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9th International Conference on Big data Computing and Communications, BigCom 2023

作者： Luo, Zhan Zhang, Lan Lai, Jiewei Wang, Xinming Tang, Chen University of Science and Technology of China School of Data Science Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350331240

Partial video copy detection (PVCD) aims to discover copy segments of query videos from a video database, which plays an important role in video copyright protection, filtering, tracking, etc. For a large-scale video database, PVCD can be divided into two stages: the first stage involves searching for video-level copies of the query video in the database, and the second stage is to further localize the copy segments within the video-level copies. Thus, two major challenges arise: (1) efficiently and effectively calculating the similarity between videos;(2) localizing mixed-duration video pairs. To address the above challenges, we propose an efficient PVCD approach for a large-scale video database, based on the Bag-of-Words (BoW) framework, which decouples video-level similarity and copy localization into cell-level. This approach consists of two modules. The first is an efficient video similarity measurement (VSM) module for the large-scale video database. VSM aggregates cell-level similarity into video-level similarity, and with a dual index, it greatly improves retrieval speed while accurately measuring spatiotemporal transformations. The second is a greedy pattern detection (GPD) module for video copy localization. GPD quickly and accurately detects similarity patterns through a greedy strategy on the similarity matrix formed by matching frames in each cell, then aggregates them into complete predicted copy segments. On the comprehensive dataset self-SVD, VSM significantly outperforms state-of-the-art methods by 7.28% in mAP, and the retrieval speed is increased by over 318 times. Additionally, for short videos at the scale of hundreds of millions, the response speed can theoretically reach seconds. On the copy localization dataset MIX, composed of mixed-duration videos, GPD also achieves the best performance. © 2023 IEEE.

关键词： Aggregates

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RAM-CG: Relation-aware Adaptive Model for Continual Graph Learning 2

RAM-CG: Relation-aware Adaptive Model for Continual Graph Le...

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2nd Asia Conference on Advanced Robotics, Automation, and Control Engineering, ARACE 2023

作者： Shen, Qinghua Liu, Guiquan University of Science and Technology of China School of Data Science Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350328363

Neural networks excel at mining the static graph, but how to learn from streaming graphs without forgetting previous knowledge is an emerging challenge and well known as continual graph learning (CGL). Despite recent progress in this area, two significant challenges persist: 1) most of existing works only manipulate on the intermediate graph embedding and ignore intrinsic properties of graphs, which limits the learning method's pertinence. 2) recent attempts obscure the transferable knowledge and lack explicit description for the transfer process. It is non-trivial to tell what and how the information is transferred across graphs. In this paper, we point out that latent relations behind graph edges, as an intrinsic graph property, can be attributed as an invariant factor for the evolving graph sequence and lead a possible knowledge transfer. Motivated by this, we design a relation-aware adaptive model, dubbed as RAM-CG, which consists of a relation analysis module to explore latent relations behind edges for message-passing and a task-awareness masking classifier to account for the shifting knowledge in the graph sequence. Extensive experiments show that RAM-CG provides significant 2.2%, 4.2% and 6.6% accuracy improvements over the state-of-the-art results on CitationNet, OGBN-arxiv and TWITCH dataset. © 2023 IEEE.

关键词： Message passing

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ALRS: An Attention Loss Function based on Row-Sparsity for Incremental Learning 8

ALRS: An Attention Loss Function based on Row-Sparsity for I...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Xia, Yingying Lu, Bo Ji, Jianmin School of Data Science University of Science and Technology of China Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781665456128

Incremental learning has received significant attention, but the problem of catastrophic forgetting remains a major challenge for existing approaches. This issue hinders models from accumulating knowledge over long stretches. To address this problem, we propose a new approach called Attention Loss function based on Row-Sparsity (ALRS) that mines significant patches by simultaneously learning patch weights and logits (class vectors) using the same parameters. We integrate the attention mechanism with the novel loss function to avoid catastrophic forgetting. This innovative approach enables the model to conflate the newly introduced classes with the existing ones, without the need to store any data or models from the previous steps' base classes. To assess its efficacy, we incorporate ALRS into the distillation loss for validation and conduct a thorough evaluation of the approach's performance on three datasets: CIFAR-100, Caltech-101, and CUBS-200-2011. Compared to LWM, which also does not store data, our method achieves an average improvement of more than 8 percentage points with an absolute advantage on CIFAR-100. Additionally, on Caltech-101 and CUBS-200-2011, our new approach provides comparable accuracy to baseline. © 2023 IEEE.

关键词： Distillation

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Probe Machine Based Computing Model for Maximum Clique Problem

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Chinese Journal of Electronics 2022年第2期31卷 304-312页

作者： CUI Jianzhong YIN Zhixiang TANG Zhen YANG Jing Department of Computer Huainan Union University School of Electronic and Information Engineering Anhui University of Science & Technology School of Mathematics Physics and Statistics Shanghai University of Engineering Science School of Mathematics and Big Data Anhui University of Science & Technology

Probe machine(PM) is a recently reported mathematic model with massive parallelism. Herein,we presented searching the maximum clique of an undirected graph with six vertices. We constructed data library containing n sublibraries, each sublibrary corresponded to a vertex in the given graph. Then, probe library according to the induced subgraph was designed in order to search and generate all maximal cliques. Subsequently,we performed probe operation, and all maximal cliques were generated in parallel. The advantages of the proposed model lie in two aspects. On one hand, solution to NP-complete problem is generated in just one step of probe operation rather than found in vast solution *** the other hand, the proposed model is highly *** work demonstrates that PM is superior to TM in terms of searching capacity when tackling NP-complete problem.

关键词： maximal cliques maximum clique problem searching capacity data library NP-complete problem computational complexity model lie induced subgraph probe operation undirected graph mathematic model graph theory parallel algorithms optimisation parallelism computing model vertex probe library probe machine

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K-Means Clustering with Local Distance Privacy

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Big data Mining and Analytics 2023年第4期6卷 433-442页

作者： Mengmeng Yang Longxia Huang Chenghua Tang the Data61 Commonwealth Scientific and Industrial Research OrganizationMelbourne 3168Australia the School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China. the School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541010China

With the development of information technology,a mass of data are generated every *** and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.K-means clustering has been widely used for cluster analysis in real ***,these analyses are based on users’data,which disclose users’*** differential privacy has attracted lots of attention recently due to its strong privacy guarantee and has been applied for clustering ***,existing K-means clustering methods with local differential privacy protection cannot get an ideal clustering result due to the large amount of noise introduced to the whole dataset to ensure the privacy *** solve this problem,we propose a novel method that provides local distance privacy for users who participate in the clustering *** of making the users’records in-distinguish from each other in high-dimensional space,we map the user’s record into a one-dimensional distance space and make the records in such a distance space not be distinguished from each *** be specific,we generate a noisy distance first and then synthesize the high-dimensional data *** propose a Bounded Laplace Method(BLM)and a Cluster Indistinguishable Method(CIM)to sample such a noisy distance,which satisfies the local differential privacy guarantee and local dE-privacy guarantee,***,we introduce a way to generate synthetic data records in high-dimensional *** experimental evaluation results show that our methods outperform the traditional methods significantly.

关键词： K-means clustering local differential privacy data analysis

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A Variational Inference-Based LSTM-enhanced Deep Neural Model for Sequential Recommendations

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IEEE Access 2025年 13卷 95945-95962页

作者： Ngaffo, Armielle Noulapeu Kamgang, Ines Raissa Djouela Maka, Ebenezer Maka Malong, Yannick University of Douala Laboratory of Computer Science Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala8580 Cameroon Department of computer engineering University of Buea Faculty of Engineering and Technology Cameroon

Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM models capture sequential dependencies and temporal dynamics, making them ideal for personalized, session-based, and sequential recommendation tasks. However, traditional LSTM-based recommender systems do not model uncertainty since they perform deterministic predictions. Moreover, those recommender models exhibit poor generalization on small datasets, and poor-handle cold-start and data sparsity problems. To overcome those limitations, this paper presents an LSTM-based recommender model that leverages an improved deep matrix factorization to accurately address the data sparsity problem. In addition, an enhanced variational inference coupled to the Evidence Lower Bound Optimization is applied on LSTM unit layers to perform uncertainty-aware predictions. Thereafter, latent vectors obtained from LSTM networks are effectively involved in the sequential recommendation process. Experiments have been conducted on several real-world datasets to evaluate our proposal and the results show the proposed model presents significant performances compared to state-of-the-art models. © 2013 IEEE.

关键词： Deep neural networks

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A Novelty Framework in Image-Captioning with Visual Attention-Based Refined Visual Features

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computers, Materials & Continua 2025年第3期82卷 3943-3964页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Mohammed ELAffendi Sajid Shah School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information ScienceDepartment University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Image captioning,the task of generating descriptive sentences for images,has advanced significantly with the integration of semantic ***,traditional models still rely on static visual features that do not evolve with the changing linguistic context,which can hinder the ability to form meaningful connections between the image and the generated *** limitation often leads to captions that are less accurate or *** this paper,we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic *** model strengthens the alignment between visual and linguistic elements,resulting in more coherent and contextually appropriate ***,we introduce two innovative modules:the Visual Weighting Module(VWM)and the Enhanced Features Attention Module(EFAM).The VWM adjusts visual features using partial attention,enabling dynamic reweighting of the visual inputs,while the EFAM further refines these features to improve their relevance to the generated *** continuously adjusting visual features in response to the linguistic context,our model bridges the gap between static visual features and dynamic language *** demonstrate the effectiveness of our approach through experiments on the MS-COCO dataset,where our method outperforms state-of-the-art techniques in terms of caption quality and contextual *** results show that dynamic visual-linguistic alignment significantly enhances image captioning performance.

关键词： Image-captioning visual attention deep learning visual features

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A Machine Learning Approach to Model Counting 36

A Machine Learning Approach to Model Counting

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Dalla, Marco Visentin, Andrea O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (纸本)9798331527235

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

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computers, Materials & Continua 2024年第11期81卷 2873-2894页

作者： Alaa Thobhani Beiji Zou Xiaoyan Kui Amr Abdussalam Muhammad Asim Naveed Ahmed Mohammed Ali Alshara School of Computer Science and Engineering Central South UniversityChangsha410083China Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic UniversityRiyadh11432Saudi Arabia

Image captioning has gained increasing attention in recent *** characteristics found in input images play a crucial role in generating high-quality *** studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption ***,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these ***,this leads to enhanced captioning network *** light of this,we present an image captioning framework that efficiently exploits the extracted representations of the *** framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language *** VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features ***,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative *** the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s *** the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve *** implementation code can be found here:https://***/althobhani/VFDICM(accessed on 30 July 2024).

关键词： Visual attention image captioning visual feature detector visual feature visual attention

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Media Power Measuring via Emotional Contagion

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Journal of Social Computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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