Reviewerly/exHarmony huggingface dataset:
Leveraging Graph Neural Networks for SLA Violation Prediction in Cloud Computing

Authors:
raw_author_name: Theodoros Theodoropoulos
raw_affiliation_string: Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece
raw_author_name: John Violos
raw_affiliation_string: Department of Software and Information Technology Engineering, &#x00C9;cole de technologie sup&#x00E9;rieure, Montreal, Canada
raw_author_name: Aris Leivadeas
raw_affiliation_string: Department of Software and Information Technology Engineering, &#x00C9;cole de technologie sup&#x00E9;rieure, Montreal, Canada
raw_author_name: Konstantinos Tserpes
raw_affiliation_string: Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece

Abstract: In this paper we examine different approaches for the prediction of Service Level Agreements (SLAs) violations that occur during the service provisioning between cloud customers and providers.Despite the fact that there are many network metrics that involve the server -client interaction, it is an open research question how these available metrics can be used by a SLA prediction mechanism.We study three different data representation models for the network characteristics, a time series, a content and a context representation.We see that a context approach using graph representations captures efficiently the associativity of clients and improves the performance of traditional SLA violation prediction models when it is combined with them.The prediction of the SLA violations takes place using neural networks, making us propose a composite SLA prediction model that leverages Graph Neural Networks (GNNs).In our research, we put special emphasis and try different variations on how we construct the graphs.We perform an extensive performance evaluation of 23 different SLA prediction models that can be grouped into the three representations categories, namely the vector models that are based on network features, sequential models that leverage the temporal evolution of QoS metrics and Graph models that take into consideration the associativity of the clients.The experimental results show that our proposed GNN-based model can significantly improve the accuracy of SLA violation prediction, making it a useful tool for Cloud and Service providers.
RAFNet: Reparameterizable Across-Resolution Fusion Network for Real-Time Image Semantic Segmentation

Authors:
raw_author_name: Lei Chen
raw_affiliation_string: College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China
raw_author_name: Huhe Dai
raw_affiliation_string: College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China
raw_author_name: Yuan Zheng
raw_affiliation_string: National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian, College of Computer Science, Inner Mongolia University, Hohhot, China

Abstract: The demand to implement semantic segmentation networks on mobile devices has increased dramatically. However, existing real-time semantic segmentation methods still suffer from a large number of network parameters, unsuitable for mobile devices with limited memory resources. The reason mainly arises from the fact that most existing methods take the backbone networks (e.g., ResNet-18 and MobileNet) as an encoder. To alleviate this problem, we propose a novel Reparameterizable Channel & Dilation (RCD) block and construct a considerably lightweight yet effective encoder by stacking several RCD blocks according to three guidelines. The strengths of the proposed encoder result in the abilities not only to extract discriminative feature representations via channel convolutions and dilated convolutions, but also to reduce computational burdens while maintaining segmentation accuracy with the help of re-parameterization technique. Except for encoder, we also present a simple but effective decoder that adopts an across-resolution fusion strategy to fuse multi-scale feature maps generated from the encoder instead of a bottom-up pathway fusion. With such an encoder and a decoder, we provide a Reparameterizable Across-resolution Fusion Network (RAFNet) for real-time semantic segmentation. Extensive experiments demonstrate that our RAFNet achieves a promising trade-off between segmentation accuracy, inference speed and network parameters. Specifically, our RAFNet with only 0.96M parameters obtains 75.3% mIoU at 107 FPS and 75.8% mIoU at 195 FPS on Cityscapes and CamVid test sets for full-resolution inputs, respectively. After quantization and deployment on a Xilinx ZCU104 device, our RAFNet obtains a favorable segmentation performance with only 1.4W power.
HybridAD: A Hybrid Model-Driven Anomaly Detection Approach for Multivariate Time Series

Authors:
raw_author_name: Weiwei Lin
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangdong, China
raw_author_name: Songbo Wang
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangdong, China
raw_author_name: Wentai Wu
raw_affiliation_string: Peng Cheng Laboratory, Guangdong, China
raw_author_name: Dongdong Li
raw_affiliation_string: School of Computer Science and Engineering, South China University of Technology, Guangdong, China
raw_author_name: Albert Y. Zomaya
raw_affiliation_string: School of Computer Science, The University of Sydney, Sydney, NSW, Australia

Abstract: Anomaly detection, in recent years, has gained increasing attention in the research and practice of time series processing. However, the task is particularly challenging with multivariate time series which complicates the temporal dependency between observations and introduces complex inter-channel correlation. Meanwhile, in order to fit a broader range of applications, robustness during both training and detection is also a critical aspect. In this paper, we propose an unsupervised, hybrid model-driven anomaly detection scheme capable of (1) transforming sequences into a fused representation of temporal dependency embeddings and inter-channel correlation embeddings, and (2) achieving robust anomaly detection using a temporal prediction network for sample-wise posterior estimation combined with a data reconstruction network to assess the source of prediction. On this basis, we develop a probability density-based anomaly scoring mechanism for online detection in multivariate time series, where the anomaly score for each observation is rectified by the reliability of the prediction source. The results of extensive experiments on five publicly available datasets show that our proposed solution outperforms various state-of-the-art anomaly detection algorithms (including DL-based and non-DL-based), achieving a performance improvement (in F1-Score) by up to 10.42%.
Clustering asymmetrical data with outliers: Parsimonious mixtures of contaminated mean-mixture of normal distributions

Authors:
raw_author_name: Mehrdad Naderi
raw_affiliation_string: Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
raw_author_name: Mehdi Jabbari Nooghabi
raw_affiliation_string: Department of Mathematical Sciences, University of Copenhagen, DK-2100 Copenhagen Ø, Denmark; Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract: Mixture modeling has emerged as a statistical tool to perform unsupervised model-based clustering for heterogeneous data. A framework of using contaminated mean-mixture of normal distributions as the components of the mixture model is designed to accommodate asymmetric data with outliers. Fourteen parsimonious variants of the postulated model are introduced by employing an eigenvalue decomposition of the component scale matrices. Simultaneously clustering and outliers detection is an outstanding advantage of the proposed model in analyzing non-normally distributed data. A computationally feasible and flexible EM-type algorithm is outlined for obtaining maximum likelihood parameter estimates. Moreover, the score vector and empirical information matrix for calculating asymptotic standard errors of the parameter estimates are derived by offering an information-based approach. The applicability of the proposed method is demonstrated through the analysis of simulated and real datasets with varying proportions of outliers.
Multi-View Projection Based Joint Geometry and Color Hole Repairing Method for G-PCC Trisoup Encoded Color Point Cloud

Authors:
raw_author_name: Wenxu Tao
raw_affiliation_string: Faculty of Information Science and Engineering, Ningbo University, Ningbo, China
raw_author_name: Gangyi Jiang
raw_affiliation_string: Faculty of Information Science and Engineering, Ningbo University, Ningbo, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
raw_author_name: Mei Yu
raw_affiliation_string: Faculty of Information Science and Engineering, Ningbo University, Ningbo, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
raw_author_name: Yun Zhang
raw_affiliation_string: School of Electronics and Communication Engineering, Sun Yat-Sen University, Shenzhen, China
raw_author_name: Zhidi Jiang
raw_affiliation_string: College of Science and Technology, Ningbo University, Ningbo, China
raw_author_name: Yo-Sung Ho
raw_affiliation_string: School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea

Abstract: Efficient compression of color point clouds (CPC) is crucial for immersive visual systems. The G-PCC standard by Moving Picture Experts Group offers effective compression solutions for static CPCs. G-PCC Trisoup, one of the G-PCC solutions, achieves high coding performance using a pruned octree, combined with triangle soup (trisoup) surface encoding. However, encoding errors introduce distortions like triangular holes, degrading visual quality. This article proposes a multi-view projection based joint geometry and color hole repairing method for G-PCC trisoup encoded color point cloud, significantly improving reconstruction quality. Firstly, a G-PCC Trisoup encoded CPC is projected onto multi-view color and geometry maps. A multi-view projection-based scheme detects triangular holes by identifying geometry discontinuities. A geometric weighting factor, considering direction, distance, and level set distance, repairs geometry maps. Additionally, a novel gated hole repairing network repairs color maps by leveraging semantic information and reducing background interference. Finally, the joint geometry and color repaired results are inversely projected to obtain the final CPCs. The proposed method is validated on distorted CPCs encoded with G-PCC Trisoup at different levels. Experimental results demonstrate effective improvement in geometry and color objective quality, leading to enhanced visual perception.
CFTO: Communication-Aware Fairness in Blockchain Transaction Ordering

Authors:
raw_author_name: Mohammad Nassar
raw_affiliation_string: Viterbi Department of Electrical and Computer Engineering, Technion, Haifa, Israel
raw_author_name: Ori Rottenstreich
raw_affiliation_string: Viterbi Department of Electrical and Computer Engineering and the Taub Department of Computer Science, Technion, Haifa, Israel
raw_author_name: Ariel Orda
raw_affiliation_string: Viterbi Department of Electrical and Computer Engineering, Technion, Haifa, Israel

Abstract: Blockchain leader-based protocols elect leaders for proposing the next block of transactions. Proposed blocks need to pass a validation routine in order to be added to the blockchain. Proposers may prioritize certain transactions based on their fees or accounts, which enables attackers to gain profits through block building, while simultaneously causing negative impacts on other users. A fair block selection follows a random selection of pending transactions among those that a proposer is aware of. We propose CFTO, a protocol that aims at encouraging fair block selection in a leader-based blockchain network while taking into account real network conditions, such as the network’s topology structure and the forwarding protocol. CFTO offers two main contributions. First, it provides incentives for acting honestly and diminishing malicious and dishonest nodes. To accomplish this, we use a reputation system, whereby each node is given a reputation score based on its actions. Second, it consists of an algorithm that evaluates the proposed blocks based on the zone structure of the network. Furthermore, we adapt the evaluation algorithm to fit the additional order constraints implied in Ethereum transaction ordering. We demonstrate the improved accuracy of CFTO in detecting fair blocks, in terms of increasing the probability of approving fair blocks and decreasing the probability of approving unfair blocks, by implementing experiments and comparing them with Helix [1], a previously proposed consensus algorithm for fair block selection. As part of our experiments, we also compare certain features with those of previous studies.
FRAD: Free-Rider Attacks Detection Mechanism for Federated Learning in AIoT

Authors:
raw_author_name: Bo Wang
raw_affiliation_string: School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China
raw_author_name: Ximeng Liu
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, China
raw_author_name: Yina Guo
raw_affiliation_string: School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, China

Abstract: The rapid development of the Artificial Intelligence of Things (AIoT) opens up a new perspective for emerging service-based applications, and becomes a major driver of diverse Federated Learning (FL) applications. However, due to the heterogeneity of nodes and the existence of free-rider attacks, some device nodes may launch free-rider attacks to obtain the global model without any contribution, which not only dampens the enthusiasm of legitimate participants but also undermines the fairness of node contributions. In this paper, we propose a free-rider attack detection mechanism (FRAD) for federated learning with deep autoencoding gaussian mixture model based on contribution and reputation. Specifically, we first model the contribution values based on the computing resource, communication cost, and data quality of each device node. Then, based on PageRank algorithms, we design an optimal reputation-based model to fairly and precisely choose benign nodes to participate in federated training under information asymmetry. Furthermore, we develop FRAD mechanism via deep autoencoding gaussian mixture model that combines historical contribution with reputation scores. Simulation results validate that the proposed mechanism in this paper outperforms the state-of-the-art baselines ( ∼×2.14 and ∼×1.1 on MNIST and CIFAR, respectively) in defending against free-rider attacks, and when the main clients are free-riders, i.e., 50% or even up to 80% of the free-riders, FRAD can still maintain a high defense performance against free-rider attacks.
Low-Rank Sparse Generative Adversarial Unsupervised Domain Adaptation for Multi-Target Traffic Scene Semantic Segmentation

Authors:
raw_author_name: Mohsen Saffari
raw_affiliation_string: Department of Computer Science, University of Tulsa, Tulsa, OK, USA
raw_author_name: Mahdi Khodayar
raw_affiliation_string: Department of Computer Science, University of Tulsa, Tulsa, OK, USA

Abstract: Semantic segmentation in diverse real-world traffic scenes is a challenging task for autonomous vehicles to have a reliable understanding of the outside environment. Although deep visual models show a remarkable performance on traffic scene segmentation, they need significant improvements when the source and target scene distributions are different. The weather and illumination differences between source and target datasets result in dramatic distribution shifts which degrade the target segmentation performance. This paper develops an unsupervised domain adaptation (UDA) model to address complex domain shift problems where the dataset, weather, and illumination of source and target traffic scenes are different. We devise a sparse adversarial multi-target UDA approach to capture powerful domain-invariant features to segment traffic scenes in different conditions. First, a sparse representation of the source traffic scenes is captured via a new spectral low-rank dictionary learning technique in the latent space of a deep encoder-decoder segmentation architecture. The feature sparsity provides high generalization capacity for our deep feature extractor which enables us to compute complex visual patterns of source images. Then, the distribution of source sparse features is learned using a generative adversarial framework. Finally, the sparse representations of source and target scenes are aligned via a sparse domain-invariant feature extractor trained by min-max optimization. The aligned features serve as domain-invariant scene representations that can best describe both source and target scenes; hence providing deep domain adaptation for traffic scene semantic segmentation. Experiments on a real-world dataset indicate the superiority of the proposed model compared to the state-of-the-art methodologies.
Transmitter Identification With Contrastive Learning in Incremental Open-Set Recognition

Authors:
raw_author_name: Xiaoxu Zhang
raw_affiliation_string: National Space Science Center, Chinese Academy of Sciences, Beijing, China
raw_author_name: Yonghui Huang
raw_affiliation_string: National Space Science Center, Chinese Academy of Sciences, Beijing, China
raw_author_name: Meiyan Lin
raw_affiliation_string: National Space Science Center, Chinese Academy of Sciences, Beijing, China
raw_author_name: Ye Tian
raw_affiliation_string: National Space Science Center, Chinese Academy of Sciences, Beijing, China
raw_author_name: Junshe An
raw_affiliation_string: National Space Science Center, Chinese Academy of Sciences, Beijing, China

Abstract: Radio frequency fingerprints are commonly exploited as a unique signature in the physical layer for distinguishing transmitters in Transmitter Identification Systems (TIS). In response to the growing demand for TIS in open dynamic scenarios, this paper proposes the Incremental Open-Set Recognition (IOSR) framework to address IOSR tasks, which involve changes in transmitter categories, component replacements, and open-set recognition. To overcome the limitations of traditional methods, the proposed framework focuses on enhancing the security, adaptability, reliability, and efficiency of TIS. Specifically, a well-designed data representation and a lightweight extractor based on supervised contrastive learning are introduced to improve inter-class discriminative ability and intra-class compactness for feature extraction. The incorporation of MobileNetV3 reduces the training parameters of the extractor while improving computational efficiency. Moreover, an adaptive evolved block is designed to mitigate catastrophic forgetting in incremental learning, preserving historical knowledge and enhancing system scalability and adaptability. Additionally, an enhanced open-set recognizer is proposed to establish a suitable open-set decision boundary through output calibration. The performance evaluation of the framework on the WiFi dataset showcases its superiority over existing approaches in the closed-set recognition, achieving an accuracy of over 99.6%. It also performs well in incremental tasks, with an accuracy exceeding 98.9%. In the open-set recognition, the framework achieves an accuracy improvement of approximately 8%. Moreover, it demonstrates superior accuracy in the IOSR task, outperforming other algorithms by more than 5.8%. Furthermore, ablation experiments provide further evidence of the effectiveness of the proposed framework, while a complexity comparison demonstrates its ability to balance computational load and accuracy.
A Secure Sidechain for Decentralized Trading in Internet of Things

Authors:
raw_author_name: Fangyu Gai
raw_affiliation_string: School of Engineering, The University of British Columbia, Kelowna, Canada
raw_author_name: Jianyu Niu
raw_affiliation_string: The University of British Columbia, Kelowna, Canada
raw_author_name: Mohammad M. Jalalzai
raw_affiliation_string: School of Engineering, The University of British Columbia, Kelowna, Canada
raw_author_name: Seyed Ali Tabatabaee
raw_affiliation_string: Blockchain&#x0040;UBC, Vancouver, Canada
raw_author_name: Chen Feng
raw_affiliation_string: School of Engineering, The University of British Columbia, Kelowna, Canada

Abstract: Sidechains allow transaction dissemination and execution outside the blockchain main network (i.e., the mainchain), enabling a scalable, efficient, and secure financial infrastructure for the Internet of Things (IoT) without trusting any central authority. Existing sidechains either have online requirements or rely on intensive computation on a central operator, which does not meet the needs of IoT for dynamic changes and high performance. This paper proposes an alternative sidechain construction, called Cumulus, which meets the needs of IoT by leveraging the classic Byzantine fault-tolerant (BFT) consensus protocols such as PBFT that have commonly been applied in permissioned blockchains. Cumulus builds BFT-based sidechains atop public blockchains (e.g., Ethereum) using smart contracts and ensures the bidirectional safety of users’ assets. Cumulus sidechains periodically interact with the mainchain and submit checkpoints through representatives selected in an efficient and decentralized manner. The experiments show that Cumulus sidechains outperform rollup-based sidechains, and state-of-the-art sidechain constructions, achieving two and three orders of magnitude improvement in throughput and latency while retaining comparable operational cost.
Multi-Party Privacy-Preserving Faster R-CNN Framework for Object Detection

Authors:
raw_author_name: Ruonan Wang
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Min Luo
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Qi Feng
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Cong Peng
raw_affiliation_string: School of Cyber Science and Engineering, Wuhan University, Wuhan, China
raw_author_name: Debiao He
raw_affiliation_string: Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

Abstract: Faster region-based conventional neural network (Faster R-CNN) is a common algorithm for object detection that identifies the object and their location information through three steps: feature extraction, region proposal network and classification. However, there are data privacy issues in the training and prediction of Faster R-CNN. Thus we design a multi-party privacy-preserving Faster R-CNN framework for object detection named SPFR. Specifically, we extend the existing sub-protocols and achieve high-precision division, exponentiation and logarithm calculation through the idea of blinding and oblivious transfer (OT) protocols. Then we design a series of key privacy-preserving protocols that satisfy the secure computational requirements based on the above-mentioned sub-protocols and prove that these protocols are security in the semi-honest adversary model. Finally the proposed protocols are implemented in Pytorch, and the experimental findings demonstrate the efficiency of the protocols in Faster R-CNN.