Reviewerly/exHarmony huggingface dataset:
Trajectory Planning via Region-Constrained Searching and Path-Based Collision Avoidance

Trajectory planning is one of essential items in the application of UAVs aiming to generate feasible trajectories. In this paper, we propose a refined trajectory planning scheme, which utilizes information of obstacles to achieve faster initial path generation, and uses information of front-end path for collision avoidance in back-end trajectory optimization. We test the refined path-searching method in several scenarios to certify its effectiveness, and design a simulation environment to test our trajectory planning scheme. The result shows that our scheme reaches good performance in collision avoidance and dynamic feasibility with far less time.
Improving DeepFake Video Detection Performance with a Noval Deep Learning Approach

With the rise in both the quantity and sophistication of deepfake videos, the need for robust detection systems to identify potentially misleading content on social media and the internet has become paramount. However, current automated face forgery detection systems still face limitations, often demonstrating bias towards the training dataset. This research paper addresses this issue by proposing a novel approach for detecting deepfake media. We introduce a custom Visual Geometry Group (VGG16) deepfake detection method that leverages convolutional neural network architectures. To evaluate the effectiveness of our approach, we utilize the deepfake detection challenge (DFDC) dataset on Kaggle to build network models and compare the performance of our custom VGG16 method against the standard VGG16. Additionally, we investigate the impact of data augmentation techniques on the performance of Convolutional Neural Network (CNN)-based deepfake detectors, examining their effect on both VGG16 and our custom VGG16 approach using the DFDC dataset. Our results demonstrate a high level of accuracy, with precision, recall, and f1-score values of 0.983, 0.975, and 0.979, respectively, and an overall accuracy of 0.986 for deepfake detection. This study presents a promising approach to enhance the accuracy of deepfake video detection, representing a crucial step towards mitigating the potential negative impacts of deepfake technology.
Animated lightning bolt generation using machine learning

In this paper, we investigate the possibility of leveraging the predictive power of machine learning to generate animated lightning bolts in the image space effciently.To this end, we selected state-of-the-art machine learning architectures based on Generative Adversarial Network (GAN) and trained them on the commonly available videos.We demonstrate that visually convincing animations are achievable even when employing a limited dataset.The visual realism of the generated sequences of lightning bolts is assessed by conducting a user study on the participants.
Automatic Retail Dataset Creation with Multiple Sources of Information Synchronization

We propose a novel methodology to automatically create and continuously extend a dataset of images of retail items by synchronizing two streams of information: (1) video recordings of the items being scanned at the Point of Sale (POS) and (2) the registry of the transactional database collected at the POS. Our approach is able to collect hundreds of thousands of images of distinct classes at virtually no cost, avoiding the expensive task of manual labeling at scale. Additionally, our approach is able to handle the dynamics of the retail scene such as the arrival of seasonal items and the dismiss of discontinued items, extending or reducing the image collection appropriately. Furthermore, images in our dataset are collected during the natural execution of daily operations, presenting natural illumination, occlusion, and view variations, which make it a rich dataset for robust modeling. Finally, in this paper we present the RI6K++ dataset, obtained using our methodology. The RI6K++ dataset contains over 500,000 images of 6,648 distinct labeled SKUs and can be used to develop retail computer-vision applications, such as automatic checkout, 3D reconstruction from 2D images, shelf auditing and smart cart.
Invariant Convolutional Networks

Neural networks are often trained on datasets, that are not fully representative of the expected query images. Many times, the difference stem from the query images being taken in sub-optimal conditions. The most common defects are rotation, scale, blur, noise and intensity & contrast change which were all thoroughly studied and described. In this paper we propose a novel neural network architecture which is invariant to such degradations by design. We incorporate the knowledge build for classical methods directly into the network architecture providing an alternative to the augmentation of the training dataset. In the experiments, the proposed solution outperforms the classical augmentation technique in both accuracy and computational resources needed.
CNN Ensemble Robust to Rotation Using Radon Transform

A great deal of attention has been paid to alternative techniques to data augmentation in the literature. Their goal is to make convolutional neural networks (CNNs) invariant or at least robust to various transformations. In this paper, we present an ensemble model combining a classic CNN with an invariant CNN where both were trained without any augmentation. The goal is to preserve the performance of the classic CNN on nondeformed images (where it is supposed to classify more accurately) and the performance of the invariant CNN on deformed images (where it is the other way around). The combination is controlled by another network which outputs a coefficient that determines the fusion rule of the two networks. The auxiliary network is trained to output the coefficient depending on the intensity of the image deformation. In the experiments, we focus on rotation as a simple and most frequently studied case of transformation. In addition, we present a network invariant to rotation that is fed with the Radon transform of the input images. The performance of this network is tested on rotated MNIST and is further used in the ensemble whose performance is demonstrated on the CIFAR-10 dataset.
Reinforcement of Medical Image Security Through Steganography and Encryption Techniques

Patients' data is stored in Electronic Health Records (EHRs) for confidentiality purposes. However, the insecure networks of hospitals and clinics puts patient information at risk. To address this ethical issue, secure communication is crucial. The Digital Imaging and Communications in Medicine (DICOM) standard governs the transmission of EHRs to maintain data integrity and confidentiality. This paper combines cryptography and steganography techniques to transmit medical information and images across hospital networks, securely. The introduced framework comprises encryption of patients' data using DNA-based Advanced Encryption Standard (AES), hiding the information within images using Least Significant Bit (LSB) embedding, compressing the stego images using Discrete Wavelet Transform (DWT), and reversal of the process at the receiver end. Simulation experiments demonstrate that this framework comprising both encryption and steganography outperforms existing methods in securing the medical data.
RWU3D: Real World ToF and Stereo Dataset with High Quality Ground Truth

This paper introduces a new dataset RWU3D that consists of ToF depth images and the corresponding amplitude images as well as high-resolution Stereo images and the respective ground truth data. The corresponding disparity and depth images are also available. The dataset is recorded for the real world scenarios that can be used to obtain a more accurate estimation of a particular scene through the fusion of ToF and Stereo characteristics. We built a system comprising of a state of the art ToF sensor, high-resolution RGB cameras, and a laser scanner for obtaining ground truth. The data from the different sensors is calibrated to one coordinate system, which is a challenging task in itself. The data is recorded for 11 different real world scenes with 770 total frames in total obtained with our system. The dataset would present new challenges for the experimentation with the ToF data and contribute towards the development of algorithms designed to analyze and improve the ToF data.
An Observation Equalization Optimization for Heterogeneous Sensors Multi-Target Assignment

A multi-UAV and multi-target assignment method based on observation equalization optimization is proposed for fixed-wing UAV standoff tracking with heterogeneous sensors. The mechanical circle scanning sensor is changed to observe only partial targets by sector scanning to improve the observation performance. A novel performance function is proposed to balance observation with current and prior FIM for each target. Then, the configuration and observability of heterogeneous sensors are considered to divide groups of UAVs and targets. Finally, the multi-target assignment with heterogeneous sensors is solved by linear programming and replanning online in time. The simulation results present some intuitive cooperative track patterns and verify the effectiveness of the proposed sector scanning mode and online replanning method, which can reduce the deviation of the observation performance for targets by 7.6% and 15.4%, respectively.
Optimized interval type-2 fuzzy logic controller based on Bio-inspired methods

Recently, interval type-2 fuzzy logic controller (IT2FLC) which is considered a simple class of the general Type-2 fuzzy logic controller (T2FLC) has shown superiority in different applications in dealing with uncertainties and minimizing its impact on the system, in fact superiority of IT2FLC over ordinary Type-1 fuzzy logic controller (T1FLC) in decreasing the effect of uncertainties in the system comes through the best choice of the unique property of IT2FLC called footprint of uncertainty (FOU) which is not included in T1FLC. Varying FOU can save a more degree of freedom in designing IT2FLC. It can be chosen using an appropriate optimization method where the selection of proper optimization method will develop the effectiveness of IT2FLC and increase its ability to minimize uncertainties in the system. Based on the above, an IT2FLC is utilized in this study with its parameters are tuned using different Bio-inspired optimization techniques to determine the effect of tuning FOU that achieves a good system performance. A comparison includes these optimization techniques for IT2FLC such as ant lion optimization (ALO), grey wolf optimization (GWO), artificial hummingbird algorithm (AHA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and T1FLC method is constructed using some performance criteria to determine the proper approach that achieves satisfactory performance. The performance and the effectiveness of the proposed control approach were investigated in Matlab/Simulink platform. Simulation results showed that IT2FLC based on bio-inspired optimization methods always outperforms T1FLC under different scenarios where it shows high stability, and it can dampen out the effect of uncertainties in the system.
Privacy Preserving Encoder Classifier for Access Control Based on Face Recognition

Processing private data to control the access of a system by identifying users and blocking unknowns is becoming increasingly important and requires the development of a privacy-preserving approach. This article presents a privacy-preserving access control method based on facial recognition. This method highlights the performance of classifiers based on generated output. A classifier based on generated output, a ResNet encoder, was used and tested on both clear data and encrypted data. The private data, the face images, were encrypted using a new perceptual image encryption method based on the discrete cosine transform.
Context-Aware Facial Expression Recognition in Group Photos

Facial expression recognition has made significant progress in controlled laboratory environments, thanks to advancements in computing power, method development, and data availability. However, recognizing expressions in real-world (in-the-wild) settings remains a challenge, particularly for expressions that are often confused with others. While misclassifications in single-faced images primarily depend on the model’s performance, the dominant expression in images containing multiple faces can help correct these misclassified expressions. By employing a two-stage classification algorithm to identify the most common expression in the image and reclassify faces with different expressions accordingly, we observed a 4.29% increase in the success rate of expression recognition among 21,270 face images. This approach leverages collective information within group images to achieve comprehensive facial expression recognition, overcoming limitations in analyzing single static images. Such advancements are crucial for real-world scenarios where facial expression recognition faces challenges due to environmental factors, the presence of multiple faces, and low-resolution or degraded image quality.
Prediction of Head-Neck Cancer Recurrence from Pet/CT Images with Havrda-Charvat Entropy

This paper proposes a loss function based on Havrda–Charvat entropy in deep neural networks for outcome prediction in head–neck cancers. Havrda-Charvat is a parameterized cross-entropy which generalizes the classical Shannon-based cross-entropy. Its parameter denoted α takes its values in ]0, ∞[ and one can recover some usual entropies, for instance Shannon for α = 1 or Gini coefficient for α = 2. In this paper, we propose to use this entropy to predict cancer recurrence by incorporating it in a neural network instead of Shannon’s entropy for better adaptability. Our deep network is composed of a double auto-encoder to extract features and a classifier to predire cancer outcome. The experiments are conducted on MICCAI challenge dataset of Head-Neck cancer. The influence of the parameter on the results is studied and an optimal interval of its values is found. A result shows that Havrda–Charvat entropy can achieve better prediction performance than Shannon entropy, which is the most widely used in prediction task nowadays.
Crowd-Counting of Images in Rainy Weather Based on GAN-U-Net Model and Multi-column CNN Counting Approaches

A count of crowds tries to determine the total number of persons in crowded places in order to avoid disruption of the safety system and maintain crowd safety. Accurate estimation of the size of crowds is a challenging process because of numerous challenges like occlusion, complicated backgrounds, varying sizes, uneven distribution, alteration of lighting, and adaptable weather. In this study, we propose a solution to two crowd counting challenges: poor weather such as rain and image scaling. We investigate crowd counting in the rainy Shanghai-Tec images. We adopt the GAN-U-Net de-raining model and the SAS-Net [1] and SGA-Net [2] crowd-counting models in our study.
Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN

This paper proposes a CNN classification network based on Bagging and stacking ensemble learning methods for breast cancer classification. The model was trained and tested on the public dataset of DDSM. The model is capable of fast and accurate classification of input images. According to our research results, for binary classification (presence or absence of breast cancer), the accuracy reached 98.84%, and for five-class classification, the accuracy reached 98.34%. The model also achieved a micro-average recall rate of 94.80% and an F1 score of 94.19%. In comparative experiments, we compared the effects of different values of bagging_ratio and n_models on the model, as well as several methods for ensemble bagging models. Furthermore, under the same parameter settings, our BSECNN outperformed VGG16 and ResNet-50 in terms of accuracy by 8.22% and 6.33% respectively.
Deep Learning-Based Radio Frequency Identification of False Base Stations

Advances in mobile and wireless communications allow to handle the continuously increasing demands on the data volume and connectivity of users. The 5G Open Radio Access Network (RAN) concept offers a flexible and inter-operable solution enabling network operators to select equipment from different vendors. However, such a step can potentially increase security risks due to emergence of the false base stations (FBS) operated with a purpose to steal private information about mobile equipment users. In this paper, we introduce a simple deep-learning (DL) based classification method, working directly with In-phase and Quadrature (I/Q) data of a radio frequency (RF) signal, to identify a device working as FBS. To operate the legitimate as well as the FBS, the srsRAN open-source software suite from Software Radio Systems (SRS), connected to three distinct software defined radio (SDR) devices, is used.
TES-Net: Temporal Excitation Shift for Action Recognition

In order to model fine-grained temporal information in human action videos, this paper proposes a new video structure - temporal excitation shift network TES. This model incorporates TES temporal excitation shift modules into each residual block of the ResNet50 network, enabling the network to better learn short-term motion features in videos. The TES module acts on the input feature map and consists of two parts: the temporal excitation module TEM allocates weights along the temporal dimension of the input feature map, highlighting video frames that have a significant impact on classification results through attention; The temporal shift module TSM partially shifts and exchanges the input feature map along the channel dimension, so that the features of each video frame obtain feature information from adjacent frames, thereby implicitly extracting motion features. The TES module can be well integrated into the ResNet50 network and time segmented network to fully capture short-term motion features and long-term temporal information in videos. The experimental results show that the TES model proposed in this article effectively extracts the spatiotemporal features of human actions in videos. It achieved recognition results of 85.63%, 49.91%, 73.5%, 95.53%, and 76.1% on the Diving48, Something-V1, Kinetic400, UCF101, and Hmdb51 datasets, respectively. The inference speed of a single video reached 0.035 seconds. Therefore, the TES model achieved a good balance between recognition accuracy and speed.
A formulation of mask optimization into QUBO model for Ising machines

Recently, Ising machines, which solve Quadratic Unconstrained Binary Optimization (QUBO) problems in a short computation time, have attracted attention. In this paper, we propose a mask optimization method using an Ising machine to obtain a mask with high fidelity to target patterns and high tolerance to process variation. In the proposed method, a mask pattern is improved by repeatedly solving QUBO problems by the Ising machine. In experiments, we applied the proposed method using the Ising machine to various patterns and evaluated it in comparison with existing methods in fidelity to target patterns, tolerance to process variation, and execution time.
GPU Occupancy Prediction of Deep Learning Models Using Graph Neural Network

Over the past few years, deep learning has been rapidly adopted in many fields. Among the various hardware accelerators specifically for deep learning computation, graphics processing units (GPUs) are mainly used. GPU occupancy—the average ratio of active warps to maximum supported warps on all streaming multiprocessors—is an essential indicator of how well GPUs are utilized. Predicting the GPU occupancy of deep learning models is critical for boosting both job runtime performance and platform resource efficiency. However, GPU occupancy prediction is challenging due to the complex factors hidden in framework runtimes and diverse architectures and hyperparameters of models. In this paper, we propose DNN-occu to predict the GPU occupancy of deep learning models. Our key observation is that models can be represented as directed acyclic computation graphs. DNN-occu extracts a set of occupancy-related features from the computational semantics of the graph nodes and edges. It also employs a novel graph neural network for better feature encoding and prediction generalization. The experiments on various configurations of real-world deep learning models show that DNN-occu achieves high accuracy for occupancy prediction (with an overall error of 9.271%) and has a strong generalization ability for unseen models. In addition, we apply DNN-occu in a trace-driven simulation of deep learning workload scheduling and achieve up to a 31.45% increase in overall GPU utilization and a 19.71% reduction in makespan.
FedGuard: Selective Parameter Aggregation for Poisoning Attack Mitigation in Federated Learning

Minimizing the attack surface of Federated Learning (FL) systems is a field of active research. FL turns out to be highly vulnerable to various threats coming from the edge of the network. Current approaches rely on robust aggregation, anomaly detection and generative models for defending against poisoning attacks. Yet, they either have limited defensive capabilities due to their underlying design or are impractical to use as they rely on constraining building blocks.We introduce FedGuard, a novel FL framework that utilizes the generative capabilities of Conditional Variational AutoEncoders (CVAE) to effectively defend against poisoning attacks with tuneable overhead in communication and computation. Whilst the idea of hardening a FL system using generative models is not entirely new, FedGuard’s original contribution is in its selective parameter aggregation operator with parameter selection being driven by synthetic validation data sampled from the CVAEs trained locally by each participating party.Experimental evaluations in a 100-client setup demonstrates FedGuard to be more effective than previous approaches against several types of attacks (label and sign flipping, additive noise, same value attacks). FedGuard successfully defends in scenarios with up to 50% malicious peers where other strategies fail. In addition, FedGuard does not require auxiliary datasets or centralized (pre-) training. It provides resilience against poisoning attacks from the very first round of federated training.