Article
Computer Science, Information Systems
Weilong Ren, Xiang Lian, Kambiz Ghazinour
Summary: This paper investigates the problem of monitoring the top-k objects with the highest ranking scores in incomplete data streams, proposing a cost-model-based data imputation approach and effective pruning strategies to reduce the search space.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Theory & Methods
Ibrahim Alghamdi, Christos Anagnostopoulos, Dimitrios P. Pezaros
Summary: An important use case of Mobile Edge Computing is task and data offloading, which is beneficial for a variety of mobile applications. By adopting Optimal Stopping Theory, the data quality-aware offloading sequential decision making problem can be addressed to minimize the expected processing time. The proposed OST models show significant improvement in minimizing the expected processing time for analytics task execution.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Information Systems
Ali Ebrahim
Summary: This paper presents a novel approach using FPGAs to accelerate the top-k heavy hitters query in data streams. It optimizes a well-known software algorithm by relaxing some requirements, resulting in a practical and scalable hardware accelerator that outperforms current state-of-the-art accelerators. The optimized FPGA accelerator, specified at the C language level, is easily reproducible with HLS tools and showed promising results on Intel Arria 10 and Stratix 10 FPGAs.
Article
Computer Science, Artificial Intelligence
Dimitris Bertsimas, Vassilis Digalakis Jr
Summary: The study presents a novel approach for frequency estimation in data streams based on optimization and machine learning. Unlike existing algorithms that rely on random hashing, this approach utilizes observed stream prefixes to hash elements and compress the frequency distribution. The researchers developed an exact mixed-integer linear optimization formulation to compute optimal or near-optimal hashing schemes for observed elements, and used machine learning for unseen elements.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Operations Research & Management Science
Katia Colaneri, Tiziano De Angelis
Summary: This paper introduces and solves a class of optimal stopping problems of recursive type, showing that in a multidimensional Markovian setting, the problem is well posed. The class of problems is applied to a stock trading model in two different market venues, enabling the determination of the optimal stopping rule in that scenario.
MATHEMATICS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Hongjie Guo, Jianzhong Li, Hong Gao, Kaiqi Zhang
Summary: The PSATop-k algorithm combines partitioning and sampling techniques to efficiently answer approximate top-k queries with selection conditions and arbitrary ranking functions. Experimental results show that PSATop-k outperforms existing algorithms, especially running 12.22 to 14.30 times faster on average than TA-based and Coreset-based methods as the result set size varies from 10 to 80.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Meng Han, Muhang Li, Zhiqiang Chen, Hongxin Wu, Xilong Zhang
Summary: High utility pattern mining over data streams has been extensively studied due to its wide range of application scenarios. A new algorithm called HUPM_Stream is proposed in this paper to efficiently mine high utility patterns over data streams. The algorithm utilizes an Ext-list structure to reduce the time complexity of the utility list join operation, an improved remaining utility pruning strategy IRS to reduce the number of join operations, and a hash table structure-based resultset maintenance strategy HRS to avoid generating the same resultset repeatedly during window sliding. Experimental results demonstrate that the proposed algorithm performs better on dense datasets.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Siwei Lyu, Yanbo Fan, Yiming Ying, Bao-Gang Hu
Summary: This work introduces the average top-k (AT(k)) loss as a new aggregate loss for supervised learning, which can better adapt to different data distributions and can be combined with different types of individual loss without significant increase in computation. The AT(k) loss is further explained from the perspective of modification of individual loss and robustness to training data distributions.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Remote Sensing
Chenglong Zhang, Wen Chen, Danan Dong, Nobuaki Kubo, Jianping Wu
Summary: With the increasing number of satellites, the risk of fixing wrong integer ambiguities can reduce the accuracy and efficiency of ambiguity resolution (AR). Therefore, partial ambiguity resolution (PAR) is proposed to solve full AR (FAR) by fixing a subset of ambiguities. The classic optimal stopping theory (OST) is introduced to dynamically identify ambiguity subsets in batch PAR, aiming to maintain accuracy and reduce processing time.
Article
Mathematics
Hugh N. Entwistle, Christopher J. Lustri, Georgy Yu. Sofronov
Summary: This article considers optimal stopping problems, where a sequence of independent random variables is drawn from a known continuous density. The expectation and variance of the optimal stopping time are derived asymptotically as the number of drawn variables becomes large. For distributions with infinite upper bound, the asymptotic behavior of these statistics depends on the algebraic power of the probability distribution decay rate in the upper limit. For densities with finite upper bound, the asymptotic behavior of these statistics depends on the algebraic form of the distribution near the finite upper bound. Explicit calculations are provided for several common probability density functions.
Article
Computer Science, Information Systems
Thu-Lan Dam, Sean Chester, Kjetil Norvag, Quang-Huy Duong
Summary: This paper introduces a task to efficiently discover the top-k most popular terms within a user-specified bounded region over a stream of social posts, and proposes a hybrid index structure and algorithms to support such queries. Experimental studies show that the proposed methods are capable of both online aggregation and accurate query processing.
INFORMATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Haodong Cheng, Meng Han, Ni Zhang, Le Wang, Xiaojuan Li
Summary: The paper proposes an efficient algorithm based on dataset projection for mining Top-K high-utility itemsets from data streams. The algorithm combines data structure optimization, new projection technology, threshold raising and pruning strategies to improve running efficiency and is suitable for dense datasets.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Engineering, Manufacturing
Behzad Hezarkhani, Mahesh Nagarajan, Chunyang Tong
Summary: This paper analyzes the optimal structures and parameters of performance-based maintenance contracts in the context of an original equipment manufacturer (OEM) contracting maintenance services to an operator. The study finds that certain commonly used performance-based contract structures may not be optimal for the OEMs. The research also considers the customer's ability to affect uptime and explores the advantages and limitations of offering menus of contracts. The findings suggest that contracts designed using the key ideas in this paper show promising results for practitioners.
PRODUCTION AND OPERATIONS MANAGEMENT
(2022)
Article
Automation & Control Systems
Yu-Jui Huang, Zhenhua Wang
Summary: In this study, we establish the existence of an optimal equilibrium for an optimal stopping problem under nonexponential discounting, where the state process is a multidimensional continuous strong Markov process. This equilibrium is a large collection of finely closed equilibria satisfying a boundary condition, based on probabilistic potential theory. This generalizes the existence of optimal equilibria for one-dimensional stopping problems found in previous literature.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2021)
Article
Computer Science, Information Systems
Xixian Han, Xianmin Liu, Jianzhong Li, Hong Gao
Summary: This study introduces a novel algorithm PTM for efficiently mining top-k high utility itemsets on massive data. PTM processes partitions based on prefixes and average transaction utility, utilizes depth-first search and subtree pruning rule to achieve fast mining results.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Theory & Methods
Kostas Kolomvatsos, Christos Anagnostopoulos
Article
Computer Science, Artificial Intelligence
Yiannis Kathidjiotis, Kostas Kolomvatsos, Christos Anagnostopoulos
APPLIED INTELLIGENCE
(2020)
Article
Computer Science, Information Systems
K. Panagidi, C. Anagnostopoulos, A. Chalvatzaras, S. Hadjiefthymiades
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
(2020)
Article
Computer Science, Theory & Methods
Fotis Savva, Christos Anagnostopoulos, Peter Triantafillou
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Hardware & Architecture
Christos Anagnostopoulos
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2020)
Article
Computer Science, Theory & Methods
Ibrahim Alghamdi, Christos Anagnostopoulos, Dimitrios P. Pezaros
Summary: An important use case of Mobile Edge Computing is task and data offloading, which is beneficial for a variety of mobile applications. By adopting Optimal Stopping Theory, the data quality-aware offloading sequential decision making problem can be addressed to minimize the expected processing time. The proposed OST models show significant improvement in minimizing the expected processing time for analytics task execution.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Theory & Methods
Kostas Kolomvatsos, Christos Anagnostopoulos
Summary: The research community has identified challenges in data processing at the Cloud, particularly related to latency and waiting times for data to travel from collection points. An Edge Computing approach is proposed as a solution, where data can be processed closer to their source. This approach aims to efficiently allocate queries to available edge nodes, utilizing Fuzzy Logic theory and a Support Vector Machine-based machine learning model. The aim is to manage uncertainties in the problem and enhance decision making in processing queries.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Hardware & Architecture
Irian Leyva-Pupo, Cristina Cervello-Pastor, Christos Anagnostopoulos, Dimitrios P. Pezaros
Summary: This paper addresses the problem of dynamic user plane function placement and chaining reconfiguration (UPCR) in a MEC environment to cope with user mobility while guaranteeing cost reductions and acceptable quality of service (QoS). The proposed heuristic algorithm, dynamic priority and cautious UPCR (DPC-UPCR), provides near-optimal solutions within significantly shorter times than the mathematical model. Additionally, the scheduling mechanism based on optimal stopping theory outperforms baseline solutions in terms of the number of reconfiguration events and QoS levels.
Article
Computer Science, Theory & Methods
Natascha Harth, Christos Anagnostopoulos, Hans-Joerg Voegel, Kostas Kolomvatsos
Summary: This study addresses the importance of utilizing device computational capacity for machine learning and analytics tasks in edge computing and IoT environments. A methodology based on federated learning principles is proposed to ensure the generalization capability of locally trained models, incorporating adaptive weighting for optimal model selection decision making. Comparative and performance evaluation demonstrate significant improvement in prediction quality compared to existing approaches.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Qianyu Long, Kostas Kolomvatsos, Christos Anagnostopoulos
Summary: This paper introduces a novel paradigm in edge computing where edge nodes reuse locally completed computations to reduce the burden on resources. The paradigm is enhanced with lightweight monitoring mechanisms to forecast violations and updates, and feasibility is studied through similarity metrics among datasets.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Kostas Kolomvatsos, Christos Anagnostopoulos
Summary: This study proposes a proactive scheme to support collaborative activities and makes decisions on the presence of services through statistical inference of service demand and node performance, filling a research gap in this field.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Panagiotis Fountas, Maria Papathanasaki, Kostas Kolomvatsos, Christos Anagnostopoulos
Summary: This paper presents a hierarchical query-driven clustering approach for efficient data mapping in remote datasets. By combining a Query-Based Learning (QBL) model with hierarchical clustering, the proposed method achieves fast response to a large number of query sets.
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2022, PART II
(2022)
Article
Engineering, Electrical & Electronic
Christos Anagnostopoulos, Tahani Aladwani, Ibrahim Alghamdi, Konstantinos Kolomvatsos
Summary: In this paper, the authors study computational offloading techniques in autonomous computing nodes at the network edge and propose a task-management mechanism based on the popularity of tasks and data availability. The mechanism utilizes a fuzzy inference system to determine the probability of executing tasks locally, offloading them to peer computing nodes, or offloading them to the Cloud.
Article
Computer Science, Artificial Intelligence
Madalena Soula, Anna Karanika, Kostas Kolomvatsos, Christos Anagnostopoulos, George Stamoulis
Summary: This paper discusses the role of edge computing in IoT and cloud computing, explores research on task allocations in the EC ecosystem, and proposes a batch processing model and two allocation models. The study demonstrates the advantages and limitations of the models in task allocations.
Article
Computer Science, Artificial Intelligence
Christos Anagnostopoulos, Peter Triantafillou
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
(2020)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)