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12th International Conference on Information and Communication Technology and System (ICTS)

作者： Kusumawardani, Dian Muklason, Ahmad Supoyo, Vicha Azthanty Inst Teknol Sepuluh Nopember Dept Informat Syst Surabaya Indonesia

ISBN: (纸本)9781728121338

Examination timetabling problem (ETP) is one of challenging combinatorial optimization problems, either scientifically or practically. In the scientific literatures, ETP has been proven as a non-deterministic polynomial (NP)-hard problem. In practice, examination timetabling is known as tedious and stressful work especially when double degree was offered by the university. In this study, two real-world datasets of ETP are introduced and solved using a combination of graph-coloring based sequential greedy algorithm and simulated annealing (SA) algorithm. Different from the state-of-the-art approaches in the literature, in which SA was employed within meta-heuristics, in this study SA was implemented within hyper-heuristics framework. The experimental results show that the proposed algorithm successfully automate the timetabling process and generates better timetable compared to the timetable generated manually. More specifically, tested over the two datasets the proposed algorithm, in terms of soft constraint penalty that should be minimized, could improve manual timetable from 39,569 to 33,649 and from 76,763 to 34, 929. In addition to new real datasets, the main contribution of this study is a better understanding on the performance of Simulated Annealing within hyper-heuristics for solving real-world examination timetabling problem.

关键词： Examination Timetabling greedy algorithm Simulated Annealing Hyper-Heuristic Java

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Active Manifold and Model-Order Reduction to Accelerate Multidisciplinary Analysis and Optimization

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AIAA JOURNAL 2021年第11期59卷 4739-4753页

作者： Boncoraglio, Gabriele Farhat, Charbel Stanford Univ Dept Aeronaut & Astronaut Durand BldgRoom 224-2 Stanford CA 94305 USA Stanford Univ Dept Aeronaut & Astronaut Aircraft Struct Durand BldgRoom 257 Stanford CA 94305 USA

A computational framework is proposed for efficiently solving multidisciplinary analysis and optimization (MDAO) problems in a relatively high-dimensional design parameter space. It relies on projection-based reduced-order models (PROMs) and a new concept of an active manifold (AM) to mitigate the curse of dimensionality during the training of the PROMs. The AM is discovered using a deep convolutional autoencoder for dimensionality reduction: it is proposed as a superior alternative to the concept of active subspace whose capabilities are limited by the associated affine approximation. The computational framework also blends the concept of a global PROM as a surrogate model of a nonlinear partial differential equation (PDE)-based behavior function with that of a database of local, linear PROMs for approximating a linear PDE-based behavior function. It is demonstrated for the solution of a flexible instance of NASA's Common Research Model for transport aircraft, where the objective function pertains to aerodynamics, the constraint relates to flutter, and the design space contains 58 structural and shape parameters. The obtained results illustrate the potential of the AM concept for mitigating the aforementioned curse of dimensionality. They also demonstrate the feasibility of the proposed computational framework for realistic MDAO problems and its ability to reduce solution time.

关键词： Model Order Reduction Aerodynamic Configurations Transport Aircraft CFD Fuselages Computational Structural Dynamics greedy algorithm Broyden Fletcher Goldfarb Shanno Damping Ratio Structural Modeling

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Structured Robust Submodular Maximization: Offline and Online algorithms

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INFORMS JOURNAL ON COMPUTING 2021年第4期33卷 1590-1607页

作者： Torrico, Alfredo Singh, Mohit Pokutta, Sebastian Haghtalab, Nika Naor, Joseph (Seffi) Anari, Nima Univ Montreal Polytech Montreal Montreal PQ H3T 1J4 Canada Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA Berlin Inst Technol D-10623 Berlin Germany Zuse Inst Berlin D-14195 Berlin Germany Cornell Univ Dept Comp Sci Ithaca NY 14853 USA Technion Israel Inst Technol IL-3200003 Haifa Israel Stanford Univ Comp Sci Dept Stanford CA 94305 USA

Constrained submodular function maximization has been used in subset selection problems such as selection of most informative sensor locations. Although these models have been quite popular, the solutions obtained via this approach are unstable to perturbations in data defining the submodular functions. Robust submodular maximization has been proposed as a richer model that aims to overcome this discrepancy as well as increase the modeling scope of submodular optimization. In this work, we consider robust submodular maximization with structured combinatorial constraints and give efficient algorithms with provable guarantees. Our approach is applicable to constraints defined by single or multiple matroids and knapsack as well as distributionally robust criteria. We consider both the offline setting where the data defining the problem are known in advance and the online setting where the input data are revealed over time. For the offline setting, we give a general (nearly) optimal bicriteria approximation algorithm that relies on new extensions of classical algorithms for submodular maximization. For the online version of the problem, we give an algorithm that returns a bicriteria solution with sublinear regret.

关键词： submodular optimization robust optimization matroid constraints greedy algorithm online learning

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Influence maximization algorithm based on Gaussian propagation model

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INFORMATION SCIENCES 2021年 568卷 386-402页

作者： Li, WeiMin Li, Zheng Luvembe, Alex Munyole Yang, Chao Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201209 Peoples R China

The influence of each entity in a network is a crucial index of the network information dissemination. greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space's circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). In recent years, social networks have become an indispensable part of modern social life. People communicate and collaborate on various social networks, with a large amount of data being generated during the communication process. This dependence on social networks has prompted extensive analysis toward finding solutions to the intricacy of influence maximization. Influence maximization is a fundamental and critical issue. In practical applications such as the spread of news, outbreak of diseases, viral marketing, and rumor control, influence maximization techniques are required. The initial solution of influence maximiza

关键词： Social network Influence maximization Gaussian propagation model greedy algorithm

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Efficient Layer Sharing for Application Deployment in Edge Computing Environment

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IEEE Transactions on Consumer Electronics 2025年

作者： Tang, Bing Ma, Yahui Liu, Yu Yang, Qing Cao, Buqing Tang, Mingdong Guangzhou Maritime University School of Computer Science Guangzhou510725 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University of Technology School of Computer Zhuzhou412007 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510006 China

Edge computing provides consumer electronics with faster and smarter responses, thereby enhancing user experience. Based on container technology, in a cloud-edge collaborative environment, edge servers pull images from image registries in the cloud and deploy them locally to serve various smart devices and consumer electronics. Leveraging image layer sharing can reduce the amount of image transmission, as locally cached layers on edge servers need not be repeatedly pulled. This paper investigates the rational deployment of applications on edge servers under a layer sharing mechanism. Considering a scenario with distributed multiple image registries, the objective is to minimize the total deployment time under the condition of image layer sharing. In this scenario, the application deployment problem can be modeled as a variable-value 0-1 multiple knapsack problem with partial item sharing. To address this improved knapsack problem, two algorithms named greedy minimum value based on layer sharing (GD-LSV) and greedy minimum value density based on layer sharing (GD-LSVD) are proposed. Experimental simulations using real layer dataset have proved the effectiveness of the proposed algorithms. Compared with representative algorithms, as the number of applications increases, the proposed algorithms shorten the total deployment time by up to 51.78% and reduce the total storage consumption by up to 22.79%. © 2025 IEEE.

关键词： Application deployment container edge computing greedy algorithm layer sharing

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How to pack directed acyclic graphs into small blocks

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DISCRETE APPLIED MATHEMATICS 2021年 288卷 91-113页

作者： Asahiro, Yuichi Furukawa, Tetsuya Ikegami, Keiichi Miyano, Eiji Yagita, Tsuyoshi Kyushu Sangyo Univ Dept Informat Sci Fukuoka 8138503 Japan Kyushu Univ Dept Econ Engn Fukuoka 8190395 Japan Kyushu Inst Technol Dept Artificial Intelligence Fukuoka 8208502 Japan Kyushu Inst Technol Dept Syst Design & Informat Fukuoka 8208502 Japan

This paper studies the following variant of clustering or laying out problems for directed acyclic graphs (DAG for short), called the minimum block transfer problem. The objective of this problem is to find a partition of a node set which satisfies the following two conditions: (i) Each element (called a block) of the partition has a size that is at most B and (ii) the maximum number of external arcs among directed paths from the roots to the leaves is minimized. Here, an external arc is defined as an arc connecting two distinct blocks. This paper mainly studies the case B = 2. First, we show that the problem is NP-hard even if the height of DAGs is three and its maximum indegree and outdegree are two and three, respectively. Then, we design (i) linear-time optimal algorithms for DAGs of height at most two, (ii) a very simple 2-approximation algorithm, and moreover, (iii) a (2 - 2/h)-approximation algorithm for the case that the height h of the input DAG is even and the other one for odd h, whose approximation ratio is 2 - 2/(h + 1). As for the inapproximability of the problem, for any epsilon > 0 and unless P = NP, we show that the problem does not admit any polynomial time (3/2 - epsilon)-approximation ((4/3 - epsilon)-approximation, resp.) algorithm if the height of the input DAGs is restricted to at most five (at least six, resp.). Also, in the case B >= 3, we show the NP-hardness and prove a (3/2 - epsilon)-inapproximability of this case. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Minimum block transfer problem Directed acyclic graph NP-hardness Approximability Inapproximability greedy algorithm

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Analysis and Optimization of Fog Radio Access Networks With Hybrid Caching: Delay and Energy Efficiency

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第1期20卷 69-82页

作者： Jiang, Yanxiang Wan, Chaoyi Tao, Meixia Zheng, Fu-Chun Zhu, Pengcheng Gao, Xiqi You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Chinese Acad Sci Key Lab Wireless Sensor Network & Commun Shanghai Inst Microsyst & Informat Technol Shanghai 200050 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

In this article, delay and energy efficiency (EE) are investigated in fog radio access networks (F-RANs) with hybrid caching. With multiple caching and transmission strategies, hybrid caching offers great flexibility for file placement and file fetching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and fronthaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Furthermore, considering the NP-hard complexity of the problem, we first theoretically analyze the optimal structure of the caching result. Then, we convert the original problem into a classification problem. We further propose a gradual-replacement greedy algorithm to obtain a near optimal hybrid caching strategy, which ensures high accuracy with low complexity. Numerical results show a significant performance gain of the proposed near optimal hybrid caching strategy over baselines and flexibility in delay-sensitive and EE-sensitive scenarios.

关键词： Fog radio access networks hybrid caching stochastic geometry delay energy efficiency greedy algorithm

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Efficient Reversible Data Hiding for JPEG Images With Multiple Histograms Modification

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第7期31卷 2535-2546页

作者： Xiao, Mengyao Li, Xiaolong Ma, Bin Zhang, Xinpeng Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Qilu Univ Technol Shandong Acad Sci Sch Cyber Secur Jinan 250353 Peoples R China Shandong Prov Key Lab Comp Networks Jinan 250353 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Most current reversible data hiding (RDH) techniques are designed for uncompressed images. However, JPEG images are more commonly used in our daily lives. Up to now, several RDH methods for JPEG images have been proposed, yet few of them investigated the adaptive data embedding as the lack of accurate measurement for the embedding distortion. To realize adaptive embedding and optimize the embedding performance, in this article, a novel RDH scheme for JPEG images based on multiple histogram modification (MHM) and rate-distortion optimization is proposed. Firstly, with selected coefficients, the RDH for JPEG images is generalized into a MHM embedding framework. Then, by estimating the embedding distortion, the rate-distortion model is formulated, so that the expansion bins can be adaptively determined for different histograms and images. Finally, to optimize the embedding performance in real time, a greedy algorithm with low computation complexity is proposed to derive the nearly optimal embedding efficiently. Experiments show that the proposed method can yield better embedding performance compared with state-of-the-art methods in terms of both visual quality and file size preservation.

关键词： Transform coding Discrete cosine transforms Histograms Distortion Image coding Quantization (signal) Rate-distortion Reversible data hiding JPEG image MHM rate-distortion optimization model greedy algorithm

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Heuristic Strategies for NFV-Enabled Renewable and Non-Renewable Energy Management in the Future IoT World

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IEEE ACCESS 2021年 9卷 125000-125031页

作者： Tipantuna, Christian Hesselbach, Xavier Unger, Walter Escuela Politec Nacl Dept Elect Telecommun & Comp Networks Quito 170517 Ecuador Univ Politecn Cataluna Dept Network Engn Barcelona 08034 Spain Rhein Westfal TH Aachen Dept Comp Sci D-52056 Aachen Germany

The ever-growing energy demand and the CO2 emissions caused by energy production and consumption have become critical concerns worldwide and drive new energy management and consumption schemes. In this regard, energy systems that promote green energy, customer-side participation enabled by the Internet of Things (IoT) technologies, and adaptive consumption mechanisms implemented on advanced communications technologies such as the Network Function Virtualization (NFV) emerge as sustainable and de-carbonized alternatives. On these modern schemes, diverse management algorithmic solutions can be deployed to promote the interaction between generation and consumption sides and optimize the use of available energy either from renewable or non-renewable sources. However, existing literature shows that management solutions considering features such as the dynamic nature of renewable energy generation, prioritization in energy provisioning if needed, and time-shifting capabilities to adapt the workloads to energy availability present a complexity NP-Hard. This condition imposes limits on applicability to a small number of energy demands or time-shifting values. Therefore, faster and less complex adaptive energy management approaches are needed. To meet these requirements, this paper proposes three heuristic strategies: a greedy strategy (greedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs) that, when deployed at the NFV domain, seeks the best possible scheduling of demands that lead to efficient energy utilization. The performance of the algorithmic strategies is validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and processing of demands. Additionally, simulation results reveal that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and

关键词： Energy management Heuristic algorithms Renewable energy sources Complexity theory Adaptive systems Adaptation models Ecosystems Energy efficiency energy management demand response NFV IoT power consumption workload scheduling genetic algorithm greedy algorithm dynamic programming renewable energy

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Face Recognition using a greedy based warping algorithm 11

Face Recognition using a greedy based warping algorithm

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11th International Conference on Advanced Computing (ICoAC)

作者： Krishnaveni, B. Sridhar, S. Anna Univ Informat Sci & Technol Dept Chennai Tamil Nadu India

ISBN: (纸本)9781728152868

Recognition of faces is an important and challenging problem of a biometric system. In this paper, a greedy based warping algorithm is proposed for face recognition, which is a variant of conventional dynamic time warping (DTW) algorithm. The algorithm is designed to improve the recognition accuracy and to reduce the time complexity of DTW. Many real-time image processing applications require a solution that is near-optimal. The proposed algorithm provides a near-optimal solution to face recognition applications with linear time and space complexity and gives a better recognition rate. In this, matching of test face image to a set of gallery face images is performed. The performance of the greedy based algorithm has been evaluated by the experimentation on standard face databases such as ORL and FEI. Experimental results show that the proposed algorithm performs well even if the face images have uncontrolled conditions such as expression, lighting, and pose variations.

关键词： biometrics face recognition DTW greedy algorithm patch Image matching

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