Reviewerly/exHarmony huggingface dataset:
Similarity-based Multi-Relational Attention Network for Social Recommendation

Graph Neural Networks have been widely used in the field of social recommendation systems. However, with the increase of diffusion depth, it tends to cause the over-smoothing problem, which inhibits its performance. In this work, we propose a similarity-based multi-relational attention network, called Similarity-based Multi-Relational Attention Network (SRAN), for social recommendation. Our model has three distinctive characteristics: (i) it tries to solve the data sparsity problem in social recommendation scenarios by using both user social relations and item homogeneous relations as supplementary information; (ii) it has an iteratively aggregating structure that mimics the structure of influence diffusion in user and item domain; (iii) it has two levels of attention mechanisms applied at the diffusion and aggregating level, enabling it to differentiate importance weights when constructing the user and item latent embeddings. Experiments conducted on two large-scale representative datasets demonstrate that the proposed algorithm outperforms previous methods substantially with 5.7% and 6.8% gains of [email protected] and [email protected] in both datasets against the state of the arts. Moreover, our SRAN alleviates the over-smoothing problem, and its performance can be further improved by increasing the diffusion depth.
Linear Substitution Pruning: Consider All Filters Together

Filter (neuron) pruning is a neural network compression approach. In previous work, the importance of each filter is generally considered individually or pairwisely. This paper shows that considering the linear relation among all filters can help us prune more efficiently. Based on this intuition, we propose a new filter pruning method, named Linear Substitution Pruning (LSP). Similar to LSP, we also propose a model compensation method, called Linear Substitution Compensation (LSC), which restores the model performance after pruning by using all remaining filters to compensate for the error caused by pruning. The experiments show that our method outperforms the state-of-the-art filter pruning methods. LSP achieves a reduction of 61.04% in flops on ResNet110 with an increase of 0.96% in top-1 accuracy at the same time, and it achieves a reduction of 51.84% in flops on ResNet50 with also an increase of 0.05% in top-1 accuracy.
An Effective Approach for Large Ontology Matching Using Multi-objective Grasshopper Algorithm

Although the population-based metaheuristic ontology matching approaches have achieved excellent results on small scale matching tasks, such methods do not solve the large ontology matching problem. In addition, the common practice of the ontology matching community is to divide the large ontology into many small fragments. Although the divide-and-conquer strategy is feasible to reduce time and space complexity, it can easily change the original structure of the ontology, resulting in reduced quality. Further, if the produced fragments are smaller, the number of fragments will increase, which introduces new time and space complexity. In this paper, an effective approach for large ontology matching using multi-objective metaheuristic Grasshopper algorithm is proposed, called GOLOM. In this approach, an ontology pruning technique is proposed to reduce time and space complexity while maintaining the original structure. An effective background knowledge is built to assist the basic matcher in a proper way. In order to demonstrate the performance of GOLOM, three large ontology matching tasks were conducted. Experimental results show that GOLOM significantly reduces time and memory complexity compared to partition-based. In terms of alignment quality, GOLOM outperforms all the state-of-the-art systems.
A Computational Trust Model Based on Hyperledger Fabric Blockchain

The core component of the blockchain is smart contracts, which are the basis for connecting countless nodes and executing transactions. In smart contracts, trust often replaces trust in the form of proof of workload, proof of identity, proof of authorization, etc. Therefore, in the transaction process of the core chain, the node usually sees a concept of quantity, and does not know the change course of the agent's trust, so that it cannot fully grasp the dynamic process of the agent and the change of the trust evaluation value. The researches of computational trust has deeply affected the global economic and social development, and have been widely used in some large platforms such as ICAO PKD trust platform, EU's eIDAS platform, US FICAM system and so on. The current solution is a combination of limited mathematical models and assumptions that are far from the operational level. Hyperledger has superior performance in data access, invocation, operability, tamperability, traceability and other aspects, and is more suitable for the study of computational trust models. This study introduces the hyperledger technology to construct a computational trust model, and the main factors affecting the computational trust model are analyzed by using the Hyperledger Fabric technology, which is as the form of one to one to correspond to each node in the hyperledger, and realizes the trust transmission of the node, by sorting each node in the consensus chain, and passes. The interface technology realizes the docking between the model and the Hyperledger Fabric system. And then this paper analyzes the composition system of the computational trust model based on Hyperledger Fabric.
Improving End-to-End Biomedical Question Answering System

Biomedical question answering refers to extracting an answer based on given questions and related documents. Existing biomedical question answering research either focuses on a specific stage, such as machine reading comprehension, or uses traditional rule-based methods and ontology with complex construction processes. In this paper, we demonstrate the application of simple but powerful neural-based approaches in improving the end-to-end biomedical question answering system. We employ the BM25-based documents retriever, BERT-based neural ranker, and an answer extraction stage using the BioBERT pre-trained language model. In view of the lack of sufficient training data in the biomedical domain, domain adaptation and data augmentation are adopted to address the question answering task, so as to further reinforce the system performance. Based on our self-built standard large-volume retrieve corpus and neural ranker corpus, we get competitive results on BioASQ8b.
Aggregating Heterogeneous Neighbors and Node Types for Numerical Reasoning over Text

Numerical reasoning machine reading comprehension (MRC) is a challenging natural language understanding task, which requires machines to have the numerical reasoning ability to add, subtract, sort and count over text to predict answers. Previous state-of-the-art models typically use graph neural networks (GNNs) to perform reasoning over heterogeneous graphs containing numbers and entities, to enhance their numerical reasoning ability. However, their GNN module treats heterogeneous graphs as homogeneous and only aggregates information of direct neighbor nodes, thus ignoring node types and not explicitly capturing the rich heterogeneous neighboring information for each node. To alleviate these issues, in this paper, we propose a heterogeneous neighbors and node types aggregated graph attention network (HNNT-GAT) for the numerical reasoning MRC task. It employs random walk with restart to sample heterogeneous neighbors for each node, and groups them by the node type, then it updates node vector with heterogeneous neighbors using BiLSTM and attention mechanism. Finally, it leverages graph attention network for numerical reasoning to predict the answer. Besides, for node initialization in the heterogeneous graph, we explicitly concatenate with node type embedding to incorporate node type information. Experimental results indicate that HNNT-GAT model offers significant and consistent improvements, outperforming previous state-of-the-art QDGAT on DROP dataset.
A Facial Expression Synthesis Method Based on Generative Adversarial Network

Recently, machine learning, especially the emergence of generative adversarial networks (GANs), has further enhanced the robustness and realism of facial expression conversion models. However, most models have flaws such as fuzziness in the details. Based on this, this article mainly studies the facial expression synthesis method based on GANs. Firstly, we created a dataset containing 127,616 expression annotations suitable for the study of facial expressions. The dataset has been tested on mainstream models with good generation results. Secondly, we propose a GAN network structure named SRFEGAN with a super-resolution synthesis module. This module helps solve the artifact problem in the process of image conversion. Experimental results on our dataset show that the average recognition accuracy rate of the generated images is 63.76% and the Frechet Inception distance (FID) is 36.581. This shows that our network can accurately synthesize the facial expression image of the subject, and the image quality is better.
Analysis of public sentiment tendency in COVID-19 period based on GA-BiLSTM

As the Internet becomes the main source of information for the public, grasping the emotional polarity of online public opinion is particularly important for relevant departments to supervise online public opinion. In order to more accurately determine the emotional polarity of public opinion in the epidemic, this paper proposes a public sentiment analysis model based on Word2vec, genetic algorithm and Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm. The Word2vec model converts the comment text into an n-dimensional vector, uses the Bi-LSTM algorithm to analyze the sentiment polarity, and uses the genetic algorithm to analyze the number of Bi-LSTM layers and the number of fully connected layers and the number of neurons in each layer of Bi-LSTM optimization. The experimental results show that the accuracy of the above model is compared with the accuracy of the Word2vec model and the LSTM model separately, and the accuracy is increased by 11.0% and 7.7%, respectively.
Historical Information-Based Intent Detection for Multiturn Dialogue

Intent detection aims to determine the intent of users, an important task in natural language processing and dialogue systems. As one of the key modules of task-based dialogue systems, intent detection directly influences the meaning analysis of spoken language and the performance of dialogue systems. However, existing intent detection algorithms mainly focus on single-turn conversation, ignoring the valuable historical conversation message. To alleviate the problem, we propose a multiturn dialogue intent detection model based on historical information. Specifically, the semantic information of user statements and system statements is extracted by a role-distinguished self-attention network. Then, semantic relevance based on context information adjustment is used to combine the system statement and user statement. Finally, long short-term memory (LSTM) is used to capture the historical message. Experimental results prove that the proposed method improves the effect of intent detection and performs better than seven mainstream baselines.
Accurate Human Mesh Reconstruction from a Video with Transformer Based Encoder

Recently 3D human motion reconstruction has attracted a lot of attentions and found many applications in fields such as virtual reality, sport video analysis and etc.. Several video-based 3D human pose and shape estimation methods have been proposed. However, these methods still failed to solve the problem of accurately matching generated 3D model to the human body regions in the images. To tackle the problem, we propose a new model named transformer encoder with sliding attention window(TESA). Our method uses the VIBE structure as the backbone and proposes a multi-head attention encoder to generate more accurate human pose and shape. The multi-head attention structure in transformer can extract local and temporal features between frames, e.g. one attention head might focus on a hand region, while another attention head might extract features of a leg. Further more, a new module named sliding attention window is proposed to get smooth human motion by applying motion constraint between the current frame and it’s neighbor frames. Our method outperforms previous video-based methods in accuracy, and achieving state-of-the-art performance.
A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Random Cooperative Learning Strategy

Multi-swarm particle swarm optimization algorithms have received much attention in recent years due to their effectiveness in maintaining the diversity of population. However, during the evolution process, the algorithm often suffers from the aggregation of particles within the sub-swarm which could make the particles fall into the local optimum. In order to solve this problem, we propose a new multi-swarm particle swarm optimization algorithm: A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Cooperative Learning Strategy Algorithm (SMS-DCLS-PSO). In the SMS-DCLS-PSO algorithm, we use the number of stagnant local optimums in the sub-swarms as the aggregation flag. When the sub-swarms reach the aggregation condition, the dynamic random cooperative learning strategy is adopted to realize the adaptive periodic information exchange between sub-swarms. To further enhance the diversity of the population, the best particle of the main population and the worst particle of the sub-swarms are respectively mutated. When the sub-swarms don't meet the aggregation conditions, the sub-swarms evolve independently which means the particles only exchange the information within their own sub-swarm. The main-swarm evaluates the local optimal position of each sub-swarm and its own local optimal position in each iteration to update the velocity. The results of the comparison with SMS-DCLS-PSO and other PSO algorithms on the latest public test suite demonstrate that SMS-DCLS-PSO has a better performance on optimization.
Re-synthesis Anomaly Detection Framework for Driving Scenes Guided by Uncertain Metrics

research-article Share on Re-synthesis Anomaly Detection Framework for Driving Scenes Guided by Uncertain Metrics Authors: Shichao Yan Academy for Engineering&Technology, Fudan University, China Academy for Engineering&Technology, Fudan University, ChinaSearch about this author , Lubin Chen Academy for Engineering&Technology, Fudan University, China Academy for Engineering&Technology, Fudan University, ChinaSearch about this author , Chunyu Wang Academy for Engineering&Technology, Fudan University, China Academy for Engineering&Technology, Fudan University, ChinaSearch about this author , Yang Liu Academy for Engineering&Technology, Fudan University, China Academy for Engineering&Technology, Fudan University, ChinaSearch about this author , Peng Zhai Academy for Engineering&Technology, Fudan University, China Academy for Engineering&Technology, Fudan University, ChinaSearch about this author , Lihua Zhang Academy for Engineering&Technology, Fudan University, China Academy for Engineering&Technology, Fudan University, ChinaSearch about this author Authors Info & Claims ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial IntelligenceMarch 2022 Pages 579–584https://doi.org/10.1145/3532213.3532301Online:13 July 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Distil Knowledge from Natural Language

Knowledge Distillation (KD) is a machine learning approach for model compression and acceleration, which is suitable for applications with limited computational resources. KD is typically performed by distilling and transferring the knowledge of large teacher models into smaller student models, to enhance the performance of the latter. Current KD models require supervision by a pretrained teacher model, whose training requires additional computational cost. In this work, we first analyze the output of the teacher, then propose a method to distill knowledge from natural language. On this basis, we propose Semantic-knowledge-based Teacher-free KD (ST-KD) to further advance model compression and acceleration methods. Our model was tested on the CIFAR10 and CIFAR100 image classification datasets, and it was experimentally demonstrated that it improved the performance of a variety of deep neural networks with virtually no additional computational cost.
Dynamic Updating of Partial Feature Maps for Image-based Real-Time Recognition of Online Handwritten Chinese Text

research-article Share on Dynamic Updating of Partial Feature Maps for Image-based Real-Time Recognition of Online Handwritten Chinese Text Authors: Yuefeng Wu Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, China Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, ChinaSearch about this author , Ying Zhong Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, China Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, ChinaSearch about this author , Da-Han Wang Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, China Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, ChinaSearch about this author , Xueke Chi Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, China Computer and Information Engineering, Xiamen University of Technology, China and Fujian Key Laboratory of Pattern Recognition and Image Understanding, Xiamen University of Technology, ChinaSearch about this author Authors Info & Claims ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial IntelligenceMarch 2022 Pages 344–349https://doi.org/10.1145/3532213.3532264Online:13 July 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Small-sample Multi-choice Reading Comprehension Method Based on Semantic Interaction and Domain Adaptation

Machine Reading Comprehension (MRC) is a task to evaluate the machine's ability to understand natural language. This task is to provide articles and questions to the machine and hope that the machine can answer as much as possible. As one of the tasks of MRC, multiple-choice reading comprehension has important research significance and is faced with challenges because it has higher requirements for semantic understanding than other types of tasks. However, the fields involved in practical application scenarios are limited, so it is difficult to obtain large-scale and high-quality training corpus, which makes the performance of the reading comprehension model difficult to meet the needs of practical application. This paper proposes a model based on semantic interaction and domain adaptation (SIDA) to solve the multiple-choice task in the small sample domain. The model interacts with the semantic relationship between different input parts by simulating human reading strategies and transfers cross-domain knowledge through extraterritorial data, which improves the performance of the model in the field of small samples.
Coded Distributed Computing Schemes via Grouping Method

Coded distributed computing introduced by Li et al. in 2015 is an efficient approach to reduce the communication load in general distributed computing frameworks such as MapReduce. But there is a big disadvantage in this scheme, that is, the numbers of required input files and output functions increase exponentially with the increase of the number of computing nodes. In this paper, we make an attempt to overcome these problems by constructing some new coded distributed computing schemes. We demonstrate that when the number of computing nodes becomes large, 1) the number of input files in the proposed scheme is much smaller than that of input files in the scheme derive by Li et al.; 2) the required number of output functions is reduced exponentially. Meanwhile, the ratio of the communication load to that of the scheme derived by Li et al. is less than 3.
A Perimeter Alert Surveillance system based on pedestrian detection and dual-threshold selection for local binocular ranging

Perimeter alert surveillance is an early warning system used for monitoring the warning area and detecting pedestrians entering by mistake or abnormal behavior. It has a wide range of application scenarios. Computer vision based surveillance is an efficient and safe method. We propose an early warning system based on pedestrian detection and binocular ranging, which is able to detect approaching pedestrians from surveillance equipment and calculate the distance between pedestrians and photographing equipment. The model detects pedestrian candidate windows from the binocular image through YOLO network, and then uses the binocular algorithm to calculate the pedestrian distance. We calculate the local disparity map in the candidate window with the consideration of the real-time performance and the proportion of the candidate window in the whole image. At the same time, we propose a dual-threshold selection (DTS) algorithm, which divides the disparity map into foreground, middle ground and background. This helps remove background and occlusion interference in the foreground and background, and improve the accuracy of distance calculation.
Learning Non-Parametric Models in Real Time via Online Generalized Product of Experts

In this work, we address the problem of online learning, where models must be continually updated from an incoming stream of data, while retaining past information. We develop an approach that is nonparametric, models uncertainty, and requires minimal hand-tuning. Our proposed algorithm, which we term online generalized product of experts (OGPoE), extends the powerful generalized product of experts (GPoE) framework to the online setting by leveraging methods for sparse, variational Gaussian process approximations, as well as nonparametric clustering. We devise a 1-D example learning problem to illustrate how our method works, and we verify that we achieve competitive results with other popular modeling approaches on a benchmark learning problem. Finally, we demonstrate how our algorithm can produce high accuracy predictions on a physical system, by learning the kinematics for a concentric tube robot, even when the robot is subject to changing, unknown loads.