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SOAR: A Synthesis Approach for Data Science API Refactoring

SOAR: A Synthesis Approach for Data Science API Refactoring

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International conference on Software Engineering (ICSE)

作者： Ansong Ni Daniel Ramos Aidan Z. H. Yang Inês Lynce Vasco Manquinho Ruben Martins Claire Le Goues Yale University New Haven USA INESC-ID/IST U. Lisboa Portugal Carnegie Mellon University USA Queen's University Queensland QC Canada U. Lisboa Lisbon Portugal INESC-ID/IST U. Lisboa Lisboa Portugal School of Computer Science Carnegie Mellon University Pittsburgh USA Carnegie Mellon University Mellon MA USA

With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source organizations often have to exert manual effort to refactor the APIs used in the code base. Moreover, due to the abundance of similar open-source libraries, data scientists working on a certain application may have an abundance of libraries to choose, maintain and migrate between. The manual refactoring between APIs is a tedious and error-prone task. Although recent research efforts were made on performing automatic API refactoring between different languages, previous work relies on statistical learning with collected pairwise training data for the API matching and migration. Using large statistical data for refactoring is not ideal because such training data will not be available for a new library or a new version of the same library. We introduce Synthesis for Open-source API Refactoring (SOAR), a novel technique that requires no training data to achieve API migration and refactoring. SOAR relies only on the documentation that is readily available at the release of the library to learn API representations and mapping between libraries. Using program synthesis, SOAR automatically computes the correct configuration of arguments to the APIs and any glue code required to invoke those APIs. SOAR also uses the interpreter's error messages when running refactored code to generate logical constraints that can be used to prune the search space. Our empirical evaluation shows that SOAR can successfully refactor 80% of our benchmarks corresponding to deep learning models with up to 44 layers with an average run time of 97.23 seconds, and 90% of the data wrangling benchmarks with an average run time of 17.31 seconds.

关键词： Training data Documentation Data science Benchmark testing Libraries Task analysis Open source software

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How Physics and Background Attributes Impact Video Transformers in Robotic manipulation: A Case Study on Planar Pushing

How Physics and Background Attributes Impact Video Transform...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Shutong Jin Ruiyu Wang Muhammad Zahid Florian T. Pokorny School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

As model and dataset sizes continue to scale in robot learning, the need to understand how the composition and properties of a dataset affect model performance becomes increasingly urgent to ensure cost-effective data collection and model performance. In this work, we empirically investigate how physics attributes (color, friction coefficient, shape) and scene background characteristics, such as the complexity and dynamics of interactions with background objects, influence the performance of Video Transformers in predicting planar pushing trajectories. We investigate three primary questions: How do physics attributes and background scene characteristics influence model performance? What kind of changes in attributes are most detrimental to model generalization? What proportion of fine-tuning data is required to adapt models to novel scenarios? To facilitate this research, we present CloudGripper-Push-1K, a large real-world vision-based robot pushing dataset comprising 1278 hours and 460,000 videos of planar pushing interactions with objects with different physics and background attributes. We also propose Video Occlusion Transformer (VOT), a generic modular video-transformer-based trajectory prediction framework which features 3 choices of 2D-spatial encoders as the subject of our case study. The dataset and source code are available at https://***.

关键词： Adaptation models Shape Image color analysis source coding Transformers Data models Robot learning Trajectory Physics Intelligent robots

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Automated Reverse Engineering for MoM-Based Microservices (ARE4MOM) Using Static analysis

Automated Reverse Engineering for MoM-Based Microservices (A...

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working ieee/IFIP conference on Software Architecture

作者： Snigdha Singh Anne Koziolek KASTEL – Institute of Information Security and Dependability Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798350359169

ISBN: (纸本)9798350359176

Context: Understanding architecture is crucial during the development of Message-oriented Middleware-based (MoM-based) microservices. Such architecture serves as a prerequisite for system maintenance, comprehension, and refactoring. However, maintaining an accurate architecture document becomes challenging due to continuous changes in the architecture throughout development. Problem: The challenge lies in maintaining the correct architectural document amid ongoing architectural modifications. While Reverse Engineering supports architecture extraction, existing approaches are limited to synchronous microservice communication and lack clear identification of microservice components and messaging interfaces. Objective: This research aims to automatically extract both the component-based architecture and behavior of MoM-based microservice systems, focusing on asynchronous communication. The primary contribution is the extraction of components and behavior models tailored for asynchronous scenarios. Method: To address the challenges, we extend the existing Software Model eXtractor (SoMoX) approach, incorporating a model-based reverse engineering concept to support asynchronous communication. Our approach automates the extraction of MoM-based microservice system architecture from its source code using static analysis. Result: The paper introduces the concepts of our approach (ARE4MOM) through a running example, demonstrating its capabilities. Evaluation: involves three GitHub case studies utilizing different MoMs and technologies for asynchronous communication. ARE4MOM successfully extracts the architecture and behavior model with a 98.1% F1 score.

关键词： Asynchronous communication Analytical models Software architecture source coding Unified modeling language Reverse engineering Microservice architectures

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Features of Creating Accounts in Linux OS

Features of Creating Accounts in Linux OS

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Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

作者： Ilia Sokolov Sergey Porshnev Valentin Baklanov Ksenia Sherbakova Dmitry Kuts Training and Scientific Center “Information Security” Ural Federal University named after the first President of Russia B. N. Yeltsin Ekaterinburg Russian Federation

ISBN: (数字)9798350362893

ISBN: (纸本)9798350362909

Currently, operating systems based on the Linux kernel and open-source software are widely used, including: Debian, Centos, openSUSE, RedHat, Slackware and others, all distributions of which include the kernel and open-source software (packages). This allows you to organize user interaction with various devices. The article presents the results of an analysis of the creation of user account files for operating systems based on the Linux kernel. We are considering using the grep utility to search for data on the hard drive. In operating system forensics, all possible behavior patterns of such source code are very important. Of particular interest are user account management processes and their traces in operating systems. The forensic application of the found methods for controlling user accounts is considered, which will increase the level of information security of the system. A theory will be put forward about the origin of the phenomenon of duplication of account file records on the hard drive, which assumes that duplication occurs due to the characteristics of the file system and the operation of the useradd utility. An experiment was conducted to confirm the theory and show the stages of creating duplicate account files, which allows you to track all changes in the account manipulation system, regardless of system logs.

关键词： File systems Linux Forensics source coding Information security Kernel Information technology

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CONAN: Contrastive Fusion Networks for Multi-view Clustering

CONAN: Contrastive Fusion Networks for Multi-view Clustering

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ieee International conference on Big Data

作者： Guanzhou Ke Zhiyong Hong Zhiqiang Zeng Zeyi Liu Yangjie Sun Yannan Xie Wuyi University Jiangmen China Polytechnic of EPE1 Foshan China

ISBN: (纸本)9781665445993

With the development of big data, deep learning has made remarkable progress on multi-view clustering. Multi-view fusion is a crucial technique for the model obtaining a common representation. However, existing literature adopts shallow fusion strategies, such as weighted-sum fusion and concatenating fusion, which fail to capture complex information from multiple views. In this paper, we propose a novel fusion technique, entitled contrastive fusion, which can extract consistent representations from multiple views and maintain the characteristic of view-specific representations. Specifically, we study multi-view alignment from an information bottleneck perspective and introduce an intermediate variable to align each view-specific representation. Furthermore, we leverage a single-view clustering method as a predictive task to ensure the contrastive fusion is working. We integrate all components into an unified framework called CONtrAstive fusion Network (CONAN). Experiment results on five multi-view datasets demonstrate that CONAN outperforms state-of-the-art methods. Our source code will be available soon at https://***/guanzhou-ke/conan.

关键词： Deep learning codes Clustering methods conferences Big Data Data mining Task analysis

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Abstracting log lines to log event types for mining software system logs

Abstracting log lines to log event types for mining software...

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International Mining Software Repositories, MSR

作者： Meiyappan Nagappan Mladen A. Vouk Department of Computer Science North Carolina State University Raleigh USA

Log files contain valuable information about the execution of a system. This information is often used for debugging, operational profiling, finding anomalies, detecting security threats, measuring performance etc. The log files are usually too big for extracting this valuable information manually, even though manual perusal is still one of the more widely used techniques. Recently a variety of data mining and machine learning algorithms are being used to analyze the information in the log files. A major road block for the efficient use of these algorithms is the inherent variability present in every log line of a log file. Each log line is a combination of a static message type field and a variable parameter field. Even though both these fields are required, the analyses algorithm often requires that these be separated out, in order to find correlations in the repeating log event types. This disentangling of the message and parameter fields to find the event types is called abstraction of log lines. Each log line is abstracted to a unique ID or event type and the dynamic parameter value is extracted to give an insight on the current state of the system. In this paper we present a technique based on a clustering technique used in the Simple Log file Clustering Tool for log file abstraction. This solution is especially useful when we don't have access to the source code of the application or when the lines in the log file do not conform to a rigid structure. We evaluated our implementation on log files from the Virtual Computing Lab, a cloud computer management system at North Carolina State University, and abstracted it to 727 unique event types.

关键词： Software systems Data mining Machine learning algorithms Algorithm design and analysis Cloud computing Debugging Data security Information security Manuals Information analysis

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The offline software framework of the Pierre Auger Observatory

The offline software framework of the Pierre Auger Observato...

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ieee Symposium on Nuclear Science (NSS/MIC)

作者： S. Argiro S.L.C. Barroso J. Gonzalez L. Nellen T. Paul T.A. Porter L. Prado M. Roth R. Ulrich D. Veberic University of Torino Italy I.N.F.N. Italy Centro Brasileiro de Pesquisas Fìsicas Northeastern University China Universidad Nacional Autónoma de México State University Russia State University of Campinas-UNICAMP Brazil Forschungzentrum Karlsruhe Nova Gorica Polytechnic

The Pierre Auger Observatory is designed to unveil the nature and the origins of the highest energy cosmic rays. The large and geographically dispersed collaboration of physicists and the wide-ranging collection of simulation and reconstruction tasks pose some special challenges for the offline analysis software. We have designed and implemented a general purpose framework which allows collaborators to contribute algorithms and sequencing instructions to build up the variety of applications they require. The framework includes machinery to manage these user codes, to organize the abundance of user-contributed configuration files, to facilitate multi-format file handling, and to provide access to event and time-dependent detector information which can reside in various data sources. A number of utilities are also provided, including a novel geometry package which allows manipulation of abstract geometrical objects independent of coordinate system choice. The framework is implemented in C++, and takes advantage of object oriented design and common open source tools, while keeping the user side simple enough for C++ novices to learn in a reasonable time. The distribution system incorporates unit and acceptance testing in order to support rapid development of both the core framework and contributed user code

关键词： Observatories Collaborative software Cosmic rays Object oriented modeling Analytical models Software design Algorithm design and analysis Collaboration Application software Machinery

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A simulated environment for early development stages of reinforcement learning algorithms for closed-loop deep brain stimulation

A simulated environment for early development stages of rein...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Sebastián Castaño-Candamil Mara Vaihinger Michael Tangermann Brain State Decoding Lab BrainLinks-BrainTools University of Freiburg Germany Autonomous Intelligent Systems Lab University of Freiburg Germany

In recent years, closed-loop adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD) has gained focus in the research community, due to promising proof-of-concept studies showing its suitability for improving DBS therapy and ameliorating related side *** main challenges faced in the aDBS control problem is the presence of non-stationary/non-linear dynamics and the heterogeneity of PD's phenotype, making the exploration of data-driven dynamics-aware control algorithms a promising research direction. However, due to the severe safety constraints related to working with patients, aDBS is a sensitive research field that requires surrogate development platforms with growing complexity, as novel control algorithms are *** our current contribution, we propose the characterization and categorization of non-stationary dynamics found in the aDBS problem. We show how knowledge about these dynamics can be embedded in a surrogate simulation environment, which has been designed to support early development stages of aDBS control strategies, specifically those based on reinforcement learning (RL) algorithms. Finally, we present a comparison of representative RL methods designed to cope with the type of non-stationary dynamics found in *** allow reproducibility and encourage adoption of our approach, the source code of the developed methods and simulation environment are made available online.

关键词： Satellite broadcasting Heuristic algorithms Gravity Training Task analysis Standards Reinforcement learning

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Implementation of fuzzy logic controller in SHPF&TCR for power quality

Implementation of fuzzy logic controller in SHPF&TCR for pow...

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International conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

作者： G. Satyanarayana Rao Y. Ravindra Reddy Department of EEE Sasi Institute of Technology &Engineering Tadepalligudem W.G Dist A.P India

Recent scenario in the distribution system is harmonics created by Non linear load and unbalance current. It effects not only the working of adjacent loads but also shorten the life of power equipment by creating excessive losses. In this paper a fuzzy controlled shunt active power filter is described to maintain the (Total Harmonic Distortion) THD within the allowable limits defined by ieee Std. 519-1992 and to reduce reactive power and improve power factor. This Filter draws the opposite harmonics containing current from the load so that source current remain sinusoidal and undistorted. Fuzzy logic controller is used to control the shunt active power filter and the performance of the shunt active filter control strategies has been evaluated in terms of harmonic mitigation and DC link voltage regulation. New MatLab code is generated for the controller. Three phase reference current wave forms generated by proposed scheme are tracked by the three-phase voltage source converter in a hysteresis band control scheme. A fully functional MATLAB based Simulink model of Shunt Active Power Filter for different types of loads has been designed based on `Instantaneous Power Theory' or `p-q theory'. The results of simulation comply with all the features described by the theory justifying employment of Shunt Active Power Filter (SAPF) in the industry.

关键词： Active filters Power harmonic filters Reactive power Harmonic analysis Power filters Voltage control

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A fuzzy controlled shunt active power filter for reducing current harmonics and reactive power compensation

A fuzzy controlled shunt active power filter for reducing cu...

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International conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Udayan Das Gupta Palash Das Md. Ahsanul Hoque Department of Electrical & Electronic Engineering International Islamic University Chittagong (IIUC) Chittagong Bangladesh

Recent scenario in the distribution system is harmonics created by Nonlinear load and unbalance current. It affects not only the working of adjacent loads but also shorten the life of power equipment by creating excessive losses. In this paper, a fuzzy controlled shunt active power filter is described to maintain the (Total Harmonic Distortion) THD within the allowable limits defined by ieee Std. 519-1992 and to reduce reactive power and improve power factor. This Filter draws the opposite harmonics containing current from the load so that source current remain sinusoidal and undistorted. Fuzzy logic controller is used to control the shunt active power filter and the performance of the shunt active filter control strategies has been evaluated in terms of harmonic mitigation and DC link voltage regulation. New MATLAB code is generated for the controller. Three-phase reference current waveforms generated by proposed scheme are tracked by the three-phase voltage source converter in a hysteresis band control scheme. A fully functional MATLAB based Simulink model of Shunt Active Power Filter for different types of load (nonlinear, unbalance, both) has been designed based on `Instantaneous Power Theory' or `p-q Theory'. The results of simulation comply with all the features described by the theory, justifying employment of Shunt Active Power Filter (SAPF) in the industry.

关键词： Generators MATLAB Computational modeling Integrated circuit modeling Mathematical model Circuit analysis

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