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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Li Yongkang Jiang Guanfeng Liu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Computing Department Macquarie University Sydney Australia

A blockchain can be taken as a decentralized and distributed public database. In order to achieve data consistency of the system nodes, the execution of a consensus algorithm is necessary and required in the case of decentralized environments. Simply speaking, the consensus is that every node agrees on some record in the blockchain. There are many kinds of consensus algorithms in blockchain environments, and each consensus algorithm has its own proper application scenario. Here we firstly analysis and compare various popular consensus algorithms in blockchain environments, and then as voting theory has systematically studied the decision-making in a group, the traditional methods of voting theory is summarized and listed, including (Position) scoring rules, Copeland, Maximin, Ranked pairs, Voting trees, Bucklin, Plurality with runoff, Single transferable vote, Baldwin rule, and Nanson rule. Finally, we introduce the voting methods from voting theory to consensus algorithms in the blockchain to improve its performance.

关键词： Blockchain Consensus algorithm Bitcoin Voting knowledge engineering Computer science Decision making

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Semantics and computation of the evolution of hybrid systems with ariadne 14

Semantics and computation of the evolution of hybrid systems...

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14th International Conference on Computability and Complexity in Analysis, CCA 2017

作者： Collins, Pieter Bresolin, Davide Gonzalez, Sanja Živanovic Geretti, Luca Villa, Tiziano Department of Data Science and Knowledge Engineering Maastricht University Bouillonstraat 8-10 Postbus 616 6200MD Netherlands Dipartimento di Informatica Università di Bologna Mura Anteo Zamboni 7 Bologna40126 Italy Department of Mathematics and Computer Science Barry University 11300 NE 2nd Avenue Miami ShoresFL33161-6695 United States Dipartimento di Informatica Università Degli Studi di Verona Strada le Grazie 15 Verona37134 Italy

In this talk we will present material on the semantics, computability, and algorithms for the evolution of hybrid dynamical systems, and an overview of the tool Ariadne for verification of hybrid systems [1]. Hybrid systems are characterised by undergoing continuous evolution interspersed by discrete jumps. They exhibit all the complexities of finite automata, nonlinear dynamic systems and differential equations, and are extremely difficult to analyze. We will consider hybrid systems in which the continuous dynamics is given by a differential equation x = f(x), with discrete jumps x' = ri(x) which occur as soon as a guard condition gi(x) ≥ 0 is activated. It is clear that the evolution of a hybrid system undergoes discontinuities, but since only continuous functions are computable, it is not clear to what extent, if any, it is possible to perform a rigorous analysis of a hybrid system. We will first show that we can define lower and upper semantics of evolution under which it is possible to compute reachable sets, and that away from discontinuity points (such as grazing or corner collision points), these semantics agree [2]. In order to perform reachability analysis, it is necessary to define the evolution over bounded initial sets of states. We show that this can be done using the operations of range, compose, flow and solve operations on functions. We will see that constrained image sets of the form {f(x) | x ∈ D | g(x) ∈ C}, are sufficient to express the evolution exactly, except for the case of degenerate (non-transverse) cross- ings [3]. The flow operation is the most computationally demanding, and we will give some details of the implementation and efficiency considerations [4]. We will give examples of reachability analysis in Ariadne, including electrical power converters and heating systems. Finally, we will outline some areas of active research, including differential inclusions [5] and modular reasoning. © 2017 14th International Conference on Computabili

关键词： Hybrid systems

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Unifying Large Language Models and knowledge Graphs: A Roadmap

arXiv

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arXiv 2023年

作者： Pan, Shirui Luo, Linhao Wang, Yufei Chen, Chen Wang, Jiapu Wu, Xindong Griffith University QLD Australia The Department of Data Science and AI Monash University Melbourne Australia The Nanyang Technological University Singapore The Faculty of Information Technology Beijing University of Technology Beijing China The Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei China The Research Center for Knowledge Engineering Zhejiang Lab Hangzhou China

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolve by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs;2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering;and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions. Copyright © 2023, The Authors. All rights reserved.

关键词： knowledge graph

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An improved elastic net with heuristic cloning strategy for traveling salesman problem

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ICIC Express Letters 2011年第11期5卷 3995-4000页

作者： Yang, Gang Yang, Nan Yi, Junyan Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China No. 59 Zhongguancun Street Haidian District Beijing 100872 China Department of Computer Science and Technology Zhejiang University of Technology No. 18 Chaowang Road Hangzhou 310014 China

Based on analysis of relationships between solution qualities and number of initial dynamic points in elastic net algorithm, we propose an improved elastic network algorithm (IENA) introduced in a heuristic cloning strategy for traveling salesman problem (TSP). The improved elastic net consists of four evolution processes with less dynamic points and gradually reducing cities, and it accelerates the convergent process without compromising the equality of solutions. IENA is conducted on some random TSP instances to verify its performance by comparison with other algorithms. © 2011 ICIC International.

关键词： Traveling salesman problem

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Higgs Boson Discovery using Machine Learning Methods with Pyspark

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Procedia Computer science 2020年 170卷 1141-1146页

作者： Mourad Azhari Abdallah Abarda Badia Ettaki Jamal Zerouaoui Mohamed Dakkon Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco Département de Statistique et Informatique de Gestion Université Abdelmalek Essaadi Tetouan Morocco

Higgs Boson is an elementary particle that gives the mass to everything in the natural world. The discovery of the Higgs Boson is a major challenge for particle physics. This paper proposes to solve the Higgs Boson Classification Problem with four Machine Learning (ML) Methods, using the Pyspark environment: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosted Tree (GBT). We compare the accuracy and AUC metrics of those ML Methods. We use a large dataset as Higgs Boson, collected from public site UCI and Higgs dataset downloaded from Kaggle site, in the experimentation stage.

关键词： Boson Higgs Spark Pyspark Machine Learning (ML) Logistic Regression (LR) Decision Tree (DT) Random Forest (RF) Gradient Boosted Tree (GBT) AUC Accuracy

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Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

arXiv

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arXiv 2023年

作者： Liu, Yixuan Zhao, Suyun Xiong, Li Liu, Yuhan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Engineering Research Center Ministry of Education on Database and BI China Information School Renmin University of China China Department of Computer Science Emory University United States

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users’ attitudes need to be satisfied locally, while a strict privacy guarantee for the global model is also required centrally. Personalized Local Differential Privacy (PLDP) is suitable for preserving users’ varying local privacy, yet only provides a central privacy guarantee equivalent to the worst-case local privacy level. Thus, achieving strong central privacy as well as personalized local privacy with a utility-promising model is a challenging problem. In this work, a general framework (APES) is built up to strengthen model privacy under personalized local privacy by leveraging the privacy amplification effect of the shuffle model. To tighten the privacy bound, we quantify the heterogeneous contributions to the central privacy user by user. The contributions are characterized by the ability of generating "echos" from the perturbation of each user, which is carefully measured by proposed methods Neighbor Divergence and Clip-Laplace Mechanism. Furthermore, we propose a refined framework (S-APES) with the post-sparsification technique to reduce privacy loss in high-dimension scenarios. To the best of our knowledge, the impact of shuffling on personalized local privacy is considered for the first time. We provide a strong privacy amplification effect, and the bound is tighter than the baseline result based on existing methods for uniform local privacy. Experiments demonstrate that our frameworks ensure comparable or higher accuracy for the global model. © 2023, CC BY.

关键词： Learning systems

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A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking

A Continuous Information Gain Measure to Find the Most Discr...

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Congress on Evolutionary Computation

作者： Matthew Stephenson Damien Anderson Ahmed Khalifa John Levine Jochen Renz Julian Togelius Christoph Salge Department of Data Science and Knowledge Engineering Maastricht University Maastricht the Netherlands Computer and Information Sciences Department University of Strathclyde Glasgow UK Game Innovation Lab Tandon School of Engineering New York University New York USA Research School of Computer Science Australian National University Canberra Australia

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways.

关键词： Games Correlation Artificial intelligence Testing Task analysis Noise measurement Technological innovation

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SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Song, Zhenbo Liu, Hongyan Lu, Zhiwu He, Jun Du, Xiaoyong Key Laboratory of Data Engineering Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China

Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they always skip long-range contextual properties. Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. On top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed respectively to learn both short-range local features and long-range contextual features. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art performance in terms of both recognition accuracy and running speed with a super-light model size (0.9M parameters). Meanwhile, for the purpose of further boosting efficiency, we introduce two simplified versions, which also achieve state-of-the-art performance and further reduce the model size to 0.8M and 0.4M respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Global Top-k Aggregate Queries Based on X-tuple in Uncertain database

Global Top-k Aggregate Queries Based on X-tuple in Uncertain...

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International Conference on Advanced Information Networking and Applications Workshops (AINAW)

作者： Dexi Liu Changxuan Wan Naixue Xiong Jong Hyuk Park Sang-Soo Yeoe School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China Department of Computer Science Georgia State University Atlanta GA USA Department of Computer Science and Engineering Seoul National University of Technology Seoul South Korea Division of Computer Engineering Mokwon University Daejeon South Korea

ISBN: (纸本)9781424467013;9780769540191

A Top-k aggregate query, which is a powerful technique when dealing with large quantity of data, ranks groups of tuples by their aggregate values and returns k groups with the highest aggregate values. However, compared to Top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. As a powerful semantic of Top-k in uncertain database, Global Top-k return k highest-ranked tuples according to their probabilities of being in the Top-k answers in possible worlds. We propose a x-tuple based method to process Global Top-k aggregate queries in uncertain database. Our method has two levels, group state generation and G-x-Top-k query processing. In the former level, group states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming based Global x-tuple Top-k query processing are employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions.

关键词： Aggregates databases knowledge engineering Query processing Computer networks Power engineering computing data engineering Power engineering and energy Dynamic programming Radar imaging

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