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Sequence-to-Sequence Multi-Hop knowledge Reasoning based on Retentive Network 7

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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7th International conference on Computer Information Science and Application Technology, CISAT 2024

作者： Su, Ruilin Zhang, Wanshan Yu, Dunhui Hubei University Wuhan China Wuhan430062 China Hubei University School of Computer Science and Information Engineering Wuhan430062 China Hubei University Hubei Key Laboratory of Big Data Intelligent Analysis and Application Wuhan430062 China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan430062 China

ISBN: (纸本)9798350375107

Multi-hop knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model's memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model's own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing. © 2024 IEEE.

关键词： Iterative decoding

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Locating Framework-specific Crashing Faults with Compact and Explainable Candidate Set 23

Locating Framework-specific Crashing Faults with Compact and...

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45th IEEE/ACM International conference on software engineering (ICSE)

作者： Yan, Jiwei Wang, Miaomiao Liu, Yepang Yan, Jun Zhang, Long Chinese Acad Sci Inst Software Technol Ctr Software Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

ISBN: (纸本)9781665457019

Nowadays, many applications do not exist independently but rely on various frameworks or libraries. The frequent evolution and the complex implementation of framework APIs induce lots of unexpected post-release crashes. Starting from the crash stack traces, existing approaches either perform application-level call graph (CG) tracing or construct datasets with similar crash-fixing records to locate buggy methods. However, these approaches are limited by the completeness of CG or dependent on historical fixing records, and some of them only focus on specific manually modeled exception types. To achieve effective debugging on complex framework-specific crashes, we propose a code-separation-based locating approach that weakly relies on CG tracing and does not require any prior knowledge. Our key insight is that one crash trace with the description message can be mapped to a definite exceptionthrown point in the framework, the semantics analysis of which can help to figure out the root causes of the crash-triggering procedure. Thus, we can pre-construct reusable summaries for all the framework-specific exceptions to support fault localization in application code. based on that idea, we design the exceptionthrown summary (ETS) that describes both the key variables and key APIs related to the exception triggering. Then, we perform static analysis to automatically compute such summaries and make a data-tracking of key variables and APIs in the application code to get the ranked buggy candidates. In the scenario of locating Android framework-specific crashing faults, our tool CrashTracker exhibited an overall MRR value of 0.91 and outperforms the state-of-the-art tool Anchor with higher precision. It only provides a compact candidate set and gives user-friendly reports with explainable reasons for each candidate.

关键词： Fault Localization Framework-specific Exception Crash Stack Trace Android Application

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Proceedings of the 17th European conference on software Architecture, ECSA 2023

Proceedings of the 17th European Conference on Software Arch...

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Proceedings of the 17th European conference on software Architecture, ECSA 2023

ISBN: (纸本)9783031663253

The proceedings contain 25 papers. The special focus in this conference is on software Architecture. The topics include: Architecting Explainable Service Robots;analysing Interoperability in Digital Twin software Architectures for Manufacturing;architecting Artificial Intelligence for Autonomous Cars: The OpenPilot Framework;designing and Evaluating Interoperable Industry 4.0 Middleware software Architecture: Reconfiguration of Robotic System;shaping IoT Systems Together: The User-System Mixed-Initiative Paradigm and Its Challenges;analyzing the Evolution of Inter-package Dependencies in Operating Systems: A Case Study of Ubuntu;a Graph-based Java Projects Representation for Antipatterns Detection;scalable Cloud-Native Pipeline for Efficient 3D Model Reconstruction from Monocular Smartphone Images;automated Reverse engineering of the Technology-Induced software System Structure;tool Support for the Adaptation of Quality of Service Trade-Offs in Service- and Cloud-based Dynamic Routing Architectures;Cloud-Native Architecture for Mixed File-based and API-based Digital Twin Exchange;what Rationales Drive Architectural Decisions? An Empirical Inquiry;learning from Each Other: How Are Architectural Mistakes Communicated in Industry?;a Query Language for software Architecture Information;extracting the Architecture of Microservices: An Approach for Explainability and Traceability;a Comparison of Static and Dynamic Analysis to Identify Microservices in Monolith Systems;architecture-based Attack Path Analysis for Identifying Potential Security Incidents;carving Sustainability into Architecture knowledge Practice;guidance Models for Designing Big Data Cyber Security Analytics Systems;automated Integration of Heteregeneous Architecture Information into a Unified Model;an Experience Report on the Design and Implementation of an Ad-hoc Blockchain Platform for Tactical Edge Applications;Designing a Reference Architecture for the C-ITS Services;towards Assessing Spread in Sets of

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The Role of Responsiveness to Change in Large Onboarding Campaigns 24th

The Role of Responsiveness to Change in Large Onboarding Cam...

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24th International conference on Agile software Development (XP)

作者： Smite, Darja Moe, Nils Brede Blekinge Inst Technol Karlskrona Sweden SINTEF Digital Trondheim Norway

ISBN: (纸本)9783031339752;9783031339769

Onboarding is a process of organizational socialization of the new hires, that includes recruitment, orientation, training, coaching and support. While onboarding individuals into an organization is a rather straightforward task, little is known about 1) onboarding hundreds of developers and 2) doing it on a distance in outsourcing situations. Furthermore, the subject of sustainable growth with respect to organizational capabilities and culture is often overlooked. This paper reports findings from an exploratory multi-case study of two large onboarding campaigns. We collected empirical data from interviews, retrospectives, onboarding documentation and onsite visits. based on the empirical study, onboarding hundreds of software engineers in a complex agile product development environment which lacks documentation and puts high demands on engineers' knowledge and skills is a challenging and costly endeavor. To save the costs and for practical reasons, large-scale onboarding is organized in batches with the first batch trained onsite, and the later batches trained internally. We report challenges faced in the two cases and discuss possible solutions. One core finding is that a good plan combined with the organizational agility, i.e., the responsiveness to change, together with organizational maturity determined the success of organizational scaling. The presented cases contribute to the scarce research on knowledge transfer and onboarding in a large-scale agile context.

关键词： Onboarding Scaling Training Teams Large-Scale Agile software engineering Empirical Case study Sustainable Organizational Growth

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A Comprahensive Analysis of knowledge Transfer Techniques for Medical Image Segmentation with Deep Learning 5th

A Comprahensive Analysis of Knowledge Transfer Techniques fo...

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5th International conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Vishvendra Jain, Garima Nidhya, M.S. Chaudhary, Chetan Department of Agriculture Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Noida India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper aims to comprehensively examine diverse knowledge switch techniques used in scientific photograph segmentation with deep mastery. Specifically, we know the latest advances in transfer, gaining knowledge of model adaptation, first-rate-tuning, and transfer mastering techniques. For each technique, we discuss the motivations, techniques, and demanding situations concerned with their software for medical picture segmentation. Similarly, we discuss a variety of open issues and destiny studies guidelines. Furthermore, we overview present applications of these methods in clinical photograph segmentation. Sooner or later, we provide a complete contrast of these processes based on the evaluation metrics hired in the literature. This paper aims to shed light on the modern state of understanding switch techniques in medical picture segmentation and to provide a roadmap for future research on this unexpectedly evolving field.. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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SDN Multi-Domain Routing for knowledge-Defined Networking 15

SDN Multi-Domain Routing for Knowledge-Defined Networking

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15th International conference on Communication software and Networks, ICCSN 2023

作者： Zhang, Yuhan Li, Jie Yu, Yinrui Fan, Zhiming Ma, Hongjun Wang, Xingwei Northeastern University Computer Science and Engineering Shenyang China

ISBN: (纸本)9798350329360

With the rapid rise of smart devices, such as smartphones and smart home devices, intelligent routing through software Defined Network(SDN) has become a research focus. Recently, the concept of knowledge-Defined Networking (KDN) has been proposed. In this paper, we propose knowledge-Defined Networking oriented SDN multi-domain routing, and a DDQN routing algorithm based on graph neural network at the knowledge layer. The communication of SDN multi-domain architecture for knowledge-Defined Networking and intelligent routing decision are realized. The network performance such as delay jitter, packet loss rate and throughput is improved compared to existing approaches in simulation. © 2023 IEEE.

关键词： Reinforcement learning

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PERFORMANCE EVALUATION OF BRAIN TUMOR SEGMENTATION based ON U-NET HYBRID MODEL 2

PERFORMANCE EVALUATION OF BRAIN TUMOR SEGMENTATION BASED ON ...

引用

2nd International conference on Power, Communication, Computing and Networking Technologies, PCCNT 2024

作者： Zhang, Yun Zhou, Ziqi Yang, Yuguang College of Artificial Intelligence Chengdu University of Information Technology Chengdu China Department of Biomedical Engineering Case School of Engineering Case Western Reserve University ClevelandOH United States School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9781837242672

Medical image segmentation of brain tumors is a crucial aspect of medical image analysis with direct implications for diagnosis and treatment decisions. In this study, we systematically evaluated the performance of U-Net and its variants (Dense U-Net, DeepResU-Net) on the BraTS18 dataset. Experiments show that the DeepResU-Net model optimally segments healthy tissue, tumor cores, and enhanced tumors. This study provides an essential theoretical basis for automated segmentation of brain tumors, highlighting the importance of choosing an appropriate model architecture to improve segmentation performance. © The Institution of engineering & Technology 2024.

关键词： Image segmentation

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An Online Portfolio Selection Algorithm with Dynamic Coreset Construction 1

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17th International conference on knowledge Science, engineering and Management (KSEM)

作者： Peng, Jing Chao, Kaiyin Chen, Geying Yin, Jianfei Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (数字)9789819754892

ISBN: (纸本)9789819754885;9789819754892

Online portfolio selection in financial markets presents a significant challenge due to the vast number of tradable assets and the prevalence of noise signals. Existing methods often struggle to dynamically select asset subsets in order to optimize capital efficiency. To address these challenges, we propose the Portfolio Selection with Dynamic Coreset Construction (PSCS) algorithm. PSCS incorporates a novel clustering process that groups assets based on their cumulative returns, creating a unique feature space to facilitate effective clustering. From each cluster, a small set of assets is carefully chosen to form a coreset using a peak-to-valley feature that captures the price change ratio within recent time windows. Leveraging this coreset, a peak-based optimization problem is formulated and solved through closed-form solutions. Experimental results demonstrate the superiority of PSCS over seven existing algorithms, highlighting its promising contribution to the field of online portfolio selection in financial markets.

关键词： Online portfolio selection Dynamic coreset construction Clustering

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Abstraction in Assumption-based Argumentation 21

Abstraction in Assumption-based Argumentation

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21st International conference on Principles of knowledge Representation and Reasoning, KR 2024

作者： Apostolakis, Iosif Saribatur, Zeynep G. Wallner, Johannes P. Institute of Software Technology TU Graz Austria Institute of Logic and Computation TU Wien Austria

ISBN: (纸本)9781956792058

Approaches to computational argumentation provide foundational ways to reason argumentatively within Artificial Intelligence (AI). The underlying formal approaches can oftentimes be classified into structured argumentation and abstract argumentation. The former prescribe rigorous workflows, starting from knowledge bases to finding arguments in favour and against claims under scrutiny, and drawing conclusions. Abstract argumentation provides formal semantics operating on arguments whose internal structure is hidden and only relations are kept for reasoning, resulting in so-called argumentation frameworks (AFs). In this work, we apply a form of existential abstraction on the prominent structured approach of assumption-based argumentation (ABA), leading to an interactive way of simplifying argumentation scenarios by abstracting irrelevant details, towards supporting explainability. Existential abstraction was shown to be promising in many areas of AI, including a recent work on AFs. We lift this approach to the structured level—which is, as we show, both not direct from AFs and can benefit from utilization of the internal structure of arguments. Among our contributions, we introduce existential abstraction on ABA via clustering assumptions, develop semantics on clustered ABA frameworks for reasoning on such clusterings, show differences to the level of AFs, and provide a prototype interactive tool that obtains faithful clusterings that do not lead to any spurious reasoning. © 2024 Proceedings of the International conference on knowledge Representation and Reasoning. All rights reserved.

关键词： knowledge representation

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Cop: Continuously Pairing of Heterogeneous Wearable Devices based on Heartbeat 1

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17th International conference on knowledge Science, engineering and Management (KSEM)

作者： He, Wenfeng Lin, Jiawei Zou, Yongpan Cao, Weipeng Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen 518107 Peoples R China

ISBN: (数字)9789819754984

ISBN: (纸本)9789819754977;9789819754984

With the development of technology, the number of wearable devices has exploded. Many users wear multiple wearable devices at the same time. These devices require pairing operations due to requirements for sharing resources, identity authentication, etc. But traditional device pairing solutions have their own shortcomings. For example, schemes based on environmental information are too dependent on the external environment. In order to solve these problems, we propose Cop in this paper, a novel device-pairing scheme based on user heartbeat, which realizes the pairing of different types of devices and devices under different motion states, and build a hardware platform verification system to verify various performances in real scenarios. At the same time, it is compared with IPI-based pairing algorithm. Experimental results show that our pairing scheme possesses advantages over IPI method in the core metrics of bit rate, coding efficiency, false acceptance rate and false rejection rate.

关键词： Heartbeat Signal Device pairing Heterogeneous Wearable Devices

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