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WOBTree:a write-optimized B+-tree for non-volatile memory

Frontiers of computer science 2021年第5期15卷 37-51页

作者： Haitao WANG Zhanhuai LI Xiao ZHANG Xiaonan ZHAO Song JIANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi'An 710072China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi'An 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’An 710072China Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTX 76010USA

The emergence of non-volatile memory(NVM)has introduced new opportunities for performance optimizations in existing storage *** better utilize its byte-addressability and near-DRAM performance,NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block *** this scenario,a cache line(usually 64 bytes)becomes the data transfer unit between volatile and non-volatile ***,the failure-atomicity of write on NVM is the memory bit width(usually 8 bytes).This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-*** this paper,we propose WOBTree,a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive *** minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write *** results show that compared with previous persistent B+-tree solutions,WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.

关键词： non-volatile memory B+-tree atomic granularity mismatch write amplification performance optimization

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Statistical Model-driven Similarity Hashing: Bridging Modalities for Efficient Unsupervised Retrieval 39

Statistical Model-driven Similarity Hashing: Bridging Modali...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kuai, Mingjin Long, Jun Yang, Zhan School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Changsha410083 China

ISBN: (纸本)157735897X

Unsupervised deep cross-modal hash retrieval aims to map multi-modal features into binary hash codes without labels, which is of interest due to its storage efficiency, query speed and convenient applications. However, existing approaches suffer from two main limitations: (1) Slightly insufficient consideration of text instance similarity, along with independent or redundant fusion to learn multi-modal similarity information. (2) They ignore the noisy adjacent correlations between multi-modal instances, leading to a lack of discriminative power in the generated hash codes. To address these challenges, we propose a new approach called Statistical Model-driven Similarity Hashing (SMSH). Specifically, we introduce Jaccard similarity when constructing the text similarity matrix. It reduces the similarity error between text instances while better considering the asymmetry of the elements in the text features. After that, we integrate the original similarity information between various modalities to construct a unified similarity matrix. The gaps between modalities are bridged while reducing the redundant information in them. In addition, we introduce a Statistical Model-driven Similarity Enhancement (SMSE) approach, which reduces the noise of similarity relations between multi-modal instances by using a Gaussian Mixture Model to keep instances with lower semantic similarity as far away from each other as possible. Experiments on three benchmark datasets demonstrate the excellent performance of the SMSH method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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The Evaluation of Trust Towards The Intention to Use of Blockchain-Based Crowdwork Systems (Case Study of Creative Industry) 8

The Evaluation of Trust Towards The Intention to Use of Bloc...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Hartono, Sugiarto Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350326093

Current crowdwork research still has issues related to trust. Workers and employers do not know each other, so problems related to trust will arise. This trust issue will influence the desire to use the crowdwork system. This study will evaluate trust in the intention to use blockchain-based crowdwork systems in creative industries. The methodology used is a quantitative approach (questionnaire) which is distributed to 200 respondents consisting of workers and employers. After that, testing the inner and outer models was carried out. The results obtained are technology, risk, and reputation which have the greatest impact on trust. The use of blockchain technology can minimize risks when using a crowdwork system, such as data that cannot be changed, data tracing can be done, and so on. The rating and review features are also important to be able to find out the user's reputation in the system. © 2023 IEEE.

关键词： Blockchain

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Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning

arXiv

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arXiv 2023年

作者： Liu, Jiayu Huang, Zhenya Zhai, Chengxiang Liu, Qi Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China University of Illinois at Urbana-Champaign United States

Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which requires machines to master mathematical logic and knowledge from solving problems. However, existing approaches are not transparent (thus not interpretable) in terms of what knowledge has been learned and applied in the reasoning process. In this paper, we propose a general Learning by Applying (LeAp) framework to enhance existing models (backbones) in a principled way by explicit knowledge learning. In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning. The learned mathematical knowledge, including word-word relations and word-operator relations, forms an explicit knowledge graph, which bridges the knowledge "learning" and "applying" organically. Moreover, for problem solving, we design a semantics-enhanced module and a reasoning-enhanced module that apply knowledge to improve the problem comprehension and symbol reasoning abilities of any backbone, respectively. We theoretically prove the superiority of LeAp's autonomous learning mechanism. Experiments on three real-world datasets show that LeAp improves all backbones' performances, learns accurate knowledge, and achieves a more interpretable reasoning process. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Case-based Explanation for Black-Box Time Series and Image Models with Applications in Smart Agriculture 30

Case-based Explanation for Black-Box Time Series and Image M...

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Doctoral Consortium of the 30th International Conference on Case-Based Reasoning, ICCBR-DC 2022

作者： Delaney, Eoin School of Computer Science University College Dublin Belfield Dublin 4 Ireland VistaMilk SFI Research Centre Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Black-box models are frequently deployed for high stakes prediction tasks in a variety of domains (e.g., disease diagnosis and agricultural prediction). The predictions of these opaque systems are often plagued by a lack of transparency, motivating novel research in eXplainable AI (XAI) aiming to understand why a certain prediction was made. One increasingly promising form of explanation is counterfactual explanation where the aim is to elucidate how a prediction could change, given some change in the input space. While the majority of existing work has focused on producing counterfactual explanations for tabular data, significantly less focus has been placed on generating and evaluating counterfactual explanations for time series and image data. Explaining predictions for these data types, arguably, presents a whole new set of issues for XAI, due to the complex and multi-dimensional nature of the data. In this research, we examine how leveraging case-based reasoning (CBR) techniques such as Nearest-Unlike-Neighbors (NUNs) can aid the generation and evaluation of explanations in these domains. We also demonstrate the inadequacies of many traditional techniques that are used to evaluate explanations and highlight the promise of CBR and user studies in the evaluation of explanations. © 2022 Copyright for this paper by its authors.

关键词： Forecasting

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CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness 39

CoDe: Communication Delay-Tolerant Multi-Agent Collaboration...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Song, Shoucheng Lin, Youfang Han, Sheng Yao, Chang Wu, Hao Wang, Shuo Lv, Kai School of Computer Science & Technology Beijing Jiaotong University Beijing China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence Beijing China

ISBN: (纸本)157735897X

Communication has been widely employed to enhance multi-agent collaboration. Previous research has typically assumed delay-free communication, a strong assumption that is challenging to meet in practice. However, real-world agents suffer from channel delays, receiving messages sent at different time points, termed Asynchronous Communication, leading to cognitive biases and breakdowns in collaboration. This paper first defines two communication delay settings in MARL and emphasizes their harm to collaboration. To handle the above delays, this paper proposes a novel framework Communication Delay-tolerant Multi-Agent Collaboration (CoDe). At first, CoDe learns an intent representation as messages through future action inference, reflecting the stable future behavioral trends of the agents. Then, CoDe devises a dual alignment mechanism of intent and timeliness to strengthen the fusion process of asynchronous messages. In this way, agents can extract the long-term intent of others, even from delayed messages, and selectively utilize the most recent messages that are relevant to their intent. Experimental results demonstrate that CoDe outperforms baseline algorithms in three MARL benchmarks without delay and exhibits robustness under fixed and time-varying delays. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Delay tolerant networks

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An Initial Step Towards Stable Explanations for Multivariate Time Series Classifiers with LIME

An Initial Step Towards Stable Explanations for Multivariate...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Han Meng Christian Wagner Isaac Triguero Computational Optimisation and Learning (COL) Lab School of Computer Science University of Nottingham Nottingham United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham United Kingdom DaSCI Andalusian Institute in Data Science and Computational Intelligence University of Granada Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

LIME, or ‘Local Interpretability Model-agnostic Explanations’ is a well-known post-hoc explanation technique for the interpretation of black-box models. While very useful, recent studies show that LIME suffers from stability problems: explanations provided for the same process can be different, making it difficult to trust their reliability. This paper investigates the stability of LIME when explaining multivariate time series classification problems. We demonstrate that due to the temporal dependency in time series data, the traditional artificial neighbour generation methods used in LIME have a higher risk of creating out-of-distribution inputs. We disucss how this behavior is one of the reasons resulting in unstable explanations. In addition, LIME weights neighbours based on user-defined hyperparameters which are problem-dependent and hard to tune, and we show how unsuitable hyperparameters can contribute to the generation of unstable explanations. As a preliminary step towards addressing these issues, we propose to employ a generative approach with an adaptive weighting method in the LIME framework. Specifically, we adopt a generative model based on variational autoencoder to create within-distribution neighbours, reducing the out-of-distribution problem, while the adaptive weight method eliminates the need for user-defined hyperparameters. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in providing more stable explanations using the LIME framework.

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Enhancing Business Intelligence Through A Semi-Distributed Blockchain Framework for Predictive Analytics On Sales Growth 2

Enhancing Business Intelligence Through A Semi-Distributed B...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mullaikodi, C. Jagannathan, Sharath Kumar Jayanthi, N. Singh, Rakhi Tomar, Yesha Jaison, B. J. N. N Institute of Engineering Department of Ece Tamilnadu Tiruvallur India Data Science Institute Saint Peter's University NJ United States Chennai India Sgt University Faculty of Commerce and Management Haryana Gurugrm India School of Management Doon University Uttarakhand Dehradun India Rmk Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371055

The cutting-edge technologies in the ever-evolving sector of data analytics has been accelerated by the requirement for efficient and secure techniques of acquiring business intelligence. This approach provides a solution to the existing issues. In order to address the issues of data integrity, security, and transparency that are associated with the existing methodologies, there is a need for a paradigm shift that will lead to a solution that is more resilient. The traditional centralised data analytics solutions have their limitations when it comes to guaranteeing the accuracy and dependability of forecasts, and this is the most important aspect of the situation. A semi-distributed blockchain paradigm is proposed as a solution to the problems that are associated with centralised systems. This paradigm spreads the computing burden while maintaining a certain degree of decentralisation in order to enhance security. The approach that is currently being considered is the construction and deployment of a blockchain network that is semi-distributed and contains data analytics techniques. Smart contracts simplify the processes of data transmission and validation among participants, hence ensuring transparency and confidence in the predictive analytics model. These findings reveal an improvement in the system efficiency, data quality, and accuracy of forecasting. © 2024 IEEE.

关键词： Business Intelligence Predictive Analytics Sales Growth Semi-Distributed Blockchain Smart Contracts

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics 38

Probabilistic Decomposed Linear Dynamical Systems for Robust...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024 Neural information processing systems foundation. All rights reserved.

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