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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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MemADet: A Representative Memory Bank Approach for Industrial Image Anomaly Detection

MemADet: A Representative Memory Bank Approach for Industria...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Min Li Jinghui He Zuobin Ying Gang Li Mingle Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the field of industrial production, anomaly detection is crucial for ensuring product quality and maintaining production efficiency. With the continuous advancement of computer vision technology, it has shown tremendous potential in industrial applications. However, the scarcity of labeled anomaly samples in real-world operating environments poses significant challenges for traditional anomaly detection techniques. To address this, we propose MemADet, a novel anomaly detection model that employs an unsupervised approach and leverages a representative memory bank. It employs a dynamic decision mechanism to control the representativeness of the features stored in the memory bank, and employs a weighted anomaly score calculation mechanism to further enhance the performance of image anomaly detection. Our evaluations indicate that MemADet performs robustly in industrial image anomaly detection across three datasets, with particularly no-table detection accuracy on the MVTec AD dataset. Its efficacy is further validated by competitive results on two additional datasets, highlighting its consistent and effective performance in various settings.

关键词： Accuracy Computational modeling Memory management Production Feature extraction Real-time systems Product design Quality assessment Reliability Anomaly detection

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Explore the Impact of Cellular Resource Allocation on Mobile UHD Video Streaming over 5G UDN

Explore the Impact of Cellular Resource Allocation on Mobile...

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IEEE Conference on Wireless Communications and networking

作者： Xinjing Yuan Lingjun Pu Xiaohang Xu Jingdong Xu Institute of Systems and Networks College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China State Key Laboratory of Integrated Services Networks (Xidian University) Xi’an China

The incoming 5G cellular network is stepping into a densification era, where various kinds of base stations are densely deployed to provide fruitful mobile services such as video streaming. In order to improve the performance of these mobile services, the way to optimally allocate cellular resources for the network-wide users is a crucial problem. In this paper, we consider the mobile Ultra-High-Definition (UHD) video streaming service, envision a 5G ultra dense network (UDN) consisting of a series of Video Base Stations (VBSs) dedicated to video streaming services for multiple users, and mainly explore the impact of cellular resource allocation on video streaming. We incorporate two important video streaming states into cellular resource allocation and formulate a streaming-aware and fairness-aware cellular resource allocation problem. To deal with the formulated problem, we provide a novel graph transformation and design an optimal matching algorithm which can be solved in polynomial time. Extensive trace-driven simulations validate the superior performance of the proposed algorithm under different user mobility patterns and network scales.

关键词： Cellular networks Base stations Correlation 5G mobile communication Conferences Bit rate Transforms

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Fairness of Large Music Models: From a Culturally Diverse Perspective

Fairness of Large Music Models: From a Culturally Diverse Pe...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Qinyuan Wang Bruce Gu He Zhang Yunfeng Li School of Music University of Sydney NSW Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CNPIEC KEXIN Ltd Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions.

关键词： Adaptation models Ethics Instruments Data science Rhythm Data models User experience Resonance Cultural differences Artificial intelligence

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Polarization attack on continuous-variable quantum key distribution

arXiv

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arXiv 2018年

作者： Zhao, Yijia Zhang, Yichen Huang, Yundi Xu, Bingjie Yu, Song Guo, Hong State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China State Key Laboratory of Advanced Optical Communication System and Network School of Electronics Engineering and Computer Science Center for Quantum Information Technology Peking University Beijing100871 China

The shot-noise unit (SNU) is a crucial factor for the practical security of a continuous-variable quantum key distribution system. In the most widely used experimental scheme, the SNU should be calibrated first and acts as a constant during the key distribution. However, the SNU of a practical system is dependent on the various parameters of the local oscillator, which can be controlled by the eavesdropper in the open channel. In this paper, we report a quantum hacking method to control the practical SNU by using the limited compensation rate of the polarization compensation. Since the compensation is only based on of the polarization measurement results of part of local oscillator pulses, the polarization of other unmeasured pulses may not be compensated correctly, which can be utilized by the eavesdropper to control the practical SNU. The simulation and experiment results indicate that the practical SNU can be controlled by the eavesdropper. Thus, the eavesdropper can use the fact that the practical SNU is no longer equals to the calibrated one to control the excess noise and final key rate. Copyright © 2018, The Authors. All rights reserved.

关键词： Shot noise

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Depthwise Separable Convolution Feature Learning for Ihomogeneous Rock Image Classification 4th

Depthwise Separable Convolution Feature Learning for Ihomoge...

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4th International Conference on Cognitive Systems and information Processing, ICCSIP 2018

作者： Zhu, Yueqin Bai, Lin Peng, Weihang Zhang, Xinyang Luo, Xiong Development and Research Center China Geological Survey Beijing100037 China Key Laboratory of Geological Information Technology Ministry of Natural Resources Beijing100037 China Geomathematics Key Laboratory of Sichuan Province Chengdu University of Technology Chengdu610059 China College of Geophysics Chengdu University of Technology Chengdu610059 China College of Management Science Chengdu University of Technology Chengdu610059 China College of Network Security Chengdu University of Technology Chengdu610059 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789811379826

The issue of classifying rock images is one of most important problems in geological detection and analysis. In order to improve rock recognition performance, we use convolution neural network (CNN) based deep learning method to classify ihomogeneous rock images intelligently. We present the depthwise separable convolution method for rock image classification, which reduces the required parameters compared to normal convolution, and achieves the separation of channels and regions. In our experiments, there are 12 kinds of common rock image data collected by us. Generally, similar rocks such as limestone and dolomite are easy to be confused, which is the same as that of rock with naked eyes. Compared with the model Inception, the rock image classification accuracy can be improved 9% by using the depth separable convolution model Xception. Meanwhile, after analyzing feature maps of granite and slate generated by the proposed model, we can easily find that the color, mineral composition and structure of the rock image are extracted successfully. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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A large family of prime-phase sequences with low correlation

A large family of prime-phase sequences with low correlation

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International Workshop on Signal Design and Its Applications in Communications (IWSDA)

作者： Ni-Gang Sun Chang-Lu Lin Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou Fujian China Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

A large family of p-phase highest coordinate sequences, where p is an odd prime, is constructed by using the highest coordinate of the generalized Kerdock codes over the ring Z pl , for l ges 3. Utilizing the local Weil bound and spectral analysis over the additive group of Z pl , we derive an estimate of the correlation of the sequences. And the result shows that these sequences have low nontrivial autocorrelation and crosscorrelation, which makes it possible as code sequences in CDMA communication systems.

关键词： Multiaccess communication Autocorrelation Sun computer science computer security Cryptography Electronic mail Spectral analysis Interference Binary sequences

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Universal designated-verifier partially blind signatures for e-commerce

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International Journal of computers and Applications 2010年第4期32卷 399-403页

作者： Cao, Tianjie Lin, Dongdai Xue, Rui School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China State Key Laboratory of Information Security of Institute of Software Chinese Academy of Sciences Beijing 100080 China

Blind signature schemes can prevent the information of a signature and message pair from leaking to the signer. Partially blind signature is a signature of which the message containing some information agreed by the signer and the signature requester is in an unblinded form. To protect the privacy of partially blind signature holders from leaking out by verifiers, we introduced universal designated-verifier partially blind signatures, in which any holder of a partially blind signature can convert the partially blind signature into a designatedverifier signature. Given the designated-verifier signature, only the designated verifier can verify that the message is signed by the signer, but he is unable to convince anyone else of this fact. In this paper, we also proposed a universal designated-verifier partially blind signature scheme from bilinear pairings based on Chow et al.'s public key infrastructure (PKI)-based partially blind signature scheme. Finally, we illustrate the application of the universal designated-verifier partially blind signatures that they can be used in untraceable electronic commerce systems and other trust negotiation systems.

关键词： Electronic commerce

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Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment

arXiv

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arXiv 2024年

作者： Xu, Angchi Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript, which is time-consuming and hard to be parallelized while training. In this work, we aim to escape from this inefficient alignment with massive but redundant frames, and instead to directly localize a few action transitions for pseudo segmentation generation, where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation, we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition, to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation, we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment

Efficient and Effective Weakly-Supervised Action Segmentatio...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Angchi Xu Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript, which is time-consuming and hard to be parallelized while training. In this work, we aim to escape from this inefficient alignment with massive but redundant frames, and instead to directly localize a few action transitions for pseudo segmentation generation, where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation, we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition, to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation, we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed. 1 1 Code is available at https://***/iSEE-laboratory/CVPR24_ATBA.

关键词： Training Visualization computer vision Semantics Noise Robustness Pattern recognition

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