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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Ramy Mohamed Marios Avgeris Aris Leivadeas Ioannis Lambadaris Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Software and IT Engineering École de technologie supérieure Montreal Canada

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

Appropriate deployment of Virtual Network Functions (VNFs) within the network infrastructure to optimize resource utilization, while fulfilling performance criteria, is critical to Network Function Virtualization (NFV). However, traditional optimization methods frequently struggle to deal with the dynamic and complex nature of the VNF placement problem. Therefore, this study proposes an online VNF placement approach that addresses these problems by leveraging Deep Reinforcement Learning (DRL) and Reward Constrained Policy Optimization (RCPO). This technique combines DRL’s adaptability with RCPO’s constraint incorporation capabilities, ensuring that the learned policies satisfy performance and resource restrictions while optimizing the overall VNF placement. Hence, the VNF placement problem is formulated as a constrained optimization problem and adapted to the DRL setting. We demonstrate that our proposed framework outperforms certain state-of-the-art VNF placement techniques when it comes to resource utilization and constraint satisfaction.

关键词： Metalearning Adaptive learning Costs Transfer learning Neural networks Optimization methods Deep reinforcement learning

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A Systematic Review of Fitts' Law in 3D Extended Reality 25

A Systematic Review of Fitts' Law in 3D Extended Reality

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Amini, Mohammadreza Stuerzlinger, Wolfgang Teather, Robert J Batmaz, Anil Ufuk Department of Computer Science & Software Engineering Concordia University MontrealQC Canada Simon Fraser University VancouverBC Canada School of Information Technology Carleton University OttawaON Canada

ISBN: (纸本)9798400713941

Fitts' law is widely used as an evaluation tool for pointing or selection tasks, evolving into diverse applications, including 3D extended reality (XR) environments like virtual, augmented, and mixed reality. Despite standards like ISO 9241:411, the application of Fitts' law varies significantly across studies, complicating comparisons and undermining the reliability of findings in 3D XR research. To address this, we conducted a systematic review of 119 publications, focusing on 122 studies that used Fitts' law in 3D XR user experiments. Our analysis shows that over half of these studies referenced Fitts' law without thoroughly investigating throughput, movement time, or error rate. We performed an in-depth meta-analysis to examine how Fitts' law is incorporated into research. By highlighting trends and inconsistencies, and making recommendations this review aims to guide researchers in designing and performing more effective and consistent Fitts-based studies in 3D XR, enhancing the quality and impact of future research. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual reality

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Fast Resource Allocation for Virtual Network Functions Chain Placement

Fast Resource Allocation for Virtual Network Functions Chain...

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Mohamed, Ramy Leivadeas, Aris Lambadaris, Ioannis Moris, Todd Djukic, Petar Carleton University Department of Systems and Computer Engineering OttawaON Canada École de technologie supérieure Department of Software and IT Engineering MontrealQC Canada Ciena Corporation OttawaON Canada

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

Network slicing enables the creation and management of a network that meets and surpasses the evolving requirements of a diverse set of new services. Within 5G network slicing, we can describe a network service as a Service Function Chain (SFC), i.e., a graph of interconnected Virtual Network Functions (VNFs) residing in a particular network slice. Thus, the optimal resource allocation for these SFCs is critical for the network operator. Nevertheless, this problem stays partially unresolved because it needs managing resources spread across various Edge and Cloud sites in distinct geographical areas. Moreover, the complexity of the problem dramatically grows when considering practical constraints like end-to-end delay, VNF affinity, processing delay, and traffic requirements between VNFs. This paper addresses this problem and proposes practical solutions for the Virtual Network Functions Chain Placement Problem (VNF-CPP) established on Integer Linear Programming (ILP) and heuristic algorithms. Furthermore, we provide theoretical analysis and experiments to demonstrate the efficiency of the proposed placement algorithms. © 2022 IEEE.

关键词： 5G mobile communication systems

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Characterizing Discussion Trend on Blockchain Development by Mining Stack Overflow

SSRN

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SSRN 2023年

作者： Ze, Xue Zhao, Tianyu Hao, Isabella Wei, Lili Department of Electrical and Computer Engineering Faculty of Engineering McGill University MontrealQC Canada Department of Software Engineering Faculty of Engineering McGill University MontrealQC Canada

Context: The importance of blockchain knowledge is becoming more apparent as the technology continues to grow and expand. With the increasing interest in web3-related technology and the hype of the metaverse, more people are seeking to understand blockchain and its potential applications. Thus, characterizing blockchain-related concerns on Stack Overflow, one of the most popular question-and-answer (Q&A) sites for programmers, is important for the concerned software development. Several research projects have explored the technological obstacles confronted by users. However, there is no existing study that analyzes the trend and change in blockchain-related discussions over the years. There is also a paucity of information on the community itself, the Stack Overflow users, and the important topics to ***: In this study, we investigated the developers' concerns associated with blockchain development by leveraging the Latent Dirichlet Allocation (LDA) technique. We also characterized the changing trend of blockchain discussions over the ***: We first collected 6,340 blockchain-related questions from Stack Overflow. Leveraging LDA, we discerned the most relevant topics and prevalent challenges encountered by blockchain developers. Subsequently, we depicted these findings using pie charts and embarked on a manual analysis to elucidate the trends in topic changes over the ***: The findings of our investigation reveal a predilection among users to post topics closely associated with "Hyperledger Fabric", "Blockchain Mechanism", and "Smart Contract". Notably, a pronounced surge in user engagement and the proliferation of discussions have been observed from 2018 to ***: Utilizing the LDA methodology, we have discerned a transformation in the scope of blockchain-related topics under discussion by users on Stack Overflow (SO) over the course of the year. Furthermore, our analysis has shed light on the evolving trends and the array o

关键词： Smart contract

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A Fast Local Community Detection Algorithm in Signed Social Networks 9

A Fast Local Community Detection Algorithm in Signed Social ...

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9th International Conference on Social Networks Analysis, Management and Security, SNAMS 2022

作者： Bakhtar, Sahar Harutyunyan, Hovhannes A. Department of Software Engineering and Computer Science Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9798350320480

Recent years have witnessed the rapid growth of social network services and consequently, research problems investigated in this area. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the group. Community detection includes exploring the community partitioning in social networks. Regarding the fact that social networks are huge, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. This problem can be defined as the detection of a community for a given node by using local information. Many networks contain both positive and negative relations. A community in signed networks is defined as a group of nodes that are densely connected by positive links within the community and negative links between communities. In this paper, considering the problem of local community detection in signed networks, a new fast algorithm, noted as AlgSP, is developed to identify a dense community for a given node in signed networks. Experimental results show that the proposed algorithm can detect the ground-truth communities independently from the starting nodes. © 2022 IEEE.

关键词： Population dynamics

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Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet

arXiv

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arXiv 2023年

作者： Rasoulian, Amirhossein Harirpoush, Arash Salari, Soorena Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

Accurate identification and quantification of unruptured intracranial aneurysms (UIAs) is crucial for the risk assessment and treatment of this cerebrovascular disorder. Current 2D manual assessment on 3D magnetic resonance angiography (MRA) is suboptimal and time-consuming. In addition, one major issue in medical image segmentation is the need for large well-annotated data, which can be expensive to obtain. Techniques that mitigate this requirement, such as weakly supervised learning with coarse labels are highly desirable. In the paper, we propose FocalSegNet, a novel 3D focal modulation UNet, to detect an aneurysm and offer an initial, coarse segmentation of it from time-of-flight MRA image patches, which is further refined with a dense conditional random field (CRF) post-processing layer to produce a final segmentation map. We trained and evaluated our model on a public dataset, and in terms of UIA detection, our model showed a low false-positive rate of 0.21 and a high sensitivity of 0.80. For voxel-wise aneurysm segmentation, we achieved a Dice score of 0.68 and a 95% Hausdorff distance of ∼0.95 mm, demonstrating its strong performance. We evaluated our algorithms against the state-of-the-art 3D Residual-UNet and Swin-UNETR, and illustrated the superior performance of our proposed FocalSegNet, highlighting the advantages of employing focal modulation for this task. The code will be available upon acceptance at https://***/ah-rasoulian/FocalSegNet. © 2023, CC0.

关键词： Risk assessment

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Decomposition Models for the Routing and Slot Provisioning Problem

Decomposition Models for the Routing and Slot Provisioning P...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Brigitte Jaumard Adham Mohammed Quang Anh Nguyen Department of Computer Science and Software Engineering Concordia University Montreal (Qc) Canada

Severa1 studies have been proposed for the Routing and Slot Assignment (RSA) problem in optical networks. In this study, we focus on exact or $\varepsilon-$optimal solution schemes. In this paper, two decomposition schemes are investigated: a first one that exploits a slot decomposition, and a second one that takes advantage of a link decomposition. In the slot decomposition, we gather the requests with the same lowest indexed frequency slot; while in the link decomposition, we put together the requests using the same first link in their routing. Computational results compare the performance of both decomposition schemes, with an indirect comparison with the best results of the literature, showing that they could solve much larger data instances than in the previous studies.

关键词： Integrated optics Computational modeling Optical variables measurement Optical fiber networks Routing

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Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning

arXiv

引用

arXiv 2023年

作者： Rasoulian, Amirhossein Salari, Soorena Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada

Intracranial hemorrhage (ICH) is a life-threatening medical emergency that requires timely and accurate diagnosis for effective treatment and improved patient survival rates. While deep learning techniques have emerged as the leading approach for medical image analysis and processing, the most commonly employed supervised learning often requires large, high-quality annotated datasets that can be costly to obtain, particularly for pixel/voxelwise image segmentation. To address this challenge and facilitate ICH treatment decisions, we introduce a novel weakly supervised method for ICH segmentation, utilizing a Swin transformer trained on an ICH classification task with categorical labels. Our approach leverages a hierarchical combination of head-wise gradient-infused self-attention maps to generate accurate image segmentation. Additionally, we conducted an exploratory study on different learning strategies and showed that binary ICH classification has a more positive impact on self-attention maps compared to full ICH subtyping. With a mean Dice score of 0.44, our technique achieved similar ICH segmentation performance as the popular U-Net and Swin-UNETR models with full supervision and outperformed a similar weakly supervised approach using GradCAM, demonstrating the excellent potential of the proposed framework in challenging medical image segmentation tasks. Our code is available at https://***/HealthX-Lab/HGI-SAM. © 2023, CC BY.

关键词： Image segmentation

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Broadcasting in interconnection networks based on node partitioning

Broadcasting in interconnection networks based on node parti...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Hovhannes A. Harutyunyan Narek Hovhannisyan Department of Computer Science and Software Engineering Concordia University Montreal QC Canada

Broadcasting is one of the information dissemination primitives where a message is passed from one node (called originator) to all other nodes in the network. With the increasing interest in interconnection networks, an extensive amount of research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network *** the scope of this paper, we will mainly focus on determining the broadcast time and near-optimal broadcasting schemes in networks. Determination of the broadcast time of any node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few network topologies. There also exist various heuristic and approximation algorithms for different network topologies. In this paper, we will present an efficient heuristic for the broadcast time problem in networks, where the nodes can be partitioned into two groups.

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Broadcasting in chains of rings

Broadcasting in chains of rings

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Hovhannes A. Harutyunyan Narek Hovhannisyan Edward Maraachlian Department of Computer Science and Software Engineering Concordia University Montreal QC Canada

Broadcasting is an information dissemination problem in an interconnection network where one node, called the originator, must distribute a message to all other nodes by placing a series of calls along the links of the network. Every time the informed nodes aid the originator in distributing the message. Finding the broadcast time of any node in an arbitrary network is NP-complete. Polynomial time solutions are known only for a few classes of networks. In this paper, we consider chains of rings. We present a linear algorithm to find the broadcast time of arbitrary chains of rings and closed chains of rings.

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