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arXiv 2024年

作者： Yue, Hao Lai, Shaopeng Yang, Chengyi Zhang, Liang Yao, Junfeng Su, Jinsong School of Informatics Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China

Cross-document Relation Extraction aims to predict the relation between target entities located in different documents. In this regard, the dominant models commonly retain useful information for relation prediction via bridge entities, which allows the model to elaborately capture the intrinsic interdependence between target entities. However, these studies ignore the non-bridge entities, each of which co-occurs with only one target entity and offers the semantic association between target entities for relation prediction. Besides, the commonly-used dataset-CodRED contains substantial NA instances, leading to the prediction bias during inference. To address these issues, in this paper, we propose a novel graph-based cross-document RE model with non-bridge entity enhancement and prediction debiasing. Specifically, we use a unified entity graph to integrate numerous non-bridge entities with target entities and bridge entities, modeling various associations between them, and then use a graph recurrent network to encode this graph. Finally, we introduce a novel debiasing strategy to calibrate the original prediction distribution. Experimental results on the closed and open settings show that our model significantly outperforms all baselines, including the GPT-3.5-turbo and InstructUIE, achieving state-of-the-art performance. Particularly, our model obtains 66.23% and 55.87% AUC points in the official leaderboard1 under the two settings, respectively, ranking the first place in all submissions since December 2023. Our code is available at https://***/DeepLearnXMU/CoRE-NEPD. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming Third-Party Libraries in Static Bug-Finding Systems 24

LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Wu, Rongxin He, Yuxuan Huang, Jiafeng Wang, Chengpeng Tang, Wensheng Shi, Qingkai Xiao, Xiao Zhang, Charles Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China The Hong Kong University of Science and Technology Hong Kong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Person Hang Zhou China

ISBN: (纸本)9798400702174

Despite the benefits of using third-party libraries (TPLs), the misuse of TPL functions raises quality and security concerns. Using traditional static analysis to detect bugs caused by TPL function is nontrivial. One promising solution would be to automatically generate and persist the summaries of TPL functions offline and then reuse these summaries in compositional static analysis online. However, when dealing with millions of lines of TPL code, the summaries designed by existing studies suffer from an unresolved paradox. That is, a highly precise form of summary leads to an unaffordable space and time overhead, while an imprecise one seriously hurts its precision or recall. To address the paradox, we propose a novel two-layer summary design. The first layer utilizes a line-sized program representation known as the program dependence graph to compactly encode path conditions, while the second layer encodes bug-type-specific properties. We implemented our idea as a tool called Libalchemy and evaluated it on fifteen mature and extensively checked open-source projects. Experimental results show that Libalchemy can check over ten million lines of code within ten hours. Libalchemy has detected 55 true bugs with a high precision of 90.16%, eleven of which have been assigned CVE IDs. Compared to whole-program analysis and the conventional design of path-sensitively precise summaries, Libalchemy achieves an 18.56× and 12.77× speedup and saves 91.49% and 90.51% of memory usage, respectively. © 2024 ACM.

关键词： Static analysis

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An Opposition-Based Learning Archerfish Hunting Optimizer for Global Optimization 6

An Opposition-Based Learning Archerfish Hunting Optimizer fo...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Ferhat, Aridj Zitouni, Farouq Limanc, Abdelhadi Lakbichi, Rihab Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

This paper introduces OBL-AHO, an enhanced Archerfish Hunting Optimizer (AHO) variant that integrates Opposition-Based Learning (OBL) to improve AHO's exploratory capabilities. By leveraging opposite solutions alongside newly generated ones, OBL-AHO broadens the search space, effectively reducing the risk of local optima entrapment-A common challenge in metaheuristic algorithms. The performance of OBL-AHO is rigorously compared with the original AHO and several prominent Metaheuristic Algorithms (MAs), including TLBO (Teaching-Learning-Based Optimization), PSO (Particle Swarm Optimization), GWO (Grey Wolf Optimizer), OOBO (OnE-To-One Based Optimizer), SSA (Salp Swarm Algorithm). Evaluations were conducted using test functions taken from CEC 2022, and the outcomes were statistically analyzed using the Friedman test. The findings demonstrate that OBL-AHO significantly improves exploration while maintaining competitive exploitation relative to its opponents. © 2024 IEEE.

关键词： Contrastive Learning

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Comparative Analysis of Four Metaheuristic Algorithms for Estimating Parameters in Solar Photovoltaic Models 6

Comparative Analysis of Four Metaheuristic Algorithms for Es...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Lakbichi, Rihab Zitouni, Farouq Limane, Abdelhadi Ferhat, Aridj Harous, Saad Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331522810

In the last decade, Metaheuristic Algorithms (MAs) have been widely employed to estimate parameters in photovoltaic (PV) models to maximize solar energy efficiency. This paper presents a comparative study of the performance of four novel and efficient MAs applied to solve the parameter extraction problem in three ubiquitous PV models: The Single-Diode Model (SDM), the Double-Diode Model (DDM), and the Photovoltaic Module Model (PMM). The primary objective of this study is to evaluate and compare the efficiency of these MAs and identify the most effective one based on the Root Mean Square Error (RMSE) between the simulated and the actual data. This work compares the performance of the Red-Tailed Hawk Optimizer (RTH), the One-To-One Based Optimizer (OOBO), the Sand Cat Swarm Optimization (SCSO), and the Archerfish Hunting Optimizer (AHO). The numerical results demonstrate that RTH significantly outperforms its counterparts in estimating parameters for the examined models. This exceptional performance underscores RTH's efficiency and effectiveness as a powerful tool for parameter estimation in PV models. Given these promising results, RTH stands out as a preferred method and holds the potential for further refinement and enhancement in the future. © 2024 IEEE.

关键词： Mean square error

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Machine Learning-Based Whole Gland Radiomics Analysis for Prostate Cancer Classification

Machine Learning-Based Whole Gland Radiomics Analysis for Pr...

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IEEE International Symposium on Biomedical Imaging

作者： Dimitris Filos Dimitris Fotopoulos Maria Anastasia Rouni Ioanna Chouvarda Laboratory of Computing Medical Informatics and Biomedical Imaging Technologies Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Prostate cancer (PCa) is the second more common cancer type in men, and the need for a non-invasive PCa diagnostic and stratification tool can now be met via quantified analysis of Multiparametric MRI. Radiomics analysis can quantify imaging information on a Region of Interest (ROI), not visible by clinicians and thus improve cancer detection. However, the delineation of cancer lesions is both challenging and time consuming. In this paper, we consider as area in focus the whole prostate gland as segmented by high performance deep learning algorithms, calculate whole gland radiomics features and employ them to develop machine learning models for the identification of clinically significant Pca (csPCa). T2 weighted and diffusion weighted images from 1500 patients were used for model development and validation. The radiomic features from the prostate gland’s core, periphery and whole volume were extracted. The radiomic features were harmonized against vendor batch effect using statistical methods and a robust repeated elastic net method was employed for feature selection. Clinical data were also considered to improve model performance. Finally, Support Vector Machines and Balanced Random Forest (BRF) models were developed, and the validation step was repeated 5 times by splitting the whole dataset in 1:3 ratio in each iteration. A balanced accuracy of 79% was achieved for the SVM using radiomics from the whole prostate gland. The final model’s good performance highlights the value of whole gland radiomics for the identification of csPCa.

关键词： Support vector machines Image segmentation Glands Predictive models Feature extraction Data models Lesions

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Gemina: A Coordinated and High-Performance Memory Deduplication Engine

Gemina: A Coordinated and High-Performance Memory Deduplicat...

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IEEE Symposium on High-Performance Computer Architecture

作者： Zhehua Zhang Suzhen Wu Wenyan You Chunfeng Du Bo Mao Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen Fujian China

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Memory deduplication is widely used to effectively reduce the memory footprint in operating systems, while huge pages are employed to enhance access performance by increasing TLB hit rates. Unfortunately, duplicate huge pages are very rare in memory, leading to most huge pages being split into base pages during deduplication, which degrades access performance by up to $\mathbf{4 3 \%}$. Although asynchronous huge page promotion attempts to elevate multiple base pages to huge pages to maximize performance, deduplication disrupts the uniformity of page attributes, limiting the effectiveness of such promotions. To address this problem, we designed Gemina, a new memory deduplication scheme that coordinates huge page management. Gemina employs a fine-grained detector to distinguish pages, redundancy and hotness, a distributor to process pages conservatively and avoid frequent switches, and an adapter to organize memory for fast conversion. This approach balances the trade-off between performance and space efficiency in memory management. Experimental evaluations show that, compared to the naive KSM-based deduplication scheme, Gemina can achieve $96.1 \%$ of the memory savings of KSM while also improving memory access performance by $\mathbf{4 3. 1 \%}$ in random access.

关键词： Limiting Operating systems Memory management Redundancy Detectors Protection Engines

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Mitigating Write Disturbance in Non-Volatile Memory via Coupling Machine Learning with Out-of-Place Updates

Mitigating Write Disturbance in Non-Volatile Memory via Coup...

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IEEE Symposium on High-Performance Computer Architecture

作者： Ronglong Wu Zhirong Shen Zhiwei Yang Jiwu Shu School of Informatics Xiamen University Xiamen Key Laboratory of Intelligent Storage and Computing Xiamen University

Non-volatile memory (NVM) opens up new opportunities to resolve scaling restrictions of main memory, yet it is still hindered by the write disturbance (WD) problem. The WD problem mistakenly transforms the values of NVM cells, hence seriously deteriorating memory reliability and downgrading access performance. Existing studies mainly mitigate the WD problem via encoding WD-prone data patterns under in-place updates, yet we find that when turning to out-of-place updates, they can gain the potential to reduce more WD errors. We present LearnWD, an approach that mitigates the WD problem in NVM via coupling machine learning with out-of-place updates. LearnWD first employs clustering algorithms to classify the stale data based on the error proneness. To perform a write operation, LearnWD carefully examines the aggressivity of new data and the error proneness of stale data, so as to speculatively minimize the resulting WD errors. We conduct extensive experiments using 15 real-world data sets with different data types, showing that LearnWD can assist a variety of data encoding schemes to further reduce 20.1% of WD errors, shorten 11.0% of write latency, and extend 21.9% of write endurance.

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Reducing Write Tail Latency of Distributed Key-Value Stores Using In-Network Chasing

Reducing Write Tail Latency of Distributed Key-Value Stores ...

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International Workshop on Quality of Service

作者： Jinghui Jiang Xiwen Fan Zhenpei Huang Kairui Zhou Qiao Xiang Lu Tang Qiang Li Jiwu Shu Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Minjiang University

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Multiple systems have explored how to use programmable switch ASICs to improve the performance of distributed systems. However, they focus on accelerating read operations and perform poorly under write-intensive workloads. In this paper, we present Gecko, a system that accelerates write operations in distributed key-value store systems using switch ASICs. The core idea of Gecko is to offload the client-side chasing mechanism, a technique deployed by production storage networks, to the programmable switch to simultaneously reduce the perceived and actual write-tail latency in distributed key-value stores. The perceived latency is the interval between the user sending a write request and the user receiving the write success, and there may be replicas that have not yet completed the write, but the actual latency requires all replicas to be successfully written. Gecko not only reduces the perceived write tail latency by deploying the chasing mechanism, but the actual write tail latency is also reduced by utilizing the capabilities of programmable switches. Specifically, Gecko’s in-network chasing design caches a write request at the switch data plane and reports success to the client when only m out of n (usually set to 2 and 3 in production networks, respectively) replicas have been successfully written to the server, and retries the cached write request if the remaining n − m replicas are not successfully written. In addition to caching the write request, Gecko also introduces novel designs to fully implement the chasing controller and a corresponding timer controller in the switch data plane, minimizing the interaction overhead between the switch control and data plane. Extensive experiments on a testbed of Barefoot Tofino switch and commodity servers show that Gecko not only substantially reduces the write tail latency by more than 2.16x caused by transient glitches at servers, but also maintains the same level of reliability as the classic three-replica write opera

关键词： Switches Tail Production Quality of service Control systems Servers Reliability

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Advances in wireless sensor networks under AI-5 G for augmented reality

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 I0001-I0003页

作者： Muhammad KHAN Visual Analytics for Knowledge Laboratory(VIS 2 KNOW Lab) Department of Applied Artificial IntelligenceSchool of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul 03063Republic of Korea

Due to the recent rapid development in the 5 G technology,the usage of sensor networks especially wireless sensor networks(WSNs)has boosted advances in the augmented reality(AR),supporting decision making in AR *** decision-making needs support and consideration of artificial intelligence(AI)techniques capable of adapting to changes in AR environments for creating systems that evolve autonomously over ***,it is important to apply new information fusion techniques that allow for the processing of information at low and high levels to improve the accuracy of such systems.

关键词： networks evolve sensor

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Blockchain Privacy Protection Based on Post Quantum Threshold Algorithm

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Computers, Materials & Continua 2023年第7期76卷 957-973页

作者： Faguo Wu Bo Zhou Jie Jiang Tianyu Lei Jiale Song Institute of Artificial Intelligence Beihang UniversityBeijing100191China Zhongguancun Laboratory Beijing100094China Key Laboratory of Mathematics Informatics and Behavioral Semantics(LMIB)Beihang UniversityBeijing100191China Bejing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing100191China LMIB and School of Mathematical Sciences Beihang UniversityBeijing100191China

With the rapid increase in demand for data trustworthiness and data security,distributed data storage technology represented by blockchain has received unprecedented *** technologies have been suggested for various uses because of their remarkable ability to offer decentralization,high autonomy,full process traceability,and tamper *** enables the exchange of information and value in an untrusted *** has been a significant increase in attention to the confidentiality and privacy preservation of blockchain *** data privacy is a critical concern in cryptography,and one of the most important protocols used to achieve this is the secret-sharing *** dividing the secret into shares and distributing them among multiple parties,no one can access the secret without the cooperation of the other ***,Attackers with quantum computers in the future can execute Grover’s and Shor’s algorithms on quantum computers that can break or reduce the currently widely used ***,centralized management of keys increases the risk of key *** paper proposed a post-quantum threshold algo-rithm to reduce the risk of data privacy leakage in blockchain *** algorithm uses distributed key management technology to reduce the risk of individual node private key leakage and provide post-quantum *** proposed privacy-preserving cryptographic algorithm provides a post-quantum threshold architecture for managing data,which involves defining users and interaction processes within the *** paper applies a linear secret-sharing solution to partition the private key of the Number Theory Research Unit(NTRU)algorithm into n *** constructs a t–n threshold that allows recovery of the plaintext only when more than t nodes participate in *** characteristic of a threshold makes the scheme resistant to collusion attacks from members whose combined credibility is less than the

关键词： Blockchain post-quantum cryptography threshold cryptography privacy protection

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