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2023 International Conference on New Trends in Computational intelligence, NTCI 2023

作者： Tao, Yu Chen, Yueqi Yang, Jie Big Data Center China Citic Bank Beijing China School of Mathematical Sciences Dalian University of Technology Liaoning China Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province Liaoning China

ISBN: (纸本)9798350380859

Binary classification frequently entails the challenge of imbalanced datasets, characterized by substantial disparities in sample sizes between the two different classes. The data imbalance usually makes it difficult for classifiers to learn positive samples, leading to unsatisfactory classification effects. Generative adversarial network (GAN) can be used as an oversampling method to rectify data imbalance by generating positive samples. However, when confronted with a multi-cluster distribution of positive samples, GAN often experiences a mode collapse, that is, only a partial distribution of a few samples can be learned, resulting in the aggregation of generated data into a cluster or a segment. To solve this problem, this paper proposes a novel method MIX-NOISE GAN. It employs Gaussian Mixture Model (GMM) to generate GMM noise that aligns with the distribution of positive samples. The GMM noise is then combined with traditional random noise as the generator input, referred to as the mixture of noise. Empirical results and visualizations from experiments conducted on simulated and benchmark datasets demonstrate that MIX-NOISE GAN significantly enhances classification performance on 17 imbalanced datasets compared to six classical sampling methods in terms of G-mean, F-measure, and AVC. © 2023 IEEE.

关键词： Generative adversarial networks

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Lightweight Storage Framework for Blockchain-Enabled Internet of Things Under Cloud Computing

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Computers, Materials & Continua 2023年第5期75卷 3607-3624页

作者： Xinyi Qing Baopeng Ye Yuanquan Shi Tao Li Yuling Chen Lei Liu School of Computer and Artificial Intelligence Huaihua CollegeHuaihua418000China Guizhou Science and Technology Innovation Service Center Co Guiyang550000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China

Due to its decentralized,tamper-proof,and trust-free characteristics,blockchain is used in the Internet of Things(IoT)to guarantee the reliability of ***,some technical flaws in blockchain itself prevent the development of these applications,such as the issue with linearly growing storage capacity of blockchain *** the other hand,there is a lack of storage resources for sensor devices in IoT,and numerous sensor devices will generate massive data at ultra-high speed,which makes the storage problem of the IoT enabled by blockchain more *** are various solutions to reduce the storage burden by modifying the blockchain’s storage policy,but most of them do not consider the willingness of *** attempt to make the blockchain more compatible with the IoT,this paper proposes a storage optimization scheme that revisits the system data storage problem from amore practically oriented *** will only store transactional data that they are directly involved *** addition,a transaction verification model is developed to enable peers to undertake transaction verification with the aid of cloud computing,and an incentive mechanism is premised on the storage optimization scheme to assure data *** results of the simulation experiments demonstrate the proposed scheme’s advantage in terms of storage and throughput.

关键词： Blockchain internet of things storage optimization transaction verification cloud computing incentive mechanism

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Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

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Multimedia Tools and Applications 2024年第42期83卷 90553-90572页

作者： Jiang, Xiaohui Zhu, Haijiang An, Ning Xie, Binjian Wei, Hao Tang, Fulin Wu, Yihong College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Multimodal Artificial Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Intelligent Coal Mining and Strata Control Beijing100013 China Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China

In many 3D reconstruction, Simultaneous Localization and Mapping (SLAM), AR, and VR systems, the camera focal length constantly changes slightly due to loose hardware, causing their performances to deteriorate. To address this problem, we propose a novel camera self-calibration method that incorporates clustering prior and nonlinear optimization. By leveraging the Kruppa equation, we roughly estimate multiple analytical solutions for the camera focal length from an image sequence. Then, robust clustering based on the Gaussian mixture model is introduced to obtain an initial camera focal length value. Finally, a nonlinear cost function is proposed to refine the initial camera focal length value. Extensive experimental results on simulation and real data demonstrate that the proposed method outperforms the state-of-the-art methods, exhibiting good robustness and high accuracy. Moreover, the proposed method can be used in real-time videos for SLAM systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cost functions

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MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

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Unified Uncertainty Estimation for cognitive Diagnosis Models

arXiv

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arXiv 2024年

作者： Wang, Fei Liu, Qi Chen, Enhong Liu, Chuanren Huang, Zhenya Wu, Jinze Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China The University of Tennessee Business Analytics and Statistics Knoxville United States iFLYTEK Research State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users’ proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Lay CO:Achieving Least Lossy Accuracy for Most Efficient RRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization

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计算机科学技术学报（英文版） 2023年第2期38卷 328-347页

作者： Shao-Feng Zhao Fang Wang Bo Liu Dan Feng Yang Liu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518057China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China

Resistive random access memory(RRAM)enables the functionality of operating massively parallel dot prod-ucts and ***-based accelerator is such an effective approach to bridging the gap between Internet of Things devices'constrained resources and deep neural networks'tremendous *** to the huge overhead of Analog to Digital(A/D)and digital accumulations,analog RRAM buffer is introduced to extend the processing in analog and in *** analog RRAM buffer offers potential solutions to A/D conversion issues,the energy consumption is still challenging in resource-constrained environments,especially with enormous intermediate data ***,criti-cal concerns over endurance must also be resolved before the RRAM buffer could be frequently used in reality for DNN in-ference *** we propose LayCO,a layer-centric co-optimizing scheme to address the energy and endurance con-cerns altogether while strictly providing an inference accuracy *** relies on two key ideas:1)co-optimizing with reduced supply voltage and reduced bit-width of accelerator architectures to increase the DNN's error tolerance and achieve the accelerator's energy efficiency,and 2)efficiently mapping and swapping individual DNN data to a correspond-ing RRAM partition in a way that meets the endurance *** evaluation with representative DNN models demonstrates that LayCO outperforms the baseline RRAM buffer based accelerator by 27x improvement in energy effi-ciency(over TIMELY-like configuration),308x in lifetime prolongation and 6x in area reduction(over RAQ)while main-taining the DNN accuracy loss less than 1％.

关键词： deep learning accelerator resistive random access memory (RRAM) energy efficiency error tolerance

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Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer 23

Tracing Knowledge Instead of Patterns: Stable Knowledge Trac...

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2023 World Wide Web Conference, WWW 2023

作者： Yin, Yu Dai, Le Huang, Zhenya Shen, Shuanghong Wang, Fei Liu, Qi Chen, Enhong Li, Xin Anhui Key Lab. of Big Data Analysis and Application University of Science and Technology of China State Key Lab. of Cognitive Intelligence Anhui Hefei China University of Science and Technology of China IFLYTEK Co. Ltd China

ISBN: (纸本)9781450394161

Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner's learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner's knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://***/yxonic/DTransformer. © 2023 ACM.

关键词： Deep neural networks

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Stabilize the latent space for image autoregressive modeling: a unified perspective 24

Stabilize the latent space for image autoregressive modeling...

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

作者： Yongxin Zhu Bocheng Li Hang Zhang Xin Li Linli Xu Lidong Bing School of Data Science University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Zhejiang University

ISBN: (纸本)9798331314385

Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencoders like VQGAN or VAE to encode pixels into a more compact latent space and learn the data distribution in the latent space instead of directly from pixels. However, this practice raises a pertinent question: Is it truly the optimal choice? In response, we begin with an intriguing observation: despite sharing the same latent space, autoregressive models significantly lag behind LDMs and MIMs in image generation. This finding contrasts sharply with the field of NLP, where the autoregressive model GPT has established a commanding presence. To address this discrepancy, we introduce a unified perspective on the relationship between latent space and generative models, emphasizing the stability of latent space in image generative modeling. Furthermore, we propose a simple but effective discrete image tokenizer to stabilize the latent space for image generative modeling by applying K-Means on the latent features of self-supervised learning models. Experimental results show that image autoregressive modeling with our tokenizer (DiGIT) benefits both image understanding and image generation with the next token prediction principle, which is inherently straightforward for GPT models but challenging for other generative models. Remarkably, for the first time, a GPT-style autoregressive model for images outperforms LDMs, which also exhibits substantial improvement akin to GPT when scaling up model size. Our findings underscore the potential of an optimized latent space and the integration of discrete tokenization in advancing the capabilities of image generative models. The code is available at https://***/DAMO-NLP-SG/DiGIT.

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Design of the Smart Bedside Table Based on Single-Chip Microcomputer 12

Design of the Smart Bedside Table Based on Single-Chip Micro...

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Shijia, Yang Weiqiang, Yang Xiehua, Yu Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province MinNan Science and Technology University Quanzhou China

ISBN: (纸本)9798350310153

With the popularization of smart home, nightstand plays an increasingly important role in people's daily life. By means of questionnaire survey and data collection, it is found that most household nightstands only play the role of storage, the bedroom space is always not enough for all kinds of furniture, the nightstand cannot be interconnected with other smart devices. The existing smart nightstands on the market have common problems: uneven functions and low practicability. In view of the above problems, combined with the smart home system, this paper studies and designs a smart nightstand with HT66F2390 microcontroller as the core. To save the bedroom space, other furniture functions are integrated, such as adaptive adjustment of night light and brightness, refrigeration and fresh-keeping, heating and insulation, drawer fingerprint unlock, LED light group changing in different occasions. LCD displays the user interface for easy operation. The smart nightstand is set up to test the environment and optimize the functions of each module. The results show that the designed intelligent bedside table can integrate the functions of other furniture. It effectively saves bedroom space and optimizes living experience. The expected design goal has been achieved. © 2022 IEEE.

关键词： User interfaces

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Design of Wrist Rehabilitation Robot 12

Design of Wrist Rehabilitation Robot

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12th International Conference on Information technology in Medicine and Education, ITME 2022

作者： Zhenfeng, Wang Key Laboratory of Big Data and Artificial Intelligence in University of Fujian Province MinNan Science and Technology University Quanzhou China

ISBN: (纸本)9798350310153

We have developed a wrist rehabilitation manipulator to help patients with wrist injuries during exercise achieve sustained and standardized rehabilitation. Based on the theory of rehabilitation medicine, a 3RRP spherical parallel three-dimensional exoskeleton repair manipulator was developed. According to the principle of ergonomics, the shape safety of the robot was optimized. The virtual prototype system is used to simulate the rehabilitation manipulator and MATLAB is used to operate the recovery manipulator. The simulation results show that the designed manipulator has three degrees of freedom, which can meet our needs. The working area is part of the spherical area, and there is no cavity. The appearance, color and humanistic dimension can enable patients to obtain better treatment experience. Conclusion: It provides some basis for the industrial design of manipulator. © 2022 IEEE.

关键词： Manipulators

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