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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Hu, Jinchang Zhang, Lyuye Liu, Chengwei Yang, Sen Huang, Song Liu, Yang College of Command and Control Engineering Army Engineering University of Pla Nanjing China Continental-NTU Corporate Lab Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Academy of Military Science Nanjing China

ISBN: (纸本)9798400702174

Open-source software (OSS) greatly facilitates program development for developers. However, the high number of vulnerabilities in open-source software is a major concern, including in Golang, a relatively new programming language. In contrast to other commonly used OSS package managers, Golang presents a distinctive feature whereby commits are prevalently used as dependency versions prior to their integration into official releases. This attribute can prove advantageous to users, as patch commits can be implemented in a timely manner before the releases. However, Golang employs a decentralized mechanism for managing dependencies, whereby dependencies are upheld and distributed in separate repositories. This approach can result in delays in the dissemination of patches and unresolved vulnerabilities. To tackle the aforementioned concern, a comprehensive investi-gation was undertaken to examine the life cycle of vulnerability in Golang, commencing from its introduction and culminating with its rectification. To this end, a framework was established by gathering data from diverse sources and systematically amalgamating them with an algorithm to compute the lags in vulnerability patching. It turned out that 66.10% of modules in the Golang ecosystem were affected by vulnerabilities. Within the vulnerability life cycle, we found two kinds of lag impeding the propagation of vulnerability fixing. By analyzing reasons behind non-lagged and lagged vul-nerabilities, timely releasing and indexing patch versions could significantly enhance ecosystem security. © 2024 ACM.

关键词： Open source software

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Fault-Tolerant Quantum Computation Using Large Spin-Cat Codes

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PRX Quantum 2024年第2期5卷 020355-020355页

作者： Sivaprasad Omanakuttan Vikas Buchemmavari Jonathan A. Gross Ivan H. Deutsch Milad Marvian Center for Quantum Information and Control Department of Physics and Astronomy Venice California 90291 USA Department of Electrical and Computer Engineering

We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous-variable cat encoding. With this, we can correct the dominant error sources, namely processes that can be expressed as error operators that are linear or quadratic in the components of angular momentum. Such codes tailored to dominant error sources can exhibit superior thresholds and lower resource overheads when compared to those designed for unstructured noise models. A key component is the cnot gate that preserves the rank of spherical tensor operators. Categorizing the dominant errors as phase and amplitude errors, we demonstrate how phase errors, analogous to phase-flip errors for qubits, can be effectively corrected. Furthermore, we propose a measurement-free error-correction scheme to address amplitude errors without relying on syndrome measurements. Through an in-depth analysis of logical cnot gate errors, we establish that the fault-tolerant threshold for error correction in the spin-cat encoding surpasses that of standard qubit-based encodings. We consider a specific implementation based on neutral-atom quantum computing, with qudits encoded in the nuclear spin of 87Sr, and show how to generate the universal gate set, including the rank-preserving cnot gate, using quantum control and the Rydberg blockade. These findings pave the way for encoding a qubit in a large spin with the potential to achieve fault tolerance, high threshold, and reduced resource overhead in quantum information processing.

关键词： Quantum computation Quantum control Quantum error correction Quantum information theory

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Hybrid consensus control for nonholonomic multi-robot systems

Hybrid consensus control for nonholonomic multi-robot system...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Davide Aloisi Andrea Cristofaro Department of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This work concerns the implementation of hybrid consensus control for nonholonomic multi-robot systems. It is shown how it is possible to attain a consensus behaviour in a multi-agent system having internal communications characterized by sampling. Furthermore, it is shown how it is possible to make the multi-agent system reach a formation with desired position and orientation by adding a virtual agent within the system. A simulation study supports and validates the theoretical results.

关键词： Protocols Consensus control Multi-robot systems Information technology Collision avoidance Multi-agent systems

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Analyzing the Performance of Deep Learning-based Techniques for Human Pose Estimation

Analyzing the Performance of Deep Learning-based Techniques ...

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Sport, Technology and Research (STAR), IEEE International Workshop on

作者： Federico Boscolo Fabrizio Lamberti Lia Morra Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798350351453

ISBN: (纸本)9798350351460

In response to the increasing demand for precise sports analytics, this study investigates advanced computer vision techniques in the context of tennis player performance analysis. In particular, we explore cutting-edge deep learning models for 3D Human Pose Estimation (HPE) to analyze player movements during strokes. Despite the prevalence of such techniques in other sports, solutions for tennis remain scarce. Our research addresses this gap by examining two deep learning HPE models adapted for this purpose. We conduct rigorous experimentation on a purposely crafted dataset, with the objective of comparing these models against an existing approach for 3D HPE inference in the tennis context. Our findings highlight the potential of HPE in enhancing movement analysis and player coaching, providing valuable insights for future applications in tennis and other sports.

关键词： Deep learning Adaptation models Solid modeling Three-dimensional displays Conferences Pose estimation Stars

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Can social media shape the security of next-generation connected vehicles?

arXiv

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arXiv 2024年

作者： Scarano, Nicola Mannella, Luca Savino, Alessandro Di Carlo, Stefano Politecnico di Torino Department of Control and Computer Engineering Turin Italy

The increasing adoption of connectivity and electronic components in vehicles makes these systems valuable targets for attackers. While automotive vendors prioritize safety, there remains a critical need for comprehensive assessment and analysis of cyber risks. In this context, this paper proposes a Social Media Automotive Threat Intelligence (SOCMATI) framework, specifically designed for the emerging field of automotive cybersecurity. The framework leverages advanced intelligence techniques and machine learning models to extract valuable insights from social media. Four use cases illustrate The framework’s potential by demonstrating how it can significantly enhance threat assessment procedures within the automotive industry. © 2024, CC BY-NC-SA.

关键词： Social networking (online)

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Towards Cyber Resilience against APTs

Towards Cyber Resilience against APTs

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2025 Joint National Conference on Cybersecurity, ITASEC and SERICS 2025

作者： Gaudenzi, Alessandro Nodari, Lorenzo Valentim, Rodolfo Giordano, Danilo Drago, Idilio Russo, Alessandra Cerutti, Federico Department of Information Engineering University of Brescia BS Brescia Italy Computer Science Department University of Turin TO Turin Italy Department of Control and Computer Engineering Politecnico di Torino TO Turin Italy Imperial College London London United Kingdom

This paper examines the Uber data breach of September 2022, where the Lapsus$ group exploited multi-factor authentication (MFA) fatigue to compromise contractor credentials. The attackers gained access to internal systems, demonstrating the sophistication and persistence of modern Advanced Persistent Threats (APTs). Using the ACRE framework, which focuses on later stages of the cyber kill chain, we highlight how effective Cyber Threat Intelligence (CTI) can systematically detect and analyse such attacks. The ACRE framework provides tools to collect, process, and analyse threat data, enabling organisations to identify APT activity and mitigate risks proactively. By applying ACRE to the Uber breach, this study demonstrates its capacity to uncover critical intelligence and improve defensive strategies. The case underscores the importance of intelligence-driven approaches in addressing the complexities of contemporary cyber threats and enhancing organisational resilience. © 2025 Copyright for this paper by its authors.

关键词： Machine learning

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Optimizing Multimodal Emotion Recognition: Evaluating the Impact of Speech, Text, and Visual Modalities

Optimizing Multimodal Emotion Recognition: Evaluating the Im...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Hongsheng Wang College of Computer and Control Engineering Northeastern Forestry University Harbin China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

This research aims to explore and optimize multimodal emotion recognition to enhance its performance. Multimodal emotion recognition involves analyzing information from different modalities—speech, vision, and text—to identify and classify emotional states accurately. This study investigates the roles of speech and text modalities and the auxiliary effect of visual modalities in multimodal emotion recognition. Study employs a two-stage multimodal information fusion neural network based on graph and attention mechanisms. The core idea is to achieve an effective fusion of multimodal information through graph convolutional networks and cross-modal attention mechanisms to improve emotion recognition performance. This study uses two datasets, Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Multimodal Emotion Lines Dataset (MELD), to analyze the impact of speech and text modalities on visual modalities and their potential side effects. Results show that while the visual modality alone is less effective, adding speech or text significantly improves performance. However, introducing the speech modality and the text modality is less beneficial. Experimental outcomes reveal that the speech modality's effect is not as significant as the text modality, and its inclusion does not always lead to positive results, sometimes even degrading overall recognition performance.

关键词： Performance evaluation Emotion recognition Visualization Attention mechanisms Accuracy Text analysis Text recognition Scientific computing Neural networks Speech recognition

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Research on the Probability Interval and Ramping Prediction Method of Wind Turbine Power

Research on the Probability Interval and Ramping Prediction ...

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第43届中国控制会议

作者： Ketao Dong Yali Zhao Qiankun Hu Peng Guo School of Control and Computer Engineering North China Electric Power University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The wind power ramp event refers to the large fluctuation of wind power caused by the sudden increase or decrease of wind power in a short time interval,which affects the safe and stable operation of the power grid *** article proposes a wind power prediction method based on the CNN-LSTM model,and the prediction interval of wind power is obtained by using a non-parametric kernel density estimation *** using interval prediction lower limit data combined with a detection method based on statistical power fluctuations and a Swing Door Trending algorithm proposed to identify wind power ramp events,wind power ramp event prediction is ultimately achieved,and so as to make countermeasures in *** results show that the proposed method is more beneficial to the prediction of wind power ramp events.

关键词： Wind power ramp event prediction wind power prediction non-parametric kernel density estimation

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Medical Named Entity Recognition Based on Multiple Features and Dynamic Fusion

Medical Named Entity Recognition Based on Multiple Features ...

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Data Science and Network Security (ICDSNS), International Conference on

作者： Yi Ren Wei Mao School of Computer and Control Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

To Address the challenges of unclear entity delineations and insufficient utilization of Semantic data, This study introduces a novel fusion approach leveraging multiple features for dynamic integration. To enrich the semantic representation of text, Textual model's embedding layer incorporates diverse techniques. Firstly, convolutional neural networks are used to implement font embedding, enriching the character representation of text through Chinese character fonts. Secondly, SoftLexicon is used to fuse word information from the dictionary and enhance entity boundary information. To achieve multi feature embedding, the word vector's semantic information is modeled using McBERT. In the feature extraction layer, long-distance inter-character semantic information is obtained by IDCNN, whereas contextual semantic information is obtained via BiLSTM. At the conclusion, the utilization of the dynamic fusion methodology is employed to accomplish the task of recognizing named entities, leveraging the conditional random field model. The model demonstrated an F1 score of 88.96% on the Chinese medical information evaluation dataset, surpassing the BERT BiLSTM CRF model by 3.81%, thereby validating its effectiveness.

关键词： Technological innovation Dictionaries Text recognition Semantics Named entity recognition Network security Feature extraction

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Smart Diagnosis of Active Systems

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Procedia computer Science 2024年 246卷 576-585页

作者： Gianfranco Lamperti Xiangfu Zhao Department of Information Engineering University of Brescia 25123 Brescia Italy School of Computer and Control Engineering Yantai University 264005 Yantai China

Automated diagnosis has always been a challenging task to AI. When model-based diagnosis is adopted, a model of the system is required in order to generate a set of diagnoses based on a collection of observations, where a diagnosis is a set of faulty components or, more generally, a set of faults ascribed to components. An active system (AS) is an asynchronous, distributed discrete-event system, whose model consists of a topology (how components are connected to one another), and a communicating automaton for each component (the mode in which a component reacts to events). A problem afflicting all model-based approaches to diagnosis is a possibly large number of diagnoses explaining the observations, which may jeopardize the task of a diagnostician in charge of monitoring the system, owing to the cognitive overload raised by an overwhelming number of faulty scenarios to examine. This is exacerbated in critical application domains, where, under uncertain conditions, an artificial agent is supposed to perform recovery actions in real-time, even in the order of milliseconds, to possibly restore the system. To make diagnosis of ASs viable in critical, real-time application domains, a Smart Diagnosis Engine is presented, which is grounded on two heuristics: (1) if a diagnosis δ is a superset of a diagnosis δ ' , then δ is ignored (minimality); (2) if the cardinality (number of faults) of a diagnosis δ is lower than the cardinality of a diagnosis δ ' , then δ is generated before δ ' (sorting). Consequently, the diagnosis output consists in a sequence of minimal diagnoses that are generated in ascending order by cardinality. As indicated by the experimental results, the overall improvement is twofold: most likely diagnoses are generated upfront, thereby supporting real-time recovery actions; also, the abductive search in the behavior space of the AS is reduced considerably, owing to the pruning of the trajectories that will not generate minimal diagnoses, thereby resulting

关键词： model-based reasoning real-time diagnosis minimal diagnosis active systems discrete-event systems finite automata uncertainty

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