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CSPs: Catchy short passwords making offline and online attacks impossible

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International Journal of Information and computer Security 2019年第3期11卷 255-274页

作者： Shen, Jaryn Zeng, Qingkai State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

This paper proposes to address online and offline attacks to passwords without increasing users' efforts in choosing and memorising their passwords. In CSPS, a password consists of two parts, a user-chosen short password and a server-generated long password. The short password should be memorised and secured by its user while the long password be encrypted and stored on the server side. To keep the secret key for protecting the long password secure, an additional sever is introduced to store the secret key and provide encryption/decryption services. On top of balloon, CSPS integrates expensive hash with secure encryption. It is mathematically proved that computationally unbounded attackers cannot succeed in offline dictionary or brute-force attacks or a combination of offline and online attacks. The criteria of security are established, which quantifies the security. To our best knowledge, CSPS is the first technique to make security quantifiable in password authentication mechanisms. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Web services

Selective oxidation of benzyl alcohol to benzaldehyde using palladium nanoparticle-loaded poly(ionic liquid)s catalysts

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Molecular Catalysis 2025年 584卷

作者： Liu, Yaping Pan, Chao Qiu, Xiaomin Zhang, Hailin Feng, Haijun Tang, Yubao Shang, Zhijie Song, Hongbing College of Chemical Engineering Qingdao University of Science and Technology Zhengzhou Road No.53 Qingdao 266042 China China University of Petroleum (Beijing) School of Chemical Engineering and Environment State Key Laboratory of Heavy Oil Processing Beijing 102249 China School of Computer and Software Shenzhen Institute of Information Technology Guangdong Shenzhen 518172 China College of Chemistry and Molecular Engineering Qingdao University of Science and Technology Zhengzhou Road No.53 Qingdao 266042 China

The conversion of readily accessible chemicals into high-value-added products is a key process in industrial chemistry. This research showcases an efficient transformation of benzyl alcohol to benzaldehyde utilizing poly(ionic liquid)s (PILs) supported Pd-based catalysts. The catalyst's properties were thoroughly characterized using FT-IR, SEM, TEM, XPS, and TG, confirming the successful fabrication of stable Pd/PILs catalysts. The porous architecture of the PILs material enhances palladium nanoparticle adhesion, while its structure promotes intimate contact between the catalyst and substrate, facilitating the oxidation process. With just 0.1 g of 1 % Pd/PIL-2 catalyst, the reaction at 110 °C, 4 MPa O2 for 4 h achieve a 82.16 % conversion of benzyl alcohol with absolute selectivity to benzaldehyde. Theoretical calculations elucidated the interactions between Pd6 clusters, PIL, and benzyl alcohol. Furthermore, the Fukui function and optimized electrostatic potential diagrams identified reactive sites, providing insights into the potential reaction mechanism.

关键词： Benzaldehyde Benzyl alcohol Green oxidation Poly(ionic liquid)s Theoretical calculations

Solving string constraints with regex-dependent functions through transducers with priorities and variables

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arXiv 2021年

作者： Chen, Taolue Hague, Matthew Han, Zhilei Hu, Denghang Flores-Lamas, Alejandro Lin, ANTHONY Widjaja Kan, Shuanglong Rümmer, Philipp Wu, Zhilin Department of Computer Science Birkbeck University of London Malet Street London United Kingdom Department of Computer Science Royal Holloway University of London Egham Hill Egham SurreyTW20 0EX United Kingdom School of Software Tsinghua University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences & University of Chinese Academy of Sciences China University of Kaiserslautern Max-Planck Institute Kaiserslautern Germany University of Kaiserslautern Kaiserslautern Germany Department of Information Technology Uppsala University Box 337 UppsalaSE-751 05 Sweden

Regular expressions are a classical concept in formal language theory. Regular expressions in programming languages (RegEx) such as JavaScript, feature non-standard semantics of operators (e.g. greedy/lazy Kleene star), as well as additional features such as capturing groups and references. While symbolic execution of programs containing RegExes appeals to string solvers natively supporting important features of RegEx, such a string solver is hitherto missing. In this paper, we propose the first string theory and string solver that natively provides such support. The key idea of our string solver is to introduce a new automata model, called prioritized streaming string transducers (PSST), to formalize the semantics of RegEx-dependent string functions. PSSTs combine priorities, which have previously been introduced in prioritized finite-state automata to capture greedy/lazy semantics, with string variables as in streaming string transducers to model capturing groups. We validate the consistency of the formal semantics with the actual JavaScript semantics by extensive experiments. Furthermore, to solve the string constraints, we show that PSSTs enjoy nice closure and algorithmic properties, in particular, the regularity-preserving property (i.e., pre-images of regular constraints under PSSTs are regular), and introduce a sound sequent calculus that exploits these properties and performs propagation of regular constraints by means of taking post-images or pre-images. Although the satisfiability of the string constraint language is generally undecidable, we show that our approach is complete for the so-called straightline fragment. We evaluate the performance of our string solver on over 195 000 string constraints generated from an open-source RegEx library. The experimental results show the efficacy of our approach, drastically improving the existing methods (via symbolic execution) in both precision and efficiency. © 2021, CC BY.

关键词： Transducers

Shifu2: A network representation learning based model for advisor-advisee relationship mining

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arXiv 2020年

作者： Liu, Jiaying Xia, Feng Wang, Lei Xu, Bo Kong, Xiangjie Tong, Hanghang King, Irwin Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology China School of Science Engineering and Information Technology Federation University Australia Australia School of Software Dalian University of Technology China Department of Computer Science University of Illinois at Urbana-Champaign United States Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. Copyright © 2020, The Authors. All rights reserved.

关键词： Search engines

Bagged Tree And ResNet Based Joint End-to-End Fast CTU Partition Decision Algorithm For Video Intra Coding

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Research Square

Research Square 2021年

作者： Li, Yixiao Li, Lixiang Fang, Yuan Peng, Haipeng Ling, Nam Information Security Center State Key Laboratory of Networking and Switching Technology National Engineering Laboratory for Disaster Backup and Recovery Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Engineering Santa Clara University Santa ClaraCA95053 United States

In the development of video coding standards, advanced ones have greatly improved the bit rate compared with those of previous generation, but also brought a huge increase in coding complexity. Coding standards, such as high efficiency video coding (HEVC), versatile video coding (VVC) and AOMedia video 2 (AV2), get the optimal encoding performance by traversing all possible combinations of coding unit (CU) partition and selecting the combination with minimum coding cost. This process of searching for the best makes up a large part of encoding complexity. To reduce the complexity of coding block partition for many video coding standards, this paper proposes an end-to-end fast algorithm for partition structure decision of coding tree unit (CTU) in intra coding. It can be extended to various coding standards with fine tuning, and is applied to the intra coding of HEVC reference software HM16.7 as an example. In the proposed method, the splitting decision of a CTU is made by a well designed bagged tree model firstly. Then, the partition problem of a 32×32 sized CU is modeled as a 17-output classification task and solved by a well trained residual network (ResNet). Jointly using bagged tree and ResNet, the proposed fast CTU partition algorithm is able to generate the partition quad-tree structure of a CTU through an end-to-end prediction process, instead of multiple decision making procedures at depth level. Besides, several effective and representative datasets are also conducted in this paper to lay the foundation of high prediction accuracy. Compared with the original HM16.7 encoder, experimental results show that the proposed algorithm can reduce the encoding time by 59.79% on average, while the BD-rate loss is as less as 2.02%, which outperforms the results of most of state-of-the-art approaches in the fast intra CU partition area. © 2021, CC BY.

关键词： Decision making

A novel Skin lesion prediction and classification technique: ViT-GradCAM

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Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) 2024年第9期30卷 e70040页

作者： Muhammad Shafiq Kapil Aggarwal Jagannathan Jayachandran Gayathri Srinivasan Rajasekhar Boddu Adugna Alemayehu School of Information Engineering Qujing Normal University Qujing China. Key Laboratory of Intelligent Sensor and System Design College of Information Engineering Qujing Normal University Qujing China. Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India. Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India. Lecturer in Software Engineering Wachemo University Hosaina Ethiopia.

BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

关键词： Grad‐CAM Multi‐Headed Self attention (MSA) linear patch projection multilayer perceptron (MLP) skin lesion transformer encoder vision transformer (ViT)

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

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arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

A comparative study of large-scale cluster workload traces via multiview analysis 21

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A comparative study of large-scale cluster workload traces v...

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data science and Systems, HPCC/SmartCity/DSS 2019

作者： Ruan, Li Xu, Xiangrong Xiao, Limin Yuan, Feng Li, Yin Dai, Dong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Institute of Software Application Technology Guangzhou AND Chinese Academy of Sciences Guangzhou China Department of Computer Science University of North Carolina Charlotte CharlotteNC United States

ISBN: (纸本)9781728120584

Understanding the characteristics of workloads of the large-scale Clusters is the key to diagnose the system bottlenecks, making optimal configuration decisions, improving the system throughput and resource usage. Due to the diversity and multiview of the workload traces, featuring the good designs and bottlenecks by analyzing the workloads under realworld scenarios becomes increasingly challenging. This paper introduces a multiview based trace comparative analysis method by comparatively characterizing how the architecture, jobs, tasks, machines, and resources usage were managed among different platforms. A case study is performed which verified the effectiveness of our method by comparatively analyzing two most representative big traces: Google trace and Alibaba 2018 trace. Quantitative findings, together with the performance bottleneck inferences and suggestions are also presented. To the best of our knowledge, we are the first to perform such comparative empirical study on these two traces using a multiview based approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, IT practitioners and users, but also can promote more researches on big trace data comparative analysis in large-scale clusters. © 2019 IEEE.

关键词： Trace analysis

Fully automated functional fuzzing of Android apps for detecting non-crashing logic bugs

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arXiv 2020年

作者： Su, Ting Yan, Yichen Wang, Jue Sun, Jingling Xiong, Yiheng Pu, Geguang Wang, Ke Su, Zhendong ETH Zurich East China Normal University Shanghai Key Laboratory of Trustworthy Computing China Nanjing University State Key Laboratory for Novel Software Technology China Visa Research United States ETH Zurich Department of Computer Science Switzerland

Android apps are GUI-based event-driven software and have become ubiquitous in recent years. Obviously, functional correctness is critical for an app's success. However, in addition to crash bugs, non-crashing functional bugs (in short as "non-crashing bugs" in this work) like inadvertent function failures, silent user data lost and incorrect display information are prevalent, even in popular, well-tested apps. These non-crashing functional bugs are usually caused by program logic errors and manifest themselves on the graphic user interfaces (GUIs). In practice, such bugs pose significant challenges in effectively detecting them because (1) current practices heavily rely on expensive, small-scale manual validation (the lack of automation);and (2) modern fully automated testing has been limited to crash bugs (the lack of test oracles). This paper fills this gap by introducing independent view fuzzing, a novel, fully automated approach for detecting non-crashing functional bugs in Android apps. Inspired by metamorphic testing, our key insight is to leverage the commonly-held independent view property of Android apps to manufacture property-preserving mutant tests from a set of seed tests that validate certain app properties. The mutated tests help exercise the tested apps under additional, adverse conditions. Any property violations indicate likely functional bugs for further manual confirmation. We have realized our approach as an automated, end-to-end functional fuzzing tool, Genie. Given an app, (1) Genie automatically detects non-crashing bugs without requiring human-provided tests and oracles (thus fully automated);and (2) the detected non-crashing bugs are diverse (thus general and not limited to specific functional properties), which set Genie apart from prior work. We have evaluated Genie on 12 real-world Android apps and successfully uncovered 34 previously unknown non-crashing bugs in their latest releases - all have been confirmed, and 22 have already been

关键词： Graphical user interfaces