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arXiv 2019年

作者： Li, Xiang Hu, Xiaolin Yang, Jian Pca Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Momenta Department of Computer Science and Technology Tsinghua University

The Convolutional Neural Networks (CNNs) generate the feature representation of complex objects by collecting hierarchical and different parts of semantic subfeatures. These sub-features can usually be distributed in grouped form in the feature vector of each layer [43, 32], representing various semantic entities. However, the activation of these sub-features is often spatially affected by similar patterns and noisy backgrounds, resulting in erroneous localization and identification. We propose a Spatial Group-wise Enhance (SGE) module that can adjust the importance of each sub-feature by generating an attention factor for each spatial location in each semantic group, so that every individual group can autonomously enhance its learnt expression and suppress possible noise. The attention factors are only guided by the similarities between the global and local feature descriptors inside each group, thus the design of SGE module is extremely lightweight with almost no extra parameters and calculations. Despite being trained with only category supervisions, the SGE component is extremely effective in highlighting multiple active areas with various high-order semantics (such as the dog's eyes, nose, etc.). When integrated with popular CNN backbones, SGE can significantly boost the performance of image recognition tasks. Specifically, based on ResNet50 backbones, SGE achieves 1.2% Top-1 accuracy improvement on the ImageNet benchmark and 1.0∼2.0% AP gain on the COCO benchmark across a wide range of detectors (Faster/Mask/Cascade RCNN and RetinaNet). Codes and pretrained models are available at https://***/implus/PytorchInsight. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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A unified syntax-aware framework for semantic role labeling

A unified syntax-aware framework for semantic role labeling

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2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

作者： Li, Zuchao He, Shexia Cai, Jiaxun Zhang, Zhuosheng Zhao, Hai Liu, Gongshen Li, Linlin Si, Luo Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Cyber Security Shanghai Jiao Tong University China Alibaba Group Hangzhou China

ISBN: (纸本)9781948087841

Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be so important for SRL with the emerging much smaller gap between syntax-aware and syntax-agnostic SRL. To comprehensively explore the role of syntax for SRL task, we extend existing models and propose a unified framework to investigate more effective and more diverse ways of incorporating syntax into sequential neural networks. Exploring the effect of syntactic input quality on SRL performance, we confirm that high-quality syntactic parse could still effectively enhance syntactically-driven SRL. Using empirically optimized integration strategy, we even enlarge the gap between syntax-aware and syntax-agnostic SRL. Our framework achieves state-of-the-art results on CoNLL-2009 benchmarks both for English and Chinese, substantially outperforming all previous models. © 2018 Association for Computational Linguistics

关键词： Syntactics

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Simpler CCA secure PKE from LPN problem without double-trapdoor 20th

Simpler CCA secure PKE from LPN problem without double-trapd...

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20th International Conference on Information and Communications security, ICICS 2018

作者： Cheng, Haitao Li, Xiangxue Qian, Haifeng Yan, Di Department of Computer Science and Technology East China Normal University Shanghai China National Engineering Laboratory for Wireless Security Xi’an University of Posts and Telecommunications Xi’an China Westone Cryptologic Research Center Beijing China Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9783030019495

The first CCA secure public key encryption (PKE) on the learning parity with noise (LPN) assumption was invented by Döttling et al. (ASIACRYPT 2012). At PKC 2014, Kiltz et al. gave a simpler and more efficient construction, where a double-trapdoor technique was introduced to handle the decryption queries in game simulation. Different from the technique, we build in the standard model the CCA secure PKE on a variant of Extended Knapsack LPN problem (which is provably equivalent to the standard LPN problem). We abstract out an ephemeral key from the LPN assumption, which can then be used to encrypt the underlying plaintext when equipped with several typical classes of cryptographic primitives. Thanks to these techniques, the decryption queries can be correctly answered (yet without relying on a double-trapdoor mechanism) during security reduction from LPN. The resulting simple proposal appears more modular and efficient. © Springer Nature Switzerland AG 2018.

关键词： Quantum cryptography

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Towards Fine-grained Fingerprinting of Firmware in Online Embedded Devices

Towards Fine-grained Fingerprinting of Firmware in Online Em...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Qiang Li Xuan Feng Raining Wang Zhi Li Limin Sun School of Computer and Information Technology Beijing Jiaotong University China School of Cyber Security University of Chinese Academy of Sciences China Department of Electrical and Computer Engineering University of Delaware USA

An increasing number of embedded devices are connecting to the Internet at a surprising rate. Those devices usually run firmware and are exposed to the public by device search engines. Firmware in embedded devices comes from different manufacturers and product versions. More importantly, many embedded devices are still using outdated versions of firmware due to compatibility and release-time issues, raising serious security concerns. In this paper, we propose generating fine-grained fingerprints based on the subtle differences between the filesystems of various firmware images. We leverage the natural language processing technique to process the file content and the document object model to obtain the firmware fingerprint. To validate the fingerprints, we have crawled 9,716 firmware images from official websites of device vendors and conducted real-world experiments for performance evaluation. The results show that the recall and precision of the firmware fingerprints exceed 90%. Furthermore, we have deployed the prototype system on Amazon EC2 and collected firmware in online embedded devices across the IPv4 space. Our findings indicate that thousands of devices are still using vulnerable firmware on the Internet.

关键词： Internet Microprogramming computer security Natural language processing Prototypes Cameras Conferences

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CCA secure multi-recipient KEM from LPN 20th

CCA secure multi-recipient KEM from LPN

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20th International Conference on Information and Communications security, ICICS 2018

ISBN: (纸本)9783030019495

We propose a novel multiple-recipient key-encapsulation mechanism (mKEM) scheme which takes multiple public keys as input and outputs a single key shared by corresponding recipients. We construct our scheme in the random oracle model based on low-noise LPN assumption which is a post-quantum problem. In the game simulation of security proof, a variant of Extended Knapsack LPN (which can be proved equivalent to standard LPN) is used to handle the decapsulation queries. The property of LPN problem provides randomness reuse property to shorten the length of the ciphertext compared with traditional way. © Springer Nature Switzerland AG 2018.

关键词： Quantum cryptography

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How good is your data? Investigating the quality of data generated during security incident response investigations

arXiv

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arXiv 2019年

作者： Grispos, George Glisson, William Bradley Storer, Tim School of Interdisciplinary Informatics College of Information Science and Technology University of Nebraska at Omaha Cyber Forensics Intelligence Center Department of Computer Science Sam Houston State University School of Computing Science College of Science and Engineering University of Glasgow

An increasing number of cybersecurity incidents prompts organizations to explore alternative security solutions, such as threat intelligence programs. For such programs to succeed, data needs to be collected, validated, and recorded in relevant datastores. One potential source supplying these datastores is an organization’s security incident response team. However, researchers have argued that these teams focus more on eradication and recovery and less on providing feedback to enhance organizational security. This prompts the idea that data collected during security incident investigations may be of insufficient quality for threat intelligence analysis. While previous discussions focus on data quality issues from threat intelligence sharing perspectives, minimal research examines the data generated during incident response investigations. This paper presents the results of a case study identifying data quality challenges in a Fortune 500 organization’s incident response team. Furthermore, the paper provides the foundation for future research regarding data quality concerns in security incident response. Copyright © 2019, The Authors. All rights reserved.

关键词： Data reduction

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Improved meet-in-the-middle attacks on reduced-round Piccolo

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Science China(Information Sciences) 2018年第3期61卷 95-107页

作者： Ya LIU Liang CHENG Zhiqiang LIU Wei LI Qingju WANG Dawu GU Department of Computer Science and Engineering University of Shanghai for Science and Technology State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University School of Computer Science and Technology Donghua University Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Department of Applied Mathematics and Computer Science Technical University of Denmark

Piccolo is a lightweight block cipher that adopts a generalized Feistel network structure with4 branches, each of which is 16 bit long. The key length is 80 or 128 bit, denoted by Piccolo-80 and Piccolo-128, respectively. In this paper, we mounted meet-in-the-middle attacks on 14-round Piccolo-80 without preand post-whitening keys and 18-round Piccolo-128 with post-whitening keys by exploiting the properties of the key schedule and Maximum Distance Separable（MDS） matrix. For Piccolo-80, we first constructed a 5-round distinguisher. Then 4 rounds and 5 rounds were appended at the beginning and at the end,respectively. Based on this structure, we mounted an attack on 14-round Piccolo-80 from the 5 th round to the 18 th round. The data, time, and memory complexities were 252chosen plaintexts, 267.44encryptions, and 264.91blocks, respectively. For Piccolo-128, we built a 7-round distinguisher to attack 18-round Piccolo-128 from the 4 th round to the 21st round. The data, time, and memory complexities were 252chosen plaintexts,2126.63encryptions, and 2125.29blocks, respectively. If not considering results on biclique cryptanalysis,these are currently the best public results on this reduced version of the Piccolo block cipher.

关键词： block cipher lightweight Piccolo meet-in-the-middle attack distinguisher

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Enjoy the untrusted cloud: A secure, scalable and efficient SQL-like query framework for outsourcing data

arXiv

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arXiv 2019年

作者： Chen, Yaxing Zheng, Qinghua Liu, Dan Yan, Zheng Sun, Wenhai Zhang, Ning Lou, Wenjing Thomas Hou, Y. School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University Xi'an Shaanxi China Department of Communications and Networking Aalto University Espoo Finland Department of Computer and Information Technology Purdue University West LafayetteIN United States Department of Computer Science and Engineering Washington University in St. Louis St. LouisMO United States Department of Computer Science Electrical and Computer Engineering Virginia Tech BlacksburgVA United States

While the security of cloud remains a concern, a common practice is to encrypt data before outsourcing them for utilization. One key challenging issue is how to efficiently perform queries over the ciphertext. Conventional crypto-based solutions, e.g. partially/fully homomorphic encryption and searchable encryption, suffer from low performance, poor expressiveness and weak compatibility. An alternative method that utilizes hardware-assisted trusted execution environment, i.e., Intel SGX, has emerged recently. On one hand, such work lacks of supporting scalable access control over multiple data users. On the other hand, existing solutions are subjected to the key revocation problem and knowledge extractor vulnerability. In this work, we leverage the newly hardware-assisted methodology and propose a secure, scalable and efficient SQL-like query framework named QShield. Building upon Intel SGX, QShield can guarantee the confidentiality and integrity of sensitive data when being processed on an untrusted cloud platform. Moreover, we present a novel lightweight secret sharing method to enable multi-user access control in QShield, while tackling the key revocation problem. Furthermore, with an additional trust proof mechanism, QShield guarantees the correctness of queries and significantly alleviates the possibility to build knowledge extractor. We implemented a prototype for QShield and show that QShield incurs minimum performance cost. Copyright © 2019, The Authors. All rights reserved.

关键词： Outsourcing

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Monolithic integration of MoS2-based visible detectors and GaN-based UV detectors

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Photonics Research 2019年第10期7卷 1127-1133页

作者： YOU WU ZHIWEN LI KAH-WEE ANG YUPING JIA ZHIMING SHI ZHI HUANG WENJIE YU XIAOJUAN SUN XINKE LIU DABING LI State Key Laboratory of Luminescence and Applications Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Shenzhen Castle Security Technology Co. Ltd.Shenzhen 518000China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China

With the increasing demand for high integration and multi-color photodetection for both military and civilian applications, the research of multi-wavelength detectors has become a new research hotspot. However, current research has been mainly in visible dual-or multi-wavelength detectors, while integration of both visible light and ultraviolet(UV) dual-wavelength detectors has rarely been studied. In this work, large-scale and high-quality monolayer MoS2 was grown by the chemical vapor deposition method on transparent free-standing GaN substrate. Monolithic integration of MoS2-based visible detectors and GaN-based UV detectors was demonstrated using common semiconductor fabrication technologies such as photolithography, argon plasma etching, and metal deposition. High performance of a 280 nm and 405 nm dual-wavelength photodetector was *** responsivity of the UV detector reached 172.12 A/W, while that of the visible detector reached 17.5 A/***, both photodetectors achieved high photocurrent gain, high external quantum efficiency, high normalized detection rate, and low noise equivalent power. Our study extends the future application of dual-wavelength detectors for image sensing and optical communication.

关键词： visible integration ultraviolet

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Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

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Bioresource technology 2025年 434卷 132773页

作者： Mudi Zhai Bomin Fu Zhaozhong Wu Junsen Wang Weijie Wang Hongtao Wang College of Environmental Science and Engineering Key Laboratory of Yangtze River Water Environment Ministry of Education Tongji University Siping Rd 1239 Shanghai 200092 China. College of Environmental Science and Engineering Key Laboratory of Yangtze River Water Environment Ministry of Education Tongji University Siping Rd 1239 Shanghai 200092 China National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China State Key Laboratory of Marine Pollution and Department of Chemistry City University of Hong Kong Hong Kong 999077 China. Electronic address: bomin_fu@***. School of Computer Science Northwestern Polytechnical University Xi'an 710129 China. Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092 China. Electronic address: hongtao@***.

Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments.

关键词： Data augmentation Generative artificial intelligence Wasserstein generative adversarial network Wastewater

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