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arXiv 2024年

作者： Cao, Jialun Chen, Zhiyong Wu, Jiarong Cheung, Shing-Chi Xu, Chang Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Code generation benchmarks such as HumanEval are widely adopted to evaluate LLMs’ capabilities. However, after consolidating the latest 24 benchmarks, we noticed three significant imbalances. First, imbalanced programming language. 95.8% of benchmarks involve Python, while only 5 benchmarks involve Java, resulting in an insufficient understanding of LLMs’ capability to generate Java code. Second, imbalanced code granularity. Function-/statement-level benchmarks account for over 83.3% of benchmarks. Only a mere handful extends to class-/project-levels, and all are limited to Python. Third, lacking advanced features. Existing benchmarks primarily assess basic coding skills (e.g., variables, operators, and control structures), while overlooking advanced Object-Oriented Programming (OOP) features (i.e., encapsulation, inheritance, and polymorphism). Considering the prevalence of these advanced features in real-world Java project development, constructing benchmarks to test LLMs on handling OOP features is necessary. To fill these gaps, we propose JavaBench, a project-level Java benchmark that exercises OOP features. It comprises four Java projects with 389 methods in 106 Java classes. The test coverage is up to 92%, and JavaBench is attested by 282 undergraduate students, reaching a 90.93/100 average score (i.e., pass rate against the test suite), ensuring the quality of documentation, code skeleton, and tests. To better evaluate LLM’s capability against JavaBench, we introduce a systematic evaluation design covering three context settings and five synthesis strategies at two granularities using three hierarchical metrics. Our extensive experiment yields several interesting findings. First, we noticed that regarding project-level Java programming, LLMs are far behind undergraduate students (no project can be correctly completed by any studied LLMs, and at most 41.17% Pass@5 in a more relaxed evaluation). Second, using method signature as prompt context may strike an ide

关键词： Students

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IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization

IOMatch: Simplifying Open-Set Semi-Supervised Learning with ...

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International Conference on computer Vision (ICCV)

作者： Zekun Li Lei Qi Yinghuan Shi Yang Gao State Key Laboratory for Novel Software Technology and National Institute of Healthcare Data Science Nanjing University School of Computer Science and Engineering Southeast University

Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled data will inevitably contain unseen-class outliers not belonging to any of the labeled classes. To deal with the challenging open-set SSL task, the mainstream methods tend to first detect outliers and then filter them out. However, we observe a surprising fact that such approach could result in more severe performance degradation when labels are extremely scarce, as the unreliable outlier detector may wrongly exclude a considerable portion of valuable inliers. To tackle with this issue, we introduce a novel open-set SSL framework, IOMatch, which can jointly utilize inliers and outliers, even when it is difficult to distinguish exactly between them. Specifically, we propose to employ a multi-binary classifier in combination with the standard closed-set classifier for producing unified open-set classification targets, which regard all outliers as a single new class. By adopting these targets as open-set pseudo-labels, we optimize an open-set classifier with all unlabeled samples including both inliers and outliers. Extensive experiments have shown that IOMatch significantly outperforms the baseline methods across different benchmark datasets and different settings despite its remarkable simplicity. Our code and models are available at https://***/nukezil/IOMatch.

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer science & technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization

arXiv

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arXiv 2023年

作者： Guo, Yanheng Zhang, Yan Wu, Linjie Li, Mengxia Cai, Xingjuan Chen, Jinjun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Shanxi Taiyuan030024 China State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

The Mobile Edge Computing (MEC) system located close to the client allows mobile smart devices to offload their computations onto edge servers, enabling them to benefit from low-latency computing services. Both cloud service providers and users seek more comprehensive solutions, necessitating judicious decisions in service deployment and task offloading while balancing multiple objectives. This study investigates service deployment and task offloading challenges in a multi-user environment, framing them as a multi-task high-dimensional multi-objective optimization (MT-HD-MOO) problem within an edge environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost as deployment objectives, network reliability is also incorporated. Furthermore, to promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective, in addition to latency, energy consumption, and cost. Additionally, this paper designs a MT-HD-MOO algorithm based on a multi-selection strategy to address this model and its solution. By employing diverse selection strategies, an environment selection strategy pool is established to enhance population diversity within the high-dimensional objective space. Ultimately, the algorithm’s effectiveness is verified through simulation experiments. © 2023, CC BY-NC-ND.

关键词： Multiobjective optimization

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MoMa: A Modular Deep Learning Framework for Material Property Prediction

arXiv

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arXiv 2025年

作者： Wang, Botian Ouyang, Yawen Li, Yaohui Wang, Yiqun Cui, Haorui Zhang, Jianbing Wang, Xiaonan Ma, Wei-Ying Zhou, Hao Tsinghua University China Department of Computer Science and Technology Tsinghua University China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University China Department of Chemical Engineering Tsinghua University China

Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a Modular framework for Materials that first trains specialized modules across a wide range of tasks and then adaptively composes synergistic modules tailored to each downstream scenario. Evaluation across 17 datasets demonstrates the superiority of MoMa, with a substantial 14% average improvement over the strongest baseline. Few-shot and continual learning experiments further highlight MoMa’s potential for real-world applications. Pioneering a new paradigm of modular material learning, MoMa will be open-sourced to foster broader community collaboration. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances

arXiv

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arXiv 2025年

作者： Dinitz, Michael Fan, Chenglin Liu, Jingcheng Upadhyay, Jalaj Zou, Zongrui Department of Computer Science Johns Hopkins University United States Department of Computer Science and Engineering Seoul National University Korea Republic of State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University China Management Science & Information Systems Department Rutgers University United States

We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected graph, we treat the weights of edges as sensitive information, and two graphs are neighbors if their edge weights differ in one edge by at most one. We obtain efficient algorithms with significantly improved bounds on a broad class of graphs which we refer to as recursively separable. In particular, for any n-vertex Kh-minor-free graph, our algorithm achieve an additive error of Õ(h(nW)1/3), where W represents the maximum edge weight;For grid graphs, the same algorithmic scheme achieve additive error of Õ(n1/4√W). Our approach can be seen as a generalization of the celebrated binary tree mechanism for range queries, as releasing range queries is equivalent to computing all-pair distances on a path graph. In essence, our approach is based on generalizing the binary tree mechanism to graphs that are recursively separable. Copyright © 2025, The Authors. All rights reserved.

关键词： Binary trees

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A Priori Estimation of the Approximation, Optimization and Generalization Errors of Random Neural Networks for Solving Partial Differential Equations

arXiv

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arXiv 2024年

作者： Xu, Xianliang Li, Ye Huang, Zhongyi College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China State Key Laboratory for Novel Software Technology Nanjing University China Department of Mathematical Sciences Tsinghua University China

In recent years, neural networks have achieved remarkable progress in various fields and have also drawn much attention in applying them on scientific problems. A line of methods involving neural networks for solving partial differential equations (PDEs), such as Physics-Informed Neural Networks (PINNs) and the Deep Ritz Method (DRM), has emerged. Although these methods outperform classical numerical methods in certain cases, the optimization problems involving neural networks are typically non-convex and non-smooth, which can result in unsatisfactory solutions for PDEs. In contrast to deterministic neural networks, the hidden weights of random neural networks are sampled from some prior distribution and only the output weights participate in training. This makes training much simpler, but it remains unclear how to select the prior distribution. In this paper, we focus on Barron type functions and approximate them under Sobolev norms by random neural networks with clear prior distribution. In addition to the approximation error, we also derive bounds for the optimization and generalization errors of random neural networks for solving PDEs when the solutions are Barron type functions. © 2024, CC BY.

关键词： Numerical methods

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BI-based Reasoning about Quantum Programs with Heap Manipulations

arXiv

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arXiv 2024年

作者： Su, Bonan Zhou, Li Feng, Yuan Ying, Mingsheng Department of Computer Science and Technology Tsinghua University Beijing China Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Ultimo Australia

We provide well-founded semantics for a quantum programming language Qwhile-hp with heap manipulations, where allocation statements follow a dirty pattern, meaning that newly allocated qubits can nondeterministically assume arbitrary initial states. To thoroughly characterize heap manipulations in the quantum setting, we develop a quantum BI-style logic that includes interpretations for separating implication (−∗) and separating conjunction (∗). We then adopt this quantum BI-style logic as an assertion language to reason about heap-manipulated quantum programs and present a quantum separation logic which is sound and relatively complete. Finally, we apply our framework to verify the correctness of various practical quantum programs and to prove the correct usage of dirty ancilla qubits. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

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Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach

Joint Mobile Edge Caching and Pricing: A Mean-Field Game App...

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International Conference on Data Engineering

作者： Yin Xu Xichong Zhang Mingjun Xiao Jie Wu An Liu Sheng Zhang School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Computer and Information Sciences Temple University Philadelphia USA School of Computer Science and Technology Soochow University Suzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines.

关键词： Economics Computational modeling Stochastic processes Pricing Games Differential games Data engineering

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AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification

arXiv

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arXiv 2022年

作者： Yang, Suorong Li, Jinqiao Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China The State Key Laboratory for Novel Software Technology School of Electronic Science and Engineering Nanjing University Nanjing210023 China

Data augmentation is a widely used technique for enhancing the generalization ability of convolutional neural networks (CNNs) in image classification tasks. Occlusion is a critical factor that affects on the generalization ability of image classification models. In order to generate new samples, existing data augmentation methods based on information deletion simulate occluded samples by randomly removing some areas in the images. However, those methods cannot delete areas of the images according to their structural features of the images. To solve those problems, we propose a novel data augmentation method, AdvMask, for image classification tasks. Instead of randomly removing areas in the images, AdvMask obtains the key points that have the greatest influence on the classification results via an end-to-end sparse adversarial attack module. Therefore, we can find the most sensitive points of the classification results without considering the diversity of various image appearance and shapes of the object of interest. In addition, a data augmentation module is employed to generate structured masks based on the key points, thus forcing the CNN classification models to seek other relevant content when the most discriminative content is hidden. AdvMask can effectively improve the performance of classification models in the testing process. The experimental results on various datasets and CNN models verify that the proposed method outperforms other previous data augmentation methods in image classification tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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