Reviewerly/exHarmony huggingface dataset:
On the Impact of Malicious and Cooperative Clients on Validation Score-Based Model Aggregation for Federated Learning

Conventional AI-based service flow remains a challenge for IoT-enabled devices since data collected by local clients is transferred to a centralized server, which contains a global machine learning (ML) model. However, this introduces privacy and security concerns for the clients, and federated Learning is positioned to overcome this problem where each client trains a local model with its local data and shares its model parameters with the centralized server instead of sharing data. Upon the receipt of all parameters, it aggregates these parameters and generates a new global model. Later this global model is distributed among the clients. Various aggregation methods have been published for increasing the global model's accuracy performance after aggregation. However, those new aggregation algorithms are not fully investigated under malicious and collaborated environments. A malicious environment is a scenario where malicious clients are present and can share parameters to degrade the aggregated model performance. On the other hand, the collaborative environment is another scenario in which some clients can share information with each other in order to collaborate. To tackle this issue, we investigate a new aggregation method called Score Based Aggregation (SBA) That aims to mitigate the impact of the model parameters from such malicious clients without keeping compromising the training accuracy. We compare our result to a baseline approach where the malicious client is varied from 20% to 50%. Numerical results suggest that the SBA aggregation helps the model maintain the convergence of accuracy at higher levels in comparison to the baseline approach.
Contrastive knowledge-augmented self-distillation approach for few-shot learning

Few-shot learning consists in training a classifier, which can be quickly adapted to new tasks with a few samples. To address the few-shot learning tasks, metric-based meta-learning methods explore the appropriate metrices to measure the similarity between the support and the query samples. However, existing methods ignore the similarity relationship between the labeled samples in the support set. Consequently, we propose a contrastive knowledge-augmented self-distillation approach to leverage the similarity relationship between a few labeled samples in the support set and allow the model to focus on more regions of images. Specifically, we calculate the classification probability of query images and each class prototype, respectively, and consider the classification probability of each class prototype as the teacher to guide the classification of query samples. Meanwhile, we design a contrast loss to bring the eigenvectors of the same class closer and push the eigenvectors of different classes further. In addition, a transformation function is implemented to allow the model to focus on more regions of the images, so as to obtain the key features. Extensive experiments are conducted on miniImageNet, tieredImageNet, and Caltech-UCSD birds 200, and the results show that our method can enhance metric-based meta-learning methods and outperforms the state-of-the-art methods.
Intelligent DRL-Based Adaptive Region of Interest for Delay-Sensitive Telemedicine Applications

Telemedicine applications have recently received substantial potential and interest, especially after the COVID-19 pandemic. Remote experience will help people get their complex surgery done or transfer knowledge to local surgeons, without the need to travel abroad. Even with breakthrough improvements in internet speeds, the delay in video streaming is still a hurdle in telemedicine applications. This imposes using image compression and region of interest (ROI) techniques to reduce the data size and transmission needs. This paper proposes a Deep Reinforcement Learning (DRL) model that intelligently adapts the ROI size and non-ROI quality depending on the estimated throughput. The delay and structural similarity index measure (SSIM) comparison are used to assess the DRL model. The comparison findings and the practical application reveal that DRL is capable of reducing the delay by 13% and keeping the overall quality in an acceptable range. Since the latency has been significantly reduced, these findings are a valuable enhancement to telemedicine applications.
Age Group Classification Based on Facial Features Using Deep Learning Method

Age group classification from a single face image using artificial intelligence applications such as virtual surveillance and marketing intelligence are a growing research area. Facial features extraction phase in a facial recognition classification model is one of the major phases as in this phase, facial elements like eyes and nose were extracted to distinguish a different person's facial features. Subsequently, this research aimed to design and build suitable age group classification model using Convolutional Neural Network (CNN) technique, utilizing the well-known, open-source UTKFace dataset. There were twelve customized CNN models built to find the best classification model for this research and every model could be differentiated by the number of convolutional layers and fully connected layers. In addition, there were two experiments conducted based on the observation of age group to further improve the performance for age group classification. These experiments were conducted by varying the number of convolutional layers, ranging from one to four convolutional layers, and three different batch sizes. Based on the analyzed results, the best classification model was the combination parameters of 64 batch size with two convolutional - three fully connected layers (2CNN-3FC) model, achieving a satisfactory accuracy of 71.40% for classification of 5 age groups (child, youth, adult, middle-age, and old). Future works for improvement and comparison purposes of the research can be made by experimenting with different deep learning method, and with other facial feature dataset.
Comparison of YOLO Series Algorithms in Mask Detection

Detecting whether people are wearing masks in major public places is of great significance in preventing virus transmission. YOLOv5 is a widely employed object detection algorithm with excellent detection speed and accuracy. This paper proposes a mask detection method based on YOLOv5. Firstly, images are obtained through web crawlers and video frame extraction, and then annotated using Labelimg to create a dedicated mask dataset suitable for mask detection. Next, the model is trained using the PyTorch deep learning framework. Finally, the performance of the trained model is assessed using three key metrics including recall rate, average precision, and mean Average Precision (mAP). The trained model is tested on a separate dataset, and the results demonstrate that YOLOv5 outperforms other detection methods in terms of detection performance, achieving an accuracy rate of 0.86, a recall rate of 0.823, and an mAP of 0.854. It effectively localizes the target regions and successfully detects the desired objects.
Hybrid Collaborative Movie Recommendation System

A recommendation system applies the data to the information discovery techniques and personalises the products for recommendations. Typically, movie recommendation systems predict what movies a user wants based on the characteristics of previously liked movies. These recommendation systems are helpful for organisations that gather data from many users and wish to offer the best possible recommendations effectively. This study developed a hybrid movie recommendation system using collaborative and content-based filtering involving Matrix Factorisation, TF-IDF. In addition, this study aims to extract the CBF features into CF to improve the recommendation system engine's performance and personalise the user's movie recommendations. The performance of the trained model was measured using RMSE, precision, recall, and F1 score. The trained model produced a low RMSE value with high precision, recall, and F1 score values. The paper's contribution is that the proposed hybrid recommendation provides a pathway for the users by helping them find movies that meet their interests.
Comparative Analysis and Evaluation of CNN Models for Deepfake Detection

Deepfake technology has become a significant concern due to its ability to create highly realistic fake videos and images, leading to the potential deception of individuals. Detecting deepfakes has become a critical research area in computer vision and multimedia forensics. This paper presents a comparative analysis of deepfake detection models, focusing on evaluating their accuracy and robustness. Four CNN models, namely ResNet-152, MobilenetV3, Convnext Large, and EffecientNetB7, were implemented and trained using a custom dataset obtained from FaceForensics++. The models were evaluated based on training accuracy, average loss, and testing accuracy. An LSTM layer was also incorporated into each model's architecture to leverage sequential information. The results demonstrate varying performance among the models, with EfficientNet B7 achieving the highest testing accuracy of 75%. The findings of this study provide insights for future research in this critical area.
Exploring the Performance of BERT Models for Multi-Label Hate Speech Detection on Indonesian Twitter

Due to its widespread distribution and the anonymity it offers users, hate speech on social media platforms, particularly Twitter, is a major problem. Because of Twitter's echo chamber algorithm and viral nature, hate speech can spread quickly and lead to societal unrest. The use of BERT-based models for hate speech identification in the Indonesian language has been studied in previous research. This study compares how well several pre-trained BERT models, IndoBERT and mBERT, perform in identifying hate speech on Twitter. The methodology includes gathering datasets, preparing the data, and utilizing the proper metrics to assess the models. IndoBERT successfully outperforms mBERT model according to the average of each evaluation metric shown. By carrying out this study, we hope to advance knowledge of techniques for identifying hate speech on Indonesian social media platforms and enhance the use of BERT models for such analysis.
Sketch Graph Representation for Multimedia Computational Communications: A Learning-Based Method

Multimedia computational communications towards 6G can improve the transmission efficiency significantly by introducing intelligent computation in the communication process. This intelligent/smart communication architecture includes multimedia representation, coding, transmission and other parts from the perspective of semantics, where multimedia semantic representation is the core part and is mainly utilized to reduce the amount of multimedia data. In this paper, sketch graph is proposed as an effective representation of images to describe the pixel variations, geometric feature distribution and structural information and has potential applications in multimedia computational communications. Specifically, we developed a learning-based method to extract sketch graphs with edge detection, sketch point detection and sketch line detection by deep neural networks (DNNs). Moreover, we designed an end-to-end extraction method and achieved real-time processing. The experimental results on several datasets demonstrated the advanced performance in terms of classification and generation tasks. Image classification results on the HumanSketch, ImageNet, and Caltech datasets showed that sketch graphs extracted by our method had better describing ability than those extracted by traditional methods and other traditional image compression methods. On the other hand, image generation results on the Cityscapes dataset indicated the potential of the sketch-graph-based image compression codec.
Incentive Mechanism Design for Vertical Federated Learning

In vertical federated learning (VFL), participants with different features of the same group of samples can train a global model cooperatively. Currently, no existing studies discuss the economic mechanism between the VFL participants. To fill this research gap, we study the incentive mechanism design with a linear reward scheme for VFL. Specifically, we model the interactions between the label owner and the data owner as a two-stage Stackelberg game. In Stage I, the label owner strategically chooses its processing speed and linear reward parameter for the data owner. In response to the label owner's decisions, the data owner will choose its optimal processing speed in Stage II. By characterizing the threshold structure of the reward parameter that incentivizes the maximum processing speed from a data owner in Stage II, we can derive the equilibrium of the two-stage Stackelberg game in closed-form. Finally, our simulation results show that as the cost coefficient of the data owner increases, the label owner will increase its reward but reduce its processing speed due to the linear cost growth but concave revenue growth.
Brain Stroke Prediction Using Random Forest Method with Tuning Parameter

Brain stroke is a disease that contributes to the number of deaths in Indonesia. The number of victims of brain stroke is still increasing to this day, so a solution is needed that can predict brain stroke attacks in patients. This research is useful so that patients can be given appropriate medical treatment to prevent brain stroke. To predict brain stroke in patients, researchers designed an application that utilizes machine learning to provide accurate predictions based on several variables. The researcher used a dataset consisting of 4981 patient data with 10 non-labeled attributes who had/not had a brain stroke. By using random forest algorithm which then performed parameter tuning, researchers managed to get an accuracy of 98.98% in predicting the occurrence of brain stroke in patients.
AdvRevGAN: On Reversible Universal Adversarial Attacks for Privacy Protection Applications

Different adversarial attack methods have been proposed in the literature, mainly focusing on attack efficiency and visual quality, e.g., similarity with the non-adversarial examples. These properties enable the use of adversarial attacks for privacy protection against automated classification systems, while maintaining utility for human users. In this paradigm, when privacy restrictions are lifted, access to the original data should be restored, for all stakeholders. This paper addresses exactly this problem. Existing adversarial attack methods cannot reconstruct the original data from the adversarial ones, leading to significant storage overhead for all privacy applications. To solve this issue, we propose AdvRevGAN, a novel Neural Network architecture that generates reversible adversarial examples. We evaluate our approach in classification problems, where we examine the case where adversarial attacks are constructed by a neural network, while the original images are reconstructed using the reverse transformation from the adversarial examples. We show that adversarial attacks using this approach maintain and even increase their efficiency, while the classification accuracy of the model in the reconstructed data can almost totally be restored.
Influence of Kernel Size and Layer Size for MAE Score and Model Fit in Image Denoising

The presence of noise in images is one of the challenges that complicate image processing. Numerous methods have been proposed to mitigate the impact of noise on denoising image performance. However, the application of CNN for denoising image performance optimization in the presence of noise is still a new area of research. In this paper, the configuration of kernel size and number of layers will be investigated in image denoising using CNN (Convolutional Neural Networks), specifically, in U-Net architecture. The images used are from BSD500 which consists of 500 natural images. Then, the hyperparameters will be evaluated in terms of many factors, such as mean absolute error (MAE) score and model fit to show its influence. The results show that the average score for MAE is 0.11 based on the model and variation of both kernel size and layer size, with the highest score found at kernel size=2 and layer size=64. The results show that to get a fit model, the experience should use epoch number 10. It shows that the influence of kernel size has a negative influence on MAE whereas layer size has a beneficial effect on model fit. However, there is an optimal point beyond which denoising results can decrease. To achieve better results, future researchers can compare it with other denoising models to ensure our findings.
Semantic-Aware Speech-to-Text Transmission Over MIMO Channels

Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this paper, we propose a semantic-aware speech-to-text transmission system over MIMO channels with single-user, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the text-related semantic features by leveraging the transformer module. Moreover, a novel neural network-enabled semantic-aware network is proposed to facilitate the transmission with high semantic fidelity, which identifies the critical semantic information and guarantees them to be recovered accurately. According to the simulation results, the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime.
Optimization-Based Quantized Federated Learning for General Edge Computing Systems

This paper investigates optimal implementations of federated learning (FL) in practical edge computing systems with possibly distinct computing and communication resources at the server and workers. First, we present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely HQFedWAvg, and analyze its convergence. HQFedWAvg adopts the proposed quantization scheme and a generalized mini-batch stochastic gradient descent (SGD) method and has several adjustable algorithm parameters to maximally adapt to the computing and communication resources at the server and workers. Next, we optimize the algorithm parameters of HQFedWAvg. The resulting challenging non-convex optimization problem is successfully tackled using several optimization techniques. Numerical results demonstrate HQFedWAvg's considerable performance gains over existing FL algorithms and interpret its function principle.
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications

5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multime-dia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an _S_FC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in large-scale scenarios. Finally, we design a large-scale cloud-based testbed including 250 HTTP Adaptive Streaming (HAS) players requesting two popular MS applications (i.e., live and VoD), conduct various experiments, and compare its effectiveness with baseline systems. Experimental results illustrate that SARENA outperforms baseline schemes in terms of users' QoE by at least 39.6%, latency by 29.3%, and network utilization by 30% in both MS services.
Improved Variance Predictions in Approximate Message Passing

In the Generalized Linear Model (GLM), the unknowns may be non-identically independent distributed (niid), as for instance in the Sparse Bayesian Learning (SBL) problem. The Generalized Approximate Message Passing (GAMP) algorithm performs computationally efficient belief propagation for Bayesian inference. The GAMP algorithms predicts the posterior variances correctly in the case of measurement matrices with (n)iid entries. In order to cover more ill-conditioned measurement matrices, the (right) rotationally invariant (RRI) model was introduced in which the (right) singular vectors are Haar distributed, leading to Vector AMP VAMP however assumes iid priors and posteriors. Here we introduce a convergent version of AMB (AMBAMP) applied to Unitarily transformed data, with a variance correction based on Haar Large System Analysis (LSA). The recently introduced reVAMP perspective shows that the resulting AMBUAMP algorithm has an underlying multivariate Gaussian posterior approximation, that does not get computed but that allows the LSA. The individual variance predictions are exact asymptotically in the RRI setting, as illustrated by a Gaussian Mixture Model example.
Blind Modulation Classification for OFDM in the Presence of Carrier Frequency Offsets

In the orthogonal frequency division multiplexing (OFDM) systems, inter-carrier interference (ICI) caused by carrier frequency offset (CFO) is considered to be one of the most crucial problems for OFDM over multipath channels, which will bring difficulties in the modulation classification of OFDM. In order to deal with the influence of ICI on subcarrier modulation classification, this paper presents a novel blind modulation classification method of OFDM systems with CFO over multipath channels. The virtual subcarrier signals and the pilot subcarrier signals with CFO are classified by using the second-order moment, and then the modulation subcarriers are classified with the fourth-order cumulants and the sixth-order cumulants. Simulations are conducted to verify the proposed method not only has a good classification performance, but also has a low computational complexity. The proposed method can reduce energy consumption and is beneficial for green radios.