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Adaptive Meta-Path Selection Based Heterogeneous Spatial Enhancement for circRNA-Disease Associations Prediction

IEEE Journal of Biomedical and Health Informatics 2024年第5期29卷 3792-3804页

作者： Zhihao Ma Guitao Cao Wenming Cao Shanghai Key Laboratory of Trustworthy Computing and MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China College of Information Engineering Shenzhen University Shenzhen China

Circular RNAs (circRNAs) play a crucial role in human biological processes as miRNA sponges, regulating gene expression and affecting disease manifestations. Establishing heterogeneous nodal feature relationships through meta-paths can effectively enhance the predictive capability of models. Previous research primarily constructed associations between meta-paths manually, and excessive noise made it difficult to capture highly correlated hidden features. Equally important is learning more about feature distributions, which is key to improving the generalization ability of algorithms. To address these challenges, we propose an adaptive meta-path selection method named AdaMH. The core scheme introduces an adaptive path selection method that automatically identifies highly relevant heterogeneous meta-paths during iterative training rounds. Considering the sparsity of data distribution, we introduce controlled random noise into the data through graph contrastive learning to ensure an even distribution of features. Subsequently, a multi-head attention mechanism is utilized to capture relationships in the high-dimensional heterogeneous feature space, enhancing feature representation capability. Comparing with state-of-the-art (SOTA) algorithms, AdaMH is the only one that surpasses a performance threshold of 0.95 across seven evaluation metrics.

关键词： Diseases Semantics Noise Feature extraction Contrastive learning Bioinformatics Adaptation models RNA Prediction algorithms Mathematical models

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t-PSI: Efficient Multi-party Private Set Intersection with Threshold 19

t-PSI: Efficient Multi-party Private Set Intersection with T...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Meng, Dan Fu, Zhihui Kong, Chao Qi, Yue Cao, Guitao Oppo Research Institute Shenzhen China Anhui Polytechnic University School of Computer and Information Wuhu China East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai China MoE Engineering Research Center of SH/HW Co-design Technology and Application Ecnu Shanghai China

ISBN: (纸本)9798350346558

Multi-party privacy set intersection (MPSI) enables multiple parties to compute the intersection of their datasets without leaking data privacy. Among existing MPSI protocols, e.g., KMPRT-based protocols, oblivious evaluation of programmable pseudo-random function (OPPRF) or oblivious pseudo-random function (OPRF) is typically used to generate pseudo random numbers, requiring frequent online interactions. To mitigate such communication overheads, we propose the threshold Privacy Set Intersection (t-PSI) protocol for multi-party, leveraging the Shamir Secret Sharing (SSS) protocol and the masking construction mechanism. We propose two types of masking construction mechanisms: Polynomial and Garbled Bloom-filter. To the best of our knowledge, this is the first Secret Sharing-based MPSI protocol. With the significant reduction of inter-party communication, the t-PSI protocol is highly scalable and can handle datasets ranging from 26 to 220. Further, with built-in fault tolerance, t-PSI works even if some parties went offline during protocol execution. As shown in the experiments, for five parties with datasets of 220 items each, the proposed t-PSI takes only 63 seconds, outperforming the state-of-the-art in [1]. © 2022 IEEE.

关键词： Fault tolerance

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When Adversarial Example Attacks Meet Vertical Federated Learning 19

When Adversarial Example Attacks Meet Vertical Federated Lea...

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ISBN: (纸本)9798350346558

Federated learning (FL) is proposed to enable efficient machine learning while protecting the privacy of user data. In non-federated scenarios, machine learning models are vulnerable to adversarial attacks. The attacker can construct elaborate adversarial examples, which can greatly reduce the performance of the victim model. It is worth exploring how FL models differ from traditional models in face of complex adversarial attacks. We are also curious about the robustness of different FL modeling methods against adversarial attacks, providing suggestions for selecting suitable FL models in practical applications. Consequently, we perform adversarial example attacks on vertical federated learning and design experiments to study how vertical federated models perform under adversarial example attacks. The simulation results show that federated models are more robust than non-federated models. Besides, in vertical federation learning, decision trees and XGBoost are much more robust than logistic regression and neural networks. © 2022 IEEE.

关键词： Decision trees

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Multi-Sentence Matching via Exploiting List-level Semantics Expansion

Multi-Sentence Matching via Exploiting List-level Semantics ...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Ruijun Sun Zhi Li Qi Liu Zhefeng Wang Xinyu Duan Baoxing Huai Nicholas Jing Yuan Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Shenzhen International Graduate School Tsinghua University Huawei Cloud

Sentence semantic matching plays a fundamental role in natural language understanding and is widely used in various tasks. Previous methods are mainly based on the One-to-One single pair matching process, while in many real application scenarios, such as user query matching in FAQ, we need to match multiple sentences with the specified query sentence. The reality is that the multi-sentence matching problem is largely unexplored. Although to some extent, we can use multiple One-to-One pair matching on the multi-sentence matching problem, the exploitation of list-level candidate semantics is still limited, which leads to inferior performance. To that end, in this paper, we present a focused study on the novel multi-sentence matching problem. To begin with, we propose a Multi-Sentence Matching Network (MSMN) with a meticulously designed Fusion Module and Contrastive Matching Module to deeply exploit the list-level candidate semantics expansion for the multi-sentence matching task. Then, we construct two multi-sentence matching datasets, i.e., mQuora and mReceipt, to bridge the gap of benchmark data in this area. Finally, we conduct extensive experiments on two datasets. The experimental results clearly demonstrate the effectiveness of our method.

关键词： Learning systems Semantics Benchmark testing Natural language processing Task analysis

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Efficient cube computing on an extended multidimensional model over uncertain data

Efficient cube computing on an extended multidimensional mod...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chunyang Wei Hongyan Li Kai Lei Tengjiao Wang Key Laboratory of High Confidence Software Technologies Ministry of Education School of Electronics Engineering and Computer Science Peking University Beijing China The Shenzhen Key Laboratory for Cloud Computing Technology and Applications (SPCCTA) Peking University Shenzhen Graduate School Shenzhen China

Data uncertainty is an inherent property in various applications due to reasons such as measurement errors, incompleteness of data and so on. While On-Line Analytical Processing (OLAP) has been a powerful method for analyzing large data warehouse, OLAP over uncertain data has become a valuable and attractive issue because of the increasingly demand for handling uncertainty in multidimensional data. In this paper, we firstly describe our UStar-Schema model that extends the traditional OLAP model to support uncertain dimension attributes in fact table, uncertain measures in fact table and uncertainty in dimension table. Then we extend the processing model of the aggregate queries and cube computing on Ustar-Schema. Secondly, we design a novel index structure called PSI-Index on UStar-Schema to improve efficiency of OLAP quering and cube computing. Furthermore, an advanced index structure called HB-Index and an efficient algorithm are proposed to accelerate iceberg cube computing based on our model using pruning techniques to eliminate huge amounts of useless computations. Finally, extensive experiments are performed to examine the efficiency and effectiveness of our proposed techniques.

关键词： Indexes Aggregates Uncertainty Computational modeling Sensors Algorithm design and analysis

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Holistic twig joins based on Sketch Tree

Holistic twig joins based on Sketch Tree

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： JunWen Ji Yue Wang Kai Lei Tengjiao Wang Key Laboratory of High Confidence Software Technologies Ministry of Education School of Electronics Engineering and Computer Science Peking University Beijing China The Shenzhen Key Laboratory for Cloud Computing Technology and Applications (SPCCTA) Peking University Shenzhen Graduate School Shenzhen China

Due to the wide spread of XML and its importance, twig query matching, which has been identified as a core operation in querying XML data, has become a research hotspot. Although a number of algorithms have been proposed recently to process a twig query holistically, existing algorithms can be further improved with the help of the DTD file. In this paper, we (1) proposed the Sketch Tree structure according to the DTD scheme and employed it to translate the queries; (2) proposed a new labeling scheme named Sketch Dewey on the basis of the Sketch Tree to encode not only the elements in XML data but also the query; (3) designed an efficient path matching algorithm called PathCheck and a novel holistic twig join algorithm called TJumperJoin utilizing the Sketch Dewey code. To answer a query, they only need to scan the elements of leaf nodes in the query and benefitting from the Sketch Dewey labeling scheme, they can take advantage of bitwise operation to make acceleration. At last, extensive experiments are performed to examine the efficiency and effectiveness of our proposed techniques.

关键词： XML Labeling Algorithm design and analysis Pattern matching Sections Databases Finite element methods

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Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion

arXiv

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arXiv 2024年

作者： Gao, Jinglong Lu, Chen Ding, Xiao Li, Zhongyang Liu, Ting Qin, Bing Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China National Key Laboratory of Information Systems Engineering Nanjing China Huawei Cloud Shenzhen China

Event Causality Extraction (ECE) aims at extracting causal event pairs from texts. Despite ChatGPT’s recent success, fine-tuning small models remains the best approach for the ECE task. However, existing fine-tuning based ECE methods cannot address all three key challenges in ECE simultaneously: 1) Complex Causality Extraction, where multiple causal-effect pairs occur within a single sentence;2) Subtask Interaction, which involves modeling the mutual dependence between the two subtasks of ECE, i.e., extracting events and identifying the causal relationship between extracted events;and 3) Knowledge Fusion, which requires effectively fusing the knowledge in two modalities, i.e., the expressive pretrained language models and the structured knowledge graphs. In this paper, we propose a unified ECE framework (UniCE) to address all three issues in ECE simultaneously. Specifically, we design a subtask interaction mechanism to enable mutual interaction between the two ECE subtasks. Besides, we design a knowledge fusion mechanism to fuse knowledge in the two modalities. Furthermore, we employ separate decoders for each subtask to facilitate complex causality extraction. Experiments on three benchmark datasets demonstrate that our method achieves state-of-the-art performance and outperforms ChatGPT with a margin of at least 30% F1-score. More importantly, our model can also be used to effectively improve the ECE performance of ChatGPT via in-context learning. © 2024, CC BY.

关键词： Knowledge graph

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Electrical Transport Properties of Few-Layer SnS2 Field-effect Transistors

Electrical Transport Properties of Few-Layer SnS2 Field-effe...

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2022 International Conference on Electrical, Electronics and Information Engineering, EEIE 2022

作者： Zhang, Lu Li, Tuo Yao, Peng Zou, Xiaofeng Ji, Chao Zhan, Yongzheng He, Jiaqi Zhang, Qing State Key Laboratory of High-end Server & Storage Technology Jinan250013 China Inspur Electronic Information Industry Co. Ltd. Jinan250013 China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. Jinan250013 China Beijing Institute of Computer Technology and Application Beijing100854 China College of Mathematics and Physics Beijing University of Chemical Technology Beijing100029 China

After the discovery of graphene in 2004, two dimensional (2D) materials have fascinated a lot of view due to the excellent properties. Nowadays, the research on 2D materials has spread to other graphene-like layer structured materials, especially transition metal dichalcogenides (TMDCs). Tin disulfide (SnS2) is a kind of TMDCs with a sizable bandgap. Here we introduce few-layer SnS2 field-effect transistors (FETs) fabricated using micromechanical exfoliation method. The FETs show n-type behavior, the on/off ratio exceeding 0.54×104 and the carrier mobility is 0.61 cm2V-1s-1. The electronic and optical characteristics of SnS2 flakes with a finite bandgap illustrate their potential applications in optoelectronics device. © Published under licence by IOP Publishing Ltd.

关键词： Field effect transistors

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EdgeCross: cloud Scale Traffic Management at Peering Edges

Proceedings of the ACM on Networking

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Proceedings of the ACM on Networking 2024年第CoNEXT4期2卷 1-23页

作者： Xiaoliang Wang Penghui Mi Yong Zhu Baoyi An Yinhua Wang Lixiang Wang Xuezhi Yu Qiong Xie Xiang Huang Mingliang Yin Chaoyang Ji Wei Sun Yihang Lv Yuhang Chen Cam-Tu Nguyen Chen Tian Xiaoming Fu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Shenzhen China Institute of Computer Science University of Göttingen Göttingen Germany

cloud providers deployed dozens of PoPs and data centers globally to serve billions of geo-distributed users. The traffic management at peering edges has become a key capability of cloud network operators to meet the diverse demands of users. With the rapid growth of cloud applications, users have recently announced new performance requirements, e.g., achieving latency as low as possible instead of maintaining a specified delay. The conventional inter-domain bandwidth allocation approach, which aims to reduce the high operating expenditures of bandwidth usage, fails to meet these new requirements. We further reveal that the flow scheduling among PoPs may fail due to the limited link capacity hidden by the cloud private backbone network controller. Therefore we seek a new traffic management at peering *** propose a new controller framework, EdgeCross, that satisfies not only users' emerging demands but maintains low operating costs. The large number of fine-grain application-aware flows and the consideration of backbone links' capacity lead to very high complexity of routing computation and verification for the controller. EdgeCross introduces a two-phase operation that first achieves the low-expense bandwidth allocation according to the standard 95th percentile billing model and then allocates specified flows to peering edges based on users' requirements. EdgeCross further reduces large memory consumption by proposing an effective routing table compression approach. The evaluation based on a production network with 16 PoPs has shown that EdgeCross can successfully process the routes of 1 billion flows in 10 seconds, reduce the average delay for performance-sensitive flows by 2 milliseconds compared to traditional BGP, and is able to save the bandwidth cost by 10-26% compared to the state-of-the-art Cascara.

关键词： cloud network flow scheduling peering edges sdn controller

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Synchronous network coding algorithm based on Galois field

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Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and technology 2012年第SUPPL.1期36卷 44-51页

作者： Li, Jiang Chen, Jia Li, Yi-Ning Li, Hui Shenzhen Key Lab of Cloud Computing Technology and Application Shenzhen Graduate School Peking University Shenzhen 518055 China Shenzhen Media Group Shenzhen 518055 China

A network coding(NC) algorithm based on hardware logic is presented. At first, a synchronous mechanism to solve the challenge of transmission-rate mismatch in the real network is proposed. Then a novel method to accelerate the dot product process over Galois field(GF) is designed. The whole work aims at achieving the wire speed of the hardware logic. Both encoding and decoding operations can be completed in several hundred nanoseconds. The test results show that the NC multicast communication using this algorithm can achieve the limit flow of the max-flow min-cut theorem, and the throughput is improved by using NC.

关键词： Network coding

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