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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Na Liu Lianming Xu Suxian Fu Li Wang School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Model-based shallow machine-learning methods and data-driven deep-learning (DL) methods have been advanced to address hyperspectral and multispectral image fusion (HS–MS fusion). Nonetheless, model-based approaches, which meticulously craft regularization terms within optimization models using hand-engineered priors, often struggle to pinpoint the optimal solution efficiently. DL-based methods, which train on extensive datasets to learn a non-linear mapping for generating a high spatial and spectral resolution image (HS 2 I), exhibit limited generalization capabilities when applied to novel and diverse test datasets. To improve the generalization ability and optimization efficiency of the existing HS–MS fusion methods, a novel deep low-rank prior (DLRP)-based self-supervised HS–MS fusion approach is devised. It incorporates a low-rank learning paradigm to produce the fused HS 2 I, subject to the constraints imposed by loss functions. Instead of deriving the solution via solving a low-rank approximation optimization problem, deep image prior (DIP) learned by a two-dimensional CNN and a one-dimensional CNN are integrated as the low-rank prior.

关键词： Training Optimization models Machine learning Signal processing Network architecture Sensors Speech processing Spatial resolution Optimization Hyperspectral imaging

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How Well Do Large Language Models Serve as End-to-End Secure Code Producers?

arXiv

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arXiv 2024年

作者： Gong, Jianian Duan, Nachuan Tao, Ziheng Gong, Zhaohui Yuan, Yuan Huang, Minlie School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory Beijing100191 China Tsinghua University Zhongguancun Laboratory Beijing China

The rapid advancement of large language models (LLMs) such as GPT-4 has revolutionized the landscape of software engineering, positioning these models at the core of modern development practices. As we anticipate these models to evolve into the primary and trustworthy tools used in software development, ensuring the security of the code they produce becomes paramount. How well can LLMs serve as end-to-end secure code producers? This paper presents a systematic investigation into LLMs’ inherent potential to generate code with fewer vulnerabilities. Specifically, We studied GPT-3.5 and GPT-4’s capability to identify and repair vulnerabilities in the code generated by four popular LLMs including themselves (GPT-3.5, GPT-4, Code Llama, and CodeGeeX2). By manually or automatically reviewing 4,900 pieces of code, our study reveals that: (1) large language models lack awareness of scenario-relevant security risks, which leads to the generation of over 75% vulnerable code on the SecurityEval benchmark;(2) LLMs such as GPT-3.5 and GPT-4 are unable to precisely identify vulnerabilities in the code they generated;(3) GPT-3.5 and GPT-4 can achieve 33.2%~59.6% success rates in repairing the insecure code produced by the 4 LLMs, but they both perform poorly when repairing self-produced code, indicating self-repair "blind spots". To address the limitation of a single round of repair, we developed a lightweight tool that prompts LLMs to construct safer source code through an iterative repair procedure based on the insights gained from our study. Experiments show that assisted by semantic analysis engines, our tool significantly improves the success rates of repair to 65.9%~85.5%. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Representation Learning with CGAN for Casual Inference

Representation Learning with CGAN for Casual Inference

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作者： Zhaotian Weng Jianbo Hong Lan Wang School of Software Tsinghua University Collage of Computer Science and Technology Jilin University College of Engineering Monash University

Conditional Generative Adversarial Nets(CGAN) is often used to improve conditional image generation ***,there is little research on Representation learning with CGAN for causal *** paper proposes a new method for finding representation learning functions by adopting the adversarial *** apply the pattern of CGAN and theoretically demonstrate the feasibility of finding a suitable representation function in the context of two distributions being *** theoretical result shows that when two distributions are balanced,the ideal representation function can be found and thus can be used to further research.

关键词： casual inference generative adversarial nets conditional generative adversarial nets representation learning

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A Cloud Computing Model for Libyan Higher Education Institutions

A Cloud Computing Model for Libyan Higher Education Institut...

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sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Najwa A. Imragaa Entesar H. Alfsai Bothaina F. Gargoum Abdelsalam M. Maatuk Department of Computer Science University of Benghazi Benghazi Libya Department of Software Engineering University of Benghazi Benghazi Libya

Cloud computing is now changing the IT landscape and playing a significant economic role for companies that develop software. Users of educational resources now have more flexibility and access because of cloud computing. Students, faculty members, and support staff all use cloud computing in academic institutions. Internet-based computing, known as "Cloud Computing," allows users to share IT resources securely and efficiently. The use of cloud computing in universities has many benefits, such as access to files, e-mail, databases, educational resources, and search applications as requested by faculty, administrators, staff, and students at a university alike. Moreover, cloud computing significantly reduces the complexity and cost of IT in universities. This paper discusses cloud computing, its structures, types, and services it provides, and then examines the possibility of implementing cloud services in universities and discovers the various opportunities and benefits of cloud services. The paper presents a proposed model for cloud computing for Libyan universities, which was applied to the University of Benghazi and produced satisfactory results and facilitated a lot of university work. Through the application of the proposed model, effective management of the technological needs of the Libyan university has been achieved, such as connecting programs and providing a platform for development, data storage and computing.

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Sensor Technology for Car Park Management System: Nile University of Nigeria

Sensor Technology for Car Park Management System: Nile Unive...

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Multidisciplinary engineering and Applied science (ICMEAS), International Conference on

作者： Abdulateef Saleh Jibrin Umar Adam Ibrahim Abubkar Ibrahim Dalhatu Software Engineering Nile University of Nigeria Abuja Nigeria Computer Science Nile University of Nigeria Abuja Nigeria

The escalating parking space problem at Nile University of Nigeria poses a growing threat due to rising enrollments. The absence of a structured parking management system compounds this issue. This project aims to develop a system to inform users about available parking slots, mitigating parking congestion, and providing valuable insights for carpark management improvements

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A Comprehensive Prediction Model for T20 and Test Match Outcomes Using Machine Learning

A Comprehensive Prediction Model for T20 and Test Match Outc...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Zoha Ahsan Shahwaiz Ghumman Attique Ur Rehman Sabeen Javaid Tahir Muhammad Ali Azka Mir Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University for Sciences and Technology Mubarak Al-Abdullah Kuwait

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Cricket is a popular sport worldwide, played with a bat and balls. This paper used classification and regression to predict the T20 and Test matches results and scores because cricket fans and analysts always want to predict which team will win the match and how much score a team will make. We have used a supervised machine learning technique to predict the score and result for T20 and Test matches. Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model. For the prediction of winning and losing, we have used Naïve Bayes, Gradient Boosted Trees, Logistic Regression, Deep learning, Decision Tree, Random Forest, and Generalized Linear Model. For Score Prediction, we have used Decision Trees, Random Forests, Gradient Boosted Trees, Generalized Linear Models, and Deep Learning. We've shown the most accurate model after evaluating the accuracy percentages of the several classifiers listed above. Our introduced model has gained an accuracy rate of 96.15% using the Gradient Boosted Trees classifier for Match Score Prediction and 71.72% accuracy by using the Generalized Linear Model for match result prediction. The Rapid Miner tool has been used to perform machine learning techniques to train models. This paper discusses the effectiveness and use of machine learning methods to develop highly accurate models for Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model.

关键词： Training Deep learning Logistic regression Fans Accuracy Computational modeling Predictive models

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Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年

作者： Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ma, Long Hou, Junhui Liu, Yuan Wang, Wenping The School of Software Shandong University Jinan250100 China The Department of Computer Science City University of Hong Kong Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore The Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States

As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero-level set, would result in sub-optimal reconstruction quality. In this paper, we propose the scaled-squared distance function (S2DF), a novel implicit surface representation for modeling arbitrary surface types. S2DF does not distinguish between inside and outside regions while effectively addressing the non-differentiability issue of UDF at the zero-level set. We demonstrate that S2DF satisfies a second-order partial differential equation of Monge-Ampere-type, allowing us to develop a learning pipeline that leverages a novel Monge-Ampere regularization to directly learn S2DF from raw unoriented point clouds without supervision from ground-truth S2DF values. Extensive experiments across multiple datasets show that our method significantly outperforms state-of-the-art supervised approaches that require ground-truth surface information as supervision for training. The code will be publicly available at https://***/chuanxiang-yang/S2DF. © 2025 IEEE. All rights reserved.

关键词： Supervised learning

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Deep Reinforcement Learning for Multi-Objective Resource Allocation in Multi-Platoon Cooperative Vehicular Networks

Deep Reinforcement Learning for Multi-Objective Resource All...

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作者： Xu, Yuanyuan Zhu, Kun Xu, Hu Ji, Jiequ Hohai University College of Computer and Information Nanjing211100 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China University of Victoria Department of Electrical and Computer Engineering VictoriaV8P 5C2 Canada

Grouping vehicles into platoons is a promising cooperative driving scenario to enhance the traffic safety and capacity of future vehicular networks. However, fast changing channel conditions in multi-platoon vehicular networks cause tremendous uncertainty for resource allocation. In addition, the unprecedented proliferation of various emerging vehicle-to-infrastructure (V2I) applications may result in some service demands with conflicting quality of experience. In this paper, we formulate a multi-objective resource allocation problem, which maximizes the transmission success ratio of intra-platoon communications and the mean opinion score (MOS) of V2I communication links. To efficiently solve this multi-objective optimization problem, we resort to a deep reinforcement learning (DRL) framework. Specifically, we divide it into a set of scalar optimization subproblems based on the weighted sum approach and model each one as a partially observable stochastic game (P-OSG), where each platoon acts as an agent and the actions taken by all platoons correspond to the resource allocation solution. We further propose a contribution-based dual-clip proximal policy optimization (CD-PPO) algorithm to deal with each subproblem, which is a DRL algorithm based on the actor-critic framework. The network parameters of all subproblems are then optimized collaboratively by using the proposed training algorithm and the neighborhood parameter transfer strategy. The desired Pareto front is obtained when all subproblems are solved. Simulation results reveal that the proposed algorithm can outperform other algorithms in terms of the MOS and transmission success ratio. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Write-Optimized Persistent Hash Index for Non-Volatile Memory

Write-Optimized Persistent Hash Index for Non-Volatile Memor...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Renzhi Xiao Dan Feng Yuchong Hu Yucheng Zhang Lanlan Cui Lin Wang School of Software Engineering Jiangxi University of Science and Technology Nanchang China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Engineering XI'AN University of Technology Xi'an China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

A hashing index provides rapid search performance by swiftly locating key-value items. Non-volatile memory (NVM) technologies have driven research into hashing indexes for NVM, combining hard disk persistence with DRAM-level performance. Nevertheless, current NVM-based hashing indexes must tackle data inconsistency challenges caused by NVM write reordering or partial writes, and mitigate rapid local wear due to frequent updates, considering NVM's limited endurance. The temporary allocation of buckets in NVM-based chained hashing to resolve hash collisions prolongs the critical path for writing, thus hampering write performance. This paper presents WOPHI, a write-optimized persistent hash index scheme for NVM. By utilizing log-free failure-atomic writes, WOPHI minimizes data consistency overhead and addresses hash conflicts with bucket pre-allocation. Experimental results underscore WOPHI's significant performance enhancements, with insertion latency slashed by up to 88.2% and deletion latency boosted by up to 82.6% compared to existing state-of-the-art schemes. Moreover, WOPHI substantially mitigates data consistency overhead, reducing cache line flushes by 59.3%, while maintaining robust write throughput for insert and delete operations.

关键词： Nonvolatile memory Europe Writing Throughput Hard disks Indexes Resource management

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DULRTC-RME: A Deep Unrolled Low-rank Tensor Completion Network for Radio Map Estimation

DULRTC-RME: A Deep Unrolled Low-rank Tensor Completion Netwo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yao Wang Xin Wu Lianming Xu Na Liu Li Wang School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Radio maps enrich radio propagation and spectrum occupancy information, which provides fundamental support for the operation and optimization of wireless communication systems. Traditional radio maps are mainly achieved by extensive manual channel measurements, which is time-consuming and inefficient. To reduce the complexity of channel measurements, radio map estimation (RME) through novel artificial intelligence techniques has emerged to attain higher resolution radio maps from sparse measurements or few observations. However, black box problems and strong dependency on training data make learning-based methods less explainable, while model-based methods offer strong theoretical grounding but perform inferior to the learning-based methods. In this paper, we develop a deep unrolled low-rank tensor completion network (DULRTC-RME) for radio map estimation, which integrates theoretical interpretability and learning ability by unrolling the tedious low-rank tensor completion optimization into a deep network. It is the first time that algorithm unrolling technology has been used in the RME field. Experimental results demonstrate that DULRTC-RME outperforms existing RME methods.

关键词： Learning systems Wireless communication Training Tensors Estimation Signal processing algorithms Training data Radio propagation Speech processing Optimization

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