Reviewerly/exHarmony huggingface dataset:
Recycling waste classification using emperor penguin optimizer with deep learning model for bioenergy production

The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk of world source strain. The waste classification was a good resolution for separating the waste from the recycled objects. It is inefficient and expensive to rely solely on manual classification of garbage and recycling sources. Convolutional neural networks (CNNs) have lately been used to classify recyclable waste, and this is the primary way for recycling the waste. This study presents a recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) model for bioenergy production. RWC-EPODL model focuses on recycling waste materials recognition and classification. When it comes to detecting and classifying trash, the RWC-EPODL model uses two stages. At the initial stage, the RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. In addition, Bayesian optimization (BO) algorithm is applied as hyperparameter optimizer of the AX-RetinaNet model. Following the EPO algorithm with a stacked auto-encoder (SAE) model, the EPO algorithm is used to fine-tune the parameters of the SAE technique for trash classification. The RWC-EPODL model's experimental validation is examined through a number of studies. The RWC-EPODL approach has a 98.96 percent success rate. The comparative result analysis reported the better performance of the RWC-EPODL model over recent approaches.
Predicting population size and termination criteria in metaheuristics: A case study based on spotted hyena optimizer and crow search algorithm

The proper configuration of a metaheuristic requires specific and advanced knowledge, for instance to decide an appropriate population size, to effectively set probability parameters, or to define a correct termination criterion. During the last years there is a trend to let the metaheuristic to autonomously self-control the internal configuration. In this way, by using learning mechanisms, the algorithm is able to learn from the solving process and automatically adapt its configuration in order to improve their performance. Following this research trend, in this paper we explore the ability to autonomously predict population size and termination criteria. The population prediction allows the metaheuristic to dynamically vary the population size during solving time. In this regard, a suitable number of agents is used for each part of the process in order to efficiently explore the space of solutions. On the other hand, predicting the termination criteria is useful as well, the smart usage of resources in the optimization field can bring several advantages in solving complex industrial problems. In this regard, since the number of iterations is estimated during solving time, the proposed architecture avoids useless processing time that does not lead to any solution improvement. We illustrate interesting results using the spotted hyena optimizer and crow search algorithm solving 24 benchmarks functions and the multidimensional knapsack problem, where the proposed approach notably competes with several state-of-the-art optimization algorithms.
An Approach for Blockchain Pool Mining Employing the Consensus Protocol Robust against Block Withholding and Selfish Mining Attacks

This paper proposes an approach for pool mining in public blockchain systems based on the employment of a recently reported consensus protocol with the puzzle based on a symmetric encryption that provides an energy–space trade-off and reduces energy consumption. The proposed architecture employs a pseudo-symmetric allocation of the resources for the blockchain consensus protocol and provides protection against certain malicious actions of the pool members, as well as a miner’s opportunity for selecting the resources required for participation in the consensus protocol. Given that the considered consensus protocol employs two resources, the proposed architecture uses this two-dimensional nature to provide resistance against block withholding and selfish mining attacks, as well as a reduction in energy spending as a trade-off with the employment of certain memory resources. The high resistance of the proposed pool mining approach against the considered attacks appears to be a consequence of the success probability of the pool in comparison with the success probability of malicious miners. Assuming appropriate selection of the puzzle hardness, the probability that malicious miners can solve the puzzle without the support of the pool manager can be arbitrarily small. Implementation of the proposed approach on a modified Ethereum platform and experimental evaluation issues have also been reported. The conceptual novelty of the proposed pool mining approach is the following: Instead of separation of the blockchain consensus protocol and control of pool miners honest work, this paper proposes an approach where honest work of miners and pool managers is provided by a dedicated application of the considered consensus protocol. Advantages of the proposal in comparison with the previously reported ones include the following: (i) high resistance against block withholding and selfish mining attacks without an additional security procedure; (ii) reduction in the energy required, and at the same time preservationthe security of the consensus protocol; (iii) flexibility of the pool miners regarding selection of the resources that should be employed providing a trade-off between required energy and memory resources. The proposed architecture was implemented employing a dedicated modification of the Ethereum platform and the performed experiments confirmed the feasibility and effectiveness of the proposal.
Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement.In this research, we specially investigated the effectiveness of outlier removal to improve estimation performance of 5 machine learning (ML) methods (Support Vector Regression, Random Forest, Ridge Regression, K-Nearest Neighbor, and Gradient Boosting Machines) for software development effort estimation (SDEE).We propose a novel discretization method based on Golden Section (dubbed as Golden Section based Adaptive Discretization, GSAD) to identify optimal number of outliers for SDEE dataset.The results signify the importance of optimal number of outliers' removal to improve estimations.Moreover, the results obtained after applying GSAD technique have been compared with IQR and Cooks' distance based outlier identification methods over 4 datasets: ISBSG Release 2021, UCP, NASA93 and China.The empirical results confirm that the performance of ML based SDEE methods is generally improving by employing GSAD and the proposed GSAD method has the ability to compete with the other prevalent outlier identification methods.
Facial Expression Classification using Fusion of Deep Neural Network in Video

For computers to recognize human emotions, expression classification is an equally important problem in the human-computer interaction area. In the 3rd Affective Behavior Analysis In-The-Wild competition, the task of expression classification includes eight classes with six basic expressions of human faces from videos. In this paper, we employ a transformer mechanism to encode the robust representation from the backbone. Fusion of the robust representations plays an important role in the expression classification task. Our approach achieves 30.35% and 28.60% for the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score on the validation set and the test set, respectively. This result shows the effectiveness of the proposed architecture based on the Aff-Wild2 dataset and our team archives 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> for the expression classification task in the 3rd Affective Behavior Analysis In-The-Wild competition.
Cyber-Physical Social Space towards Blockchain and Smart Specialisation Solutions

The paper presents some general and specific aspects of research on applications that blockchain technology can find in Cyber-Physical Social Space. An electronic supply chain model based on blockchain data authentication technology is presented. The main features are cybersecurity of the system and opportunities to protect customers from unauthorized transactions. The main elements of the technology and the focus areas in the implementation of blockchain applications are discussed.
Polarimetric dehazing based on fusing intensity and degree of polarization

Image dehazing is an indispensable approach to address the image degeneration in hazy environment. In recent years, polarimetric methods have been widely investigated for image dehazing. In the basic algorithm of polarimetric dehazing, in addition to the dehazed intensity, the degree of polarization (DoP) can also be reconstructed with the assistance of Stokes parameters. Considering the additional information from the DoP, the polarimetric dehazing method is improved with the fusion of the intensity and the DoP. The wavelet transform is exploited to make fusion, and different fusion rules are designed for the low-frequency and the high-frequency components, respectively. The proposed method can improve the quality of polarimetric dehazed image, especially for the transparent object that is hard to be handled with previous methods. The validity of the proposed method is demonstrated in experiments.
Efficiency of two-stage aerobic-reagent technology for the pre-treatment of different age leachates of municipal solid waste landfills

Experimental lab-scale results on the efficiency of two-stage aerobic-reagent technology of leachate pre-treatment for different age leachates on examples of medium-to-old age leachate of Chervonohrad MSW landfill and young-to-medium leachate of Hrybovychi MSW landfill (Lviv region, Ukraine) are presented in the paper. Kinetics of aerobic pre-treatment of leachates is approximated using the simple exponential model and two-parameter modified exponential model. Optimal dosage of aqueous reagent solutions of polyacrylamide, aluminium sulphate, iron (II) sulphate and hydrogen peroxide are obtained experimentally. Treatment effects at optimum dosage reach 95.9% by ammonium nitrogen and 90.2% by COD allowing discharge of pre-treated leachates to WWTP.
TokenAware: Accurate and Efficient Bookkeeping Recognition for Token Smart Contracts

Tokens have become an essential part of blockchain ecosystem, so recognizing token transfer behaviors is crucial for applications depending on blockchain. Unfortunately, existing solutions cannot recognize token transfer behaviors accurately and efficiently because of their incomplete patterns and inefficient designs. This work proposes TokenAware , a novel online system for recognizing token transfer behaviors. To improve accuracy, TokenAware infers token transfer behaviors from modifications of internal bookkeeping of a token smart contract for recording the information of token holders (e.g., their addresses and shares). However, recognizing bookkeeping is challenging, because smart contract bytecode does not contain type information. TokenAware overcomes the challenge by first learning the instruction sequences for locating basic types and then deriving the instruction sequences for locating sophisticated types that are composed of basic types. To improve efficiency, TokenAware introduces four optimizations. We conduct extensive experiments to evaluate TokenAware with real blockchain data. Results show that TokenAware can automatically identify new types of bookkeeping and recognize 107,202 tokens with 98.7% precision. TokenAware with optimizations merely incurs 4% overhead, which is 1/345 of the overhead led by the counterpart with no optimization. Moreover, we develop an application based on TokenAware to demonstrate how it facilitates malicious behavior detection.
EQAdap: Equipollent Domain Adaptation Approach to Image Deblurring

In this paper, we present an end-to-end unsupervised domain adaptation approach to image deblurring. This work focuses on learning and generalizing the complex latent space of the source domain and transferring the extracted information to the unlabeled target domain. While fully supervised image deblurring methods have achieved high accuracy on large-scale vision datasets, they are unable to well generalize well on a new test environment or a new domain. Therefore, in this work, we introduce a novel Bijective Maximum Likelihood loss for the unsupervised domain adaptation approach to image deblurring. We evaluate our proposed method on GoPro, RealBlur_J, RealBlur_R, and HIDE datasets. Through intensive experiments, we demonstrate our state-of-the-art performance on the standard benchmarks.
Visual Deep Learning-Based Explanation for Neuritic Plaques Segmentation in Alzheimer’s Disease Using Weakly Annotated Whole Slide Histopathological Images

Quantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL) models such as UNet have been successfully used for automatic segmentation of histopathological whole slide images (WSI) of biological tissues. In this study, we propose a DL-based methodology for semantic segmentation of tau lesions (i.e., neuritic plaques) in WSI of postmortem patients with AD. The state of the art in semantic segmentation of neuritic plaques in human WSI is very limited. Our study proposes a baseline able to generate a significant advantage for morphological analysis of these tauopathies for further stratification of AD patients. Essential discussions concerning biomarkers (ALZ50 versus AT8 tau antibodies), the imaging modality (different slide scanner resolutions), and the challenge of weak annotations are addressed within this seminal study. The analysis of the impact of context in plaque segmentation is important to understand the role of the micro-environment for reliable tau protein segmentation. In addition, by integrating visual interpretability, we are able to explain how the network focuses on a region of interest (ROI), giving additional insights to pathologists. Finally, the release of a new expert-annotated database and the code ( https://github.com/aramis-lab/miccai2022-stratifiad.git ) will be helpful for the scientific community to accelerate the development of new pipelines for human WSI processing in AD.
Analysis of the Exploration of Security and Privacy for Healthcare Management Using Artificial Intelligence: Saudi Hospitals

A large component of the Health Information Systems now comprises numerous independent apps created in the past that need to be merged to provide a more uniform service. In addition to affecting the Intelligent Health Board Functionality and dependability, the quality of these additional apps may also have an impact. A critical characteristic of the SHS's management and upkeep is the SHS's reliance on the real benefits provided to it. In speaking, an HMIS (Healthcare Management Information System) is a computer-based device that benefits medical practitioners to perform their duties more efficiently by coordinating all of their data. Even though these systems are widely used by most of the world, there is a significant need to comprehend these technologies and indeed the potential they provide. Healthcare data warehouses in Saudi Arabia have evolved through time, and this research examines how key service improvements in Saudi present varied viewpoints on how premium initiative help may be attained in health as well as how this could be done. When it comes to understanding how different types of medical professionals interact with healthcare systems throughout history, researchers developed stages of the maturity model.
MultiMediate'22

Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroup Interaction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.
Investigation on out-of-plane displacement measurements of thin films via a mechanical constraint-based 3D-DIC technique

Thin film is functioning as a key material in surface engineering, and accurate measurement of its deformation are particularly important for the reliability and stability assessment of coating-substrate structures. However, the displacement field measured by the conventional three-dimensional subset-based digital image correlation (3D-DIC) method has no spatial continuity; thus, it cannot precisely reflect the true deformation of thin films. To improve the measurement accuracy, a 3D-DIC method considering mechanical constraints (3DMC-DIC) was proposed in this paper. In this method, a mesh with 8-noded quadrilateral shell elements satisfying spatial continuity was employed to determine the out-of-plane displacements of all nodes based on trusted points, which are the speckle points with higher measurement accuracy obtained by the conventional subset-DIC method. The whole out-of-plane displacement field was then inversely obtained based on the optimized displacements of these nodes. A blister test was performed, and the results show that the out-of-plane displacement field of thin film with spatial continuity and high accuracy on specimen edge can be obtained by the 3DMC-DIC method.
Experiences of web‐based extended reality technologies for physics education

Abstract Extended reality (XR) technologies such as augmented reality (AR) and virtual reality (VR) are being increasingly used for education and skill development through the development of interactive learning environments (ILEs) with XR implementations. For physics education, these ILEs with XR are capable of improving the visual representation of educational content, as well as stimulating the cognitive process of learning with interactive experiences. In this study, we focused on evaluating students' perceptions of the usage of web‐based XR technologies for education, through a learning tool that implements virtual reality and augmented reality environments, with the purpose of providing students the necessary didactic material to learn physics. An experimental process was carried out with a sample of 70 undergrad students who used FisicARtivo, a web‐based learning tool on kinematics and dynamics concepts that incorporates XR. After working with the tool, students completed a comprehensive survey about their motivational perceptions regarding the use of XR technologies for physics education. According to the results, both VR and AR showed significant effects on motivation. However, there was a higher positive impact on students' learning motivation when they used FisicARtivo in AR mode in comparison to VR mode.
SC-GAN: Subspace Clustering based GAN for Automatic Expression Manipulation

In recent years, the topics of facial attribute manipulation and decomposition have gained great popularity in computer vision and human computer interaction. Even though such methods have been preliminarily employed in some photo beautification applications, it still remains challenging due to the highly-versatile facial attributes and their drastic appearance changes subject to variation of deformation, illumination, pose, etc. The prevailing problems are especially severe when we are faced with group photos involving many faces. To overcome such critical limitations and discover more meaningful visual attributes and their possible decompositions, we develop a subspace clustering based generative adversarial network (SC-GAN) in this paper. Our SC-GAN can simultaneously decompose multiple subspaces and generate diverse samples correspondingly, thus the training of the generative models could be more effectively guided by facial attribute and its decomposition and manipulation in a natural and meaningful fashion. Our SC-GAN incorporates the SIFT K-means cluster, which could split the holistic semantic facial space into different subspaces without supervision, and help the new GAN generate more convincing results within specific subspaces. Extensive experiments and comprehensive evaluations confirm that, our method can greatly reduce the unexpected influences caused by portrait diversities and outperform the state-of-the-art facial attribute manipulation approaches.1
SABRINA: A Stochastic Subspace Majorization-Minimization Algorithm

A wide class of problems involves the minimization of a coercive and differentiable function F on $${\mathbb {R}}^N$$ whose gradient cannot be evaluated in an exact manner. In such context, many existing convergence results from standard gradient-based optimization literature cannot be directly applied and robustness to errors in the gradient is not necessarily guaranteed. This work is dedicated to investigating the convergence of Majorization-Minimization (MM) schemes when stochastic errors affect the gradient terms. We introduce a general stochastic optimization framework, called StochAstic suBspace majoRIzation-miNimization Algorithm SABRINA that encompasses MM quadratic schemes possibly enhanced with a subspace acceleration strategy. New asymptotical results are built for the stochastic process generated by SABRINA. Two sets of numerical experiments in the field of machine learning and image processing are presented to support our theoretical results and illustrate the good performance of SABRINA with respect to state-of-the-art gradient-based stochastic optimization methods.
Application of Continuous Wavelet Transform and Artificial Naural Network for Automatic Radar Signal Recognition

This article aims to propose an algorithm for the automatic recognition of selected radar signals. The algorithm can find application in areas such as Electronic Warfare (EW), where automatic recognition of the type of intra-pulse modulation or the type of emitter operation mode can aid the decision-making process. The simulations carried out included the analysis of the classification possibilities of linear frequency modulated pulsed waveform (LFMPW), stepped frequency modulated pulsed waveform (SFMPW), phase coded pulsed waveform (PCPW), rectangular pulsed waveforms (RPW), frequency modulated continuous wave (FMCW), continuous wave (CW), Stepped Frequency Continuous Wave SFCW) and Phase Coded Continuous Waveform (PCCW). The algorithm proposed in this paper is based on the use of continuous wavelet transform (CWT) coefficients and higher-order statistics (HOS) in the feature determination of selected signals. The Principal Component Analysis (PCA) method was used for dimensionality reduction. An artificial neural network was then used as a classifier. Simulation studies took into account the presence of noise interference with signal-to-noise ratio (SNR) in the range from −5 to 10 dB. Finally, the obtained classification efficiency is presented in the form of a confusion matrix. The simulation results show a high recognition test accuracy, above 99% with a signal-to-noise ratio greater than 0 dB. The article also deals with the selection of the type and parameters of the wavelet. The authors also point to the problems encountered during the research and examples of how to solve them.