Reviewerly/exHarmony huggingface dataset:
Threat Capability of Stubborn Mining in Imperfect GHOST Bitcoin Blockchain

Bitcoin is the largest PoW blockchain, which currently uses the longest-chain protocol for chain selection and is vulnerable to various attacks like stubborn mining attack. As a variant of selfish mining attack, stubborn mining attack usually has 7 types of strategies, each of which does damage to the blockchain system. GHOST is another chain-selection protocol, which has been demonstrated to make the blockchain system more secure than the longest-chain protocol under selfish mining attack. There were studies on stubborn mining in perfect GHOST blockchains and they only studied two types of stubborn mining strategies. But it is a fact of life that the blockchain is an imperfect network due to ubiquitous network congestion and/or attacks. This paper aims to explore a simulation-based approach to quantitatively evaluate the threat capability of all 7 stubborn mining strategies. We first develop all stubborn strategies in imperfect GHOST blockchains. Then we evaluate miner revenues and system throughput over different network conditions. The results show that the lead-fork-stubborn strategy is the dominant strategy for attackers when they have more than 33% total computing power. The stubborn attackers with less than 20% total computing power lose their revenue whichever stubborn mining strategy is used. The blockchain with high network quality still has the risk of significant throughput downgrade. Our work can help the stubborn mining attack detection and secure blockchain system design.
Analysing Covertness of Tor Bridge Request

Tor bridges are hidden entrances of Tor network. Users can exploit bridges to hide their visits of Tor. To restrict hidden Tor visits, many attacks focus on bridge information discovery or bridge traffic detection. But these attacks are less effective because bridges' information cannot be discovered thoroughly and its traffic are often obfuscated. In the paper, we present a novel attack to stop hidden Tor visits. We observe that users need to request information of bridges from a database before visiting Tor network. Thus, attackers can stop Tor visits by detecting the process of bridge requests rather than bridge itself. To verify our attack's feasibility, we analyse covertness of the most widely-used bridge request tool, which imitates normal network request when communicating with bridge database. After comparing with five types of typical web request, we find that this tool fails to imitate in packet time, size and direction, for example, the variation of simulated packet sizes are more dynamic than normal. Based on the three imitation vulnerabilities, we train machine learning algorithms to detect bridge request. Extensive experiments demonstrate that bridge request can be identified with high accuracy and very low false-positive rates in real-world. In conclusion, our work paves a new way to block evasive Tor visits.
Protecting Legitimate SEI Security Approaches from Phase-Based Obfuscation Attacks

Specific Emitter Identification (SEI) has proven to be an effective means for passively identifying emitters using unique and distinct features that are unintentionally imparted to waveforms during their formation and transmission. Primarily, the assumption is that the to-be-identified emitters are passive devices incapable or unwilling to resist SEI. However, cost-effective software-defined radios and open-source deep learning algorithms are leading investigators to challenge this assumption. They show that previously exploited features can be modified to reduce or defeat SEI. Recently, RF-Veil has been proposed to combat such attacks by providing emitters with an active means to obfuscate their waveform features. The result is an eavesdropping and impersonation attack resilient SEI process. Despite RF-Veil's security and privacy focus, it is fair to assume that nefarious actors will attempt to abuse it to thwart legitimate SEI security processes. Therefore, this work investigates the identification of nefarious emitters that employ RF-Veil to thwart legitimate SEI security processes. The results show that there is an inherent Residual Phase Offset (RPO) present in preambles that are not removed in traditional phase offset correction. Removing RPO improves SEI performance when using the phase representation of IQ samples and significantly reduces RF-Veil's negative impact on SEI.
A Framework of Robust Congestion Mitigation with Traffic Estimation, Split Ratio Optimisation and Path Planning

Extensive traffic micro-bursts as well as limited capabilities of network infrastructures result in unpredictable network congestions which have large impacts on the quality of service. In order to effectively and robustly mitigate network congestions, this paper proposes a framework which incorporates future traffic estimation with edge learning, split ratio optimisation via the multi-step linear programming, as well as path planning using a revised column generation algorithm. The combination of traffic prediction and the proposed optimisation framework takes future traffic into consideration and guarantees the robustness of segment routing process. The revised column generation algorithm enables flexible rerouting of traffic distributed to congested links and significantly reduces congestion areas. Extensive experiments on a specific traffic dataset and three benchmark datasets show the feasibility and effectiveness of the proposed algorithms.
A Simple Yet Effective Data Augmentation for Human Pose Estimation

Accurate occluded key point identification is a challenge and hot topic for human pose estimation. To make the occluded or invisible keypoint better, data augmentation play an important role which makes the network overcome complex case. In this paper, we want to apply the mosaic and mix-up technique which is a powerful method to tackle the problem. Furthermore, data augmentation demonstrates its superiority over other methods without enlarging the computational cost. Correspondingly, the proposed work focuses on powerful data augmentation for occluded keypoints. First, following a human detection in the detector network, feed the human proposal region into the data augmentation, which makes the network can learn more about the occluded cases. The data after data augmentation then apply to train for the pose estimator. The estimator collects more information in occluded keypoints, illustrating higher precision efficiency. The outputs of our experiments would also demonstrate a distinction between the use of mix-up and mosaic data augmentation and existing approaches. The predicted joint heatmaps are more accurate than the baseline technique despite using the same amount of parameters due to the transition to a high-resolution network (HRNet) for the pose estimator. Regarding AP, the proposed design outperforms the baseline network which is HRNet by 1.0 points, but in the occluded case, the pose estimator performs much better. Additionally, the COCO 2017 benchmarks, now accessible as an open and the most popular dataset for pose estimation, were used to train the proposed network.
Rao-Blackwellized Monte Carlo Data Association With Deep Metric For Object Tracking

We propose a deep Rao-Blackwellized Monte Carlo data association particle filter (DeepRBMCDA) which is a modification of the existing RBMCDA using Hungarian association. It uses YOLOv7 detected bounding box with deep ReIdentification (ReID) descriptors to track detected objects in Bayesian way. In our work, we demonstrate our performance on a diverse GMOT-40 dataset which contains sequences of varying class objects of similar appearance. We evaluate our tracker and compare its performance with state-of-the-art trackers. We obtain comparable multi object tracking accuracy (MOTA), multi object tracking precision (MOTP), localization accuracy (LocA), and multi object detection accuracy (MODA), improved mostly tracked (MT), reduced mostly lost (ML), and lowest fragmentation (Frag). We also perform the ablation study which reports highest higher order tracking accuracy (HOTA), HOTA combined LocA (HOTALocA), MOTA, identity switching (IDSW), MT, ML, Frag, and identity based F1 score (IDFI) on tracking ground-truth labels. Using particle filter for object tracking provides robustness which can be helpful in diverse dynamic tracking scenarios.
Green Blockchain and Smart Homes: A Systematic Review

Over the last few decades, one of the biggest challenges facing technology, especially the Internet of Things (IoT), has been system vulnerability, centralization, the inefficient ways of storing and transforming data, making these systems vulnerable to fraud, hacking and other forms of manipulation. Additionally, they often charged high fees and long processing times. The advent of blockchain solves many problems through the use of specific techniques and efficiency interventions such as decentralization manner without the help of any third party, high security, consensus deals, anonymity and others. On the other hand, there are also disadvantages. In this context, many research has focused on the use of green blockchain (GBC) to improve performance and mitigate the problems and significant impacts of traditional blockchain and therefore create a positive green think minding. In this paper, we query the Scopus database with keywords to retrieve relevant publications to explore the rationale behind the need for sustainable development and the actual green blockchain technology being used in smart homes.
EVS2vec: A Low-dimensional Embedding Method for Encrypted Video Stream Analysis

The rise in video streaming has led to an increase in network traffic, with encrypted video streams playing a significant role in illegal video detection. However, there are challenges in performing content analysis of encrypted video streams, including label limitations and complex calculations. In this paper, we proposed a low-dimensional embedding method based on Byte Rate Sequences (BRS), named EVS2vec (Encrypted Video Stream to Vector), to solve these problems effectively. It can represent the content of encrypted video streams with low-dimensional vectors by mapping the indefinite-length sequence into a low-dimensional Euclidean space. EVS2vec can thereby be applied for not only supervised analysis but also unsupervised analysis. Furthermore, using BRS can also save the time overhead on fine-grained network traffic parsing. In order to ensure the content-related distinguishability of the embedding result, inspired by contrastive learning, we designed a network structure based on Recurrent Neural Network (RNN) with self-attention mechanism in EVS2vec and trained it using siamese network. The experiments on a public dataset show that EVS2vec saves storage overhead while containing enough video content information. EVS2vec can achieve a high accuracy of similarity threshold, reaching 96.71%. An 8-dimensional fingerprint for each video is constructed. Moreover, classification and clustering analysis can also be performed with acceptable results.
Is Partial Model Aggregation Energy-Efficient for Federated Learning Enabled Wireless Networks?

This work aims to address two of the main challenges for federated learning (FL), i.e., the limited communication resources and the data heterogeneity across devices. To this end, we first devise a novel FL framework with partial model aggregation (PMA), which only aggregates the lower layers of neural networks responsible for feature extraction while the upper layers corresponding to complex pattern recognition remain at devices for personalization. This design is able to address the data heterogeneity and reduce the transmitted information in wireless channels. Then, we maximize the scheduled data sample volume by joint optimizing the device scheduling, bandwidth allocation, computation and communication time division. Specifically, our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the optimal device scheduling. Experimental results on the CIFAR-10 dataset show that the proposed PMA-FL improves 11.6% accuracy compared with the state-of-art benchmarks, and the proposed joint dynamic device scheduling and resource optimization approach achieves slightly higher accuracy than the considered benchmarks but reduced 25% energy or 12.5% time budgets.
DIDs-Assisted Secure Cross-Metaverse Authentication Scheme for MEC-Enabled Metaverse

With the popularization of emerging technologies such as artificial intelligence, 5G and beyond, extended reality and blockchain, the next generation Internet is rapid expansion. “Metaverse” as an evolving paradigm of next-generation Internet, can be recognized as a fully immersive, hyper spatiotemporal and self-sustaining virtual shared space, and its concept is continuous development and evolution. It is moving from imagination to the coming reality, but it is still far from being realized. One of reasons is that distinct sub-metaverses deploying their services on heterogeneous blockchains results in major problems for interoperability, preventing the implementation of seamless integrated metaverse. Facing the challenge, this paper proposes a decentralized identifiers (DIDs) assisted secure cross-metaverse authentication scheme for MEC-enabled metaverse, which is based on a novel designed infrastructure build on MEC and blockchain. In addition, the proposed scheme adopts DIDs, which can not only achieve the secure cross-metaverse authentication, but also increase the decentralization of the metaverse. In addition, the adoption of ID-based aggregate signature can reduce the overhead of computation, communication and storage.
Federated Representation Learning Through Clustering

Federated self-supervised learning (FedSSL) methods have proven to be very useful in learning unlabeled data that is distributed to multiple clients, possibly heterogeneously. However, there is still a lot of room for improvement for FedSSL methods, especially for the case of highly heterogeneous data and a large number of classes. In this paper, we introduce a new way of thinking to approach the FedSSL problems. Specifically, we propose optimizing the representations through the more difficult task of clustering. The resulting federated representation learning through clustering (FedRLC) scheme utilizes i) a crossed KL divergence loss with a data selection strategy during local training and ii) a dynamic upload on local cluster centers during communication updates. Experimental results show that FedRLC achieves state-of-the-art results on widely used benchmarks even with highly heterogeneous settings and datasets with a large number of classes such as CIFAR-100.
A Comparative Analysis of Fake Image Detection in Generative Adversarial Networks and Variational Autoencoders

The rise of deep fake technology has led to growing concerns about its potential misuse for propaganda, disinformation, and even cybercrimes. Deep fake detection has thus become a crucial research area to prevent the spread of fake content and protect digital authenticity. This research investigates the detection of fake human face images using deep learning models. The study utilizes a combination of the CelebA-HQ and FFHQ datasets to create real image labels. Two generative models, a Style-based generator and a Variational Autoencoder, are trained to generate GAN-fake and VAE-fake images, respectively. The deep learning models are then trained and evaluated using both real and fake images. The research focuses on two scenarios: binary net and multi-class net. In the binary net scenario, ResNet50 achieves the highest accuracy of 99.34%, along with excellent precision, recall, and F1-score. VGG16 and VGG19 also perform well in distinguishing between fake and real images. In the multi-class net scenario, ResNet50 again achieves the highest accuracy of 95.25% and balanced F1-score. VGG16 and VGG19 maintain competitive performance, while other models show slightly lower accuracy. These results provide insights into the effectiveness of different deep learning models for detecting fake human face images. ResNet50 consistently performs well in both scenarios, while VGG16 and VGG19 offer reliable alternatives.
Detection of Cancer Cells in Tissue Histopathology Images Using a Convolutional Neural Network

Cancer is a chronic disease with a high increase in the number of cases worldwide. In women, breast cancer is the most common cancer. One way to detect breast cancer is through histopathologic images of the tissue. Currently, histopathology detection is done manually, so it has shortcomings in the form of human limitations in classifying large numbers of images. In this research, a Convolutional Neural Network (CNN) model is developed to detect cancer cells in histopathology images automatically. The dataset used to train the model is 120,000 histopathology images labeled 1 (cancer) and 0 (normal) in the same ratio. Training the model is done with a batch size of 128 and epochs of 30. The confusion matrix and AUC-ROC method measure accuracy. From this study, the CNN model proved accurate in predicting tissues with cancer cells with a precision value of 94.76 percent, recall of 98.07 percent, and F-1 score of 96.39 percent.
An Analysis of Hate Speech Detection Techniques

Hate speech is any act of provoking or insulting another person or group based on their ethnicity, religion, race, gender, sexual orientation, physical ability, or other characteristics. This can be done in a variety of ways. Because of this, a study was conducted that focused on ascertaining the most effective technique for identifying hate speech. It was discovered that LSTM, a form of recurrent neural network (RNN) that is particularly good at modeling sequential data, is the most successful technique to apply. Many natural language processing (NLP) activities may be performed with it, including hate speech detection, which makes LSTM have advantages over other techniques, both in terms of long-term memory mechanisms. LSTM, also able to handle long and complex text sequences, capable of recognizing more complex patterns, able to understand and consider the context in the text, to recognize the meaning of a word or phrase in a broader context. Then, based on the dataset obtained from the Kaggle website with a total of 24783 data, then the dataset is divided into training data and validation data, with a ratio of training data to validation data. of 80%: 20%. Then, the testing step results in an accuracy value of 87.10%.
Using Locality-Sensitive Hashing for Rendezvous Search

The multichannel rendezvous problem is a fundamental problem for neighbor discovery in many IoT applications.The existing works in the literature focus mostly on improving the worst-case performance, and the average-case performance is often not as good as that of the random algorithm.As IoT devices (users) are close to each other, their available channel sets, though they might be different, are similar.Using the locality-sensitive hashing (LSH) technique in data mining, we propose channel hopping algorithms that exploit the similarity between the two available channel sets to increase the rendezvous probability.For the synchronous setting, our algorithms have the expected time-to-rendezvous (ETTR) inversely proportional to a well-known similarity measure called the Jaccard index.For the asynchronous setting, we use dimensionality reduction to speed up the rendezvous process.Our numerical results show that our algorithms can outperform the random algorithm in terms of ETTR.
DIBR-Based Collaborative Computation in Edge Network for Multiplayer Online VR Game

Due to the real-time and realistic immersive experience, online multiplayer interactive virtual reality (VR) is becoming more and more popular. To provide necessary VR immersive environments, delay-sensitive rendering tasks require a large number of high-resolution metadata, bringing significant challenges to computing processing and offloading transmission. Fortunately, the Metaverse-based edge networking and computing provide a possible approach to reuse similar view data in an adaptive collaborative transmit route. In this paper, we proposed a collaborative rendering method for multiplayer online VR games based on Depth Image-Based Rendering (DIBR), with a high data rate target under strict delay requirements by cooperatively synthesizing similar but compute-intensive views. Specifically, players are clustered into different groups by the proposed adaptive synthesis view matching method to choose appropriate views for players in need at first. Then, a multi-object optimizing problem is formulated considering on-orthogonal multiple access (NOMA) and device-to-device (D2D). A block coordinate descent (BCD) method via invoking the semidefinite relaxation (SDR) method is proposed to solve this non-convex problem. Simulation results show the proposed algorithm has reached an optimal maximum minimum data rate, reducing the cost of computation and communication resources.
Privacy-Preserving Federated Primal-Dual Learning for Non-Convex Problems With Non-Smooth Regularization

Recently, the federated learning (FL) has been a machine learning paradigm for the preservation of data privacy, though high communication cost and privacy protection are still the main concerns of FL. However, in many practical applications, the trained model needs certain nature or characteristics, such as sparseness in classification, otherwise learning performance loss is inevitable. In order to upgrade the learning performance, a suitable non-smooth regularizer (e.g., $\ell_{1}$-norm for the model sparseness) can be added to the loss function (often non-convex) in the considered optimization problem. This paper proposes a novel primal-dual learning algorithm to handle such non-smooth regularization aided non-convex FL problems, that yields much superior learning performance over some state-of-the-art FL algorithms under privacy guarantee by means of differential privacy. Finally, some experimental results are provided to demonstrate the efficacy of the proposed algorithm.
Convergence Analysis and Optimization of Over-the-Air Federated Meta-Learning

By moving the model computation from the cloud to edge devices, federated learning (FL) preserves user privacy without sending raw data to the centralized server. However, the personalized data generated by different edge devices are often statistically heterogeneous, which degrades the performance of FL significantly. In addition, frequent information exchange between the server and devices imposes heavy communication overhead for the distributed model training of FL in spectral-limited wireless networks. With the aim of overcoming the statistical challenge in a communication-efficient manner, we propose an over-the-air federated meta-learning (Air-FedML) framework from the perspectives of integrating algorithm design and wireless transmission, which enables edge devices to collaboratively learn a shared model with good adaptation to heterogeneous data. To gain theoretical insights, we derive a closed-form expression of the convergence upper bound for the proposed Air-FedML framework to capture the effect of wireless communications on learning performance. Then, we formulate a non-convex optimization problem to minimize the derived upper bound by jointly optimizing the transmit and receive strategies under the constraints of power budget and aggregation error. Numerical results verify the effectiveness of the designed algorithm and show the superiority of our Air-FedML in adapting to statistically heterogeneous data compared to existing FL schemes.