检索结果-内蒙古大学图书馆

Editorial: Temporal structure of neural processes coupling sensory, motor and cognitive functions of the brain, volume II

学校读者我要写书评

暂无评论

Frontiers in Computational Neuroscience 2023年 17卷 1291744页

作者： Gupta, Daya Shankar Banerjee, Arpan Piras, Federica Roy, Dipanjan School of Pharmacy South University SavannahGA United States Cognitive Brain Dynamics Lab National Brain Research Center Gurugram India Laboratory of Neuropsychiatry Department of Clinical Neuroscience and Neurorehabilitation IRCCS Santa Lucia Foundation Rome Italy Centre for Brain Science and Applications School of Artificial Intelligence and Data Science Indian Institute of Technology Jodhpur India

Boenke et al. reported “on the group level: (1) Veridical auditory TOJs, relative to non-veridical, were associated with higher (pre-stimulus) 20 Hz (beta) power over central electrodes, and (2) veridical visual TOJs showed higher (pre-stimulus) 10, 15 Hz (alpha beta) power over parieto-occipital electrodes.” [...]in this study, “the individual-level modulation pattern was variable and included activations opposite to the group mean.” [...]they argue that there are different strategies (reflected in opposite directions of modulations of brain oscillations) used by the brain leading to the same outcome, namely veridical TOJs in their study. [...]as advised by the authors, a general description of brain activity during the processing of incoming information should account for variability in modulation directions at both the group and individual levels. Furthermore, desynchronization frees up neurons from the restriction of spiking only during a certain phase of oscillations. [...]desynchronization will promote the temporal coupling between neuronal activities, given the constraints of interactions involving external stimuli. The spatiotemporal separation, representing three groups of features, was about 90%. Since spatiotemporal separation does not exceed 100% in the above example, this mechanism may not lead to an increase in entropy, given the completion of a task.

关键词： consciousness perception binding voluntary control temporal motor noise active inference embodied embedded cognition

Large Language Models for Generative Information Extraction: A Survey

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Xu, Derong Chen, Wei Peng, Wenjun Zhang, Chao Xu, Tong Zhao, Xiangyu Wu, Xian Zheng, Yefeng Wang, Yang Chen, Enhong State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Hefei230000 China Department of Data Science City University of Hong Kong Hongkong999077 Hong Kong Jarvis Research Center Tencent YouTu Lab Beijing100029 China Anhui Conch Information Technology Engineering Co. Ltd. Wuhu241000 China

Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks, in this study, we survey the most recent advancements in this field. We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques, and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on a thorough review conducted, we identify several insights in technique and promising research directions that deserve further exploration in future studies. We maintain a public repository and consistently update related works and resources on GitHub (LLM4IE repository). Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

Survey on data Analysis in Social Media:A Practical Application Aspect

学校读者我要写书评

暂无评论

Big data Mining and Analytics 2020年第4期3卷 259-279页

作者： Qixuan Hou Meng Han Zhipeng Cai the College of Computing Georgia Institute of TechnologyAtlantaGA 30332USA Data-driven Intelligence Research(DIR)Lab of College of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30060USA the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA

Social media has more than three billion users sharing events,comments,and feelings throughout the *** serves as a critical information source with large volumes,high velocity,and a wide variety of *** previous studies on information spreading,relationship analyzing,and individual modeling,etc.,have been heavily conducted to explore the tremendous social and commercial values of social media *** survey studies the previous literature and the existing applications from a practical *** outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques,such as topic analysis,time series analysis,sentiment analysis,and network *** that,we present the impacts of such applications in three different areas,including disaster management,healthcare,and ***,we list existing challenges and suggest promising future research directions in terms of data privacy,5 G wireless network,and multilingual support.

关键词： social media topic analysis time series analysis sentiment analysis network analysis disaster management bio-surveillance business intelligence

Discriminative versus Generative Approaches to Simulation-based Inference

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sluijter, Benjamin Diefenbacher, Sascha Bhimji, Wahid Nachman, Benjamin Leiden Institute of Physics Universiteit Leiden Leiden2300 RA Netherlands Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Berkeley Lab BerkeleyCA94720 United States Berkeley Institute for Data Science University of California BerkeleyCA94720 United States

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihiood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

A Generative Adversarial Active Learning Method for Effective Outlier Detection

学校读者我要写书评

暂无评论

A Generative Adversarial Active Learning Method for Effectiv...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Mohamed Jaward Bah Ji Zhang Ting Yu Feng Xia Zhao Li Shuigeng Zhou Hongzhi Wang Big Data Intelligence Research Center Zhejiang Lab Hangzhou China University of Southern Queensland School of Sciences Toowoomba Australia Institute of Innovation Science and Sustainability Federation University Australia Ballarat Australia Zhejiang University China Fudan University School of Computer Science Shanghai China Harbin Institute of Technology Massive Data Computing Lab China

Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem.

关键词： Learning systems Games Benchmark testing Generative adversarial networks Generators labeling Task analysis

Towards a Sustainable Future: Analyzing Water Quality Changes for Environmental Sustainability

学校读者我要写书评

暂无评论

Towards a Sustainable Future: Analyzing Water Quality Change...

Smart Technology (ICE-SMARTec), IEEE International Conference on

作者： Nur Suhaili Mansor Hapini Awang Nor Hazlyna Harun Mohamad Ghozali Hassan Sarkin Tudu Shehu Malami Noraishah Shamsuddin Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Malaysia Disaster Management Institute (DMI) School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Nigeria ICI in Smart Manufacturing Sirim Bhd Shah Alam Malaysia

The development of industrial areas is frequently thought to have a good impact on society because it helps to meet the population's requirements. However, it also negatively impacts the environment, such as river pollution. If a country experiences rapid economic growth without considering the effects of industrial development on the environment, then human well-being will be affected. In this context, advanced development is attained by a developed nation while ensuring the well-being of society. Uncontrolled and unsustainable development increases environmental pollution, adversely affecting human health. As water is essential for human life, water resources, such as rivers, must be properly managed and protected to ensure human well-being. Thus, the study conducted on the changes in the water quality of the Kim Kim River provides insights into how unintended consequences of urban and industrial development can be identified and addressed using three sets of satellite imagery data from 2015, 2019 and 2020. The study was led to identify research problems and achieve the study objectives to obtain research results. The results of image classification analyses indicate that the development area near Sungai Kim Kim increased to 50%, with 38% from industrial areas. The RMSE for the image classification is 1.51, significantly better than the minimum allowed value of 2.00. It provides an understanding of the change in river water quality because of urban and industrial development. By highlighting the importance of sustainable development and the need to balance economic growth with environmental protection, this study contributes to the development of effective policies and strategies for the sustainable development of industrial areas.

关键词：

BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean label

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Hu, Shengshan Zhou, Ziqi Zhang, Yechao Zhang, Leo Yu Zheng, Yifeng He, Yuanyuan Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Information Technology Deakin University VIC3216 Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security HUST Wuhan430074 China Cluster and Grid Computing Lab HUST Wuhan430074 China

Due to its powerful feature learning capability and high efficiency, deep hashing has achieved great success in large-scale image retrieval. Meanwhile, extensive works have demonstrated that deep neural networks (DNNs) are susceptible to adversarial examples, and exploring adversarial attack against deep hashing has attracted many research efforts. Nevertheless, backdoor attack, another famous threat to DNNs, has not been studied for deep hashing yet. Although various backdoor attacks have been proposed in the field of image classification, existing approaches failed to realize a truly imperceptive backdoor attack that enjoys invisible triggers and clean label setting simultaneously, and they also cannot meet the intrinsic demand of image retrieval backdoor. In this paper, we propose BadHash, the first generative-based imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label. Specifically, we first propose a new conditional generative adversarial network (cGAN) pipeline to effectively generate poisoned samples. For any given benign image, it seeks to generate a natural-looking poisoned counterpart with a unique invisible trigger. In order to improve the attack effectiveness, we introduce a label-based contrastive learning network labCLN to exploit the semantic characteristics of different labels, which are subsequently used for confusing and misleading the target model to learn the embedded trigger. We finally explore the mechanism of backdoor attacks on image retrieval in the hash space. Extensive experiments on multiple benchmark datasets verify that BadHash can generate imperceptible poisoned samples with strong attack ability and transferability over state-of-the-art deep hashing schemes. © 2022, CC BY.

关键词： Deep neural networks

Multi-task representation learning with multi-view graph convolutional networks

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Huang, Hong Song, Yu Wu, Yao Shi, Jia Xie, Xia Jin, Hai National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multi-task multi-view learning in this paper. We first explain the feasibility and advantages of multi-task multi-view learning for these two tasks. Then we propose a novel model named as MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multi-view graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network datasets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks. © 2021, CC BY.

关键词： data mining

Grace: An Efficient Parallel SPARQL Query System over Large-Scale RDF data

学校读者我要写书评

暂无评论

Grace: An Efficient Parallel SPARQL Query System over Large-...

International Conference on Computer Supported Cooperative Work in Design

作者： Xiang Kang Yuying Zhao Pingpeng Yuan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

As a markup language for describing web resources, RDF is often used to represent graph data. SPARQL is a standard query language for RDF data, which is convenient in querying RDF. As RDF data grows rapidly, how to deal with complex queries over large-scale data in a reasonable time still remains many challenges. The existing RDF query systems often fail to respond within a reasonable time when dealing with complex SPARQL queries. Therefore, we propose an efficient parallel SPARQL query system and novel in three aspects. First, the proposed design provides a dynamic selectivity estimation strategy and generates an optimal query plan for parallel query processing. Second, the new design proposes a parallel processing model to maximize the parallelism of the system. Finally, chunk-based task distribution strategy is implemented to assist the parallel processing model. Based on the proposed design, we implement an efficient parallel query system (Grace). Extensive experiments on LUBM and BTC benchmarks show that Grace outperforms RDF-3X, Virtuoso, TripleBit and achieves a good performance of scalability on the number of threads.

关键词： Distribution strategy Scalability Query processing Markup languages Instruction sets Estimation Parallel processing