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IEEE Conference on Global Communications (GLOBECOM)

作者： Yihang Tao Jun Wu Xi Lin Shahid Mumtaz Soumaya Cherkaoui School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Faculty of Science and Engineering Waseda University Japan Department of Engineering Nottingham Trent University UK Department of Computer and Software Engineering Polytechnique Montreal Canada

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.

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Adaptive dialogue management using intent clustering and fuzzy rules

Adaptive dialogue management using intent clustering and fuz...

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作者： Griol, David Callejas, Zoraida Molina, Jose Manuel Sanchis, Araceli Department of Software Engineering University of Granada Granada Spain Departement of Computer Science Universidad Carlos III de Madrid Madrid Spain

Conversational systems have become an element of everyday life for billions of users who use speech-based interfaces to services, engage with personal digital assistants on smartphones, social media chatbots, or smart speakers. One of the most complex tasks in the development of these systems is to design the dialogue model, the logic that provided a user input selects the next answer. The dialogue model must also consider mechanisms to adapt the response of the system and the interaction style according to different groups and user profiles. Rule-based systems are difficult to adapt to phenomena that were not taken into consideration at design-time. However, many of the systems that are commercially available are based on rules, and so are the most widespread tools for the development of chatbots and speech interfaces. In this article, we present a proposal to: (a) automatically generate the dialogue rules from a dialogue corpus through the use of evolving algorithms, (b) adapt the rules according to the detected user intention. We have evaluated our proposal with several conversational systems of different application domains, from which our approach provided an efficient way for adapting a set of dialogue rules considering user utterance clusters. © 2020 John Wiley & Sons Ltd

关键词： Personal digital assistants

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Flight Delay Prediction Based on Gradient Boosting Ensemble Techniques

Flight Delay Prediction Based on Gradient Boosting Ensemble ...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Rahemeen Khan Salas Akbar Tooba Ali Zahed Department of Software Engineering Bahria University Karachi Pakistan Department of Computer Science Bahria University Karachi Pakistan

ISBN: (纸本)9781665464789

In recent years, the volume of airline transportation has increased with the rapid development of aviation. With an increased demand for flights, aviation is confronted with the issue of flight delays, which becomes a series of issues that must be addressed efficiently. Correct flight delay prediction can improve airport operations efficiency and passenger travel comfort. The current study uses Gradient boosting ensemble models to build a machine learning flight delay prediction model. The Airline dataset was subjected to three different gradient boosting techniques: CatBoost, LightGBM, XGBoost, and Decision tree. to validate the performance and efficiency of the proposed method, a comparative analysis between the top performed Boosting techniques with other Ensemble Techniques is performed. CatBoost improves prediction accuracy while maintaining stability, according to the comparison results on the given dataset.

关键词： Atmospheric modeling Transportation Predictive models Boosting Airports Stability analysis Delays

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Enhancing Diversity in Multi-Objective Feature Selection

Enhancing Diversity in Multi-Objective Feature Selection

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Congress on Evolutionary Computation

作者： Sevil Zanjani Miyandoab Shahryar Rahnamayan Azam Asilian Bidgoli Sevda Ebrahimi Masoud Makrehchi Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Paculty of Science Wilfrid Laurier University Waterloo ON Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly en-hancing model performance, interpretability, and resource efficiency across diverse domains. In population-based optimization methods, the generation of diverse individuals holds utmost importance for adequately exploring the problem landscape, particularly in highly multi-modal multi-objective optimization problems. Our study reveals that, in line with findings from sev-eral prior research papers, commonly employed crossover and mutation operations lack the capability to generate high-quality diverse individuals and tend to become confined to limited areas around various local optima. This paper introduces an augmen-tation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II. This enhancement is achieved through two key components: the genuine initialization method and the substitution of the worst individuals with new randomly generated individuals as a re-initialization approach in each generation. The proposed multi-objective feature selection method undergoes testing on twelve real-world classification problems, with the number of features ranging from 2,400 to nearly 50,000. The results demonstrate that replacing the last front of the population with an equivalent number of new random individuals generated using the genuine initialization method and featuring a limited number of features substantially improves the population's quality and, consequently, enhances the performance of the multi-objective algorithm.

关键词： Sociology Pipelines Optimization methods Evolutionary computation Feature extraction Data models Distance measurement

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Healthcare Professionals’ Perceptions Towards Ai Integration in Jordan's Health Sector

SSRN

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SSRN 2024年

作者： Klaib, Ahmad F. Alhosanie, Tasneem N. Mahdin, Ladees W. Altatar, Enas J. Software Engineering Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Information Systems Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

PurposeThe impact of AI on healthcare is widely recognized there remains a scarcity of studies examining how doctors perceive and approach its use in medicine. This study aims to gather insights from healthcare providers in Jordan concerning the advantages of integrating AI into practices, their perspectives on AI applications in healthcare, and their views on the future role of AI in replacing key tasks within health *** survey was conducted among healthcare professionals working at facilities in Jordan. An online questionnaire was used to collect data on demographics, attitudes toward using AI for tasks, and opinions on the benefits of AI adoption. Categorical variables were presented as counts and percentages, while the continuous variables were interpreted as mean and standard deviation. The associations between the determinants and the outcomes were done using one-way ANOVA. Any test with a P-value 0.05 was considered *** total of 612 healthcare professionals participated in the survey with females comprising a majority of respondents (52.8%). The majority of respondents showed optimism about AI’s potential to improve and revolutionize the field, although there were concerns about AI replacing human roles. Generally, physical therapists, medical researchers, and pharmacists displayed openness to incorporating AI into their work routines. Younger individuals aged between 18 and 40 seemed accepting of AI in the domain. A significant portion of participants believed that AI could negatively impact job opportunities and reduce the time needed for diagnosing conditions, but did not find any correlation, between responses and *** conclude, the results of this study suggest that healthcare professionals, in Jordan, hold receptive views on incorporating artificial intelligence in the medical field similar to their counterparts in developed nations. However, there is a concern about the implications of AI, on job stability a

关键词： Artificial intelligence

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Fairness in Visual Clustering: A Novel Transformer Clustering Approach

arXiv

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arXiv 2023年

作者： Nguyen, Xuan-Bac Duong, Chi Nhan Savvides, Marios Roy, Kaushik Churchill, Hugh Luu, Khoa The Department of Computer Science and Computer Engineering University of Arkansas Fayetteville United States The Department of Physics University of Arkansas Fayetteville United States The Department of Computer Science and Software Engineering Concordia University Montreal Canada Carnegie Mellon University United States North Carolina A&T State University United States

Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal. It is because of the limitation of large-scale balanced data with well-annotated labels for sensitive or protected attributes. In this paper, we first evaluate demographic bias in deep clustering models from the perspective of cluster purity, measured by the ratio of positive samples within a cluster to their correlation degree. This measurement is adopted as an indication of demographic bias. Then, a novel loss function is introduced to encourage a purity consistency for all clusters to maintain the fairness aspect of the learned clustering model. Moreover, we present a novel attention mechanism, Cross-attention, to measure correlations between multiple clusters, strengthening faraway positive samples and improving the purity of clusters during the learning process. Experimental results on a large-scale dataset with numerous attribute settings have demonstrated the effectiveness of the proposed approach on both clustering accuracy and fairness enhancement on several sensitive attributes. Copyright © 2023, The Authors. All rights reserved.

关键词： Population statistics

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Battle royale optimizer with a new movement strategy

arXiv

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arXiv 2022年

作者： Akan, Sara Akan, Taymaz Department of Computer Programing Ayvansaray University Istanbul Turkey Department of Software Engineering Ayvansaray University Istanbul Turkey

Gamed-based is a new stochastic metaheuristics optimization category that is inspired by traditional or digital game genres. Unlike SI-based algorithms, individuals do not work together with the goal of defeating other individuals and winning the game. Battle royale optimizer (BRO) is a Gamed-based metaheuristic optimization algorithm that has been recently proposed for the task of continuous problems. This paper proposes a modified BRO (M-BRO) in order to improve balance between exploration and exploitation. For this matter, an additional movement operator has been used in the movement strategy. Moreover, no extra parameters are required for the proposed approach. Furthermore, the complexity of this modified algorithm is the same as the original one. Experiments are performed on a set of 19 (unimodal and multimodal) benchmark functions (CEC 2010). The proposed method has been compared with the original BRO alongside six well-known/recently proposed optimization algorithms. The results show that BRO with additional movement operator performs well to solve complex numerical optimization problems compared to the original BRO and other competitors. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

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Recursive Least-Square-Based Parameter Estimation for Dynamic State Estimation in Power Grids

Recursive Least-Square-Based Parameter Estimation for Dynami...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Shahin Riahinia Amir Ameli Mohsen Ghafouri Abdulsalam Yassine Department of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada Department of Software Engineering Lakehead University Thunder Bay ON Canada

Nowadays, Dynamic State Estimation (DSE) has become integral to power system control and real-time contingency analysis. The efficacy of DSE in wide-area power systems is contingent upon several factors, most notably the accuracy of information regarding the system topology, generation, and load. Given that these parameters can vary dynamically, it becomes imperative to accurately estimate ongoing changes; since misestimations can compromise control and protective actions within power grids. This paper introduces a Recursive Least Squares (RLS) approach, which is grounded on the inverse power flow problem, to effectively estimate the reduced admittance matrix (Y-bus) for DSE applications based on the measurements received from Phasor Measurement Units (PMUs). The proposed RLS estimation technique offers reliable estimates of system parameters despite their dynamic behavior (both smooth and sudden changes) in the presence of measurement noise. The efficacy of the proposed method is validated on the IEEE 14-bus test system using diverse DSE scenarios.

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Convex optimization

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RTL implementation of MCMC-based constraints solver 16

RTL implementation of MCMC-based constraints solver

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Ahmed, Moemen Ahmed, Youssef Nagy, Younan Adbel-Rahman, Manar Salah, Khaled El-Kharashi, M. Watheq Khan, Ayub Department of Computer and Systems Faculty of Engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Fremont United States

ISBN: (纸本)9783800755899

Functional hardware verification is one of the most challenging areas in the hardware design cycle. With the increase in the complexity and size of the design, the time needed for verification becomes the largest part of the total design time. The most recent technique in practical verification is constrained random simulation. This method needs a solver to produce random input stimuli that satisfies a pre-defined set of input constraints. The efficiency of the overall verification process depends critically on the speed of the constraints solver and the distribution of the generated solutions. In this paper, we propose hardware acceleration for RTL constraints solver integrated with SystemVerilog. The solver is based on Markov Chains Monte Carlo methods. © VDE VERLAG GMBH.

关键词： Monte Carlo methods

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