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IEEE European Test Symposium (ETS)

作者： Han Yan Shuai Chen Junying Huang Jing Ye Huawei Li Xiaowei Li State Key Lab of Processors Institute of Computing Technology CAS University of Chinese Academy of Sciences Rock-Solid Security Lab. Binary Semiconductor Co. Ltd. CASTEST Co. Ltd.

ISBN: (数字)9798350349320

ISBN: (纸本)9798350349337

Elliptic curve cryptography (ECC) is widely used in public key encryption, but its high-speed deployment faces challenges due to algorithmic and arithmetic complexity. In this paper, we present a high-performance ECC processor for the elliptic curve point multiplication (ECPM) of NIST P-256. Our approach employs a fully pipelined architecture featuring a 7-stage, 256-bit multiplier operating at a high frequency. To manage the data flow of the ECPM operation process, we devise a controller equipped with configurable instructions, which provides ECPM operations with higher flexibility to meet diverse contextual requirements. Additionally, we introduce a compact pipeline schedule to reduce ECPM computation clock cycles. The proposed LUT-based design achieves ECPM computation in 0.039 ms on FPGA (Virtex-7 platform) and 0.037 ms on ASIC (90nm technology), requiring only 10712 clock cycles.

关键词： Schedules Pipelines Public key Process control Computer architecture NIST Elliptic curve cryptography

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Guest Editorial: Ubiquitous Intelligence for Internet of Vehicles

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IEEE Internet of Things Magazine 2023年第3期6卷 52-54页

作者： Peng, Haixia Cheng, Nan Ye, Qiang Fang, He Yan, Shen Choo, Kim-Kwang Raymond Duong, Trung Q. California State University Long Beach Department of Computer Engineering and Computer Science Long BeachCA United States School of Telecommunications Engineering Xidian University State Key Lab. of ISN Shaanxi China Memorial University of Newfoundland Department of Computer Science NL Canada Soochow University China Intelligent Automotive Solution BU Huawei Technologies Co. Ltd Policy Standard & Patent Dept. China The University of Texas at San Antonio Cloud Technology Endowed United States Royal Academy of Engineering Queen's University Belfast United Kingdom

As one of the prominent networking paradigms in the realm of the Internet of Things (IoT), the Internet of Vehicles (IoV) facilitates seamless information dissemination and task processing among vehicles equipped with onboard sensing, communication, computing, and storage capabilities. With the advancements in artificial intelligence (AI) techniques, there is a vision to achieve pervasive intelligence within the IoV ecosystem. Various network entities, including connected vehicles, wireless base stations, edge/cloud servers, and aerial/space-assisted devices (such as drones and satellites), are expected to interact efficiently to perceive, reason, and make intelligent decisions based on contextual awareness. These advancements aim to enhance networking and computing effectiveness. The realization of an intelligent, safe, and ubiquitous IoV heavily relies on highly responsive task computing, adaptive networking, and efficient resource control to meet the increasingly diverse requirements of vehicular applications.

关键词： Special issues and sections Internet of Vehicles Intelligent systems Ubiquitous computing

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Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network

Continuous Travel Time Prediction for Transit Signal Priorit...

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International Conference on Intelligent Transportation

作者： Xiong Gang Wenwen Kang Feiyue Wang Fenghua Zhu Yisheng Lv Xisong Dong Jukka Riekki Susanna Pirttikangas State Key Lab. of Manage. & Control for Complex Syst. Institute of Automation Chinese Academy of Sciences Beijing Beijing CN The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan CHINA Faculty of Information Technology and Electrical Engineering University of Oulu Finland

It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information Systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.

关键词： Vehicles Predictive models Object oriented modeling Training Artificial neural networks Data models

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A lightweight music texture transfer system

arXiv

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arXiv 2018年

作者： Peng, Xutan Li, Chen Cai, Zhi Shi, Faqiang Liu, Yidan Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing SKLSDE Lab. Beihang University State Key Laboratory of VR Technology and Systems Beihang University Department of Psychology Beihang University

Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks are far from practical application. In this paper, we initiatively propose a novel system for transferring the texture of music, and release it as an open source project. Its core algorithm is composed of a converter which represents sounds as texture spectra, a corresponding reconstructor and a feed-forward transfer network. We evaluate this system from multiple perspectives, and experimental results reveal that it achieves convincing results in both sound effects and computational performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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Green Federated Learning over cloud-RAN with Limited Fronthaul and Quantized Neural Networks

Green Federated Learning over Cloud-RAN with Limited Frontha...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Jiali Wang Yijie Mao Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over cloud-RAN.

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Transfer Learning with Dynamic Adversarial Adaptation Network

Transfer Learning with Dynamic Adversarial Adaptation Networ...

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IEEE International Conference on Data Mining (ICDM)

作者： Chaohui Yu Jindong Wang Yiqiang Chen Meiyu Huang Beijing Key Lab. of Mobile Computing and Pervasive Device Inst. of Comp. Tech Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Microsoft Research Asia Beijing China Qian Xuesen Lab. of Space Technology China Academy of Space Technology Beijing China

The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global source and target distributions, or pay attention to align subdomains based on multiple discriminators. However, in real applications, the marginal (global) and conditional (local) distributions between domains are often contributing differently to the adaptation. There is currently no method to dynamically and quantitatively evaluate the relative importance of these two distributions for adversarial learning. In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions. To the best of our knowledge, DAAN is the first attempt to perform dynamic adversarial distribution adaptation for deep adversarial learning. DAAN is extremely easy to implement and train in real applications. We theoretically analyze the effectiveness of DAAN, and it can also be explained in an attention strategy. Extensive experiments demonstrate that DAAN achieves better classification accuracy compared to state-of-the-art deep and adversarial methods. Results also imply the necessity and effectiveness of the dynamic distribution adaptation in adversarial transfer learning.

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques 38

Suppress Content Shift: Better Diffusion Features via Off-th...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is availab.e at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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User-inspired posterior network for recommendation reason generation

arXiv

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arXiv 2021年

作者： Zhan, Haolan Zhang, Hainan Chen, Hongshen Shen, Lei Lan, Yanyan Ding, Zhuoye Yin, Dawei State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Data Science Lab *** Baidu Inc. Beijing China

Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers’ attention as well as improving user experience. A simple and effective way is to extract keywords directly from the knowledge-base of products, i.e., attributes or title, as the recommendation reason. However, generating recommendation reason from product knowledge doesn’t naturally respond to users’ interests. Fortunately, on some E-commerce websites, there exists more and more user-generated content (user-content for short), i.e., product question-answering (QA) discussions, which reflect user-cared aspects. Therefore, in this paper, we consider generating the recommendation reason by taking into account not only the product attributes but also the customer-generated product QA discussions. In reality, adequate user-content is only possible for the most popular commodities, whereas large sums of long-tail products or new products cannot gather a sufficient number of user-content. To tackle this problem, we propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users’ interests with a posterior multiple QA discussions module, and generating recommendation reasons containing the product attributes as well as the user-cared aspects. Experimental results show that our model is superior to traditional generative models. Additionally, the analysis also shows that our model can focus more on the user-cared aspects than baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Sales

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Web image clustering by consistent utilization of visual features and surrounding texts

Web image clustering by consistent utilization of visual fea...

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13th ACM International Conference on Multimedia, MM 2005

作者： Gao, Bin Liu, Tie-Yan Qin, Tao Zheng, Xin Cheng, Qian-Sheng Ma, Wei-Ying Microsoft Research Asia Sigma Center No. 49 Zhichun Road Beijing 100080 China LMAM Dept. of Information Science School of Mathematical Sciences Beijing 100871 China MSP Laboratory Dept. of Electronic Engineering Tsinghua University Beijing 100084 China Key Lab. of Pervasive Computing Dept. of Computer Science and Technology Tsinghua University Beijing 100084 China

ISBN: (纸本)1595930442

Image clustering, an important technology for image processing, has been actively researched for a long period of time. Especially in recent years, with the explosive growth of the Web, image clustering has even been a critical technology to help users digest the large amount of online visual information. However, as far as we know, many previous works on image clustering only used either low-level visual features or surrounding texts, but rarely exploited these two kinds of information in the same framework. To tackle this problem, we proposed a novel method named consistent bipartite graph co-partitioning in this paper, which can cluster Web images based on the consistent fusion of the information contained in both low-level features and surrounding texts. In particular, we formulated it as a constrained multi-objective optimization problem, which can be efficiently solved by semi-definite programming (SDP). Experiments on a real-world Web image collection showed that our proposed method outperformed the methods only based on low-level features or surround texts. Copyright © 2005 ACM.

关键词： Image processing

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Verifying Safety of Neural Networks from Topological Perspectives

arXiv

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arXiv 2023年

作者： Liang, Zhen Ren, Dejin Xue, Bai Wang, Ji Yang, Wenjing Liu, Wanwei National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Changsha410073 China Chinese Academy of Sciences State Key Lab. of Computer Science BeijingBAI XUE China

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper, we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property and the open map property of NNs are mainly exploited, which establish rigorous guarantees between the boundaries of the input set and the boundaries of the output set. The exploitation of these two properties facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs), and the open map is a less strict property and easier to satisfy compared with the homeomorphism property. For NNs establishing either of these properties, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed *** Codes 68Q60, 68T07 Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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