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3rd Asia Pacific Information technology Conference, APIT 2021

作者： Adhikari, Saurabh Plewnia, Christian Netramai, Chayakorn Lichter, Horst The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Thailand Research Group Software Construction RWTH Aachen University Germany

ISBN: (纸本)9781450388108

The usage of compute resources by data processing jobs may change over time, requiring careful resource planning when an organization operates these resources itself in an on-premise private cloud. Ideally, the currently available resources always match the need of jobs executed on them. This way the resources would neither be overutilized, which is usually undesirable as the jobs might take longer, nor underutilized, which causes unnecessary costs for unused resources. When an organization decides to extend its private cloud resources, it can still take months until the servers are bought, delivered, and installed. Thus, the resources have to be planned carefully in advance. Estimating the future resource needs is difficult and influenced by many factors. In our experience, creating the estimate is often a manual process supported by self-designed spreadsheets;these spreadsheets are maintained by a single person from time to time and might even be replaced completely if someone else assumes that person's responsibility. However, this approach does not lead to transparent and verifiable forecasts that enable collaboration and learning from past decisions. This paper addresses the problem of generating a transparent and verifiable compute resource usage forecast by proposing a simulation approach. It requires a user to model an estimate of the future workload development of the data processing jobs as well as the current compute resource setup. The simulation can then be run to identify possible future resource bottlenecks. This can be repeated for different scenarios, including situations of failing resources as well as the addition of resources to compensate for bottlenecks and failures. We further provide a first qualitative case study of this approach that demonstrates its potential. © 2021 ACM.

关键词： Spreadsheets

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Atoms of confusion in java

arXiv

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arXiv 2021年

作者： Langhout, Chris Aniche, Maurício Software Engineering Research Group Delft University of Technology Delft Netherlands

Although writing code seems trivial at times, problems arise when humans misinterpret what the code actually does. One of the potential causes are "atoms of confusion", the smallest possible patterns of misinterpretable source code. Previous research has investigated the impact of atoms of confusion in C code. Results show that developers make significantly more mistakes in code where atoms are present. In this paper, we replicate the work of Gopstein et al. to the Java language. After deriving a set of atoms of confusion for Java, we perform a two-phase experiment with 132 computer science students (i.e., novice developers). Our results show that participants are 2.7 up to 56 times more likely to make mistakes in code snippets affected by 7 out of the 14 studied atoms of confusion, and when faced with both versions of the code snippets, participants perceived the version affected by the atom of confusion to be more confusing and/or less readable in 10 out of the 14 studied atoms of confusion. Copyright © 2021, The Authors. All rights reserved.

关键词： Atoms

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Multi-physical Field Collaborative Simulation Optimization technology and Reliability Analysis of Power Amplifiers 1st

Multi-physical Field Collaborative Simulation Optimization T...

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Li, Zhenbing Huang, Junjie Jia, Shilin Zhang, Jinrong Fu, Jialong Fang, Xiaochuan Li, Gang Wen, Guangjun School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Antennas and Electromagnetics Research Group School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9789819712762

As the core RF front-end device of wireless communication systems, the performance of millimeter wave power amplifier chips determines the communication distance/quality of the system. In order to improve the communication distance of the system, power amplifiers are often required to operate in a saturated state, which can cause severe warping and deformation of PA chips due to high temperature accumulation. This seriously affects the performance of RF front-end, and has become a key issue that needs to be urgently solved in the industry. This paper takes MATLAB as the core to build a Circuit-Electromagnetic-Thermal-Force Multiple Physical Field collaborative simulation and optimization platform. In HFSS, using vbs script to achieve fully parameterized modeling and full band circuit electromagnetic joint simulation. In ANSYS Workbench, thermodynamic simulation is implemented using SCDM and Mechanical in IronPython environment. In this paper, a PA chip based on GaN process is simulated by using the Multi-Physical Field collaborative simulation and optimization platform. Aiming at its high temperature agglomeration problem, the number and layout of transistor count are optimized, and the maximum temperature is successfully reduced by about 70 ℃. In order to compensate for the circuit performance degradation caused by layout, this paper built an automatic optimization platform to optimize it, and the gain increased by about 8.4 dB compared with that before optimization, which proved the availability, reliability and progressiveness of the multi-physical field collaborative simulation optimization platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Power amplifiers

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Self-Supervised Entity Alignment Based on Multi-Modal Contrastive Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2031-2033页

作者： Bo Liu Ruoyi Song Yuejia Xiang Junbo Du Weijian Ruan Jinhui Hu the School of Software Engineering Xi’an Jiaotong UniversityXi’an 710000 the China Electronics Technology Group Corporation(CETC)Key Laboratory of Smart City Model Simulation and Intelligent Technology the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS)Shenzhen 518000China the Chinese University of Hong Kong Shenzhen 518000China Tencent Shenzhen 518000China the CETC Key Laboratory of Smart City Model Simulation and Intelligent Technology the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS)Shenzhen 518000China the Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055

Dear Editor,This letter proposes an unsupervised entity alignment method,which realizes integration of multiple multi-modal knowledge graphs *** recent years,Large-scale multi-modal knowledge graphs(LMKGs),containing text and image,have been widely applied in numerous knowledge-driven topics,such as question answering,entity linking,information extraction,reasoning and recommendation.

关键词： modal linking letter

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Assessing the Security of GitHub Copilot Generated Code - A Targeted Replication Study

arXiv

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arXiv 2023年

作者： Majdinasab, Vahid Bishop, Michael Joshua Rasheed, Shawn Moradidakhel, Arghavan Tahir, Amjed Khomh, Foutse Department of Computer and Software Engineering Polytechnique Montreal Canada School of Mathematical and Computational Sciences Massey University New Zealand Information & Communication Technology Group UCOL - Te Pūkenga New Zealand

AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced from public repositories), which may contain bugs and vulnerabilities. Several concerns have been raised about the security of the code generated by these models. Recent studies have investigated security issues in AI-powered code generation tools such as GitHub Copilot and Amazon CodeWhisperer, revealing several security weaknesses in the code generated by these tools. As these tools evolve, it is expected that they will improve their security protocols to prevent the suggestion of insecure code to developers. This paper replicates the study of Pearce et al., which investigated security weaknesses in Copilot and uncovered several weaknesses in the code suggested by Copilot across diverse scenarios and languages (Python, C and Verilog). Our replication examines Copilot’s security weaknesses using newer versions of Copilot and CodeQL (the security analysis framework). The replication focused on the presence of security vulnerabilities in Python code. Our results indicate that, even with the improvements in newer versions of Copilot, the percentage of vulnerable code suggestions has reduced from 36.54% to 27.25%. Nonetheless, it remains evident that the model still suggests insecure code. © 2023, CC BY.

关键词： Network security

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Enhancing Web Service Anomaly Detection via Fine-grained Multi-modal Association and Frequency Domain Analysis 25

Enhancing Web Service Anomaly Detection via Fine-grained Mul...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Xixuan Yang Xin Huang Chiming Duan Tong Jia Shandong Dong Ying Li Gang Huang Peking University Beijing China Nanyang Normal University Nanyang Henan China Peking University Beijing China and National Key Laboratory of Data Space Technology and System Beijing China Alibaba Group Hangzhou Zhejiang China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400713316

Anomaly detection is crucial for ensuring the stability and reliability of web service systems. Logs and metrics contain multiple information that can reflect the system's operational state and potential anomalies. Thus, existing anomaly detection methods use logs and metrics to detect web service systems' anomalies through data fusion approaches. They associate logs and metrics using coarse-grained time window alignment and capture the normal patterns of system operation through reconstruction. However, these methods have two issues that limit their performance in anomaly detection. First, due to asynchrony between logs and metrics, coarse-grained time window alignment cannot achieve a precise association between the two modalities. Second, reconstruction-based methods suffer from severe overgeneralization problems, resulting in anomalies being accurately reconstructed. In this paper, we propose a novel anomaly detection method named FFAD to address these two issues. On the one hand, FFAD employs graph-based alignment to mine and extract associations between the modalities from the constructed log-metric relation graph, achieving precise associations between logs and metrics. On the other hand, we improve the model's fit to normal data distributions through Fourier Frequency Focus, thereby enhancing the effectiveness of anomaly detection. We validated the effectiveness of our model on two real-world industrial datasets and one open-source dataset. The results show that our method achieves an average anomaly detection F1-score of 93.6%, representing an 8.8% improvement over previous state-of-the-art methods.

关键词： anomaly detection

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FilterIE: A Joint multimodel information extraction model under two-stage visual data filtering

FilterIE: A Joint multimodel information extraction model un...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yihan Zhao Jun Cheng Tao Meng Xiaohua Wu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu P.R. China Midea Building Technology Midea Group (Shanghai) Co. Ltd Shanghai P.R. China YiBin Park of UESTC Chengdu P.R. China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multimodal information extraction has garnered increasing attention in the domain of natural language processing. However, current approaches largely overestimate the role of visual information, thereby neglecting the impact of noise contained in visual data on the task. It was observed that the accuracy of multimodal information extraction models does not always improve with the increase in the amount of information. To tackle these issues, we introduce a joint multimodal information extraction model (FliterIE) that incorporates a two-stage visual information filtering process. Specifically, we implement a two-stage filtering process for visual information based on the matching degree of multimodal data, resulting in a collection of visual information. This collection encompasses visual information at both holistic and object-level granularities, aiming to obtain valuable visual representations. We further propose a joint model strategy to combine the results of the unimodal information extraction model with the multimodal information extraction model by dynamically matching weights. Our experiments validate the effectiveness of our approach across three widely used datasets: Twitter-2015, Twitter-2017, and MNRE. We achieve state-of-the-art performance in named entity recognition and comparable performance to the state-of-the-art model in information extraction tasks.

关键词： Visualization Matched filters Accuracy Noise Neural networks Named entity recognition Information filters

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Deformation-Aware Segmentation Network Robust to Motion Artifacts for Brain Tissue Segmentation using Disentanglement Learning

arXiv

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arXiv 2024年

作者： Jung, Sunyoung Choi, Yoonseok Al-Masni, Mohammed A. Jung, Minyoung Kim, Dong-Hyun Department of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science College of Software and Convergence Technology Sejong University Seoul Korea Republic of Cognitive Science Research Group Korea Brain Research Institute Daegu Korea Republic of

Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances, making segmentation difficult. This study proposes a novel deep learning framework that demonstrates superior performance in both motion correction and robust brain tissue segmentation in the presence of artifacts. The core concept lies in a complementary process: a disentanglement learning network progressively removes artifacts, leading to cleaner images and consequently, more accurate segmentation by a jointly trained motion estimation and segmentation network. This network generates three outputs: a motion-corrected image, a motion deformation map that identifies artifact-affected regions, and a brain tissue segmentation mask. This deformation serves as a guidance mechanism for the disentanglement process, aiding the model in recovering lost information or removing artificial structures introduced by the artifacts. Extensive in-vivo experiments on pediatric motion data demonstrate that our proposed framework outperforms state-of-the-art methods in segmenting motion-corrupted MRI scans. The code is available at https://***/SunYJhxppy/Multi-Net. © 2024, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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MKGL: Mastery of a Three-Word Language

arXiv

引用

arXiv 2024年

作者： Guo, Lingbing Bo, Zhongpu Chen, Zhuo Zhang, Yichi Chen, Jiaoyan Lan, Yarong Sun, Mengshu Zhang, Zhiqiang Luo, Yangyifei Li, Qian Zhang, Qiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China Zhejiang University Ant Group Joint Laboratory of Knowledge Graph China Ant Group China Department of Computer Science The University of Manchester United Kingdom School of Computer Science and Engineering Beihang University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Technology Zhejiang University China

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs. © 2024, CC BY.

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The Research and Implementation of Network Slices and Resources Scheduling

The Research and Implementation of Network Slices and Resour...

引用

Artificial Intelligence technology (ACAIT), Asian Conference on

作者： Jinfeng Wang Kun Xie Weiliang Liu Chengcheng Li Yasheng Zhang Dept. Software Engineering Beijing University of Posts and Telecommunications Beijing China Dept. Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China Dept. The 54th Research Institute of CETC China Electronics Technology Group Corporation Limited Shijiazhuang China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

This thesis advocates for the enhancement of network data throughput and service capabilities through the establishment of device identity class isolation underpinned by SDN/NFV technologies. It proposes a network slicing mechanism to segregate devices into dedicated network slices with independently allocated resources. Further, a novel resource scheduling algorithm based on Deep Deterministic Policy Gradient (DDPG) is introduced to ensure efficient resource distribution.

关键词： Scheduling algorithms Network slicing Bandwidth Throughput Deep reinforcement learning Network function virtualization Resource management software defined networking Artificial intelligence

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