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Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum learning for Real-World Software engineering Tasks

Should Code Models Learn Pedagogically? A Preliminary Evalua...

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international Mining Software Repositories, MSR

作者： Kyi Shin Khant Hong Yi Lin Patanamon Thongtanunam The University of Melbourne

ISBN: (数字)9798331501839

ISBN: (纸本)9798331501846

learning-based techniques, especially advanced pretrained models for code have demonstrated capabilities in code understanding and generation, solving diverse software engineering (SE) tasks. Despite the promising results, current training approaches may not fully optimize model performance, as they typically involve learning from randomly shuffled training data. Recent work shows that Curriculum learning (CL) can improve performance on code-related tasks through incremental learning based on the difficulty of synthetic code. Yet, the effectiveness of CL with conventional difficulty measures in SE tasks remains largely unexplored. In this study, we explore two conventional code metrics: code length and cyclomatic complexity to determine the difficulty levels. We investigate how the pre-trained code model (CodeT5) learns under CL, through the tasks of code clone detection and code summarization. Our empirical study on the CodeXGLUE benchmark showed contrasting results to prior studies, where the model exhibited signs of catastrophic forgetting and shortcut learning. Surprisingly, model performance saturates after only the first quartile of training, potentially indicating a limit in the model’s representation capacity and/or the task’s inherent difficulty. Future work should further explore various CL strategies with different code models across a wider range of SE tasks for a more holistic understanding.

关键词： Training Measurement Codes Incremental learning Current measurement Training data Software data models data mining Software engineering

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Exploring Flavors Through AI: The Future of Culinary Taste Prediction

Exploring Flavors Through AI: The Future of Culinary Taste P...

引用

international conference on Software engineering Research, Management and Applications (SERA)

作者： Cemil Emre Yavas Jongyeop Kim Lei Chen dept. Information Technology Georgia Southern University Statesboro GA

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

This study assesses the capacity of artificial intelligence (AI) algorithms to mimic a human's sense of taste, specifically in the context of wines. We utilize wine data and machine learning tests to compare the performance of ML models, including Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine, against that of a real sommelier. Our findings show that the Random Forest model outperforms all others in accuracy. Moreover, our results uncover new insights into wine tasting. While our human tongue can detect 11 variables from our dataset, only four of these variables are used by the brain to discern wine flavors. This discovery challenges the previously held belief in the complexity of our sensory system. Our methodology paves the way for future research by streamlining data collection and enhancing its cost efficiency and accuracy by focusing on these essential variables rather than the entire set of eleven.

关键词： Support vector machines Logistic regression Machine learning algorithms Tongue Costs Accuracy Prediction algorithms

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Efficient Remote Memory Paging for Disaggregated Memory Systems 1

引用

22nd international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Wang, Tao Liu, Haikun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Memory disaggregation has attracted increasing attention in recent years because it is a cost-efficient approach to scale memory capacity for applications in a data center. However, the latency of remote memory access is a major concern in disaggregated memory systems. This paper presents VAndI, a virtual memory paging mechanism that allows applications to use remote memory pools transparently. VAndI enables effective data caching and prefetching mechanisms to address the problem of high access latency in disaggregated memory systems. VAndI exploits a low-complexity cache replacement strategy to optimize the asynchronous staging queue so that the remote write latency can be significantly reduced. VAndI can also prefetch data in multi-granularity from a remote memory pool in an adaptive manner, and thus further improves the hit rate of the local cache to reduce the read latency of remote memory. Our extensive experiments using micro-benchmarks show that VAndI can improve the performance of typical remote paging system–Infiniswap by up to 15 × –102 ×. VAndI can also improve the performance of state-of-the-art disaggregated memory system–Valet by 1.2 × –2.7 ×. For typical machine learning workloads, VAndI can achieve 20% to 80% performance improvement compared with the state-of-the-art Valet. © 2023, Springer Nature Switzerland AG.

关键词： Paging systems

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learning Cyber Threat Intelligence Knowledge Graph Embedding with Heterogeneous Relation Networks Based on Multi-Head Relational Graph Attention 19

Learning Cyber Threat Intelligence Knowledge Graph Embedding...

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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： Wang, Xuren Chen, Rong Song, Binghua An, Jungang Jiang, Jun Wang, Jian Yang, Peian Capital Normal University Information Engineering College Beijing China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350346558

Cyber threat intelligence (CTI) Knowledge Graphs Embedding (KGE) complete the tasks such as KG complementation by retaining the structural information of the graph and mining the implicit relations between entities, which can help security organizations analyze potential threat information and improve the cybersecurity protection capabilities. However, learning graph data in CTI is challenging for the data sparsity and their heterogeneity, it is critical to consider how to deal with complex graph data and aggregate multiple types of semantic information simultaneously. To solve the heterogeneity of KGEs in CTI, we propose a heterogeneous Cyber Threat Intelligence GNNs model Relational-Multi-Head-Graph-Attention-Networks (R-MGAT). Specifically, the importance of different relations is firstly learned through a multi-head attention mechanism. Then the neighbor features of different entities are learned under each relation. Finally, the weight entities and the relation-based feature are aggregated to generate the embedding representation. In this way, our model could capture various types of semantic information and selectively aggregate informative features. What's more, we manually constructed a CTI KG by annotating APT reports in response to the lack of public datasets in CTI fields. We use entity classification and link prediction to verify our R-MGAT model. A series of benchmark experiments illustrate that RMGAT can generate competitive performance in KGE. © 2022 IEEE.

关键词： Knowledge graph

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TESCO: Multiple Simulations based AI-augmented Fog computing for QoS Optimization 19

TESCO: Multiple Simulations based AI-augmented Fog computing...

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作者： Iftikhar, Sundas Raj, Uttkarsh Tuli, Shreshth Golec, Muhammed Chowdhury, Deepraj Gill, Sukhpal Singh Uhlig, Steve Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Imperial College London Department of Computing London United Kingdom International Institute of Information Technology Department of Electronics and Communication Engineering Naya Raipur India

ISBN: (纸本)9798350346558

Fog computing is one of the most widely used paradigms for analyzing and computing the data locally, instead of sending data to remote cloud servers. The main objective of fog computing is to lower the latency of services compared to cloud systems while also reducing the requirement of network bandwidth across computing devices. But as in a fog environment, more data is processed locally, fog nodes usually receive more requests daily, leading to system contention and processing overload. To tackle this, the state-of-the-art relies on machine learning coupled with simulations to forecast processing loads and limit the chances of node contention. However, prior work performs a single simulation to test whether a resource management decision, such as task placement, is optimal or not. This usually ignores the stochastic aspects in the environment or the noise in simulations. To resolve this, we proposed a novel approach called TESCO to perform simulations to test multiple candidate scheduling decisions and decide the optimal one. We demonstrate that TESCO outperforms state-of-the-art scheduler (COSCO) that uses single simulations with low overheads. © 2022 IEEE.

关键词： Fog computing

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Cost-Based Lightweight Storage Automatic Decision for In-database Machine learning 22nd

Cost-Based Lightweight Storage Automatic Decision for In-Dat...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Cui, Shuangshuang Wang, Hongzhi Gu, Haiyao Xie, Yuntian Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9783030908881;9783030908874

Storage structure decision for a database aims to automatically determine the effective storage structure according to the data distribution and workload. With the integration of machine learning and database becoming closer, complex machine learning tasks are directly executed in database, and need the support of efficient storage structure. The existing storage decision methods are mainly oriented to common workloads and rely on the decision of experienced DBAs, which has low efficiency and high risk of error. Thus, an automated storage structure decision method for in-database machine learning is urgently needed. We propose a cost-based lightweight row-column storage automatic decision system. To the best of our knowledge, this is the first storage structure selection for machine learning tasks. Extensive experiments show that the accuracy of the storage structure above 90%, shorten the task execution time by about 85%, and greatly reduce the risk of decision error.

关键词： AI for DB Row and column storage data partition

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FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated learning

FedRSM: Representational-Similarity-Based Secured Model Uplo...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Gengxiang Chen Sheng Liu Xu Yang Tao Wang Linlin You Feng Xia School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen China School of Computing Technologies Royal Melbourne Institute of Technology Melbourne Australia

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As a novel learning paradigm, Federated learning (FL) aims at protecting privacy by avoiding raw data shifting between distributed clients and central servers. However, recent researches demonstrate the vulnerability of FL against gradient-based privacy attacks, in which, gradients intercepted by malicious adversaries may result in data leakage. Current defense methods suffer from performance drops, low privacy guarantees, and high communication costs. Motivated by this, we propose FedRSM, a Representational-Similarity-Based Secured Model Uploading for Federated learning. FedRSM splits Deep Neural Networks (DNNs) into layers, calculates Representational Dissimilarity Vector (RDV), measures the similarity between local RDV and global RDV of each model layer, and constructs secured local model to be uploaded based on Representational Consistency Alteration (RCA). According to the evaluation result, FedRSM can improve testing accuracy by up to 2%, significantly reduce communication costs, and avoid data leakage under different model complexities.

关键词： Training data privacy Adaptation models Costs Federated learning Computational modeling data models

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Machine-learning-enabled Decision Support for Screwdriving Process

Machine-learning-enabled Decision Support for Screwdriving P...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Zhenkai Yang Poorya Ghafoorpoor Yazdi Sebastian Thiede Department of Design Production and Management Chair of Manufacturing Systems University of Twente Enschede Netherlands

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Screwdriving as one of the most typical processes in production is facing massive amounts of data. These data empower engineers, managers, and manufacturers to understand the screwing process and make decisions. However, the prerequisites are the ability to turn the abundance of data into knowledge and the intensive cooperation between humans and machines. Nowadays, technologies like big data analysis, machine learning (ML), and artificial intelligence (AI) can be integrated into Cyber-Physical Production Systems (CPPS). They help to get a better insight into the manufacturing processes. However, utilizing these techniques can require human efforts and expertise. In this paper, we show that machine learning can support decision-making in the screwdriving process. The proposed approach is validated on an industrial-scale screwdriving case study in a learning factory. Under the CPPS framework, a data analysis and machine learning toolbox is developed. Our test results show that this toolbox can help users understand the screwdriving process with less expert knowledge. The random forest algorithm as the best fit could effectively identify the screwing condition with an accuracy of 0.93 and F1 score of 0.90.

关键词： Machine learning algorithms data analysis Accuracy Manufacturing processes Decision making User interfaces Reliability engineering Prediction algorithms Production facilities Random forests

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Linear Concept Approximation for Multilingual Document Recommendation 22nd

Linear Concept Approximation for Multilingual Document Recom...

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22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

作者： Salamon, Vilmos Tibor Tashu, Tsegaye Misikir Horváth, Tomáš ELTE - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Telekom Innovation Laboratories Pázmány Péter sétány 1/C Budapest1117 Hungary Kombolcha Institute of Technology College of Informatics Wollo University Kombolcha208 Ethiopia Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia

ISBN: (纸本)9783030916077

In this paper, we proposed Linear Concept Approximation, a novel multilingual document representation approach for the task of multilingual document representation and recommendation. The main idea is in creating representations by using mappings to align monolingual representation spaces using linear concept approximation, that in turn will enhance the quality of content-based Multilingual Document Recommendation Systems. The experimental results on JRC-Acquis have shown that our proposed approach outperformed traditional methods on the task of multilingual document recommendation. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Strategies for Effective EEG Preprocessing

Strategies for Effective EEG Preprocessing

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learning and Technology conference (L&T)

作者： Nema Salem Nora Alamoudi Lama Mustafa Fatima Bagais Electrical Research Laboratory College of Engineering Effat University Jeddah Saudi Arabia Department of Electrical and Computer Engineering College of Engineering Effat University Jeddah Saudi Arabia

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The administration of anesthesia is critical for ensuring the safety of surgical procedures, yet it comes with inherent risks associated with medication mistakes. Monitoring EEG signals during anesthesia is crucial for understanding patient physiology; however, the accuracy of this information heavily relies on effectively eliminating noise. Decreasing noise in EEG signals is vital for accurately interpreting data, ultimately enhancing patient safety and refining anesthesia management. This research concentrates on an advanced preprocessing technique for EEG signals, employing segmentation, detrending, variational mode decomposition (VMD), and noise reduction through Gaussian filtering, moving averages, Independent Component Analysis (ICA), and Discrete Wavelet Transform (DWT). The study utilized EEG data collected during surgeries and applied these preprocessing approaches to reduce noise and artifacts. Our results indicate that employing the Discrete Wavelet Transform (DWT) for denoising is robust and produces superior outcomes compared to other techniques investigated, achieving a high Signal-To-Noise Ratio (SNR) of 43.23 dB.

关键词： Noise reduction Surgery Transforms Anesthesia Wavelet analysis Electroencephalography Discrete wavelet transforms Safety Monitoring Signal to noise ratio

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