Reviewerly/exHarmony huggingface dataset:
Clustering asymmetrical data with outliers: Parsimonious mixtures of contaminated mean-mixture of normal distributions

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

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

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

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

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

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

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

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.
ATAL: Active Learning Using Adversarial Training for Data Augmentation

Active learning (AL) tries to maximize the model’s performance when the labeled dataset is limited, and the annotation cost is high. Although it can be efficiently implemented in deep neural networks (DNNs), it is questionable whether the model can maintain the ability to generalize well when there are significant distributional deviations between the labeled and unlabeled datasets. In this paper, we consider introducing adversarial training and adversarial samples into active learning to mitigate the problem of degraded generalization performance due to different data distributions. In particular, our proposed adversarial training active learning (ATAL) has two advantages, and one is that adversarial training by different networks enables the network to have better prediction performance and robustness with limited labeled samples. And the other is that the adversarial samples generated by the adversarial training can effectively expand the labeled dataset so that the designed query function can efficiently select the most informative unlabeled samples based on the expanded labeled dataset. Extensive experiments have been performed to verify the feasibility and efficiency of our proposed method, i.e., CIFAR-10 demonstrates the effectiveness of our method — new state-of-the-art robustness and accuracy are achieved.
Identity-Based Multiproxy Signature With Proxy Signing Key for Internet of Drones

A multi-proxy signature scheme constructs a legitimate signature by a proxy group when an original signer is absent. In this paper, we newly propose an efficient and provably secure ID-Based Multi-Proxy Signature (IBMPS) from bilinear pairings based on a random proxy signing key. We observe that a proxy signing key from a Proxy Key Generation (PKG) makes IBMPS much more efficient and secure than directly using a private key for a multi-proxy signature. We show formal security results under the CDH assumption with PKG queries in the random oracle. Our scheme not only preserves provable security but also achieves the least computation cost in the signing and verification phase than other IBMPS schemes. To show its applicability in Internet-of-Drones (IoD), which usually demands greater efficiency due to the limited resources while the drones are in flight, we also present a practical on/offline IBMPS for an IoD setting. Our on/offline IBMPS scheme just requires one point scalar addition for generating a proxy signature in terms of each proxy signer. Considering that our IBMPS schemes can be practical in urgent situations, e.g., surveillance and rescue operations, we show our implementation of IBMPS schemes on a Raspberry Pi drone platform along with the relevant schemes. We demonstrate that our schemes are considerably more efficient than others with respect to computation time and energy consumption by the number of proxy signers.
Joint Edge Server Selection and Data Set Management for Federated-Learning-Enabled Mobile Traffic Prediction

To realize intelligent network management for future 6G-mobile edge computing (MEC) systems, mobile traffic prediction is crucial. Most of the previous machine learning-driven prediction approaches adopt traditional centralized training paradigm wherein mobile traffic data should be transferred to a central server. To exploit the distributed and parallel processing nature of MEC servers for training mobile traffic prediction models in a fast and secure manner, we propose a novel federated learning (FL) framework wherein locally trained prediction models over MEC servers are aggregated into a global model with joint optimization of MEC server selection and dataset management for FL participation. From mathematical investigations of the influence of MEC server participation and dataset utilization on the global model accuracy and training costs, including both training latency and energy consumption in the FL process, we first formulate an optimization problem for balancing the accuracy-cost trade-off by considering a linear accuracy estimation model. Here, the optimization problem is designed using mixed-integer nonlinear programming, which is generally known as NP-hard. We then leverage a number of relaxation techniques to develop near-optimal yet plausible algorithm based on linear programming. Furthermore, for practical concern, the proposed problem is extended by considering a concave accuracy estimation model; a genetic-based heuristic approach to the extension is proposed for determining the suboptimal solution. The numerical and simulation results suggest that our proposed framework can be effective for building mobile traffic prediction models in a more cost-efficient manner while maintaining competitive prediction accuracy.
Enhanced Syntactic and Semantic Graph Convolutional Network With Contrastive Learning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a given specific aspect in the sentence. Recent studies focus on leveraging graph convolutional neural networks to encode both syntactic and semantic information. However, current syntactic parsers, which are not specifically for ABSA, introduce noise to the syntactic information. Besides, ongoing studies ignore the distinctiveness of semantics and syntax. To address these issues, we proposed an enhanced syntactic and semantic graph convolutional network (GCN) with contrastive learning in this article. An aspect-oriented syntactic graph is constructed with aspect-specific perturbed masking for reducing the syntactic noise, and a semantic graph is established with self-attention weights from bidirectional encoder representation from transformers (BERT). The semantic and syntactic representations are further enhanced by both sentiment polarity-based supervised contrastive learning and syntactic reliability-based unsupervised contrastive learning. Furthermore, label embeddings of syntactic reliability are learned to determine the weights of syntactic and semantic information. Extensive experiments on four publicly available datasets demonstrate that our model is more competitive than the state-of-the-arts.
E<sup>3</sup>-UAV: An Edge-Based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles

Motivated by the advances in deep learning techniques, the application of Unmanned Aerial Vehicle (UAV)-based object detection has proliferated across a range of fields, including vehicle counting, fire detection, and city monitoring. While most existing research studies only a subset of the challenges inherent to UAV-based object detection, there are few studies that balance various aspects to design a practical system for energy consumption reduction. In response, we present the E$^3$-UAV, an edge-based energy-efficient object detection system for UAVs. The system is designed to dynamically support various UAV devices, edge devices, and detection algorithms, with the aim of minimizing energy consumption by deciding the most energy-efficient flight parameters (including flight altitude, flight speed, detection algorithm, and sampling rate) required to fulfill the detection requirements of the task. We first present an effective evaluation metric for actual tasks and construct a transparent energy consumption model based on hundreds of actual flight data to formalize the relationship between energy consumption and flight parameters. Then we present a lightweight energy-efficient priority decision algorithm based on a large quantity of actual flight data to assist the system in deciding flight parameters. Finally, we evaluate the performance of the system, and our experimental results demonstrate that it can significantly decrease energy consumption in real-world scenarios. Additionally, we provide four insights that can assist researchers and engineers in their efforts to study UAV-based object detection further.
Improving Adversarial Robustness With Adversarial Augmentations

Deep neural networks (DNNs)-based applications are extensively being researched and applied in the Internet of Things (IoT) devices in daily lives due to impressive performance. Recently, adversarial attacks pose a significant threat to the security of DNNs, adversarial training has emerged as a promising and effective defense approach for defending against such attacks. However, existing adversarial training methods have shown limited success in defending against attacks unseen during training, thereby undermining their effectiveness. Besides, generating adversarial perturbations for adversarial training requires massive expensive labeled data, which is a critical obstacle in the IOT applications of robust DNNs. In this paper, we first explore the effective data augmentations by implementing adversarial attacks with self-supervised in latent space. Then, we propose new loss metric functions that can avoid collapse phenomenon of contrastive learning (CL) by measuring the distances between adversarial augmented pairs. Based on extracted adversarial features in self-supervised CL, we propose a novel Adversarial Robust Learning (ARL) method, which implements adversarial training without any labels and obtains more general robust encoder network. Our approach is validated on commonly used benchmark datasets and models, where it achieves comparable adversarial robustness against different adversarial attacks when compared to supervised adversarial training methods. Additionally, ARL outperforms state-of-the-art self-supervised adversarial learning techniques in terms of achieving higher robustness and clean prediction accuracy for the downstream classification task.
Secure and Efficient Bloom-Filter-Based Image Search in Cloud-Based Internet of Things

Image search is a hot topic, which has played a significant role in various Internet of Things (IoT) applications, such as disease diagnosis, face recognition, and fingerprint recognition. Meanwhile, the proliferation of images has led image owners to outsource images to the cloud for reducing local storage and computation burdens. Therefore, image search without compromising privacy over cloud has received considerable attention and extensively explored in the literature. Many Bloom filter-based schemes have been put forth in past years, however most of them suffer from high storage overhead, low false positive rate, and even expose the values in Bloom filter. To solve these challenges, in this paper, we first design a Merged and Repeated Indistinguishable Bloom Filter (MRIBF) index structure, which can reduce the storage overhead and achieve adaptive security with a low false positive rate. Then, with the MRIBF, we propose a secure and efficient Bloom Filter-based Image Search scheme (BFIS) to achieve a faster-than-linear and more accurate search. Detailed theoretical analysis shows that our scheme is really accurate and secure. Extensive experiments demonstrate that our scheme is indeed efficient and feasible.