Reviewerly/exHarmony huggingface dataset:
Development of a Novel Lightweight CNN Model for Classification of Human Actions in UAV-Captured Videos

There has been increased attention paid to autonomous unmanned aerial vehicles (UAVs) recently because of their usage in several fields. Human action recognition (HAR) in UAV videos plays an important role in various real-life applications. Although HAR using UAV frames has not received much attention from researchers to date, it is still a significant area that needs further study because of its relevance for the development of efficient algorithms for autonomous drone surveillance. Current deep-learning models for HAR have limitations, such as large weight parameters and slow inference speeds, which make them unsuitable for practical applications that require fast and accurate detection of unusual human actions. In response to this problem, this paper presents a new deep-learning model based on depthwise separable convolutions that has been designed to be lightweight. Other parts of the HarNet model comprised convolutional, rectified linear unit, dropout, pooling, padding, and dense blocks. The effectiveness of the model has been tested using the publicly available UCF-ARG dataset. The proposed model, called HarNet, has enhanced the rate of successful classification. Each unit of frame data was pre-processed one by one by different computer vision methods before it was incorporated into the HarNet model. The proposed model, which has a compact architecture with just 2.2 million parameters, obtained a 96.15% success rate in classification, outperforming the MobileNet, Xception, DenseNet201, Inception-ResNetV2, VGG-16, and VGG-19 models on the same dataset. The proposed model had numerous key advantages, including low complexity, a small number of parameters, and high classification performance. The outcomes of this paper showed that the model’s performance was superior to that of other models that used the UCF-ARG dataset.
Camouflaged Object Detection Based on Ternary Cascade Perception

Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the background. In this paper, we focused on the accurate detection of camouflaged objects, conducting an in-depth study on COD and addressing the common detection problems of high miss rates and low confidence levels. We proposed a ternary cascade perception-based method for detecting camouflaged objects and constructed a cascade perception network (CPNet). The innovation lies in the proposed ternary cascade perception module (TCPM), which focuses on extracting the relationship information between features and the spatial information of the camouflaged target and the location information of key points. In addition, a cascade aggregation pyramid (CAP) and a joint loss function have been proposed to recognize camouflaged objects accurately. We conducted comprehensive experiments on the COD10K dataset and compared our proposed approach with other seventeen-object detection models. The experimental results showed that CPNet achieves optimal results in terms of six evaluation metrics, including an average precision (AP)50 that reaches 91.41, an AP75 that improves to 73.04, and significantly higher detection accuracy and confidence.
Optimization of the closed-loop controller of a discontinuous capsule drive using a neural network

Abstract In this paper the construction of a neural-network based closed-loop control of a discontinuous capsule drive is analyzed. The foundation of the designed controller is an optimized open-loop control function. A neural network is used to determine the dependence between the output of the open-loop controller and the state of the system. Robustness of the neural controller with respect to variation of parameters of the controlled system is analyzed and compared with the original optimized open-loop control. It is expected that the presented method can facilitate the construction of closed-loop controllers for which alternative methods are not effective, such as non-smooth or discontinuous ones.
Blockchain-Based Practical and Privacy-Preserving Federated Learning with Verifiable Fairness

Federated learning (FL) has been widely used in both academia and industry all around the world. FL has advantages from the perspective of data security, data diversity, real-time continual learning, hardware efficiency, etc. However, it brings new privacy challenges, such as membership inference attacks and data poisoning attacks, when parts of participants are not assumed to be fully honest. Moreover, selfish participants can obtain others’ collaborative data but do not contribute their real local data or even provide fake data. This violates the fairness of FL schemes. Therefore, advanced privacy and fairness techniques have been integrated into FL schemes including blockchain, differential privacy, zero-knowledge proof, etc. However, most of the existing works still have room to enhance the practicality due to our exploration. In this paper, we propose a Blockchain-based Pseudorandom Number Generation (BPNG) protocol based on Verifiable Random Functions (VRFs) to guarantee the fairness for FL schemes. Next, we further propose a Gradient Random Noise Addition (GRNA) protocol based on differential privacy and zero-knowledge proofs to protect data privacy for FL schemes. Finally, we implement both two protocols on Hyperledger Fabric and analyze their performance. Simulation experiments show that the average time that proof generation takes is 18.993 s and the average time of on-chain verification is 2.27 s under our experimental environment settings, which means the scheme is practical in reality.
Diversity-driven automated web API recommendation based on implicit requirements

With the rapid growth of the web API sharing community, Mashup development has become a popular way for software developers to quickly create Mashup applications. Mashup development allows developers to quickly implement the functionality they want by combining a chosen set of web APIs, which greatly improves development efficiency. However, the existence of numerous web API candidates make it difficult to select the appropriate API quickly. Most existing automated web API recommendation approaches focus on the developer's description of requirements but typically overestimate the developer's ability to fully describe their application's requirements, thus ignoring their implicit and diversified functional requirements. In this paper, we propose a novel automated approach called DI-RAR to retrieve and recommend web API groups for Mashup creation. Specifically, we formulate an automated web API recommendation task as a nondeterministic polynomial problem. First, a self-attention model assigns weights to query to distinguish the core and non-core requirements. then Dynamic planning retrieval generates steiner trees to retrieve API groups and uncovers strongly related implicit requirements to enrich the mashup's functions. Finally we apply simhash technique to filter similar results and finally provide diverse API groups. experiments based on a real-world dataset are performed to demonstrate the feasibility and efficiency of DI-RAR.
Multiscale structural feature transform for multi-modal image matching

Robust and reliable multi-modal image matching is an essential and challenging technique in the multi-modality involved scenarios for image fusion, image mosaic, and visual navigation. Existing image matching methods cannot simultaneously solve the scale and rotation distortion caused by different viewpoints and the nonlinear radiation distortion caused by different imaging environments or mechanisms. To address these problems, a multiscale structural feature transform method is proposed for multi-modal image matching, which consists of three blocks: multiscale structural feature detector, symmetric log-polar descriptor, and point matching with position correction. The multiscale structural feature detector constructs the phase congruency map based Difference of Gaussian image pyramid for the scale-invariant feature points detection to tackle the nonlinear radiation distortion. The feature points are detected within 10-neighborhood instead of 26-neighborhood to produce sufficient points on the phase congruency map with very sparse texture. The symmetric log-polar descriptor utilizes the multiscale structure principal direction and symmetric bins to improve the robustness of the descriptor to rotation and nonlinear radiation distortion. At last, the point matching with position correction corrects the position of feature points to compensate the position offset between different images caused by the scale and rotation distortion and the nonlinear radiation distortion, which increases the number of correct matches and improves the transformation consistency between matched points. In the experiment, the proposed method in this paper is compared with seven state-of-the-art methods. Experimental results verify the superiority of the proposed method and its robustness to the scale and rotation distortion and the nonlinear radiation distortion. In addition, we demonstrate the effectiveness of the proposed three blocks by the ablation experiments.
CNN-Wavelet scattering textural feature fusion for classifying breast tissue in mammograms

Visual interpretation from radiologists employs computer-aided diagnosis (CAD) to make clinical diagnoses by analyzing breast tissue images and assessing their texture. Aside from needing more training images for Convolutional Neural Network (CNN), selecting textural feature input to the deep learning-based CAD is challenging to control since the entire feature map is extracted automatically and only considers spatial domain analysis. Hence, integrating spatial and frequency information from the images requires complex feature representation in a classification problem. Wavelet representation could improve the feature descriptors in acquiring information from the spatial-frequency analysis. However, wavelet transforms’ shift insensitivity may affect class feature representation by suppressing high-frequency information. This study uses CNN, and wavelet scattering (WS) features to classify fatty and fibroglandular tissue with benign and malignant masses from digital mammograms to overcome CNN overfitting based on a limited dataset. Scattered WS coefficients formed from layers of wavelet dilations and averaging are utilized to retain the loss of high-frequency signal from the images, while simultaneously, the added CNN features improve sparse image representation resulting from convoluted spatial information of the images. Finally, the model is cascaded with an ensemble classifier for classifying fatty and fibroglandular tissue and mass images, n = 112, from the INbreast mammogram dataset. The best performance of mass and breast tissue classification models reach 98.0 % and 99.3 % on 10-fold cross-validation accuracy. By combining textural features from WS and deep learning model architecture, the model may adopt a training system with fewer data without augmentation to achieve good performance and low complexity costs.
GAN2X: Non-Lambertian Inverse Rendering of Image GANs

2D images are observations of the 3D physical world depicted with the geometry, material, and illumination components. Recovering these underlying intrinsic components from 2D images, also known as inverse rendering, usually requires a supervised setting with paired images collected from multiple viewpoints and lighting conditions, which is resource-demanding. In this work, we present GAN2X, a new method for unsupervised inverse rendering that only uses unpaired images for training. Unlike previous Shape-from-GAN approaches that mainly focus on 3D shapes, we take the first attempt to also recover non-Lambertian material properties by exploiting the pseudo paired data generated by a GAN. To achieve precise inverse rendering, we devise a specularity-aware neural surface representation that continuously models the geometry and material properties. A shading-based refinement technique is adopted to further distill information in the target image and recover more fine details. Experiments demonstrate that GAN2X can accurately decompose 2D images to 3D shape, albedo, and specular properties for different object categories, and achieves state-of-the-art performance for unsupervised single-view 3D face reconstruction. We also show its applications in downstream tasks including real image editing and lifting 2D GANs to decomposed 3D GANs.
NILRNN: A Neocortex-Inspired Locally Recurrent Neural Network for Unsupervised Feature Learning in Sequential Data

Abstract Unsupervised feature learning refers to the problem of learning useful feature extraction functions from unlabeled data. Despite the great success of deep learning networks in this task in recent years, both for static and for sequential data, these systems can in general still not compete with the high performance of our brain at learning to extract useful representations from its sensory input. We propose the Neocortex-Inspired Locally Recurrent Neural Network: a new neural network for unsupervised feature learning in sequential data that brings ideas from the structure and function of the neocortex to the well-established fields of machine learning and neural networks. By mimicking connection patterns in the feedforward circuits of the neocortex, our system tries to generalize some of the ideas behind the success of convolutional neural networks to types of data other than images. To evaluate the performance of our system at extracting useful features, we have trained different classifiers using those and other learnt features as input and we have compared the obtained accuracies. Our system has shown to outperform other shallow feature learning systems in this task, both in terms of the accuracies achieved and in terms of how fast the classification task is learnt. The results obtained confirm our system as a state-of-the-art shallow feature learning system for sequential data, and suggest that extending it to or integrating it into deep architectures may lead to new successful networks that are competent at dealing with complex sequential tasks.
Smart Contracts for Managing the Chain-of-Custody of Digital Evidence: A Practical Case of Study

The digital revolution is renewing many aspects of our lives, which is also a challenge in judicial processes, such as the Chain-of-Custody (CoC) process of any electronic evidence. A CoC management system must be designed to guarantee them to maintain its integrity in court. This issue is essential for digital evidence’s admissibility and probative value. This work has built and validated a real prototype to manage the CoC process of any digital evidence. Our technological solution follows a process model that separates the evidence registry and any evidence itself for scalability purposes. It includes the development of an open-source smart contract under Quorum, a version of Ethereum oriented to private business environments. The significant findings of our analysis have been: (1) Blockchain networks can become a solution, where integrity, privacy and traceability must be guaranteed between untrustworthy parties; and (2) the necessity of promoting the standardization of CoC smart contracts with a secure, simple process logic. Consequently, these contracts should be deployed in consortium environments, where reliable, independent third parties validate the transactions without having to know their content.
Deep ensemble learning enables highly accurate classification of stored red blood cell morphology

Changes in red blood cell (RBC) morphology distribution have emerged as a quantitative biomarker for the degradation of RBC functional properties during hypothermic storage. Previously published automated methods for classifying the morphology of stored RBCs often had insufficient accuracy and relied on proprietary code and datasets, making them difficult to use in many research and clinical applications. Here we describe the development and validation of a highly accurate open-source RBC morphology classification pipeline based on ensemble deep learning (DL). The DL-enabled pipeline utilized adaptive thresholding or semantic segmentation for RBC identification, a deep ensemble of four convolutional neural networks (CNNs) to classify RBC morphology, and Kalman filtering with Hungarian assignment for tracking changes in the morphology of individual RBCs over time. The ensembled CNNs were trained and evaluated on thousands of individual RBCs from two open-access datasets previously collected to quantify the morphological heterogeneity and washing-induced shape recovery of stored RBCs. Confusion matrices and reliability diagrams demonstrated under-confidence of the constituent models and an accuracy of about 98% for the deep ensemble. Such a high accuracy allowed the CNN ensemble to uncover new insights over our previously published studies. Re-analysis of the datasets yielded much more accurate distributions of the effective diameters of stored RBCs at each stage of morphological degradation (discocyte: 7.821 ± 0.429 µm, echinocyte 1: 7.800 ± 0.581 µm, echinocyte 2: 7.304 ± 0.567 µm, echinocyte 3: 6.433 ± 0.490 µm, sphero-echinocyte: 5.963 ± 0.348 µm, spherocyte: 5.904 ± 0.292 µm, stomatocyte: 7.080 ± 0.522 µm). The effective diameter distributions were significantly different across all morphologies, with considerable effect sizes for non-neighboring classes. A combination of morphology classification with cell tracking enabled the discovery of a relatively rare and previously overlooked shape recovery of some sphero-echinocytes to early-stage echinocytes after washing with 1% human serum albumin solution. Finally, the datasets and code have been made freely available online to enable replication, further improvement, and adaptation of our work for other applications.
Fiber Bundle Image Reconstruction Using Convolutional Neural Networks and Bundle Rotation in Endomicroscopy

Fiber-bundle endomicroscopy has several recognized drawbacks, the most prominent being the honeycomb effect. We developed a multi-frame super-resolution algorithm exploiting bundle rotation to extract features and reconstruct underlying tissue. Simulated data was used with rotated fiber-bundle masks to create multi-frame stacks to train the model. Super-resolved images are numerically analyzed, which demonstrates that the algorithm can restore images with high quality. The mean structural similarity index measurement (SSIM) improved by a factor of 1.97 compared with linear interpolation. The model was trained using images taken from a single prostate slide, 1343 images were used for training, 336 for validation, and 420 for testing. The model had no prior information about the test images, adding to the robustness of the system. Image reconstruction was completed in 0.03 s for 256 × 256 images indicating future real-time performance is within reach. The combination of fiber bundle rotation and multi-frame image enhancement through machine learning has not been utilized before in an experimental setting but could provide a much-needed improvement to image resolution in practice.
Deep Clustering-Based Anomaly Detection and Health Monitoring for Satellite Telemetry

Satellite telemetry data plays an ever-important role in both the safety and the reliability of a satellite. These two factors are extremely significant in the field of space systems and space missions. Since it is challenging to repair space systems in orbit, health monitoring and early anomaly detection approaches are crucial for the success of space missions. A large number of efficient and accurate methods for health monitoring and anomaly detection have been proposed in aerospace systems but without showing enough concern for the patterns that can be mined from normal operational telemetry data. Concerning this, the present paper proposes DCLOP, an intelligent Deep Clustering-based Local Outlier Probabilities approach that aims at detecting anomalies alongside extracting realistic and reasonable patterns from the normal operational telemetry data. The proposed approach combines (i) a new deep clustering method that uses a dynamically weighted loss function with (ii) the adapted version of Local Outlier Probabilities based on the results of deep clustering. The DCLOP approach effectively monitors the health status of a spacecraft and detects the early warnings of its on-orbit failures. Therefore, this approach enhances the validity and accuracy of anomaly detection systems. The performance of the suggested approach is assessed using actual cube satellite telemetry data. The experimental findings prove that the suggested approach is competitive to the currently used techniques in terms of effectiveness, viability, and validity.
Secure Modern Wireless Communication Network Based on Blockchain Technology

Sixth-generation (6G) wireless networking studies have begun with the global implementation of fifth-generation (5G) wireless systems. It is predicted that multiple heterogeneity applications and facilities may be supported by modern wireless communication networks (MWCNs) with improved effectiveness and protection. Nevertheless, a variety of trust-related problems that are commonly disregarded in network architectures prevent us from achieving this objective. In the current world, MWCN transmits a lot of sensitive information. It is essential to protect MWCN users from harmful attacks and offer them a secure transmission to meet their requirements. A malicious node causes a major attack on reliable data during transmission. Blockchain offers a potential answer for confidentiality and safety as an innovative transformative tool that has emerged in the last few years. Blockchain has been extensively investigated in several domains, including mobile networks and the Internet of Things, as a feasible option for system protection. Therefore, a blockchain-based modal, Transaction Verification Denied conflict with spurious node (TVDCSN) methodology, was presented in this study for wireless communication technologies to detect malicious nodes and prevent attacks. In the suggested mode, malicious nodes will be found and removed from the MWCN and intrusion will be prevented before the sensitive information is transferred to the precise recipient. Detection accuracy, attack prevention, security, network overhead, and computation time are the performance metrics used for evaluation. Various performance measures are used to assess the method’s efficacy, and it is compared with more traditional methods.