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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Anpeng Sun Jun Lu College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Transformer-based sequential recommendation is highly powerful as it can capture short-term and long-term sequential recommendation. It plays a crucial role in personalized recommendation systems, aiming to extract dynamic and diverse preference information from users’ realtime growing behaviors. This is mainly due to its unique selfattention mechanism, which utilizes item-item interactions within the sequences. However, in online platforms, due to the interweaving of items in user behavior, user behavior data inevitably contains noise. Currently, most methods model user preferences in the time domain, overlooking the influence of frequency patterns on user behavior. To address this issue, this paper shifts the perspective to the frequency domain and proposes a novel contrast-enhanced filtering model for sequential recommendation based on the bidirectional Transformer, called CEFRec. Specifically, the paper designs a filtering layer on top of the bidirectional Transformer, allowing learnable filters to operate on different layers’ spectra. This enables the model to capture various frequency patterns and adaptively attenuate noise information within the frequency domain. Next, the model combines the cloze task mask and dropout masks to generate high-quality positive samples and utilizes contrastive learning to enhance the quality of user embeddings learned from the frequency domain. Finally, experimental results on three public benchmark datasets demonstrate that the CEFRec model outperforms state-of-theart sequential recommendation models.

关键词： Adaptation models Time-frequency analysis Electric potential Filters Image processing Noise Contrastive learning Transformers Time-domain analysis Recommender systems

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Influence Maximization in Microblog Social Network

Influence Maximization in Microblog Social Network

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International Conference on Information Science and Control Engineering (ICISCE)

作者： Zhengyi Ma Yan Yang School of Computer Science Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing Harbin Heilongjiang Province China

In recent years, the continuous rise of social networks has promoted the research of maximizing influence. Therefore, it is more and more important to mine a group of users with the largest influence range in social network, which is also of great significance for the promotion of some products. To solve the above problems, this paper studies the influence maximization in microblog social network, and proves that the problem is NP hard, and the objective function of the problem satisfies monotonicity and submodule. An effective algorithm is proposed to solve this problem. The algorithm fully considers the nature of microblog social network and microblog users, and measures the influence of users themselves with activity, communication power and coverage. Based on the independent cascade model, a communication model is designed according to the influence of users and the interaction times between users. Through the experiment on the real microblog data set, it is proved that the algorithm has good performance in the propagation range and operation efficiency, and the propagation effect is more in line with the reality.

关键词： Information science Power measurement Control engineering Social networking (online) Blogs Linear programming

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ShuffleVITNet: Mobile-Friendly Vision Transformer With Less-Memory

ShuffleVITNet: Mobile-Friendly Vision Transformer With Less-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xinyu Zhao Jun Lu College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traditional Convolutional Neural Networks (CNNs) can efficiently acquire local features, while ViT uses a Transformer structure that captures global contextual information. In this paper, a new high-performance lightweight target detector is designed by combining the respective advantages of CNN and ViT: ShuffleViTNet. In this paper, we improve the VIT Block in MobileVIT, firstly, we use channel-by-channel convolution to replace the normal convolution to reduce the memory access cost (MAC), number of parameters (Parmas) and floating-point operations (FLOPs) in the model inference. The 1×1 convolution is then used to fuse the local features learned by the convolution with the global features learned by the Transofmer, which further lightens the model and allows for easier modification of the number of channels and migration to other lightweight neural networks. Finally a lightweight network friendly to mobile devices is constructed using the improved VIT Block. On the ImageNet-1k dataset, ShuffleViTNet achieves 75.2% Top-1 accuracy, the model’s floating-point operation is 536.5 MFLOPs, which is 44.0% less compared to MobileViT-XS, and the cost of memory accesses for model inference is saved by about 25.6%, and the required memory is saved by about 66.37%. For the object detection task on the MS-COCO dataset, ShuffleViTNet obtains higher mAP accuracy. The experimental results show that ShuffleViTNet is a high-performance lightweight network and can be well applied to downstream tasks.

关键词： Accuracy Costs Convolution Computational modeling Neural networks Memory management Object detection

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BIVD: Improved Side Adapter Network based on BiFormer

BIVD: Improved Side Adapter Network based on BiFormer

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Liyuan Hu Jun Lu College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Currently, most open vocabulary models rely on CLIP (Contrastive Language-Image Pre-training) to classify masked regions, but CLIP models still have many drawbacks. The traditional transformer model is context aware through self attention, but this local context based mechanism is limited by the sequence length of information, it can also impact model effect. Inspired by the article BiFormer, This article replaces the traditional transformer model in the Side Adapter Network (SAN) model with a variant module BIVD block based on BiFormer blocks. This model follows the latest transformer model and extracts features using a four layer feature pyramid structure, For the reduced spatial resolution caused by patch merging, add a Visual-Decoder to reshape the visual vector sequence into a spatial feature map, and restore it to the original resolution through convolution and upsampling. This can enhance the extracted feature information through a feature pyramid without affecting the fusion with CLIP layer features. But the cost is increasing the cost of the model. This article was tested on 5 commonly used datasets and showed a stable improvement in accuracy.

关键词： Adaptation models Vocabulary Visualization Costs Semantic segmentation Feature extraction Transformers Decoding Spatial resolution Context modeling

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Multi-pattern matching methods based on numerical computation

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Telkomnika - Indonesian Journal of Electrical Engineering 2013年第3期11卷 1497-1505页

作者： Jun, Lu Zhuo, Zhang Juan, Mo Xingfeng, Lv College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

Multi-pattern matching methods based on numerical computation are advanced in this paper. Firstly it advanced the multiple patterns matching algorithm based on added information. In the process of accumulating of information, the select method of byte-accumulate operation will affect the collision odds , which means that the methods or bytes involved in the different matching steps should have greater differences as much as possible. In addition, it can use balanced binary tree to manage index to reduce the average searching times, and use the characteristics of a given pattern set by setting the collision field to eliminate collision further. In order to reduce the collision odds in the initial step, the information splicing method is advanced, which has greater value space than added information method, thus greatly reducing the initial collision odds. Multiple patterns matching methods based on numerical computation fits for large multi-pattern matching. © 2013 Universitas Ahmad Dahlan.

关键词： Numerical methods

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Research on Linkage Disequilibrium Method Based on OpenMP

Research on Linkage Disequilibrium Method Based on OpenMP

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International Conference on Information Science and Control Engineering (ICISCE)

作者： Jun Lu Jun Li Ruiqing Jing Qiong Wang Cheng Chang Jiaxing Guo College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781509025367

Linkage disequilibrium method is applied for the research on inferring population genetics, LD mapping, haploid type diversity analysis and so on. Soybean genotypes are adopted as the data source and linkage disequilibrium parallel algorithm is implemented by OpenMP technology. In this algorithm, single nucleotide polymorphism sites are divided by using sliding windows into groups, adjacent sites allele in a window of each chromosome are parallel calculated and store the LD results. According to the experimental data, the serial and parallel algorithms are compared and analyzed. The conclusion shows that the OpenMP parallel technology can effectively improve the efficiency of linkage disequilibrium analysis method. It is a realistic significance for processing massive biological information data.

关键词： Biological cells Couplings Genetics Instruction sets Sociology Statistics parallel algorithms

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Time constraint-based team formation in social networks

Time constraint-based team formation in social networks

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International Conference on Mechatronic Science, Electric Engineering and Computer

作者： Xue Han Yong Liu Xingkai Guo Xianyi Wu Xiaolong Song School of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University

ISBN: (纸本)9781479925667

We investigate the problem of discovering a team of experts from a social network. Given a task with the skill set and the time span length, our goal is to obtain a team that covers all the skills, and have a low communication cost. Furthermore, each member of the team must have common free time whose length is no less than the span length required by the task. Previous works only attempt to cover the required skills and minimize the communication cost without taking the common free time of the individuals into account. We are the first to consider time factor in team formation and thus propose a novel team formation formulation with time constraint. We show that the problem is NP-hard. We first extend the RarestFirst algorithm to deal with our new problem, then propose two novel pruning strategies, called time pruning and skill pruning respectively, and an efficient algorithm called TSP-TF, to efficiently obtain an effective team for a given task. Experimental results show that TSP-TF outperforms the generalized RarestFirst algorithm by more than an order of magnitude.

关键词： Algorithm design and analysis Social network services Time factors Collaboration Data mining Time complexity databases

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User Local and Global Interest Depth Interaction for Recommendation Algorithm

User Local and Global Interest Depth Interaction for Recomme...

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International Conference on Information Science and Control Engineering (ICISCE)

作者： Xufeng Zhang Yan Yang School of Computer Science Technology Heilongiiang University Harbin China Key Laboratory of Database and Parallel Computing Harbin Heilongiiang Province China

Using auxiliary information such as social networks or item attributes to improve the performance of recommender systems is a key issue in the research of recommender systems. It plays a very important role in alleviating the interactive sparsity of collaborative filtering methods. In response to this problem, a recommendation algorithm that user local and global interest depth interaction is proposed, which analyzes the user’s implicit preferences from two perspectives: based on the user’s historical behavior records, using a fully connected neural network to extract the user’s local interests; based on the user’s historical behavior items, through the association between entities in the knowledge graph, mines the entities associated with the user’s historical behavior, forming a multi-layer global interest layer to generalize user interests. The user’s local interest vector activates the item entity in the global interest layer to generate the user’s global interest vector. Finally, the deep neural network is used to complete the feature interaction of users’ local interests, global interests and recommended items. The user’s local and global interests accurately describe user preferences and improve the accuracy of click-through rate prediction. Experiments show that the recommendation performance of the algorithm on the two public data sets is better than the comparison baseline and the classic recommendation algorithm.

关键词： Knowledge engineering Deep learning Information science Control engineering Social networking (online) Neural networks Feature extraction

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International Conference on Information Science and Technology (ICIST)

作者： Zhongqing Wang Yan Yang Yingli Zhong School of Computer Science Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

Graph similarity search is an important research problem in many applications, such as finding result graphs that have a similar structure to a given entity in biochemistry, data mining, and pattern recognition. Top-k graph similarity search is one of graph similarity search tasks, which aims to find the top-k graphs that are most similar to the query graph in a given graph database. In this paper, the top-k similarity search problem based on the graph edit distance is studied according to the corollary of the partitioned similarity theorem. Firstly, in order to speed up the online search process and avoid scanning each graph in the database one by one, an offline hierarchical inverted index is constructed to satisfy top-k search. Secondly, the offline hierarchical inverted index is used to filter the candidate graphs online and verify them, which reduces the time of searching the graphs. Finally, the good performance of the algorithm in running time and scalability is verified by running the similarity algorithm on real dataset and synthetic dataset.

关键词： Information science databases Scalability Filtering algorithms Search problems Partitioning algorithms Indexes

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A New Feature Fusion Method Based on Pre-Training Model for Sequence Labeling

A New Feature Fusion Method Based on Pre-Training Model for ...

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International Conference on Data Storage and Data Engineering (DSDE)

作者： Shichuan Yu Yan Yang School of computer science and technology Heilongjiang University Harbin China Heilongjiang Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin China

To fuse vocabulary features into the pre-training model is the mainstream data feature processing method for sequence labelling tasks. In general, the feature fusion methods that have been proposed at present are direct fusion outside the pre-training model or fusion of lexical features using attention mechanism. However, the study found that this way of vocabulary enhancement does not conform to the word formation rules of modern Chinese. In the Chinese language, it is easy to fuse irrelevant or even incorrect lexical features into the sequence using the above feature processing methods, which is bad for the experimental results of the Chinese sequence labelling task. To solve these problems, we propose to use Cosine Similarity Adapter to process lexical features in Chinese sequence labelling tasks. CSBERT is a hybrid model using this structure based on BERT, which conforms to the word formation rules of modern Chinese to a certain extent. It can fuse the features of the word into the character or eliminate the features of the word in the character according to the cosine similarity between the character vector and a word vector. The experimental results show that CSBERT has better ability to label Chinese sequences than the benchmark model. CSBERT has achieved the best experimental results such as F1-Score on 7 open datasets and the best ability of multi-label classification, which proves that the model has good practical value.

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