神经网络的连动句识别(Recognition of serial-verb sentences based on Neural Network)
连动句是具有连动结构的句子, 是汉语中的特殊句法结构, 在现代汉语中十分常见且使用频繁。连动句语法结构和语义关系都很复杂, 在识别中存在许多问题, 对此本文针对连动句的识别问题进行了研究, 提出了一种基于神经网络的连动句识别方法。本方法分两步:第一步, 运用简单的规则对语料进行预处理;第二步, 用文本分类的思想, 使用BERT编码, 利用多层CNN与BiLSTM模型联合提取特征进行分类, 进而完成连动句识别任务。在人工标注的语料上进行实验, 实验结果达到92. 71%的准确率, F1值为87. 41%。
基于深度学习的实体关系抽取研究综述(Review of Entity Relation Extraction based on deep learning)
作为信息抽取的一项核心子任务, 实体关系抽取对于知识图谱、智能问答、语义搜索等自然语言处理应用都十分重要。关系抽取在于从非结构化文本中自动地识别实体之间具有的某种语义关系。该文聚焦句子级别的关系抽取研究, 介绍用于关系抽取的主要数据集并对现有的技术作了阐述, 主要分为:有监督的关系抽取、远程监督的关系抽取和实体关系联合抽取。我们对比用于该任务的各种模型, 分析它们的贡献与缺 陷。最后介绍中文实体关系抽取的研究现状和方法。
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search
The most common solution for this is to compute an approximate risk by replacing the 0-1 loss with a surrogate one.
Continuous-time edge modelling using non-parametric point processes
The mutually-exciting Hawkes process (ME-HP) is a natural choice to model reciprocity, which is an important attribute of continuous-time edge (dyadic) data.
SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation
In this paper, we propose a Simple framework for Contrastive Learning of Acronym Disambiguation (SimCLAD) method to better understand the acronym meanings.
PSG: Prompt-based Sequence Generation for Acronym Extraction
In this paper, we propose a Prompt-based Sequence Generation (PSG) method for the acronym extraction task.
Unsupervised Learning of Compositional Scene Representations from Multiple Unspecified Viewpoints
When observing a visual scene that contains multiple objects from multiple viewpoints, humans are able to perceive the scene in a compositional way from each viewpoint, while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold.
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Many existing algorithms learn robust policies by modeling the disturbance and applying it to source environments during training, which usually requires prior knowledge about the disturbance and control of simulators.
PROMPT: Learning Dynamic Resource Allocation Policies for Network Applications
A growing number of service providers are exploring methods to improve server utilization and reduce power consumption by co-scheduling high-priority latency-critical workloads with best-effort workloads.
Compositional Scene Representation Learning via Reconstruction: A Survey
In this survey, we first outline the current progress on reconstruction-based compositional scene representation learning with deep neural networks, including development history and categorizations of existing methods from the perspectives of the modeling of visual scenes and the inference of scene representations; then provide benchmarks, including an open source toolbox to reproduce the benchmark experiments, of representative methods that consider the most extensively studied problem setting and form the foundation for other methods; and finally discuss the limitations of existing methods and future directions of this research topic.
Remote blood pressure measurement via spatiotemporal mapping of a short-time facial video
Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases.
Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video
However, due to the weak correlations and huge gaps of the semantic features between the textual question and visual answer, existing methods adopting visual span predictor perform poorly in the TAGV task.
Multi-Unit Diffusion Auctions with Intermediaries
This paper studies multi-unit auctions powered by intermediaries, where each intermediary owns a private set of unit-demand buyers and all intermediaries are networked with each other.
Neural Compression-Based Feature Learning for Video Restoration
The temporal features usually contain various noisy and uncorrelated information, and they may interfere with the restoration of the current frame.
Prompt-based System for Personality and Interpersonal Reactivity Prediction
This paper describes our proposed method for the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Personality Prediction (PER) and Reactivity Index Prediction (IRI).
A Comprehensive Survey on Data-Efficient GANs in Image Generation
Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis.
Hierarchical Locality Sensitive Hashing for Structured Data: A Survey
Data similarity (or distance) computation is a fundamental research topic which fosters a variety of similarity-based machine learning and data mining applications.
A Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation
Question Answering (QA) is a Natural Language Processing (NLP) task that can measure language and semantics understanding ability, it requires a system not only to retrieve relevant documents from a large number of articles but also to answer corresponding questions according to documents.
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification
This paper describes the contribution of the LingJing team’s method to the Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022 shared task on Emotion Classification.
STD-NET: Search of Image Steganalytic Deep-learning Architecture via Hierarchical Tensor Decomposition
We propose a normalized distortion threshold to evaluate the sensitivity of each involved convolutional layer of the base model to guide STD-NET to compress target network in an efficient and unsupervised approach, and obtain two network structures of different shapes with low computation cost and similar performance compared with the original one.
Adversarial Reconfigurable Intelligent Surface Against Physical Layer Key Generation
The development of reconfigurable intelligent surfaces (RIS) has recently advanced the research of physical layer security (PLS).
Cross-domain Federated Object Detection
Federated learning can enable multi-party collaborative learning without leaking client data.
Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation
To fully leverage the visual information for both scene understanding and dialogue generation, we propose the scene-aware prompt for the MDUG task.
Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction
Previous works on human motion prediction follow the pattern of building a mapping relation between the sequence observed and the one to be predicted.
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection
At first, we propose a novel clear memory-augmented module (CMAM), which combines the encoding and memoryencoding in a way of forgetting and inputting, thereby repairing abnormal foregrounds and preserving clear backgrounds.
Style Spectroscope: Improve Interpretability and Controllability through Fourier Analysis
We connect Fourier amplitude and phase with Gram matrices and a content reconstruction loss in style transfer, respectively.
Align-smatch: A Novel Evaluation Method for Chinese Abstract Meaning Representation Parsing based on Alignment of Concept and Relation
Therefore, to make up for the vacancy of Chinese AMR parsing evaluation methods, based on AMR evaluation metric smatch, we have improved the algorithm of generating triples so that to make it compatible with concept alignment and relation alignment.
The First International Ancient Chinese Word Segmentation and POS Tagging Bakeoff: Overview of the EvaHan 2022 Evaluation Campaign
This paper presents the results of the First Ancient Chinese Word Segmentation and POS Tagging Bakeoff (EvaHan), which was held at the Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) 2022, in the context of the 13th Edition of the Language Resources and Evaluation Conference (LREC 2022).
Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning
In this paper, we first analyze the generalization bound of the aggregation model produced from knowledge distillation for the client domains, and then describe two challenges, server-to-client discrepancy and client-to-client discrepancy, brought to the aggregation model by the domain discrepancies.
Knowledge Transfer with Visual Prompt in multi-modal Dialogue Understanding and Generation
In this work, we propose a knowledge transfer method with visual prompt (VPTG) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle visual dialogue tasks.
Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction
This process offers a natural way to obtain the "whitened" latents without any trainable parameters, and human motion prediction can be regarded as the reverse diffusion process that converts the noise distribution into realistic future motions conditioned on the observed sequence.
Slippage-robust Gaze Tracking for Near-eye Display
In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality.
Compositional Scene Modeling with Global Object-Centric Representations
Inspired by such an ability of humans, this paper proposes a compositional scene modeling method to infer global representations of canonical images of objects without any supervision.
Chinese Character Recognition with Radical-Structured Stroke Trees
In this paper, we represent each Chinese character as a stroke tree, which is organized according to its radical structures, to fully exploit the merits of both radical and stroke levels in a decent way.
Artificial Text Detection with Multiple Training Strategies
As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media.
Adversarial Example Defense via Perturbation Grading Strategy
In addition, the proposed method does not modify any task model, which can be used as a preprocessing module, which significantly reduces the deployment cost in practical applications.
Learning Trustworthy Model from Noisy Labels based on Rough Set for Surface Defect Detection
In the surface defect detection, there are some suspicious regions that cannot be uniquely classified as abnormal or normal.
Kainate receptor modulation by NETO2
Glutamate-gated kainate receptors (KARs) are ubiquitous in the central nervous system of vertebrates, mediate synaptic transmission on post-synapse, and modulate transmitter release on pre-synapse.
Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation
With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs).
Weakly-Supervised Text Instance Segmentation
Text segmentation is a challenging vision task with many downstream applications.
Uncertainty-aware U-Net for Medical Landmark Detection
To estimate the uncertainty, we propose a module named Pyramid Covariance Predictor to predict the covariance matrices of the target Gaussian distributions, which determine the distributions of landmarks and represent the uncertainty of landmark annotation.
Provably Convergent Subgraph-wise Sampling for Fast GNN Training
During the message passing (MP) in GNNs, subgraph-wise sampling methods discard messages outside the mini-batches in backward passes to avoid the well-known neighbor explosion problem, i. e., the exponentially increasing dependencies of nodes with the number of MP iterations.
Mechanism Design for Ad Auctions with Display Prices
In this paper, we study ad auctions with display prices from the perspective of mechanism design, in which advertisers are asked to submit both the costs and prices of their products.
DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback
Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration.
RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments
Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership.
Meta-Auxiliary Learning for Adaptive Human Pose Prediction
Predicting high-fidelity future human poses, from a historically observed sequence, is decisive for intelligent robots to interact with humans.
Frequency Decomposition to Tap the Potential of Single Domain for Generalization
Then, a new method that learns through multiple frequency domains is proposed.
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model
Specifically, we first extract prototypes from the labeled data on the server and send them to the clients.
Collaborative Chinese Text Recognition with Personalized Federated Learning
In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations.
Motion Information Propagation for Neural Video Compression
Meanwhile, besides assisting frame coding at the current time step, the feature from context generation will be propagated as motion condition when coding the subsequent motion latent.
Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging
As to the orthographic projection problem, we propose a novel Viewing Direction-aided Positional Encoding (VDPE) strategy.
Enhanced Fine-grained Motion Diffusion for Text-driven Human Motion Synthesis
The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently.
A Proxy Attack-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks
This paper presents a Proxy attack-Free Strategy (PFS) designed to identify efficient poisoning samples based on individual similarity and ensemble diversity, effectively addressing the mentioned concern.
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions
We provide a gradient backpropagation highway for low-rank adapters which eliminates the need for expensive backpropagation through the frozen pre-trained model, resulting in substantial savings of training memory and training time.
OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for Object-Centric Learning
The results demonstrate the shortcomings of state-of-the-art methods for learning meaningful representations from real-world data, despite their impressive performance on complex synthesis datasets.
Prototypes as Explanation for Time Series Anomaly Detection
Detecting abnormal patterns that deviate from a certain regular repeating pattern in time series is essential in many big data applications.
GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts
In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts.
ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces
The proposed ForensicsForest family is composed of three variants, which are {\em ForensicsForest}, {\em Hybrid ForensicsForest} and {\em Divide-and-Conquer ForensicsForest} respectively.
Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
In this paper, we rethink the role of the classifier in person Re-ID, and advocate a new perspective to conceive the classifier as a projection from image features to class prototypes.
Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification
Images with similar so-called fine-grained attributes (e. g., clothes and viewpoints) are encouraged to cluster together.
Ranked #2 on
Person Re-Identification
on PRCC
(mAP metric)
Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach
For multiple Unmanned-Aerial-Vehicles (UAVs) assisted Mobile Edge Computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multi-type tasks.
Test-time Personalizable Forecasting of 3D Human Poses
Current motion forecasting approaches typically train a deep end-to-end model from the source domain data, and then apply it directly to target subjects.
Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks
In this study, we focus on improving the poisoning efficiency of backdoor attacks from the sample selection perspective.
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization
In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces.
Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networks
In this paper, we study a digital twin (DT)-empowered integrated sensing, communication, and computation network.
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation
Few-shot image generation aims to train generative models using a small number of training images.
One-Shot Federated Learning with Classifier-Guided Diffusion Models
Leveraging the extensive knowledge stored in the pre-trained diffusion model, the synthetic datasets can assist us in surpassing the knowledge limitations of the client samples, resulting in aggregation models that even outperform the performance ceiling of centralized training in some cases, which is convincingly demonstrated in the sufficient quantification and visualization experiments conducted on three large-scale multi-domain image datasets.
NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search
Deep convolutional neural networks (CNNs) have been widely used in surface defect detection.
Federated Transformed Learning for a Circular, Secure, and Tiny AI
Deep Learning (DL) is penetrating into a diverse range of mass mobility, smart living, and industrial applications, rapidly transforming the way we live and work.
Efficient Trigger Word Insertion
With the boom in the natural language processing (NLP) field these years, backdoor attacks pose immense threats against deep neural network models.
Unsupervised Object-Centric Learning from Multiple Unspecified Viewpoints
This ability is essential for humans to identify the same object while moving and to learn from vision efficiently.
A Study on Training and Developing Large Language Models for Behavior Tree Generation
The core contribution of this paper lies in the design of a BT generation framework based on LLM, which encompasses the entire process, from data synthesis and model training to application developing and data verification.
Towards Generative Abstract Reasoning: Completing Raven's Progressive Matrix via Rule Abstraction and Selection
In the odd-one-out task and two held-out configurations, RAISE can leverage acquired latent concepts and atomic rules to find the rule-breaking image in a matrix and handle problems with unseen combinations of rules and attributes.
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across multiple sectors, including Software Engineering (SE).
SA-MDKIF: A Scalable and Adaptable Medical Domain Knowledge Injection Framework for Large Language Models
In the next stage, we train the skill router using task-specific downstream data and use this router to integrate the acquired skills with LLMs during inference.
Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies.
Learning-augmented Online Minimization of Age of Information and Transmission Costs
To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee.
Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm
Drawing on recent advancements in diffusion models for text-to-image generation, identity-preserved personalization has made significant progress in accurately capturing specific identities with just a single reference image.
Uncertainty-Aware Deep Video Compression with Ensembles
Deep learning-based video compression is a challenging task, and many previous state-of-the-art learning-based video codecs use optical flows to exploit the temporal correlation between successive frames and then compress the residual error.
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system.
Chaos in Motion: Unveiling Robustness in Remote Heart Rate Measurement through Brain-Inspired Skin Tracking
To address these issues, we regard the remote heart rate measurement as the process of analyzing the spatiotemporal characteristics of the optical flow signal in the video.
Soil Fertility Prediction Using Combined USB-microscope Based Soil Image, Auxiliary Variables, and Portable X-Ray Fluorescence Spectrometry
This study explored the application of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis to rapidly assess soil fertility, focusing on critical parameters such as available B, organic carbon (OC), available Mn, available S, and the sulfur availability index (SAI).
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge
Existing methods typically generate these faces by blending real or fake faces in color space.
Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning
To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods.
Improved Consistent Weighted Sampling Revisited
Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets.
Data Structures and Algorithms
Epoch-evolving Gaussian Process Guided Learning
In this paper, we propose a novel learning scheme called epoch-evolving Gaussian Process Guided Learning (GPGL), which aims at characterizing the correlation information between the batch-level distribution and the global data distribution.
Knowledge-Guided Object Discovery with Acquired Deep Impressions
The proposed ADI framework focuses on the acquisition and utilization of knowledge, and is complementary to existing deep generative models proposed for compositional scene representation.
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer
For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages.
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
To the best of our knowledge, SKR is the first attempt to address the time-consuming nature of data generation for learning neural operators.
Detection of Deep Network Generated Images Using Disparities in Color Components
In this paper, we address the problem of detecting deep network generated (DNG) images by analyzing the disparities in color components between real scene images and DNG images.
Multimedia
A New Perspective on Stabilizing GANs training: Direct Adversarial Training
Quite a number of methods have been proposed to stabilize the training of GANs, the focuses of which were respectively put on the loss functions, regularization and normalization technologies, training algorithms, and model architectures.
Bilateral Personalized Dialogue Generation with Contrastive Learning
Generating personalized responses is one of the major challenges in natural human-robot interaction.
Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing Images
In supervised deep learning, learning good representations for remote--sensing images (RSI) relies on manual annotations.
ADBCMM : Acronym Disambiguation by Building Counterfactuals and Multilingual Mixing
To address the above issue, this paper proposes an new method for acronym disambiguation, named as ADBCMM, which can significantly improve the performance of low-resource languages by building counterfactuals and multilingual mixing.
Rectangular Bounding Process
Stochastic partition models divide a multi-dimensional space into a number of rectangular regions, such that the data within each region exhibit certain types of homogeneity.
Improving Resistance to Adversarial Deformations by Regularizing Gradients
Improving the resistance of deep neural networks against adversarial attacks is important for deploying models to realistic applications.
MCTSteg: A Monte Carlo Tree Search-based Reinforcement Learning Framework for Universal Non-additive Steganography
Recent research has shown that non-additive image steganographic frameworks effectively improve security performance through adjusting distortion distribution.
One-shot Federated Learning without Server-side Training
Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection.
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing
A recent success in semantic dependency parsing shows that graph neural networks can make significant accuracy improvements, owing to its powerful ability in learning expressive graph representations.
Visual Answer Localization with Cross-modal Mutual Knowledge Transfer
In this paper, we propose a cross-modal mutual knowledge transfer span localization (MutualSL) method to reduce the knowledge deviation.
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
In this paper, we propose a federated adaptive prompt tuning algorithm, FedAPT, for multi-domain collaborative image classification with powerful foundation models, like CLIP.
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
However, existing backdoor attack methods make unrealistic assumptions, assuming that all training data comes from a single source and that attackers have full access to the training data.
A Review for Weighted MinHash Algorithms
In this review, we mainly categorize the Weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash algorithms to real-valued weighted MinHash ones (particularly the Consistent Weighted Sampling scheme).
Data Structures and Algorithms
Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture
The method is based on a Convolutional Neural Network (CNN) that is trained to solve the estimation as a standard regression problem.
Raven's Progressive Matrices Completion with Latent Gaussian Process Priors
In this paper we aim to solve the latter one by proposing a deep latent variable model, in which multiple Gaussian processes are employed as priors of latent variables to separately learn underlying abstract concepts from RPMs; thus the proposed model is interpretable in terms of concept-specific latent variables.
Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian Process
However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem.
Compositional Law Parsing with Latent Random Functions
The automatic parsing of these laws indicates the model's ability to understand the scene, which makes law parsing play a central role in many visual tasks.
Abstracting Concept-Changing Rules for Solving Raven's Progressive Matrix Problems
Finally, we conduct experiments to illustrate the interpretability of CRAB in concept learning, answer selection, and global rule abstraction.
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions
Generalization across different environments with the same tasks is critical for successful applications of visual reinforcement learning (RL) in real scenarios.
Learning to Locate Visual Answer in Video Corpus Using Question
We introduce a new task, named video corpus visual answer localization (VCVAL), which aims to locate the visual answer in a large collection of untrimmed instructional videos using a natural language question.
Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution
The appealing features of RSD-OA include that: (1) RSD-OA is invariant to visual distractions, as it is conditioned on the predefined subsequent action sequence without task-irrelevant information from transition dynamics, and (2) the reward sequence captures long-term task-relevant information in both rewards and transition dynamics.
Large Language Models Need Holistically Thought in Medical Conversational QA
The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care.
Payload-Independent Direct Cost Learning for Image Steganography
This framework directly learns universal costs that can be applied to any payload.
Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping
Different from existing methods, the proposed method disentangles the attributes of an object into ``shape'' and ``appearance'' which are modeled separately by the mixture weights and the mixture components.
VPAI_Lab at MedVidQA 2022: A Two-Stage Cross-modal Fusion Method for Medical Instructional Video Classification
Given an input video, the MedVidCL task aims to correctly classify it into one of three following categories: Medical Instructional, Medical Non-instructional, and Non-medical.
