Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)...
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Edge caching has emerged as a promising application paradigm in 5G networks,and by building edge networks to cache content,it can alleviate the traffic load brought about by the rapid growth of Internet of Things(IoT)services and *** to the limitations of Edge Servers(ESs)and a large number of user demands,how to make the decision and utilize the resources of ESs are *** this paper,we aim to minimize the total system energy consumption in a heterogeneous network and formulate the content caching optimization problem as a Mixed Integer Non-Linear programming(MINLP).To address the optimization problem,a Deep Q-Network(DQN)-based method is proposed to improve the overall performance of the system and reduce the backhaul traffic *** addition,the DQN-based method can effectively solve the limitation of traditional reinforcement learning(RL)in complex *** results show that the proposed DQN-based method can greatly outperform other benchmark methods,and significantly improve the cache hit rate and reduce the total system energy consumption in different scenarios.
In this letter, we investigate the application of the subgradient method to design efficient algorithm for linear programming (LP) decoding of binary linear codes. A major drawback of the original formulation of LP de...
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In this letter, we investigate the application of the subgradient method to design efficient algorithm for linear programming (LP) decoding of binary linear codes. A major drawback of the original formulation of LP decoding is that the description complexity of the feasible region is exponential in the check node degrees of the code. In order to tackle the problem, we propose a processing technique for LP decoding with the subgradient method, whose complexity is linear in the check node degrees. Consequently, a message-passing type decoding algorithm can be obtained, whose per-iteration complexity is extremely low. Moreover, if the algorithm converges to a valid codeword, it is guaranteed to be a maximum likelihood codeword. Simulation results on several binary linear codes with short lengths suggest that the performances of LP decoding based on the subgradient method and the state-of-art LP decoding implementation approach are comparable.
Electromagnetic theory is central to physics. An undergraduate major in physics typically takes a semester or a year of electromagnetic theory as a junior or senior, and a graduate student in physics typically takes a...
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Electromagnetic theory is central to physics. An undergraduate major in physics typically takes a semester or a year of electromagnetic theory as a junior or senior, and a graduate student in physics typically takes an additional semester or year at amore advanced level. In fall 2023, the author taught his undergraduate electricity and magnetism class using numerical methods in Haskell in parallel with traditional analytical methods. This article describes what functional programming has to offer to physics in general, and electromagnetic theory in particular. We give examples from vector calculus, the mathematical language in which electromagnetic theory is expressed, and electromagnetic theory itself.
This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition,...
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This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set programming (ASP). These include problems related to the dependency or conflicts between concerns, how to mitigate an issue, and what the most suitable mitigation strategy for a given issue would be. It then shows how ASP can be used to develop an implementation that addresses the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.
Autoencoders are powerful models for non-linear dimensionality reduction. However, their neural network structure makes it difficult to interpret how the high dimensional features relate to the lowdimensional embeddin...
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ISBN:
(数字)9783031295737
ISBN:
(纸本)9783031295720;9783031295737
Autoencoders are powerful models for non-linear dimensionality reduction. However, their neural network structure makes it difficult to interpret how the high dimensional features relate to the lowdimensional embedding, which is an issue in applications where explainability is important. There have been attempts to replace both the neural network components in autoencoders with interpretable genetic programming (GP) models. However, for the purposes of interpretable dimensionality reduction, we observe that replacing only the encoder with GP is sufficient. In this work, we propose the Genetic programming Encoder for Autoencoding (GPE-AE). GPE-AE uses a multi-tree GP individual as an encoder, while retaining the neural network decoder. We demonstrate that GPE-AE is a competitive non-linear dimensionality reduction technique compared to conventional autoencoders and a GP based method that does not use an autoencoder structure. As visualisation is a common goal for dimensionality reduction, we also evaluate the quality of visualisations produced by our method, and highlight the value of functional mappings by demonstrating insights that can be gained from interpreting the GP encoders.
Modern society needs specialists able to adapt to the increasing complexity of developed technologies. The training of these specialists from the basic level to the one that allows advanced research in the field of co...
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Student attrition is a long-standing problem in computerscience (CS), as in many other disciplines, and it has gained momentum in the academic sphere. This study employs bibliometric analysis to shed light on the res...
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Student attrition is a long-standing problem in computerscience (CS), as in many other disciplines, and it has gained momentum in the academic sphere. This study employs bibliometric analysis to shed light on the research stream of student attrition within CS. Bibliometric analysis is a popular technique for evaluating published scientific articles when empirical contributions are producing voluminous research streams. We collected 1310 articles from the Web of science and Scopus databases, published over a period of 22 years from 2000 to 2022, to analyze the most relevant publication venues in the study of attrition in CS. Further analysis revealed the most cited institutions, countries, key themes, and other conceptual information. Keywords, such as "retention," "computerscience education," "gender," "introductory programming," and "student success" emerged as dominant themes in attrition studies. As researchers work intensively to reduce attrition within CS, these thematic areas may continue to shape the future direction of attrition studies. Our study provides a comprehensive overview of research hotspots, thematic areas, and future directions for attrition studies in CS. This outcome could be valuable for young and emerging scholars who are starting their careers and looking to identify research hotspots in this field of interest.
Learners have some difficulty regulating their own programming learning process, self-regulated programming learning interventions can be designed to solve this problem and help stimulate SRL. In this paper, we examin...
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Contribution: In this article, the authors present findings and insights on the efficacy of using an educational block-based programming (BBP) environment--Blocks4DS, to teach the binary search tree (BST). Background:...
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Contribution: In this article, the authors present findings and insights on the efficacy of using an educational block-based programming (BBP) environment--Blocks4DS, to teach the binary search tree (BST). Background: For a decade, BBP environments have been a hot topic in the computerscience education (CSEd) community to promote interactive active learning of programming. However, little attention has been paid to BBP environments' efficacy on nonintroductory courses like data structures and algorithms (DS&A). DS&A courses are challenging to students due to levels of abstraction that could be reduced by syntax-free features existing in BBP interfaces. Research Questions: 1) Can undergraduate computing-major students learn about the BST using Blocks4DS? 2) Do undergraduate computing-major students understand better BSTs when learning with a BBP environment? and 3) How do undergraduate computing-major students perceive Blocks4DS for nonintroductory computerscience (CS) topics? Methodology: A mixed-method study was designed, using a control and intervention group with 83 first and second-year CS students, to evaluate the efficacy of Blocks4DS compared to traditional instructional methods (e.g., whiteboard and pseudocode). The authors evaluated its efficacy based on students' conceptual understanding and perceptions. Findings: It was found that, regardless of prior experience with text-based programming languages and instructional approaches, students introduced to the BST with Blocks4DS gained significant conceptual understanding, and performed as well as peers instructed with pseudocode. Also, 83.3% of students found the tool engaging and 72.3% found it useful to learn DS&A. This suggests that Blocks4DS can be used to teach DS&A.
Memoization is a computational technique for speeding up the complexity of computer algorithms. It stores the previously calculated results and invokes them later in the body of the algorithm when necessary. On the ot...
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