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ShellFusion: Answer Generation for Shell Program ming Tasks via knowledge Fusion 22

ShellFusion: Answer Generation for Shell Program ming Tasks ...

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ACM/IEEE 44th International conference on software engineering (ICSE)

作者： Zhang, Neng Liu, Chao Xia, Xin Treude, Christoph Zou, Ying Lo, David Zheng, Zibin Sun Yat Sen Univ Sch Software Engn Guangzhou Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Huawei Software Engn Applicat Technol Lab Shenzhen Peoples R China Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia Queens Univ Dept Elect & Comp Engn Kingston ON Canada Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781450392211

Shell commands are widely used for accomplishing tasks, such as network management and file manipulation, in Unix and Linux platforms. There are a large number of shell commands available. For example, 50,000+ commands are documented in the Ubuntu Manual Pages (MPs). Qyite often, programmers feel frustrated when searching and orchestrating appropriate shell commands to accomplish specific tasks. To address the challenge, the shell programming community calls for easy-to-use tutorials for shell commands. However, existing tutorials (e.g., TLDR) only cover a limited number of frequently used commands for shell beginners and provide limited support for users to search for commands by a task. We propose an approach, i.e., ShellFusion, to automatically generate comprehensive answers (including relevant shell commands, scripts, and explanations) for shell programming tasks. Our approach integrates knowledge mined from Q&A posts in Stack Exchange, Ubuntu MPs, and TLDR tutorials. For a query that describes a shell programming task, ShellFusion recommends a list of relevant shell commands. Specifically, ShellFusion retrieves the top-n Q&A posts with questions similar to the query and detects shell commands with options (e. g., ls -t) from the accepted answers of the retrieved posts. Next, ShellFusion filters out irrelevant commands with descriptions in MP and TLDR that share little semantics with the query, and further ranks the candidate commands based on their similarities with the query and the retrieved posts. To help users understand how to achieve the task using a recommended command, ShellFusion generates a comprehensive answer for each command by synthesizing knowledge from Q&A posts, MPs, and TLDR. Our evaluation of 434 shell programming tasks shows that ShellFusion significantly outperforms Magnum (the state-of-the-art natural language-to-Bash command approach) by at least 179.6% in terms of MRR@K and MAP@K. A user study conducted with 20 shell programmers further

关键词： Shell Programming Answer Generation knowledge Fusion

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knowledge Graph Enhanced Multimodal Transformer for Image-Text Retrieval 40

Knowledge Graph Enhanced Multimodal Transformer for Image-Te...

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40th IEEE International conference on Data engineering, ICDE 2024

作者： Zheng, Juncheng Liang, Meiyu Yu, Yang Li, Yawen Xue, Zhe Beijing Key Laboratory of Intelligent Communication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798350317152

Image-text retrieval is a fundamental cross-modal task that aims to align the representation spaces between the image and text modalities. Existing cross-modal image-text retrieval methods independently generate embeddings for images and text, introduce interaction-based networks for cross-modal inference, and then achieve retrieval by using matching metrics. However, they overlook the semantic relationship between the coarse-grained and fine-grained representations within each modality, failing to capture the consistency of representations across different modalities, which affects the semantic learning of cross-modal representations, and makes it difficult to align modalities in semantic space. Consequently, these previous works inevitably suffer from low retrieval accuracy or high computational costs. In this paper, instead of directly fusing two cross-modal het-erogeneous spaces, we propose an multimodal knowledge enhanced multimodal transformer network framework to combine coarse-grained and fine-grained representation learning into a unified framework, capturing alignment information between targets, constructing a global semantic graph, and ultimately align multimodal representations in the semantic space. In our approach, images generate semantic and spatial graphs to represent visual information, while sentences generate text graphs based on semantic relationships between words, and they are used for intra-modal graph network inference. Subsequently, the generated global and local embeddings are fused into an enhanced multimodal transformer framework, effectively imple-menting cross-modal interaction processes by leveraging prior implicit semantic information from the multimodal knowledge graph. Furthermore, compared to simply matching words with image regions, our method proposes a bidirectional fine-grained matching method to filter the salient regions and words of images and texts, remove the interfering noise information, and realize bidirectional fine-

关键词： Embeddings

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knowledge Reuse of CAD Data in Parallel Development of Multiple Wiring Harness Variants 19th

Knowledge Reuse of CAD Data in Parallel Development of Multi...

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19th IFIP WG 5.1 International conference on Product Lifecycle Management (PLM)

作者： Eder, Kevin Tas, Onur Neckenich, Jonas Winter, Roland Zielbauer, Uwe Paetzold, Kristin Mercedes Benz AG HPC X973 D-71059 Sindelfingen Germany Tech Univ Dresden George Bahr Str 3c D-01069 Dresden Germany

ISBN: (纸本)9783031251818;9783031251825

The high dependency of a wiring harness on its surrounding components requires changes to be made almost daily during development. To ensure operability, these adjustments have to be carried out in some cases even after the data freeze of a development phase. Since these changes may also be relevant for subsequent development phases, redundant design efforts are the consequence. One way to increase the efficiency of these processes is to reuse knowledge. This paper reviews the state of the art of knowledge reuse in engineering design and in software development. Furthermore, an overview of the structure of a wiring harness, as well as the knowledge contained in the corresponding CAD data is presented. Thereupon, all scenarios are specified in which knowledge reuse in the wiring harness development could be possible. Subsequently, a methodology for complete, partial and assisted reuse is specified enabling the transfer of gained knowledge into other CADdata sets of wiring harnesses. In addition, the conditions that have to be met for the application of each reuse method are presented. Finally, the requirements needed for the implementation of the shown methodology into a CAD system are specified.

关键词： knowledge reuse knowledge transfer knowledge management knowledge-based engineering Computer aided design (CAD)

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A Study of DM-TPACK Evaluation Methods for Middle School Mathematics Teachers 24

A Study of DM-TPACK Evaluation Methods for Middle School Mat...

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8th International conference on Education and Multimedia Technology, ICEMT 2024

作者： Zhang, En Chen, Ruxian Chen, Sailan Chen, Yuanhua Wang, Ying Yin, Ruiyao Institute of Computing Science and Technology Guangzhou University Guangzhou China Institute of Mathematics and Information Science Guangzhou University Guangzhou China Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9798400717611

The theoretical framework of Technological Pedagogical Content knowledge (TPACK) provides a foundation for the integration of information technology and disciplines. Dynamic mathematical software, as the most widely used mathematics discipline software by mathematics teachers, can effectively improve classroom efficiency and improve students' mathematical thinking. This is exemplified by China's first leading dynamic mathematics discipline teaching tool in the mobile Internet environment: NetPad. In light of the ongoing evolution of information technology, junior high school mathematics teachers must consistently enhance their capacity to leverage dynamic mathematics technology for instructional purposes, in order to align with the evolving demands of teacher development in the contemporary era. Therefore, this paper analyzes the research status of TPACK measurement methods, based on the relevant theoretical studies of TPACK, takes the research of core component TPACK as the entry point, integrates the dynamic mathematical technology, divides the dimensions of junior high school mathematics teachers' Dynamic Mathematics Technological Pedagogical Content knowledge (DM-TPACK) level, refers to the junior high school mathematics teachers' TPACK scale by Yuanmei Duan, constructs a DM-TPACK evaluation scale and uses Think Aloud Method and Delphi Method to revise and improve, forming the final DM-TPACK questionnaire for junior high school mathematics teachers. Finally, the questionnaire was subjected to a reliability test, and the results showed that the evaluation instrument had good internal consistency and reliability. This study provides a basis for measuring the TPACK level of junior high school mathematics teachers' integration of dynamic mathematical techniques, and provides a reference for the development of a TPACK assessment tool for teachers. © 2024 Copyright held by the owner/author(s).

关键词： Teaching

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Situational Development of Low-Code Applications in Manufacturing Companies 22

Situational Development of Low-Code Applications in Manufact...

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ACM/IEEE 25th International conference on Model Driven engineering Languages and Systems (MODELS)

作者： Kirchhoff, Jonas Weidmann, Nils Sauer, Stefan Engels, Gregor Paderborn Univ Dept Comp Sci Paderborn Germany

ISBN: (纸本)9781450394673

Companies show an increasing interest in low-code development platforms to facilitate application development by domain experts without sophisticated software development knowledge. Thus, companies aim for a more efficient development of more effective applications since domain experts as so-called citizen developers are no longer limited by the availability and domain knowledge of trained software developers. Nevertheless, efficiency and effectiveness of application development is traditionally also largely influenced by the use of a suitable software development method. Domain experts are, however, not trained in software development methods. This introduces a risk of domain experts creating unusable applications or exceeding the designated time frame of a project (or both). In this paper, we therefore propose an initial version of a situational software development method which supports domain experts in manufacturing companies during the low-code development of applications. The method can be tailored based on situational factors, considering application requirements, features of the used low-code development platform, and characteristics of the development team. We also present feedback corroborating the usefulness of our method and future extension points based on expert interviews.

关键词： low-code end-user development software development method

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UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style 29th

UMAIR-FPS: User-aware Multi-modal Animation Illustration Rec...

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29th International conference on Database Systems for Advanced Applications (DASFAA)

作者： Kang, Yan Lin, Hao Yang, Mingjian Lee, Shin-Jye Yunnan Univ Natl Pilot Sch Software Kunming 650106 Yunnan Peoples R China Yunnan Key Lab Software Engn Kunming 650106 Yunnan Peoples R China Natl Yang Ming Chiao Tung Univ Inst Management Technol Hsinchu Taiwan

ISBN: (纸本)9789819755547;9789819755554

The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users has become a challenge. However, existing anime recommendation systems (RS) have focused on text features but still need to integrate image features. In addition, most multi-modal (MM) RS research is constrained by tightly coupled datasets, limiting its applicability to illustrations RS. We propose the User-aware Multimodal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps. In the feature extract phase, for image features, we are the first to combine painting style with semantic features to construct a dual-output image encoder for enhancing representation. For text features, we obtain embeddings based on fine-tuning Sentence-Transformers by incorporating domain knowledge that composes a variety of anime text pairs from multilingual mappings, entity relationships, and term explanation perspectives, respectively. In the MM fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism to weight MM features dynamically according to user features at the interaction level and employ the DCN-V2 module to model bounded-degree MM crosses effectively. UMAIR-FPS surpasses the SOTA baselines on large real-world datasets.

关键词： Anime illustration recommendation Painting style features Multi-modal feature extraction Multi-modal feature fusion

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Learning to Score: Tuning Cluster Schedulers through Reinforcement Learning 11

Learning to Score: Tuning Cluster Schedulers through Reinfor...

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11th IEEE International conference on Cloud engineering (IC2E)

作者： Asenov, Martin Deng, Qiwen Yeung, Gingfung Barker, Adam Huawei Cent Software Inst Edinburgh Res Ctr Shenzhen Peoples R China Univ St Andrews Sch Comp Sci St Andrews Fife Scotland

ISBN: (纸本)9798350343946

Efficiently allocating incoming jobs to nodes in large-scale clusters can lead to substantial improvements in both cluster utilization and job performance. In order to allocate incoming jobs, cluster schedulers usually rely on a set of scoring functions to rank feasible nodes. Results from individual scoring functions are usually weighted equally, which could lead to sub-optimal deployments as the one-size-fits-all solution does not take into account the characteristics of each workload. Tuning the weights of scoring functions, however, requires expert knowledge and is computationally expensive. This paper proposes a reinforcement learning approach for learning the weights in scheduler scoring algorithms with the overall objective of improving the end-to-end performance of jobs for a given cluster. Our approach is based on percentage improvement reward, frame-stacking, and limiting domain information. We propose a percentage improvement reward to address the objective of multi-step parameter tuning. The inclusion of frame-stacking allows for carrying information across an optimization experiment. Limiting domain information prevents overfitting and improves performance in unseen clusters and workloads. The policy is trained on different combinations of workloads and cluster setups. We demonstrate the proposed approach improves performance on average by 33% compared to fixed weights and 12% compared to the best-performing baseline in a lab-based serverless scenario.

关键词： scheduling scoring functions reinforcement learning tuning

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An Ontology-based knowledge Modeling towards Eco-Design for Additive Manufacturing 31

An Ontology-based Knowledge Modeling towards Eco-Design for ...

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31st CIRP conference on Life Cycle engineering, LCE 2024

作者： Wang, Yanan Peng, Tao Kim, Samyeon Xiong, Yi Tang, Yunlong Tang, Renzhong State Laboratory of Fluid Power Components and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Department of Mechanical Engineering Jeonju University Jeonju-si Jeollabuk-do55069 Korea Republic of School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China Mechanical and Aerospace Engineering Department Monash University Melbourne3800 Australia Materials Science and Engineering Department Monash University Melbourne3800 Australia Zhejiang Province Key Laboratory of Advanced Manufacturing Technology School of Mechanical Engineering Zhejiang University Hangzhou310058 China

Owing to the unique technological merits of additive manufacturing (AM), design for AM, particularly eco-design for AM, has been gaining attention in improving the sustainable performance of AM-fabricated (AMed) parts. The main challenge in harvesting potential sustainability benefits is how to capture the impact of design on sustainable performance. Though several existing studies adopted quantitative evaluation on AM sustainability and investigated the relationships between design information and sustainability information of AMed part, the sustainability information is still unavailable in the design phase. This hardly guides eco-design for AM in practice. To support the increasing industrial applications of AM, this paper illustrates related knowledge, that is, material-process-structure-ecoProperty (MPSeP) relationships to connect design and sustainability information in eco-design for AM. An ontology-based method is proposed to model the MPSeP relationships of the AMed part in a structured way. An example of an AMed hydraulic valve body was used to demonstrate the MPSeP modeling process. It is discussed that how the constructed model effectively assists AM designers through providing eco-design recommendation, and enables design software tools to perform sustainability analysis towards eco-design for AM. © 2024 The Author(s).

关键词： 3D printing

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A Measurement Study on Interprocess Code Propagation of Malicious software 14th

A Measurement Study on Interprocess Code Propagation of Mali...

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14th EAI International conference on Digital Forensics and Cyber Crime (ICDF2C)

作者： Jenke, Thorsten Liessem, Simon Padilla, Elmar Bruckschen, Lilli Fraunhofer FKIE Zanderstr 5 D-53177 Bonn Germany

ISBN: (纸本)9783031565823;9783031565830

The propagation of code from one process to another is an important aspect of many malware families and can be achieved, for example, through code injections or the launch of new instances. An in-depth understanding of how and when malware uses interprocess code propagations would be a valuable aid in the analysis of this threat, since many dynamic malware analysis and unpacking schemes rely on finding running instances of malicious code. However, despite the prevalence of such propagations, there is little research on this topic. Therefore, in this work, we aim to extend the state-of-the-art by measuring both the behavior and the prevalence of interprocess code propagations of malicious software. We developed a method based on API-tracing for measuring code propagations in dynamic malware analysis. Subsequently, we implemented this method into a proof-of-concept implementation as a basis for further research. To gain more knowledge on the prevalence of code propagations and the code propagation techniques used, we conducted a study using our implementation on a real-world data set of 4853 malware samples from 1747 families. Our results show that more than a third (38.13%) of the executables use code propagation, which can be further classified into four different topologies and 24 different code propagation techniques. We also provide a list of the most significant representative malware samples for each of these topologies and techniques as a starting point for researchers aiming to develop countermeasures against code propagation.

关键词： Malware Code Injections Code Propagation

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DSESL: A Deep Stacking Ensemble Model for Synthetic Lethality Prediction

DSESL: A Deep Stacking Ensemble Model for Synthetic Lethalit...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Zhuang Wang, Xiaowen Li, Yulong Zhu, Hongming Liu, Qin School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9781665410205

Synthetic lethality (SL) refers to the phenomenon that simultaneous mutation of two genes is lethal to cells, while mutation of either gene alone is not lethal. Exploiting this genetic interaction holds immense clinical potential for selectively killing cancer cells without harming normal cells. Given the vast genomic combinatorial space, relying solely on wet lab experiments for screening synthetic lethal gene pairs is impractical, leading to the emergence of various computational methods. Existing computational methods often rely on single-feature extraction methods or single data sources for prediction, resulting in poor predictive accuracy when faced with unseen genes. In this work, we propose a novel deep ensemble model for synthetic lethality prediction based on a stacking strategy (DSESL). Firstly, leveraging the publicly available SynLethKG knowledge graph, we learn gene embed dings at three different focus-levels: single-entity single-relation, single-entity multi-relation, and multi-entity multi-relation, constructing three sub-models for prediction from the knowledge graph. Additionally, we incorporate signaling pathway data and utilize graph neural network-based methods to construct a pathway sub-model. Finally, we adopt a stacking strategy-based ensemble approach to effectively integrate the prediction results from different sub-models. based experimental results, our proposed DSESL model outperforms existing state-of-the-art SL prediction methods in all three prediction scenarios. The source code of DSESL is available at https://***/TOJSSE-iData/DSESL/. © 2024 IEEE.

关键词： Prediction models

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