Reviewerly/exHarmony huggingface dataset:
Gaussian Processes for Efficient Photovoltaic Power Prediction

Photovoltaic (PV) systems play a pivotal role in the transition towards sustainable energy sources. Grid operators rely on dependable forecasts to balance supply and demand dynamics, ensure grid stability, and optimize energy dispatch strategies. Accurate prediction of PV power enables effective integration of solar energy into the electrical grid. Grid operators need reliable forecasts to balance supply and demand, manage grid stability, and optimize energy dispatch. This study explores the use of Gaussian Process Regression (GPR) models for accurate PV power prediction, specifically comparing GPR with four different kernel functions: Rational Quadratic (QR), Squared Exponential (SE), Matern 5/2 (M52), and Exponential (Exp). Real-world data from a grid-connected PV system in Algeria is employed for evaluation. Results demonstrate that the GPR with SE kernel outperforms the other kernels, exhibiting the highest prediction accuracy based on statistical scores such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
Gesture Recognition in Indonesian Sign Language Using Hybrid Deep Learning Models

Sign language comprises unique hand gestures used to communicate between the hearing and hearing-impaired communities, with variations based on regions or countries. Due to the increasing global prevalence of hearing loss, there is a growing need for efficient sign language recognition systems. This paper presents the development of a hybrid CNN-LSTM model specifically designed to recognize static alphabetical gestures in Indonesian Sign Language (BISINDO). Primary data was collected to address the issue of publicly available dataset. These self-collected images underwent augmentation and preprocessing stages prior to model training. The architecture and hyperparameter configuration of the hybrid model were fine-tuned using the Randomized Search CV method. Performance analysis was conducted on both the hybrid model, its constituent models, and state-of-the-art model. The experimental results revealed remarkable performance by the hybrid model with excellent accuracies of 99.60%, 84.87%, and 98.00% on the training, validation, and testing sets respectively, along with a macro average score of 0.98, indicating high precision, recall, and F1-score across all the classes. However, it was observed that the VGG-16 and CNN exhibited slightly superior performance compared to the hybrid model, while the LSTM demonstrated the least favorable performance among the models.
Autogenous Spatialization for Arbitrary Loudspeaker Setups

Autogenous spatialization is an experimental approach for distributing audio signals to loudspeaker systems, based on the signals’ properties and the configuration of the loudspeaker setup. Time-domain and frequency-domain features of incoming audio streams are extracted and serve as control signals. The audio streams are then distributed to individual loudspeakers, based on a mapping between these control signals and properties of the loudspeakers, such as position and orientation. Results of the approach can range from a localizable panning between loudspeakers to alienating modulation effects and spatial sound synthesis applications. The proposed system is implemented as a C++ toolbox to allow the integration into various ecosystems for music production and sound design. External objects are provided for Pure Data, which allows the flexible testing and exploration of the spatialization approach.
Detecting Flood in Real-Time Weather Data Using Anomaly Detection Algorithm

This paper presents flood detection using anomaly detection algorithms in real-time weather data. Nine different attributes are used to detect suspicious weather that can cause floods. The weather dataset was acquired from Kelantan, Malaysia. In this paper, the collected dataset will be sent from the chosen Internet of Things (IoT) pipeline to three different anomaly detection algorithms. These algorithms are multi-threaded autonomous anomaly detection (MAAD), robust random cut forest (RRCF), and outlier detection in feature-evolving data streams (xStream). Our evaluation demonstrates that the MAAD algorithm has a 68.5% precision score and a 0.01 false alarm rate, which is better than the RRCF and xStream for detecting floods.
Rate-Privacy-Storage Tradeoff in Federated Learning with Top $r$ Sparsification

We investigate the trade-off between rate, privacy and storage in federated learning (FL) with top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> sparsification, where the users and the servers in the FL system only share the most significant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r^{\prime}$</tex> fractions, respectively, of updates and parameters in the FL process, to reduce the communication cost. We present schemes that guarantee information theoretic privacy of the values and indices of the sparse updates sent by the users at the expense of a larger storage cost. To this end, we generalize the scheme to reduce the storage cost by allowing a certain amount of information leakage. Thus, we provide the general trade-off between the communication cost, storage cost, and information leakage in private FL with top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> sparsification, along the lines of two proposed schemes.
Mobility-Aware Asynchronous Federated Learning for Edge-Assisted Vehicular Networks

Vehicular networks enable vehicles support some real-time applications through training data. Due to the limited computing capability of vehicles, vehicles usually transmit data to a road side unit (RSU) deployed along the road to process data collaboratively. However, vehicles are usually reluctant to share data with each other due to the inevitable data privacy. For the traditional federated learning (FL), vehicles train the data locally to obtain a local model and then upload the local model to the RSU to update the global model through aggregation, thus the data privacy can be protected through sharing model instead of raw data. The traditional FL requires to update the global model synchronously, i.e., the RSU needs to wait for all vehicles to upload local models to update the global model. However, vehicles may usually drive out of the coverage of the marked RSU before they obtain their local models through training, which reduces the accuracy of the global model. In this paper, a mobility-aware vehicular asynchronous federated learning (AFL) is proposed to solve this problem, where the RSU updates the global model once it receives a local model from a vehicle where the mobility of vehicles, amount of data and computing capability are taken into account. Simulation experiments validate that our scheme outperforms the conventional AFL scheme.
Distributed Learning in Heterogeneous Environment: Federated Learning with Adaptive Aggregation and Computation Reduction

Although federated learning has achieved many breakthroughs recently, the heterogeneous nature of the learning environment greatly limits its performance and hinders its realworld applications.The heterogeneous data, time-varying wireless conditions and computing-limited devices are three main challenges, which often result in an unstable training process and degraded accuracy.Herein, we propose strategies to address these challenges.Targeting the heterogeneous data distribution, we propose a novel adaptive mixing aggregation (AMA) scheme that mixes the model updates from previous rounds with current rounds to avoid large model shifts and thus, maintain training stability.We further propose a novel staleness-based weighting scheme for the asynchronous model updates caused by the dynamic wireless environment.Lastly, we propose a novel CPUfriendly computation-reduction scheme based on transfer learning by sharing the feature extractor (FES) and letting the computinglimited devices update only the classifier.The simulation results show that the proposed framework outperforms existing stateof-the-art solutions and increases the test accuracy, and training stability by up to 2.38%, 93.10% respectively.Additionally, the proposed framework can tolerate communication delay of up to 15 rounds under a moderate delay environment without significant accuracy degradation.
A Lossless Virtual Reality Based Alternative to Eye-Tracking for Attention Quantification

Precise measurement of visual attention is crucial for various fields such as psychology, neuroscience, and human-computer interaction. Recent scientific literature has focused on eye-tracking techniques to quantify visual allocation or to predict it. Eye tracking is certainly an efficient and accurate technique, but it comes with a high cost and a limited context of use. In this paper, we propose a novel computational model that accurately quantifies attention allocation in a virtual reality setting. The experimental results demonstrate that our model performs seamlessly with minimum hardware requirements. Our findings suggest that this model has the potential to measure spatio-visual attention allocation accurately and reliably over extended periods, and could be a serious alternative to eye-tracking techniques in a variety of usage, such as therapy interventions.
Knowledge Distillation for Human Pose Estimation Using Channel Dropout Strategy

The attention and utilization of big models have become a trend recently. From large-scale convolutional neural network (CNN) models to vision transformers (ViTs) which contain much more parameters, more and more big models are being adopted in every kind of task. Accuracy requirement is a major reason for using large models and the rapid development of computing hardware, which enables training for large-scale models, also stimulates further demanding of model accuracy reversely. But for mobile or edge computing, there is an inevitable trade-off between accuracy and real-time inference ability due to limited computing power on the terminal end. To make this trade-off more favourable for us, we proposed Knowledge Distillation with Channel Dropout Strategy (CDKD), which applies both the intermediate distillation and final distillation associated with the channel dropout strategy, managing to promote the accuracy of a small model while maintaining the same amount of parameters and FLOPs. In the experiments on the COCO2017 validation set, with same setup, ResNet-18 model training with our method outperfomed the baseline model.
Robust Distributed Swarm Learning for Intelligent IoT

In this paper, we study a communication-efficient distributed learning scheme through a holistic integration of federated learning (FL) and particle swarm optimization, called DSL, which is suitable for the implementation of intelligent IoT applications. Since only one selected optimum from all local devices need to report its local model updates to the parameter server, the communication cost of DSL is much reduced compared to its counterpart of standard FL. However, the DSL is vulnerable to adversarial attackers. To achieve Byzantine-resilient DSL, we propose to introduce a shared dataset for scoring local updates to screen attackers. We further provide the convergence analysis to theoretically demonstrate that CB-DSL is superior than the standard FL. Experiment results show that the learning performance of our proposed CB-DSL outperforms the existing benchmarks with only a small amount of globally shared data. It enjoys higher robustness against Byzantine attacks than the vanilla DSL, and has better communication efficiency than the standard FL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code can be found at: https://github.com/fuanxiyin/CB-DSL.git..
Depth Learner: An Advanced Learning Method with Edge Map for Monodepth2

When creating a deep learning model for estimating the depth of images, constructing a training dataset using stereo images presents a significant challenge. Therefore, using monocular images for depth estimation provides numerous benefits in terms of dataset acquisition. Monodepth2 is one of the prominent techniques for monocular depth estimation. By employing a self-supervised approach, Monodepth2 eliminates the need for ground truth, making the acquisition of the training dataset much easier. Nonetheless, a challenge faced by Monodepth2 is the issue of blurred boundaries in the output depth maps. To address this concern, the paper proposes a modified architecture of Monodepth2, resulting in enhanced accuracy and sharper boundaries in the output depth maps.
Malaysian Banknote Recognition App for the Visually Impaired Using Deep Learning

Banknotes are used worldwide, enabling people to exchange products and services. However, visually impaired and blind (VIB) individuals may face difficulties handling banknotes, especially faded or worn banknotes. Therefore, this research proposed a real-time banknote recognition system using Single Shot Detector (SSD) Mobilenetv2 algorithm. The SSD Mobilenetv2 algorithm is efficient, fast, and suitable for low-end devices, making it a suitable technique for this research. A dataset of six Malaysian banknote images (RM1,5,10,20,50, and 100) was augmented and pre-processed was used in this research. Several experiments were conducted to optimize the model's performance using Python, TensorFlow, and Keras. The optimized model achieved 77% accuracy with an average speed suitable for mobile device implementation, using an input size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$320\times 320$</tex> , 80:20 data split, 32 batch size, and 30,000 training steps. The application was able to detect the notes under various real-time settings like illumination, occlusion, or even half-folded notes, with an audible output voice. Subsequently, further development is necessary to enhance the accessibility and user-friendliness of the application. Increasing the dataset's size and variation of banknotes would help improve the accuracy, and adding vibration alerts when a note is detected would also be beneficial. These improvements empower visually impaired individuals, enhancing their independence and interaction with the world.
Finding a Path to a Dynamic Object Using the A* Algorithm

This paper proposes an algorithm that finds the optimal path for the intruder with the robot when an intruder occurs in a museum or a building with a large interior. This algorithm assumes that intelligent CCTV has found the intruder's coordinate system in real time. When intelligent CCTV gives the robot coordinate system, the robot approaches the intruder by applying A* algorithm for the coordinate system of the intruder moving in real time, finding the path every moment. The biggest limitation of this study is not to be applied on the simulator. This algorithm uses python grid function to find and track route, instead of using an algorithm by organizing in the virtual environment. In the future, it is necessary to improve the algorithm by putting the actual environment in a simulator and configuring it as a coordinate plane.
Vehicle Tracking System in Drone Imagery with YOLOv5 and Histogram

In this study, we introduce a vehicle tracking system for drone imagery, utilizing the real-time object detection network YOLOv5 to get vehicle location and cropped images. The system analyzes the segmented regions' histograms, compares them with previous frames, and identifies identical objects for tracking. The algorithm is designed to compare objects within a specific radius using coordinate information, enhancing histogram comparison efficiency. The MOTA (Multi-Object Tracking Accuracy) showed 90%, but the limited environment of data usage and experiments must be considered. The findings suggest that the real-time performance of the vehicle tracking system can be applied in various fields such as traffic control, vehicle management, and accident response.
Study on the Correlation of Trainable Parameters and Hyperparameters with the Performance of Deep Learning Models

Trainable parameters and hyperparameters are critical to the development of a deep learning model. However, the components have typically been studied individually, and most studies have found it difficult to investigate the effects of their combination on model performance. We are interested in examining the correlation between the number of trainable parameters in a deep learning model and its performance metrics under different hyperparameters. Specifically, we want to study the effects of using either the Adam or SGD optimizers at three varying learning rates. We use six pre-trained models whose trainable parameters have been quantitatively defined using two strategies: (1) freezing the convolutional basis with partially trainable weights and (2) training the whole model with most trainable weights to obtain a set of trainable parameters. Our experimental result shows a positive correlation between the trainable parameters and the test accuracy regardless of the level of the learning rate. However, for the generalization of the model, it was not guaranteed that a higher number of trainable parameters would lead to higher accuracy and F1 score. We have shown that the correlation between trainable parameters and model generalization becomes positive by using Adam with the smallest learning rate.
Secure Federated Learning with Model Compression

Although federated Learning (FL) has become very popular recently, FL is vulnerable to gradient leakage attacks. Recent studies have shown that clients' private data can be reconstructed from shared models or gradients by attackers. Many existing works focus on adding privacy protection mechanisms to prevent user privacy leakage, such as differential privacy (DP) and homomorphic encryption. However, these defenses may cause an increase of computation and communication costs or degrade the performance of FL, and do not consider the impact of wireless network resources on the FL training process. Herein, we propose a defense method, weight compression, to prevent gradient leakage attacks for FL over wireless networks. The gradient compression matrix is determined by the user's location and channel conditions. Moreover, we also add Gaussian noise to the compressed gradients to strengthen the defense. This joint learning, wireless resource allocation and weight compression matrix is formulated as an optimization problem with the objective of minimizing the FL loss function. To find the solution, we first analyze the convergence rate of FL and quantify the effect of the weight matrix on FL convergence. Then, we seek the optimal resource block (RB) allocation by exhaustive search or ant colony optimization (ACO), and then use CVX toolbox to obtain the optimal weight matrix to minimize the optimization function. Our simulation results show that the optimized RB can accelerate the convergence of FL.
Communication-Efficient Personalized Federated Learning for Digital Twin in Heterogeneous Industrial IoT

As an up-coming digitalization technology, the digital twin (DT) offers a viable implementation for dynamic perception and intelligent decision-making in the industrial IoT (IIoT). For synchronizing the real-time information between the device and its DT, communication is fundamental to the digital twin system. Federated learning (FL) based DT model framework could be seen as an emerging paradigm to avoid large communication loads and high data leakage. However, the current digital twin model constructed scheme based on FL is not suitable for the heterogeneous IIoT scenario. Due to the different data distribution and different tasks among the devices, it leads to severe performance degradation when the personalized requirements of the DT model are ignored. In this paper, we propose a DT model framework based on personalized federated learning (PFL) to perform well for individual devices. Considering the historical personalized knowledge forgetting problem, the personalized federated learning with self-knowledge distillation (DTPFLsd) algorithm is proposed to avoid severe performance degradation and unnecessary time consumption of DT modeling. The numerical results compare with state-of-the-art FL-based DT architecture called DTWN and demonstrate the effectiveness and robustness of the proposed DTPFLsd scheme.
Privacy Budget-Aware Incentive Mechanism for Federated Learning in Intelligent Transportation Systems

Vehicles on the road generate a large amount of data, which often can be used to train models for destination prediction and traffic flow prediction in intelligent transportation systems (ITS). To break down the information silos and further strengthen privacy protection, we leverage federated learning and differential privacy in this paper. In order to motivate the participation of data owners, we further devise a single-leader multi-follower Stackelberg game incentive mechanism which accounts for the heterogeneous privacy budgets and participation costs of vehicle owners. Due to the lack of prior knowledge, deep reinforcement learning is used to obtain the approximate solution for each player to achieve the Stackelberg equilibrium. Our proposed framework is capable of achieving a solution close to the Nash Equilibrium.
VCGAN: Variational Collaborative Generative Adversarial Network for Recommendation Systems

Personalized recommendation has been receiving great attention because of its huge commercial value, but recommendation is also regarded as a difficult task due to the sparsity and noise in the real data. To address these issues and boost the recommendation performance, we propose an effective framework called Variational Collaborative Generative Adversarial Network (VCGAN), which improves the correlation between generated samples and real-world data by using the GAN-based structure. The generative network predicts the scores of the user's top-N items based on her profile and behavior, and the discriminative model aims to distinguish the predicted scores and maximize the objective function by learning from the ground truth. To avoid the high dimensionality problem, VCGAN adopts Auto-encoder (AE) to produce the latent vector of side information and performs adversarial training under the condition that using Variational Auto-encoder (VAE) as the generator. Without the high dimensionality problem, side information can be leveraged in the deep model to solve rating sparsity. Moreover, discovering richer latent representations through VAE helps the generator capture the distribution of real data more precisely. To evaluate the performance of our proposed model, we conduct extensive experiments on four real-world datasets against various baselines. The analysis and experimental results show the superiority of VCGAN over the compared methods.