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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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Formula-Oriented Compositional Minimization in Model Checking

Formula-Oriented Compositional Minimization in Model Checkin...

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2010 19th IEEE Asian Test Symposium(第19届IEEE亚洲测试技术学术会议 ATS 2010)

作者： Bowen Chen Haihua Shen Wenhui Zhang Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Acade Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Acade State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijin

This paper presents a new approach to reduce finite state machines with respect to a CTL formula to alleviate state explosion problem. Reduction is achieved by removing parts useless to the formula of original machines. The main contribution of this paper is to exploit relations among subformulas of the CTL formula so as to gain more reduction, as well as to extend traditional pruning method, which handles only existential formulas, to handle universal formulas. Based on this kind of reduction, verification of a large system, which usually consists of several components, can be done by evaluating properties on a reduced version of the system, which is built by composing components of the system one by one while doing reduction after each composition. Experimental results show the effectiveness of the approach. Especially when a property is written in a more detailed way, that is to describe the system part by part, the approach has a great potential.

关键词： Formula-Oriented Compositional Minimization CTL Model Checking Formal Verification

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AIoT Bench: Towards Comprehensive Benchmarking Mobile and Embedded Device Intelligence 1st

AIoT Bench: Towards Comprehensive Benchmarking Mobile and Em...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Luo, Chunjie Zhang, Fan Huang, Cheng Xiong, Xingwang Chen, Jianan Wang, Lei Gao, Wanling Ye, Hainan Wu, Tong Zhou, Runsong Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Frontier Science and Technology Beijing China Dover United Kingdom China National Institute of Metrology Beijing China China Software Testing Center Beijing China

ISBN: (纸本)9783030328122

Due to increasing amounts of data and compute resources, the deep learning achieves many successes in various domains. Recently, researchers and engineers make effort to apply the intelligent algorithms to the mobile or embedded devices. In this paper, we propose a benchmark suite, AIoT Bench, to evaluate the AI ability of mobile and embedded devices. Our benchmark (1) covers different application domains, e.g. image recognition, speech recognition and natural language processing;(2) covers different platforms, including Android and Raspberry Pi;(3) covers different development frameworks, including TensorFlow and Caffe2;(4) offers both end-to-end application workloads and micro workloads. © 2019, Springer Nature Switzerland AG.

关键词： Benchmarking

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Middleware Fault Tolerance Support for the BOSS Embedded Operating System

Middleware Fault Tolerance Support for the BOSS Embedded Ope...

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Workshop on Intelligent Solutions in Embedded Systems

作者： F. Afonso C. Silva S. Montenegro A. Tavares Department of Industrial Electronics University of Minho Guimaraes Portugal Fraunhofer Institute for Computer Architecture and Software Technology Berlin Germany

Critical embedded systems need a dependable operating system and application. Despite all efforts to prevent and remove faults in system development, residual software faults usually persist. Therefore, critical systems need some sort of fault tolerance to deal with these faults and also with hardware faults at operation time. This work proposes fault-tolerant support mechanisms for the BOSS embedded operating system, based on the application of proven fault tolerance strategies by middleware control software which transparently delivers the added functionality to the application software. Special attention is taken to complexity control and resource constraints, targeting the needs of the embedded market

关键词： Middleware Fault tolerant systems Operating systems Application software Redundancy Yarn Hardware Fault tolerance Control systems Embedded software

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Towards memory-efficient processing-in-memory architecture for convolutional neural networks

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ACM SIGPLAN Notices 2017年第5期52卷 81-90页

作者： Wang, Yi Zhang, Mingxu Yang, Jing College of Computer Science and Software Engineering Shenzhen University China Experimental and Innovation Practice Center Harbin Institute of Technology Shenzhen China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

Convolutional neural networks (CNNs) are widely adopted in artificial intelligent systems. In contrast to conventional computing centric applications, the computational and memory resources of CNN applications are mixed together in the network weights. This incurs a significant amount of data movement, especially for highdimensional convolutions. Although recent embedded 3D-stacked Processing-in-Memory (PIM) architecture alleviates this memory bottleneck to provide fast near-data processing, memory is still a limiting factor of the entire system. An unsolved key challenge is how to efficiently allocate convolutions to 3D-stacked PIM to combine the advantages of both neural and computational processing. This paper presents Memolution, a compiler-based memory efficient data allocation strategy for convolutional neural networks on PIM architecture. Memolution offers thread-level parallelism that can fully exploit the computational power of PIM architecture. The objective is to capture the characteristics of neural network applications and present a hardware-independent design to transparently allocate CNN applications onto the underlining hardware resources provided by PIM. We demonstrate the viability of the proposed technique using a variety of realistic convolutional neural network applications. Our extensive evaluations show that, Memolution significantly improves performance and the cache utilization compared to the baseline scheme. © 2017 ACM.

关键词： Memory architecture

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Modeling LTCC-based Microchannels Using a Network Approach

Modeling LTCC-based Microchannels Using a Network Approach

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Electronics System-Integration technology Conference, ESTC

作者： G. Schlottig L. Rebenklau J. Uhlemann C. Nytsch-Geusen K.-j. Wolter Electronics Packaging Laboratory Technische Universitat Dresden Dresden Germany Fraunhofer Institute for Computer Architecture and Software Technology Berlin Germany

Low temperature cofiring ceramic (LTCC) combines hybrid technology, biocompatibility, chemical resistance and ease of fabrication to serve as a construction material for microfluidics. Due to the lack of CAD tools for MEMS this paper applies a model for the description of stationary flow in LTCC channels of non circular cross sections. Sample devices for validation are fabricated by laser structuring and standard LTCC process. The substrates consist of 130 mum thick layers and are connected horizontally to a tubing system. Characteristic length scales range between 131 mum and 159 mum. Deviations stay within the assumed uncertainty and confirm the theory. Results show that applying solutions of geometry description in accepted fluid mechanics models gives an appropriate tool for flow description. Implementing the model as a network description in the describing language MOSILAB can allow dimensioning and optimization under functional restrictions, e.g. pressure drop or geometry as well as communication with other modeling tools

关键词： Microchannel Solid modeling Chemical technology Temperature Ceramics Immune system Optical device fabrication Biological materials Building materials Optical materials

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Progress in distributed operating systems and distributed systems management

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1990年

作者： Schreoder-Preikschat W. Zimmer W. Gesellschaft fur Mathematik und Datenverarbeitung. European Workshop on Progress in Distributed Operating Systems and Distributed Systems Management

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BEESAT: A Pico Satellite for the On Orbit Verification of Micro Wheels

BEESAT: A Pico Satellite for the On Orbit Verification of Mi...

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International Conference on Recent Advances in Space Technologies (RAST)

作者： Hakan Kayal Frank Baumann Klaus Briess Sergio Montenegro Institute of Aeronautics and Astronautics Department of Astronautics Technical University Berlin Berlin Germany Frauenhofer Institute Computer Architecture and Software Technology Berlin Germany

The Technical University of Berlin has the objective to develop such components, which are not available yet. The first device under development is the so called "micro wheel", which is designed to be used in pico satellites, allowing a precise 3-axis attitude stabilization. The verification of these wheels in space will be done by BEESAT, the first pico satellite of TU Berlin. The main objective of BeeSat is the on orbit verification of newly developed micro wheels for pico satellite applications. These wheels, with the size of a one Euro coin in diameter, supported by DLR (FKZ 50JR0552) and developed in cooperation with Astro-und Feinwerktechnik, will drastically improve the actuation capabilities of pico satellites.

关键词： Wheels Satellite broadcasting Satellite ground stations Cameras Payloads Earth Testing computer architecture software Space technology

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Emotional evaluation of architectural interior forms based on personality differences using virtual reality

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Frontiers of Architectural Research 2020年第1期9卷 138-147页

作者： Maryam Banaei Ali Ahmadi Klaus Gramann Javad Hatami School of Architecture and Environmental Design Iran University of Science and TechnologyTehranIran Faculty of Computer Engineering K.N.Toosi UniversityTehranIran Department of Psychology and Ergonomics Biological Psychology and NeuroergonomicsBerlin Institute of TechnologyBerlinGermany Center for Advanced Neurological Engineering University of CaliforniaSan DiegoCAUSA School of Software University of Technology SydneySydneyAustralia Department of Psychology University of TehranTehranIran

Recent studies have shown that architectural interior forms could impact the affective state of ***,the direct relation of specific forms with specific affective states is difficult to *** addition,no systematic categorization of architectural forms and their relation to emotional states *** investigation of the impact of architectural features on inhabitants'emotions is further complicated by the use of two-dimensional images of forms in laboratory investigations,which cannot perceive real-world ***,the interior form consists of a combination of different forms rather than only pure forms,which was considered in previous *** study aimed to rill these gaps by evaluating interior forms on the basis of clustering different images of built living rooms throughout history as well as their impact on *** study used pleasure,arousal,and dominance ratings with an emphasis on individual differences in *** sample rooms were created based on formal clusters of architectural *** showed a relationship between forms and emotional states for different personality *** work provided a novel approach on the influence of architecture on emotion by considering systematic form categorization and combinations,personality differences,and a virtual reality setup.

关键词： Architectural interior form PAD Self-assessment manikin Personality Virtual reality

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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