Reviewerly/exHarmony huggingface dataset:
Combining Computer Vision Techniques and Intraframe Noise Methods to Detect a Deepfake

Deep fakes are synthetic videos created by deep learning algorithms that can convincingly depict individuals saying or doing things they never did. With the proliferation of deepfake videos in social media and the potential for them to cause significant harm, deep fake detection has become a pressing issue. In this study, we offer a unique method for detecting deepfakes by combining computer vision methods with intraframe noise. The proposed approach involves extracting features from the video frames, including texture, color, and edges, and then adding a layer of intraframe noise to the video frames. We tested the proposed method on a variety of benchmark datasets and found that it achieved high accuracy in detecting deepfake videos.
Exploring the Impact of Artificial Intelligence and Digital Transformation on Auditing Practices in Saudi Arabia: A Cross-Sectional Study

The advent of digital technologies and the increasing adoption of artificial intelligence (AI) have transformed various industries and firms, including auditing. Manual auditing procedures have proven to be time-consuming and labor-intensive, leading to lower audit quality and higher costs. The integration of digital transformation and AI in auditing practices offers potential solutions to enhance efficiency and effectiveness in Saudi Arabia. Therefore, this research focuses on examining the impact of digital technologies and AI on audit efficiency, effectiveness, challenges faced during adoption, adjustments in auditors' roles, and the regulatory and ethical considerations arising from the integration. A cross-sectional research design was adopted to collect data from a sample of 400 participants through an online survey, analyzed by statistical tools, including descriptive statistics and correlation and examined the relationships between variables. The findings indicate a positive relationship between the level of digital transformation and the adoption of AI in auditing practices in Saudi Arabia, along with significant challenges, such as resistance to change, technological infrastructure, skills gap, and regulatory compliance concerns, paving a way for further advancements in the field of auditing and digital transformation.
Trustworthy residual vehicle value prediction for auto finance

Abstract The residual value (RV) of a vehicle refers to its estimated worth at some point in the future. It is a core component in every auto finance product, used to determine the credit lines and the leasing rates. As such, an accurate prediction of RV is critical for the auto finance industry, since it can pose a risk of revenue loss by over‐prediction or make the financial product incompetent through under‐prediction. Although there are a number of prior studies on training machine learning models on a large amount of used car sales data, we had to cope with real‐world operational requirements such as compliance with regulations (i.e., monotonicity of output with respect to a subset of features) and generalization to unseen input (i.e., new and rare car models). In this paper, we describe how we addressed these practical challenges and created value for our business at Hyundai Capital Services, the top auto financial service provider in Korea.
Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

Abstract The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman filter for high dimensional linear Gaussian state space models. EnKF methods have also been developed for parameter inference of static Bayesian models with a Gaussian likelihood, in a way that is analogous to likelihood tempering sequential Monte Carlo (SMC). These methods are commonly referred to as ensemble Kalman inversion (EKI). Unlike SMC, the inference from EKI is asymptotically biased if the likelihood is non-linear and/or non-Gaussian and if the priors are non-Gaussian. However, it is significantly faster to run. Currently, a large limitation of EKI methods is that the covariance of the measurement error is assumed to be fully known. We develop a new method, which we call component-wise iterative EKI (CW-IEKI), that allows elements of the covariance matrix to be inferred alongside the model parameters at negligible extra cost. This novel method is compared to SMC on a linear Gaussian example as well as four examples with non-linear dynamics (i.e. non-linear function of the model parameters). The non-linear examples include a set of population models applied to synthetic data, a model of nitrogen mineralisation in soil that is based on the Agricultural Production Systems Simulator, a model predicting seagrass decline due to stress from water temperature and light, and a model predicting coral calcification rates. On our examples, we find that CW-IEKI has relatively similar predictive performance to SMC, albeit with greater uncertainty, and it has a significantly faster run time.
Lightweight Image Inpainting By Stripe Window Transformer With Joint Attention To CNN

Image inpainting is an important task in computer vision.As admirable methods are presented, the inpainted image is getting closer to reality.However, the result is still not good enough in the reconstructed texture and structure based on human vision.Although recent advances in computer hardware have enabled the development of larger and more complex models, there is still a need for lightweight models that can be used by individuals and small-sized institutions.Therefore, we propose a lightweight model that combines a specialized transformer with a traditional convolutional neural network (CNN).Furthermore, we have noticed most researchers only consider three primary colors (RGB) in inpainted images, but we think this is not enough.So we propose a new loss function to intensify color details.Extensive experiments on commonly seen datasets (Places2 and CelebA) validate the efficacy of our proposed model compared with other state-ofthe-art methods.
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?

There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks.In FL, model parameters are periodically communicated from participating devices, training on their own local datasets, to a central server which aggregates them into a global model.While FL has privacy/security advantages due to raw data not leaving the devices, it is still susceptible to several adversarial attacks.In this work, we reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions.In this capacity, we develop an attack framework in which compromised FL devices perturb their local datasets using adversarial evasion attacks.As a result, the training process of the global model significantly degrades on in-distribution signals (i.e., signals received over channels with identical distributions at each edge device).We compare our work to previously proposed FL attacks and reveal that as few as one adversarial device operating with a lowpowered perturbation under our attack framework can induce the potent model poisoning attack to the global classifier.Moreover, we find that more devices partaking in adversarial poisoning will proportionally degrade the classification performance.
Bandwidth-Efficient Edge Video Analytics via Frame Partitioning and Quantization Optimization

The surging penetration of video cameras drives the rapid growth of video frames processed on the mobile edge. However, the scarce bandwidth and limited edge computing resources hinder edge video analytics at scale. Observing the non-uniform distribution of objects in one frame, we find the quality requirements and importance for video analytics of different regions vary across one frame. Hence, we propose Abate, a content-aware video coding and analytics scheme, to achieve bandwidth-efficient video analytics. This scheme consists of two phases, i.e., frame partitioning and quantization optimization. Taking structural features of frames into account, frames are subtly divided into small blocks in the first phase. Then, the quality of each of the blocks is properly controlled by quantization parameters based on contents in the block, taking into account the balance between video data volume and analytic accuracy. Extensive experimental results on real-world datasets show that, compared to existing benchmarks, the proposed system and algorithm can effectively save 50% bandwidth while achieving 10% higher accuracy.
Common Randomness Generation from Sources with Countable Alphabet

We study a standard two-source model for common randomness (CR) generation in which Alice and Bob generate a common random variable with high probability of agreement by observing independent and identically distributed (i.i.d.) samples of correlated sources on countably infinite alphabet.The two parties are additionally allowed to communicate as little as possible over a noisy memoryless channel.In our work, we give a single-letter formula for the CR capacity for the proposed model and provide a rigorous proof of it.This is a challenging scenario because some of the finite alphabet properties, namely of the entropy can not be extended to the countably infinite case.Notably, it is known that the Shannon entropy is in fact discontinuous at all probability distributions with countably infinite support.
Analysis of the prediction performance of decision tree-based algorithms

Due to their many benefits, decision trees are extensively utilized to solve a variety of classification problems in the real world. Consequently, creating efficient and effective decision trees remains a common challenge for researchers in the fields of machine learning and data mining. In the literature, various decision tree algorithms with different attribute selection criteria are introduced. Although the currently available decision tree algorithms achieve different performances, none of them can produce optimal decision trees for a range of data sets. In this study, the efficacy of two traditional decision tree techniques is contrasted with the efficacy of two more recent decision tree approaches. On eleven real-world data sets, the four methods are compared in terms of four evaluation metrics: classification accuracy, tree depth, leaf nodes, and tree construction time.
Comparative Quality Analysis of GPT-Based Multiple Choice Question Generation

Assessment is an essential part of education, both for teachers who assess their students as well as learners who auto-evaluate themselves. A popular type of assessment questions are multiple-choice questions (MCQ), as they can be automatically graded and can cover a wide range of learning items. However, the creation of high quality MCQ items is nontrivial. With the advent of Generative Pre-trained Transformer (GPT), considerable effort has been recently made regarding Automatic Question Generation (AQG). While metrics have been applied to evaluate the linguistic quality, an evaluation of generated questions according to the best practices for MCQ creation has been missing so far. In this paper, we propose an analysis of the quality of automatically generated MCQs from 3 different GPT-based services. After producing 150 MCQs in the domain of computer science, we analyse them according to common multiple-choice item writing guidelines and annotate them with identified docimological issues. The dataset of annotated MCQs is available in Moodle XML format. We discuss the different flaws and propose solutions for AQG service developers.
Attention-Based Feature Compression for CNN Inference Offloading in Edge Computing

This paper studies the computational offloading of CNN inference in device-edge co-inference systems. Inspired by the emerging paradigm semantic communication, we propose a novel autoencoder-based CNN architecture (AECNN), for effective feature extraction at end-device. We design a feature compression module based on the channel attention method in CNN, to compress the intermediate data by selecting the most important features. To further reduce communication overhead, we can use entropy encoding to remove the statistical redundancy in the compressed data. At the receiver, we design a lightweight decoder to reconstruct the intermediate data through learning from the received compressed data to improve accuracy. To fasten the convergence, we use a step-by-step approach to train the neural networks obtained based on ResNet-50 architecture. Experimental results show that AECNN can compress the intermediate data by more than 256 × with only about 4% accuracy loss, which outperforms the state-of-the-art work, BottleNet++. Compared to offloading inference task directly to edge server, AECNN can complete inference task earlier, in particular, under poor wireless channel condition, which highlights the effectiveness of AECNN in guaranteeing higher accuracy within time constraint.
Efficient and Privacy-Preserving Geo-Social-Based POI Recommendation Over Encrypted Data

With the development of location based service and online social networking, geo-social-based points of interest (POIs) recommendation has received wide attention, which comprehensively considers the geographic and social factors. The popularity of cloud computing techniques have driven the emerging trend of outsourcing the geo-social-based POI recommendation service to the cloud. However, the cloud server is not fully trusted, leading to the raising concerns of data privacy. Although many privacy-preserving schemes have been proposed for the geo-social-based POI recommendation, they can only return approximate query results. Aiming at addressing this issue, in this paper, we propose an efficient and privacy-preserving geo-social-based POI recommendation scheme, called TRIPE, with accurate query results. Specifically, we first leverage the Quadtree to organize the geographic data and the MinHash method to index the social data. Then, we design a Quadtree-based POI filtering algorithm and a MinHash-based POI filtering algorithm to filter out some POIs that do not meet geo-social POI recommendation threshold. Meanwhile, we employ the BGV homomorphic encryption to protect the privacy of Quadtree-based/MinHash-based POI filtering algorithms and propose our TRIPE scheme based on these algorithms. Security analysis shows that TRIPE is privacy-preserving, and experimental results show that TRIPE is efficient.
Blockchain-Based Cloud Storage System with Enhanced Optimization and Integrity Preservation

Cloud storage system provides on-demand and pay-per-use storage models with low computing costs. However, this storage system suffers from various security risks. Blockchain technology is an advanced technique that stores data in a distributed manner, and once the data are stored, it cannot be altered. Therefore, a blockchain-based distributed architecture has been proposed in this paper for the cloud storage system. The proposed scheme utilizes the functionality of the smart contract to provide security features to the stored cloud data. The proposed scheme encodes the data before uploading it to the cloud server to achieve confidentiality. Furthermore, the proposed system includes an enhanced optimization technique and a challenge-response-based integrity checking mechanism, which provides a more secure cloud environment. The proposed technique also minimizes the system bandwidth cost using an enhanced fruit fly optimization algorithm that optimizes the node failure repair process. The experimental results and performance evaluation show that the proposed scheme is secured and reliable for the cloud computing environment.
FedCom: Byzantine-Robust Federated Learning Using Data Commitment

Federated learning is a promising distributed edge learning methodology that allows multiple clients to collaboratively train statistical models without disclosing private training data. However, there may exist Byzantine clients launching data/model poisoning attacks to compromise global model's performance or convergence. Most of the existing Byzantine-robust FL schemes are either ineffective against several advanced poisoning attacks, and their robustness suffers from further degradation when local datasets are highly non-independently and identically distributed (non-IID). To address these issues, we propose FedCom, which could achieve data/model poisoning tolerant FL under practical non-IID data partitions, even the attackers do not honestly follow FedCom's protocol. The cardinal design of FedCom is privacy-respectfully asking clients to generate the commitments, which commit and verify the honesty of their local data distributions or local model updates. An extensive performance evaluation demonstrates FedCom's superior performance compared to the state-of-the-art Byzantine-robust schemes under various rigorous settings, even the attackers do not honestly follow the protocol of FedCom and fabricate the commitments.
Over-the-Air Aggregation-Based Federated Learning in Cache-Enabled Wireless Edge Networks

In conventional federated learning (FL), dataset at the parameter server (PS) is not usually considered but may enhance the performance of FL if available. The benefit of leveraging the dataset at PS can be multiplied in over-the-air aggregation-based FL where combating local gradient update distortion against channel fading is inordinately consequential. In this paper, an over-the-air aggregation framework for communication efficient FL is investigated in cache-enabled wireless edge networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to reduce the number of channel uses necessary for the model convergence and to avoid the overhead incurred by power scale coordination and global channel state information (CSI) acquisition at BS. We present a sufficient condition for convergence to a stationary point without convexity assumption on the objective function. Based on the sufficient condition, a power control method is optimized to facilitate the model convergence without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reduce the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy.
Adversarial Poisoning Attacks on Federated Learning in Metaverse

Metaverse is envisioned to be a human-centric framework, and provide a new concept of living by offering comprehensively immersive experience for users in education, medicine and entertainment domain. Since a large amount of private data is generated at each user for accessing Metaverse, the emerging federated learning (FL) provides an effective solution to address the potential privacy leakage of data sharing by adopting the mechanism of local training and global model aggregation. However, the model aggregation is susceptible to adversarial poisoning attacks. This imposes critical issues for the privacy-preserving mechanism in Metaverse. In this research, we develop two poisoning attacks in order to emulate the behaviour of adversaries possibly existing in practical Metaverse scenarios. First, we develop a data poisoning attack using Bayesian optimisation to search for the optimal parameters of generating adversarial examples to conduct reversed adversarial training. Second, we develop a model poisoning attack where we apply layer optimisation using Bayesian optimisation to search the optimal weights for the convolutional layer in order to induce uncertainty in the classification. Numerical results show that both attack schemes can cause attacks that can not be recognised by the FL server, and layer optimisation is a stronger poisoning attack.
Immersive networked music performance systems: identifying latency factors

Music playing with binaural systems has thus far received remarkably little attention by the research community. Such an activity is particularly relevant for the case of networked music performances (NMPs), where geographically displaced musicians play together over a telecommunication network. Recent findings obtained in simulated settings have shown the preference of musicians for spatialized listening during collaborative playing using headphones, as opposed to listening with conventional stereophonic systems. This result has highlighted the need to enhance current NMP systems with the spatial rendering of the acoustic scene, leading to the development of immersive networked music performance (INMP) systems. A crucial aspect that needs to be addressed is the contribution of the spatial audio system to the overall latency of the audio processing and transmission chain between the network nodes. In this paper, we provide an overview of the INMP topic and identify the latency contributions of the components of an INMP system. We then relate such components to state-of-the-art hardware and software systems. Based on our analysis, we draw concluding remarks and discuss the open challenges for progressing the INMP field.
GroupSecAgg: Information Theoretic Secure Aggregation with Uncoded Groupwise Keys

Secure aggregation, which is a core component of federated learning, aggregates locally trained models from distributed users at a central server, without revealing any other information about the local users' data. This paper follows a recent information theoretic secure aggregation problem with user dropouts, where the objective is to characterize the minimum communication cost from the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}$</tex> users to the server during the model aggregation. All existing secure aggregation protocols let the users share and store coded keys to guarantee security. On the motivation that uncoded groupwise keys are more convenient to be shared and could be used in large range of practical applications, this paper is the first to consider uncoded groupwise keys, where the keys are mutually independent and each key is shared by a group of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{S}$</tex> users. We show that if <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{S}$</tex> is beyond a threshold, a new secure aggregation protocol with uncoded groupwise keys, referred to as GroupSecAgg, can achieve the same optimal communication cost as the best protocol with coded keys. The experiments on Amazon EC2 show the considerable improvements on the key sharing and model aggregation times compared to the state-of-the art.
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under friendly environments. However, these methods perform unstably under low signal-to-noise ratio (SNR) scenarios. To address this problem, we propose a robust RED method, which is a hybrid network of denoising autoencoder and deep metric learning (DML). Specifically, denoising autoencoder is adopted to mitigate noise interference and then improve its robustness under low SNR while DML plays an important role to improve the feature discrimination. Several typical experiments are conducted to evaluate the proposed RED method on an automatic dependent surveillance-Broadcast dataset and an IEEE 802.11 dataset and also to compare it with existing RED methods. Simulation results show that the proposed method achieves better RED performance and higher noise robustness with more discriminative semantic vectors than existing methods.