Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review on Email Spam Classification with Machine Learning Methods

This comprehensive review delves into the realm of email spam classification, scrutinizing the efficacy of various machine learning methods employed in the ongoing battle against unwanted email communication. The paper synthesizes a wide array of research findings, methodologies, and performance metrics to provide a holistic perspective on the evolving landscape of spam detection. Emphasizing the pivotal role of machine learning in addressing the dynamic nature of spam, the review explores the strengths and limitations of popular algorithms such as Naive Bayes, Support Vector Machines, and neural networks. Additionally, it examines feature engineering, dataset characteristics, and evolving threats, offering insights into the challenges and opportunities within the field. With a focus on recent advancements and emerging trends, this review aims to guide researchers, practitioners, and developers in the ongoing pursuit of robust and adaptive email spam classification systems.
Environmental and Climate Impacts of the Metaverse

This paper delves into the environmental and climate impacts of Metaverse technologies and the social implications of these impacts. Current literature primarily focuses on the potential negative or positive effects of the Metaverse on the environment and climate change without offering practical solutions and approaches to mitigate these effects. Furthermore, little consideration is given to the social implications of these environmental impacts on global issues such as social inequalities, digital divides, and corporate social responsibility. This integrative literature review aims to provide a deeper understanding of how Metaverse technologies may impact environmental sustainability and the climate while offering solutions to mitigate the relevant negative impacts on the environment and their associated social implications. This integrative literature review is grounded in Dwivedi et al.’s (2022) three concepts used to explain the pollution-getting effects of the Metaverse: 1) the development, use, and application of various digital technologies and tools consume a significant amount of energy, 2) data storage, processing, and transmission for the use of the Metaverse can lead to substantial emission of CO2, and 3) e-waste from electronic products could have significant effects on the environment. The research methodology adopted was an integrative literature review design in which multiple sources were analyzed to provide a comprehensive understanding of the topic. Findings revealed five critical areas in which the Metaverse will likely have a significant impact on environmental sustainability and the climate and associated social implications: (a) energy consumption, (b) e-waste, (c) rare earth metals and conflict minerals, (d) changes in physical travel, and (e) encouraging sustainable behaviors. The findings underscore the role of renewable and alternative resources, recycling initiatives, social inclusivity, and cross-sector collaboration in mitigating the relevant negative impacts. These findings have significant implications for environmental sustainability and for ensuring a just and equitable society where the Metaverse offers equal opportunities to different populations. Recommendations for future research include more experimental studies that test different sustainable energy sources and hardware solutions for the Metaverse and more replication studies that consider the social implications of Metaverse sustainability solutions, especially in developing nations and among disadvantaged populations.
Enhancing the Unlinkability of Circuit-Based Anonymous Communications with k-Funnels

Anonymous communication systems are essential tools for preserving privacy and freedom of expression. However, traffic analysis attacks make it challenging to maintain unlinkability in circuit-based anonymity networks like Tor, enabling adversaries to deanonymize communications. To address this problem, we introduce k-funnel, a new security primitive that enhances the unlinkability of circuit-based anonymity networks, and we present BriK, a Tor pluggable transport that implements k-funnels. k-Funnels offer k-anonymity to a group of k clients by jointly tunneling their circuits' traffic through a bridge while ensuring that the client-generated flows are indistinguishable. BriK incorporates several defense mechanisms against traffic analysis attacks, including traffic shaping schemes, synchronization protocols, and approaches for monitoring exposure to statistical disclosure attacks. Our evaluation shows that BriK is able to support web browsing and video streaming while offering k-anonymity. We evaluate the security of BriK against traffic correlation attacks leveraging state-of-the-art deep learning classifiers without considering auxiliary information and find it highly resistant. Although k-funnels require the cooperation of mutually trusted clients, limiting their coordination, our work presents a new practical solution to strengthen unlinkability in circuit-based anonymity systems.
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality

Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate. By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.
A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles

In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation using only a single image as input. We present state-of-the-art Vision Transformers for these tasks and combine them into a multitask model. Through multiple experiments on four different street image datasets, we demonstrate that the multitask approach significantly reduces inference time while maintaining high accuracy for both tasks. Additionally, we show that changing the size of the Transformer-based backbone can be used as a trade-off between inference speed and accuracy. Furthermore, we investigate the use of synthetic data for pre-training and show that it effectively increases the accuracy of the model when real-world data is limited.
A Closed-Form Solution for Graph Signal Separation Based on Smoothness

Using smoothness criteria to separate smooth graph signals from their summation is an approach that has recently been proposed [1] and shown to have a unique solution up to the uncertainty of the average values of source signals. In this correspondence, closed-form solutions of both exact and approximate decompositions of that approach are presented. This closed-form solution in the exact decomposition also answers the open problem of the estimation error. Additionally, in the caseofGaussiansourcesignalsinthepresenceofadditiveGaussiannoise, it is shown that the optimization problem of that approach is equivalent to the Maximum A Posteriori (MAP) estimation of the sources
A Sustainable Ecology of Mobility: DAO-Based Autonomous Vehicular Services

As a brief summary of the Distributed/Decentralized Hybrid Workshop on sustainable vehicular service ecology, this letter discusses the challenges of autonomous vehicular services and the potential of decentralized autonomous organizations and operations (DAOs) to address them. It outlines the future research issues on the integration of blockchain-based TRUE DAOs (TAOs) within vehicular services, including the ecological framework and the corresponding organizational, cooperational, executional and application technologies, to support a sustainable ecology of mobility.
Enabling Two-way Communication of Deaf Using Saudi Sign Language

Disabled people are facing many difficulties communicating with others and involving in society. Modern societies have dedicated significant efforts to promote the integration of disabled individuals into their societies and services. Currently, smart healthcare systems are used to facilitate disabled people. The objective of this paper is to enable two-way communication of deaf individuals with the rest of society, thus enabling their migration from marginal elements of society to mainstream contributing elements. In the proposed system, we developed three modules; the sign recognition module (SRM) that recognizes the signs of a deaf individual, the speech recognition and synthesis module (SRSM) that processes the speech of a non-deaf individual and converts it to text, and an Avatar module (AM) to generate and perform the corresponding sign of the non-deaf speech, which were integrated into the sign translation companion system called Saudi deaf companion system (SDCS) to facilitate the communication from the deaf to the hearing and vice versa. This paper also contributes to the literature by utilizing our self-developed database, the largest Saudi Sign Language (SSL) database—the King Saud University Saudi-SSL (KSU-SSL). The proposed SDCS system performs 293 Saudi signs that are recommended by the Saudi Association for Hearing Impairment (SAHI) from 10 domains (healthcare, common, alphabets, verbs, pronouns and adverbs, numbers, days, kings, family, and regions).
Analysis of Deep Learning Model Combinations and Tokenization Approaches in Sentiment Classification

Sentiment classification is a natural language processing task to identify opinions expressed in texts such as product or service reviews. In this work, we analyze the effects of different deep-learning model combinations, embedding methods, and tokenization approaches in sentiment classification. We feed non-contextualized (Word2Vec and GloVe) and contextualized (BERT and RoBERTa/XLM-RoBERTa) embeddings and also the output of the pretrained BERT and RoBERTa/XLM-RoBERTa models as input to neural models. We make a comprehensive analysis of eleven different tokenization approaches, including the commonly used subword methods and morphologically motivated segmentations. Experiments are conducted on three English and two Turkish datasets from different domains. The results show that BERT-and RoBERTa-/XLM-RoBERTa-based and contextualized embeddings outperform other neural models. We also observe that using words in raw or preprocessed form, stemming the words, and applying WordPiece tokenizations give the most promising results in the sentiment analysis task. We ensemble the models to find out which tokenization approaches produce better results together.
Two-dimensional Parallel Spatio-Temporal Pyramid Pooling for Hand Gesture Recognition

Hand Gesture Recognition (HGR) plays a crucial role in user-friendly interactions between humans and computers. In recent years, using the Convolutional Neural Network (CNN) has improved the accuracy of image processing problems. Inspired by the high recognition rate of CNN and its efficiency, we propose a model for hand gesture recognition based on CNN and evaluate the results using images with plain and complex backgrounds. Recognizing different hand signs by Two-Dimensional Parallel Spatio-Temporal Pyramid Pooling (2DPSTPP) features with deep learning methods reduces the size of the map, minimizes training complexity, and by paying attention to more details, improves detection performance. The effectiveness of the proposed method is evaluated using regular cross-validation tests on six datasets, namely American Sign Language (ASL), the NUS hand posture dataset I, the NUS hand posture dataset II, the digits dataset, the hand gesture dataset, and the leap gesture recognition dataset.
Multiple Tasks-based Multi-source Domain Adaptation Using Divide-and-conquer Strategy

In single-source unsupervised domain adaptation (SUDA), it is often assumed that a single-source domain can cover all target domain features. However, the limitation of labeled samples means that a model trained on a labeled source domain cannot always cover all target representations in practice. Therefore, multi-source unsupervised domain adaptation (MSUDA) has recently become an attractive topic because it can provide richer information than SUDA. In the MSUDA setting, multiple labeled source datasets and an unlabeled target dataset are available. The differently labeled source domains follow distinct distributions to provide different contributions to the target domain. Therefore, when combining multiple source domains into one source domain, the model tends to focus on whichever source domain makes a dominant contribution to the target domain, which induces bias in learning in the MSUDA setting. To solve this problem, this paper proposes a divide-and-conquer-based MSUDA framework that divides the MSUDA problem into multiple tasks (SUDAs) that it then conquers using multiple task-specific models. Each task is a pair that consists of a single source domain and a target domain, and the tasks provide different views on the target domain because each task has a different source domain. Then, they cooperate to supplement their knowledge via collaborative learning. This cooperation between multiple views can suppress noisy information and preserve critical information, thus mitigating the negative transfer problem during DA and significantly boosting the classification accuracy on the target domain as a result. The proposed method achieved state-of-the-art performance on several real-world visual domain adaptation datasets.
An adversary model of fraudsters’ behaviour to improve oversampling in credit card fraud detection

Imbalanced learning jeopardizes the accuracy of traditional classification models, particularly for what concerns the minority class, which is often the class of interest. This paper addresses the issue of imbalanced learning in credit card fraud detection by introducing a novel approach that models fraudulent behavior as a time-dependent process. The main contribution is the design and assessment of an oversampling strategy, called "Adversary-based Oversampling" (ADVO), which relies on modeling the temporal relationship among frauds. The strategy is implemented by two learning approaches: first, an innovative regression-based oversampling model that predicts subsequent fraudulent activities based on previous fraud features. Second, the adaptation of the state-of-the-art TimeGAN oversampling algorithm to the context of credit card fraud detection. This adaptation involves treating a sequence of frauds from the same card as a time series, from which artificial frauds’ time series are generated. Experiments have been conducted using real credit card transaction data from our industrial partner, Worldline S.A, and a synthetic dataset generated by a transaction simulator for reproducibility purposes. Our findings show that an oversampling approach incorporating time-dependent modeling of frauds provides competitive results, measured against common fraud detection metrics, compared to traditional oversampling algorithms.
Data Poisoning Attacks with Hybrid Particle Swarm Optimization Algorithms against Federated Learning in Connected and Autonomous Vehicles

As a state-of-the-art distributed learning approach, federated learning has gained much popularity in connected and autonomous vehicles (CAVs). In federated learning, models are trained locally, and only model parameters instead of raw data are exchanged to aggregate a global model. Compared with traditional learning approaches, the enhanced privacy protection and relieved network bandwidth provided by federated learning make it more favorable in CAVs. On the other hand, poisoning attack, which can break the integrity of the trained model by injecting crafted perturbations to the training samples, has become a major threat to deep learning in recent years. It has been shown that the distributed nature of federated learning makes it more vulnerable to poisoning attacks. In view of this situation, the strategies and attacking methods of the adversaries are worth studying. In this paper, two novel optimization-based black-box and clean-label data poisoning attacking methods are proposed. Poisoning perturbations are generated using particle swarm optimization hybrid with simulated annealing and genetic algorithm respectively. The attacking methods are evaluated by experiments conducted on the example of traffic sign recognition system on CAVs, and the results show that the prediction accuracy of the global model is significantly downgraded even with a small portion of poisoned data using the proposed methods.
Revocation Speedrun: How the WebPKI Copes with Fraudulent Certificates

The TLS ecosystem depends on certificates to bootstrap secure connections. Certificate Authorities (CAs) are trusted to issue these correctly. However, as a result of security breaches or attacks, certificates may be issued fraudulently and need to be revoked prematurely. Revocation, as a reactive measure, is fundamentally damage control and, as such, time is critical. Therefore, measuring reaction delay is the first step to identifying how well the revocation system functions. In this paper we attempt to characterize the current performance of the WebPKI in dealing with fraudulent certificates. We present measurements of each step in the revocation process: the detection of certificate issuance through Certificate Transparency (CT) monitoring, the administrative revocation process at popular CAs, and the revocation checking behavior of end-user clients, both in a controlled virtualized environment and in the wild. We perform two live measurements, in 2022 and 2023, respectively, to provide a longitudinal comparison. We find that detection and revocation of fraudulent certificates is quick and efficient when leveraging CT and can be completed within 6.5 hours on average. Furthermore, CT is being increasingly enforced by some browsers. However, ∼83% of the clients we observed, across popular browsers, brands and OSes, completely disregard a certificate's status, whileall of the studied browsers still display soft-fail behavior, making them vulnerable to attackers capable of interfering with the network. Of the clients that do check revocation, we find that 35% can be made to accept a revoked certificate through the use of OCSP Stapling. We expect this number to grow with client-side adoption of OCSP Stapling [RFC6961]. Current OCSP expiration times allow a revoked certificate to remain fully valid for up to 7 days for the majority of CAs, exposing clients to attacks.
SPADA: A Sparse Approximate Data Structure Representation for Data Plane Per-flow Monitoring

Accurate per-flow monitoring is critical for precise network diagnosis, performance analysis, and network operation and management in general. However, the limited amount of memory available on modern programmable devices and the large number of active flows force practitioners to monitor only the most relevant flows with approximate data structures, limiting their view of network traffic. We argue that, due to the skewed nature of network traffic, such data structures are, in practice, heavily underutilized, i.e. sparse, thus wasting a significant amount of memory. This paper proposes a Sparse Approximate Data Structure (SPADA) representation that leverages sparsity to reduce the memory footprint of per-flow monitoring systems in the data plane while preserving their original accuracy. SPADA representation can be integrated into a generic per-flow monitoring system and is suitable for several measurement use cases. We prototype SPADA in P4 for a commercial FPGA target and test our approach with a custom simulator that we make publicly available, on four real network traces over three different monitoring tasks. Our results show that SPADA achieves 2× to 11× memory footprint reduction with respect to the state-of-the-art while maintaining the same accuracy, or even improving it.
Optimizing Spatial Sensing Performance with Kriging and SRGAN – A Feasibility Study

Several studies have aimed at improving sensor performance through deep learning. In this paper, we explore the feasibility of using advanced machine learning techniques, specifically Kriging and Super Resolution Generative Adversarial Networks, to enhance the performance and accuracy of simulated force sensitive resistor matrices with a low number of sensors. Kriging is a geostatistical method that uses spatial correlations to interpolate or predict values at unsampled locations, while Super Resolution Generative Adversarial Networks are a type of generative adversarial network that can generate high-resolution images from low-resolution inputs. Our results suggest that machine learning techniques can provide a powerful tool for enhancing the accuracy performance of force sensing technologies, with potential applications in a wide range of fields, especially in autonomous vehicles.
Improving the Adversarial Robustness of Deep Neural Networks via Efficient Two-Stage Training

Despite the widespread applications of deep neural networks (DNNs), the potential threats of adversarial examples remain considerable concerns. In this paper, we propose a new proactive defense method based on a two-stage training procedure in order to enhance the adversarial robustness of DNNs. The first stage intends to map the input samples into a feature embedding space with high separability, while the second stage fixes the feature generator and learns the parameters only for the last layer for classification. Different from most of the existing state-of-the-art methods, our method neither spends extensive costs to generate adversarial information, nor constructs a complex penalty function to force the model satisfying specific restrictions under subjective assumptions. Instead, our method cuts off the interactions between the parameter updating for the deep feature generator and the classifier, discovers the relationship between adversarial robustness and separability in embedding space, results in a better interpretability. Substantial experiments on various network structures deployed on benchmark data sets including MNIST, FASHION MNIST and CIFAR10 and their adversary from different attacks demonstrate the rationality of our method.