检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Chen, Shilin Wang, Xingwang Sun, Yafeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China

As the volume of data stored on local devices increases, users turn to edge devices to help with processing tasks. Developing offloading schemes is challenging due to the varying configurations of edge devices and user preferences. While traditional methods provide schemes for offloading in various scenarios, they face unavoidable challenges, including the requirement to manage device workloads in real-time, significant computational costs, and the difficulty of balancing multi-objectives in offloading schemes. To solve these problems, we propose the Task Offloading Decision Optimizer, which offers efficient multi-objective offloading schemes considering real-time device workload and user preference. The proposed offloading scheme contains three goals: reducing task execution time, decreasing device energy consumption, and lowering rental costs. It comprises two important parts: Scheme Maker and Scheme Assistor. Scheme Maker utilizes deep reinforcement learning, optimizes the internal architecture, and enhances the performance of the operation. It optimizes buffer storage to generate dependable multi-objective offloading schemes considering real-time environmental conditions. Scheme Assistor utilizes the data in the Scheme Maker buffer to enhance efficiency by reducing computational costs. Extensive experiments have proved that the proposed framework efficiently provides offloading schemes considering the real-time conditions of the devices and the users, and it offers offloading schemes that enhance task completion rate by 50%. Compared to the baseline, the task execution time is reduced by 12.1%, and the device energy consumption is reduced by 11.4%. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Closed Loop Networks for Open-Set Semi-Supervised Learning

SSRN

引用

SSRN 2023年

作者： Ouyang, Jihong Meng, Qingyi Li, Ximing Zhang, Zhengjie Li, Changchun Wang, Wenting College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science University of Texas Dallas United States

Open-Set Semi-Supervised Learning (OS-SSL) refers to the task of learning classifiers with labeled and unlabeled instances, but the unlabeled data may contain the instances associated with unseen labels, dubbed as Out-Of-Distribution (OOD) instances. The main idea of OS-SSL is to detect and filter out the OOD instances to avoid the negative effects for classifier training. The current methods treat the detection of OOD instances and the classification of seen labels as two specific tasks, and achieve OS-SSL by using a shared backbone with two task-specific heads. However, we found that such way may result in a conflict problem between the two specific heads by observing the gradients of the shared backbone parameters. To solve this problem, we develop a novel OS-SSL method, namely Closed LOop NEtworks (CLONE), which employs two respective networks to detect OOD instances and classify seen labels, but makes a feedback loop between them. We can train CLONE in an end-to-end manner efficiently. We empirically compare CLONE with the existing OS-SSL methods on several benchmark datasets. The experimental results indicate that CLONE can outperform existing baseline methods in most settings. © 2023, The Authors. All rights reserved.

关键词： Cloning

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient UAV-assisted Communications via Collaborative Beamforming

Energy Efficient UAV-assisted Communications via Collaborati...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tingting Zheng Yanheng Liu Geng Sun Mushu Li Conghao Zhou Xuemin Sherman Shen College of Computer Science and Technology Jilin University Changchun China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

In this paper, we propose collaborative beamforming (CB) in unmanned aerial vehicle (UAV)-assisted communication networks to improve transmission data rate with minimum energy consumption. Specifically, CB allows a group of UAVs forming a virtual element antenna array (VEAA) and transmitting data collaboratively in a synchronous manner through a high-gain mainlobe (ML) beam. The goal is to optimize the deployment locations of UAVs in the VEAA and excitation current weights for performing CB transmissions considering the energy cost for UAV deployment. Accordingly, we formulate an Energy-Efficient Communication Multi-objective Optimization Problem (EECMOP) to jointly maximize the transmission rate and minimize the maximum sidelobe level (SLL) as well as UAV energy consumption. Then, we propose an Enhanced Multi Objective Ant Lion Optimizer (EMOALO) algorithm which incorporates a chaos theory to develop chaotic initialization and adjustable mode operators for solving the problem. Simulation results demonstrate the effectiveness of the EMOALO algorithm in improving energy efficiency for UAV-assisted communication networks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Task-Oriented Multi-Modal Mutual Learning for Vision-Language Models

Task-Oriented Multi-Modal Mutual Learning for Vision-Languag...

引用

International Conference on Computer Vision (ICCV)

作者： Sifan Long Zhen Zhao Junkun Yuan Zichang Tan Jiangjiang Liu Luping Zhou Shengsheng Wang Jingdong Wang College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin China Baidu VIS University of Sydney Zhejiang University

Prompt learning has become one of the most efficient paradigms for adapting large pre-trained vision-language models to downstream tasks. Current state-of-the-art methods, like CoOp and ProDA, tend to adopt soft prompts to learn an appropriate prompt for each specific task. Recent CoCoOp further boosts the base-to-new generalization performance via an image-conditional prompt. However, it directly fuses identical image semantics to prompts of different labels and significantly weakens the discrimination among different classes as shown in our experiments. Motivated by this observation, we first propose a class-aware text prompt (CTP) to enrich generated prompts with label-related image information. Unlike CoCoOp, CTP can effectively involve image semantics and avoid introducing extra ambiguities into different prompts. On the other hand, instead of reserving the complete image representations, we propose text-guided feature tuning (TFT) to make the image branch attend to class-related representation. A contrastive loss is employed to align such augmented text and image representations on downstream tasks. In this way, the image-to-text CTP and text-to-image TFT can be mutually promoted to enhance the adaptation of VLMs for downstream tasks. Extensive experiments demonstrate that our method outperforms the existing methods by a significant margin. Especially, compared to CoCoOp, we achieve an average improvement of 4.03% on new classes and 3.19% on harmonic-mean over eleven classification benchmarks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Scalable Precise computation of Shannon Entropy

arXiv

引用

arXiv 2025年

作者： Lai, Yong Tong, Haolong Xu, Zhenghang Yin, Minghao College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Information Technology Northeast Normal University Changchun130017 China

Quantitative information flow analyses (QIF) are a class of techniques for measuring the amount of confidential information leaked by a program to its public outputs. Shannon entropy is an important method to quantify the amount of leakage in QIF. This paper focuses on the programs modeled in Boolean constraints and optimizes the two stages of the Shannon entropy computation to implement a scalable precise tool PSE. In the first stage, we design a knowledge compilation language called ADD[∧] that combines Algebraic Decision Diagrams and conjunctive decomposition. ADD[∧] avoids enumerating possible outputs of a program and supports tractable entropy computation. In the second stage, we optimize the model counting queries that are used to compute the probabilities of outputs. We compare PSE with the state-of-the-art probably approximately correct tool EntropyEstimation, which was shown to significantly outperform the existing precise tools. The experimental results demonstrate that PSE solved 55 more benchmarks compared to EntropyEstimation in a total of 441. For 98% of the benchmarks that both PSE and EntropyEstimation solved, PSE is at least 10× as efficient as EntropyEstimation. Copyright © 2025, The Authors. All rights reserved.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

IoT Device Identification via A Bio-Inspired Feature Selection Approach

IoT Device Identification via A Bio-Inspired Feature Selecti...

引用

IEEE International Conference on Communications (ICC)

作者： Boxiong Wang Hui Kang Geng Sun Jiahui Li College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

The rapid development of the Internet-of-Things (IoT) also brings security and other problems. Device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. Feature selection is an effective data preprocessing technique in IoT device identification, which can improve the performance of classification and reduce computational complexity. In this paper, we propose a novel wrapper feature selection approach based on the improved binary honey badger algorithm (IBHBA) to select features in IoT traffic. Four improved factors are employed in IBHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. Moreover, a binary mechanism is adopted to make the algorithm more suitable for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that IBHBA outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Object Attribute Matters in Visual Question Answering

arXiv

引用

arXiv 2023年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding. Specifically, we design an attribute fusion module and a contrastive knowledge distillation module. The attribute fusion module constructs a multimodal graph neural network to fuse attributes and visual features through message passing. The enhanced object-level visual features contribute to solving fine-grained problem like counting-question. The better object-level visual-language alignment aids in understanding multimodal scenes, thereby improving the model’s robustness. Furthermore, to augment scene understanding and the out-of-distribution performance, the contrastive knowledge distillation module introduces a series of implicit knowledge. We distill knowledge into attributes through contrastive loss, which further strengthens the representation learning of attribute features and facilitates visual-linguistic alignment. Intensive experiments on six datasets, COCO-QA, VQAv2, VQA-CPv2, VQA-CPv1, VQAvs and TDIUC, show the superiority of the proposed method. © 2023, CC BY.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Why Misinformation is Created? Detecting them by Integrating Intent Features

arXiv

引用

arXiv 2024年

作者： Wang, Bing Fu, Bo Li, Ximing Pei, Songwen Li, Changchun Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun Jilin China Liaoning Normal University Liaoning Dalian China University of Shanghai for Science and Technology Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Jilin University China

Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinformation, namely Misinformation Detection (MD), has become an active research topic receiving widespread attention. As a complex phenomenon, the veracity of an article is influenced by various aspects. In this paper, we are inspired by the opposition of intents between misinformation and real information. Accordingly, we propose to reason the intent of articles and form the corresponding intent features to promote the veracity discrimination of article features. To achieve this, we build a hierarchy of a set of intents for both misinformation and real information by referring to the existing psychological theories, and we apply it to reason the intent of articles by progressively generating binary answers with an encoder-decoder structure. We form the corresponding intent features and integrate it with the token features to achieve more discriminative article features for MD. Upon these ideas, we suggest a novel MD method, namely Detecting Misinformation by Integrating Intent featuRes (Dm-inter). To evaluate the performance of Dm-inter, we conduct extensive experiments on benchmark MD datasets. The experimental results validate that Dm-inter can outperform the existing baseline MD methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

TYPE-SUPERVISED SEQUENCE LABELING BASED ON THE HETEROGENEOUS STAR GRAPH FOR NAMED ENTITY RECOGNITION

arXiv

引用

arXiv 2022年

作者： Wen, Xueru Zhou, Changjiang Tang, Haotian Liang, Luguang Jiang, Yu Qi, Hong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China

Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts. The traditional sequence labeling methodology ignores the nested entities, i.e. entities included in other entity mentions. Many approaches attempt to address this scenario, most of which rely on complex structures or have high computation complexity. The representation learning of the heterogeneous star graph containing text nodes and type nodes is investigated in this paper. In addition, we revise the graph attention mechanism into a hybrid form to address its unreasonableness in specific topologies. The model performs the type-supervised sequence labeling after updating nodes in the graph. The annotation scheme is an extension of the single-layer sequence labeling and is able to cope with the vast majority of nested entities. Extensive experiments on public NER datasets reveal the effectiveness of our model in extracting both flat and nested entities. The method achieved state-of-the-art performance on both flat and nested datasets. The significant improvement in accuracy reflects the superiority of the multi-layer labeling strategy. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised multi-label learning with balanced binary angular margin loss 24

Semi-supervised multi-label learning with balanced binary an...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ximing Li Silong Liang Changchun Li Pengfei Wang Fangming Gu College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Computer Network Information Center Chinese Academy of Sciences China and University of Chinese Academy of Sciences Chinese Academy of Sciences China

ISBN: (纸本)9798331314385

Semi-supervised multi-label learning (SSMLL) refers to inducing classifiers using a small number of samples with multiple labels and many unlabeled samples. The prevalent solution of SSMLL involves forming pseudo-labels for unlabeled samples and inducing classifiers using both labeled and pseudo-labeled samples in a self-training manner. Unfortunately, with the commonly used binary type of loss and negative sampling, we have empirically found that learning with labeled and pseudo-labeled samples can result in the variance bias problem between the feature distributions of positive and negative samples for each label. To alleviate this problem, we aim to balance the variance bias between positive and negative samples from the perspective of the feature angle distribution for each label. Specifically, we extend the traditional binary angular margin loss to a balanced extension with feature angle distribution transformations under the Gaussian assumption, where the distributions are iteratively updated during classifier training. We also suggest an efficient prototype-based negative sampling method to maintain high-quality negative samples for each label. With this insight, we propose a novel SSMLL method, namely Semi-Supervised Multi-Label Learning with Balanced Binary Angular Margin loss (S2ML2-BBAM). To evaluate the effectiveness of S2ML2-BBAM, we compare it with existing competitors on benchmark datasets. The experimental results validate that S2ML2-BBAM can achieve very competitive performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：