Privacy-preserving combinatorial auctions, also known as sealed-bid combinatorial auctions, allow bidders to place bids on combinations of homogeneous or heterogeneous items without revealing the bidding prices (excep...
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ISBN:
(纸本)9789819609567;9789819609574
Privacy-preserving combinatorial auctions, also known as sealed-bid combinatorial auctions, allow bidders to place bids on combinations of homogeneous or heterogeneous items without revealing the bidding prices (except for the winning ones) to any individual party. There is a significant lack of literature addressing this crucial and practical issue. We are bridging this gap by introducing two novel protocols for centralized and distributed auction systems. In the centralised approach, we integrate homomorphic encryption and DGK/Veugen secure comparison in a 2-server protocol for sealed-bid auctions. In the distributed approach we introduce a novel protocol based on homomorphic encryption and perturbation to allow an auction issuer and bidders to collaborate without any third party.
Linear codes related to applications in Galois Geometry often require a certain divisibility of the occurring weights. In this paper we present an algorithmic framework for the classification of linear codes over fini...
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ISBN:
(纸本)9783031645280;9783031645297
Linear codes related to applications in Galois Geometry often require a certain divisibility of the occurring weights. In this paper we present an algorithmic framework for the classification of linear codes over finite fields with restricted sets of weights. The underlying algorithms are based on lattice point enumeration and integer linear programming. We present new enumeration and non-existence results for projective two-weight codes, divisible codes, and additive F-4-codes.
This paper presents a method for the optimized reconfiguration of radial distribution systems that explicitly considers the protection systems constraints. A fully automated method based on graph analysis is proposed ...
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Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in e...
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Can current robotic technologies truly replicate the full scope and intricacies of human labour?In practice,the adoption of robots remains limited,especially in open,unstructured environments commonly encountered in everyday scenarios such as services,healthcare,agriculture,construction,and numerous other *** the perspective of general robotic manipulation,the challenges arise from three factors.(1)High operational barriers:human operators are obliged to master specialized robotic programming languages and gain a deep understanding of the tasks at *** tasks need to be broken down into action-level robotic programs,which results in high labour costs.(2)Limited autonomous task execution:robots lack the capability to independently plan and execute actions required to achieve the target *** limitation renders them unsuitable for deployment in open,unstructured environments that demand sophisticated interaction and seamless collaboration with humans.
This research full paper describes our experience in teaching parallel programming for students without previous knowledge of basic concepts of computing, comparing their levels of learning. The use of parallel softwa...
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ISBN:
(数字)9781728189611
ISBN:
(纸本)9781728189628
This research full paper describes our experience in teaching parallel programming for students without previous knowledge of basic concepts of computing, comparing their levels of learning. The use of parallel software grew considerably in recent years due to the increasing availability of multi and many-core devices. The evolution of hardware and software resources collaborated for a remarkable computational processing power offered by parallel programs. However, parallel programming is taught usually in more advanced years of the undergraduate computer courses, due to its supposed prerequisites as sequential programming, operating systems, computer architectures and others. Postponing parallel programming teaching hinder students to apply parallelism other subjects, reducing the probability of these future professionals think on parallel solutions naturally. We executed 05 experiments teaching parallel programming subjects for 252 students. We analyzed whether students without prerequisites could learn parallel programming in the same level verified with students with prior computing knowledge. We used three different teaching methodologies: traditional, Problem Based Learning (PBL), and Team-Based Learning (TBL). The teaching and learning evaluation took into account such metrics: parallelism thinking of students, use of programming-model, correct output of the program, source-code readability and satisfaction of the students. The paper shows that it is possible to teach parallel programming to students without previous knowledge of computing, obtaining high scores and interest in such learning. Our results contribute positively to disseminate parallel programming, which is vital to extract performance from nowadays computers.
The importance of social network alignment (SNA) for various downstream applications, such as social network information fusion and e-commerce recommendation, has prompted numerous professionals to develop and share S...
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The importance of social network alignment (SNA) for various downstream applications, such as social network information fusion and e-commerce recommendation, has prompted numerous professionals to develop and share SNA tools. However, malicious actors can exploit these tools to integrate sensitive user information, thereby posing cybersecurity risks. Although many researchers have explored attacking SNA (ASNA) through network modification attacks to protect users, practical feasibility remains challenging. In this study, we propose an effective node injection attack via a dynamic programming framework (DPNIA) to address the problem of modeling and solving ASNA within a limited time and balancing the costs and benefits. DPNIA models ASNA as a problem of maximizing the number of confirmed incorrect correspondent node pairs with greater similarity scores than the pairs between existing nodes, thereby making ASNA solvable. A cross-network evaluation method is employed directly to identify node vulnerabilities, facilitating progressive attacking from easy to difficult. In addition, an optimal injection strategy searching method based on dynamic programming is used to determine which links should be added between the injected and existing nodes, thereby enhancing the effectiveness of the attack at a low cost. Experiments on four real-world datasets demonstrated that DPNIA consistently and significantly surpasses various baselines when attacking both multiple networks simultaneously and a single network.
The integration of Large Language Models (LLMs) into education marks a significant advancement toward personalized and adaptive learning environments, particularly in programming education. Addressing the limitations ...
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programming language detection from source code excepts remains an active research field, which has already been addressed with machine learning and natural language processing. Identifying the language of short code ...
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programming language detection from source code excepts remains an active research field, which has already been addressed with machine learning and natural language processing. Identifying the language of short code snippets poses both benefits and challenges across various scenarios, such as embedded code analysis, forums, Q&A systems, search engines, source code repositories, and text editors. Existing approaches for language detection typically require multiple lines or even the entire file contents. In this article, we propose a character- level deep learning model designed to predict the programming language from a single line of code. To this aim, we construct a balanced dataset comprising 434.18 million instances across 21 languages, significantly exceeding the size of existing datasets by three orders of magnitude. Leveraging this dataset, we train a deep bidirectional recurrent neural network that achieves a 95.07% accuracy and macro-F1 score fora single-line code. To predict the programming language of multiple lines (e.g., code snippets) and entire files, we build a stacking ensemble meta-model that leverages our single-line model to efficiently recognize the language of multiple lines of code. Our system outperforms the state-of-the-art approaches not only fora single line of code, but also for snippets of 5 and 10 lines and whole files of source code. We also present PLangRec, an open-source language detection system that includes our trained models. PLangRec is freely available as a user-friendly web application, a web API, and a Python desktop program.
Python has become the de facto language for scientific computing. programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the ...
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Python has become the de facto language for scientific computing. programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High-Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. This work presents a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of automatic optimizations powered by a data-centric intermediate representation. We show performance results and scaling across CPU, GPU, FPGA, and the Piz Daint supercomputer (up to 23,328 cores), with 2.47x and 3.75x speedups over previous-best solutions, first-ever Xilinx and Intel FPGA results of annotated Python, and up to 93.16% scaling efficiency on 512 nodes. Our benchmarks were reproduced in the Student Cluster Competition (SCC) during the Supercomputing Conference (SC) 2022. We present and discuss the student teams' results.
Learning programming is perceived as hard by many students. To support students, many e-assessment and intelligent tutoring systems have been developed. These systems can automatically evaluate student submissions and...
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ISBN:
(纸本)9798400706004
Learning programming is perceived as hard by many students. To support students, many e-assessment and intelligent tutoring systems have been developed. These systems can automatically evaluate student submissions and provide feedback. Despite comprehensive research on feedback modalities, little is known about students' confidence in the correctness of their submissions when requesting feedback. Also, educators hope that students get more confident and that automatically provided feedback helps students to improve and better self-assess their work. In this paper, first-semester students are asked about their confidence in passing a requested syntax or function test before the test results are revealed to them. Students can request feedback from each of the two provided test types twice in arbitrary order. The self-rated confidence, test outcomes, time needed to enter the confidence, and correlations are analyzed in detail. The results show that the majority of students has a high confidence in their submitted work. However, students frequently over-estimate the correctness and only few under-estimate it. There is a correlation between students' confidence in their submissions and their actual performance, but this cannot be used to make reliable predictions. The test pass rate for highly confident students is higher for syntax tests than for function tests and students need more time for entering their confidence for syntax than for function tests. Over the semester, the self-rated confidence decreases. When tests are reattempted, both correctness and self-assessment abilities show improvement.
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