Reviewerly/exHarmony huggingface dataset:
India’s photovoltaic potential amidst air pollution and land constraints

India aims for ambitious solar energy goal to fulfill its climate commitment but there are limited studies on solar resource assessment considering both environmental and land availability constraints. The present work attempts to address this issue using satellite-derived air pollution, radiation, and land use data over the Indian region. Surface insolation over India has been decreasing at a rate of -0.29 ± 0.19 Wm-2 y-1 between 2001 and 2018. Solar resources over nearly 98%, 40%, and 39% of the Indian landmass are significantly impacted by aerosols, clouds, and both aerosols and clouds respectively. Only 29.3% of the Indian landmass is presently suitable for effective solar photovoltaic harnessing, but this is further declining by -0.21% annually, causing a presumptive loss of 50 GW solar potential, translating 75 TWh power generation. Lowering two decades of aerosol burden can make 8% additional landmass apt for photovoltaic use. Alleviating aerosol-induced dimming can fast-track India's solar energy expansion.
Human motion pattern recognition based on the fused random forest algorithm

In this paper, a fused random forest algorithm named (PSO-RF)-(KNN-HC) is proposed for the recognition of seven human motion patterns, including flat walking, sitting, standing, going up the stairs, going down the stairs, going up the slope and going down the slope. A particle swarm optimization (PSO) method is used to find the optimal parameters of the random forest model and build the optimal classification model. In the decision process of the random forest, the algorithm of k-nearest neighbors-hierarchical clustering (KNN-HC) is applied to select the decision trees for new recognition samples and calculate the voting weights of each tree, which improves the classification accuracy of the random forest model for multi-classification problems. In the data processing stage, the motion data are analyzed from view of the frequency domain using the fast Fourier transform (FFT) to divide the data segments in cycles and perform feature extraction. Finally, the proposed algorithm is validated against other machine learning algorithms based on a self-constructed human motion dataset through a real motion data acquisition platform, and the effectiveness of the proposed method is also validated on an open source dataset.
Buyer Beware, or Buyer Unaware? Examining the Correlates of Consumer Fraud Victimization Acknowledgment in the United States

Victims’ acknowledgment of their experiences as criminal events is a crucial first step to any criminal justice involvement. Works examining the topic have identified several factors associated with acknowledgment, including the seriousness of the offense, repeat victimization, help-seeking behaviors, and distress. Despite this, works examining acknowledgment have been restricted to interpersonal offenses, limiting understandings of whether correlates of acknowledgment can apply in other specific domains, such as for consumer fraud. Using a sample of consumer fraud victims from the National Crime Victimization Survey Supplemental Fraud Survey ( n = 411), findings indicate that distress and help-seeking increased odds of acknowledgment, while repeat victimization was not associated with acknowledgment. Findings are discussed in light of future directions for research and policy.
A system dynamics model in the humanitarian supply chain based on blockchain technology

One of the most important issues in the Humanitarian Supply Chain (HSC) is the delivery of goods at the right time and at the right cost, the ability to track goods, and increase trust among supply chain members to increase synergy. This study explored the impact of Blockchain Technology (BT) in the HSC and the extent to which these goals are covered using a system dynamics model. To do this, first, the previous studies in the literature were reviewed. Then, the effective variables were determined by surveying the subject-matter experts. Furthermore, the relationships between the identified variables were evaluated by developing a cause-effect model. Then, by designing the stock-flow models and related tests, the relevant model was validated, and finally, two scenarios of the mild and server economic crisis were evaluated. For each scenario, three policies of using BT, increasing the workforce, and increasing the suppliers' transport capacity were reviewed. The results of simulating the model and the policies showed that using BT is the best policy to achieve the goals during a severe economic crisis. Furthermore, when the economic crisis is mild, if the demand for goods is very high, this policy is also effective. However, increasing the workforce is a more effective policy when the demand for goods is low.
Optimized Bayesian convolutional neural networks for invasive breast cancer diagnosis system

Deep learning (DL) has depicted unprecedented results in the diagnosis of invasive ductal carcinoma (IDC). However, most DL methods are not able to infer the uncertainty in their predictions, and are often too confident which may cause undesirable consequences. The ability of DL models to express their uncertainty in their predictions is particularly important in diagnosing diseases. Therefore, this paper proposes an uncertainty-aware approach called optimized Bayesian convolutional neural network (OBCNN) for the automated detection of IDC from histopathology images and is capable of estimating and inferring the uncertainty in its predictions. The proposed OBCNN employs an approximate Bayesian version of a pre-trained convolutional neural network (CNN) model named ResNet101V2 that is fine-tuned to the histopathology images of IDC. The Bayesian approximation is performed by adding Monte Carlo dropout (MC-dropout) to ResNet101V2 architecture. Due to the effect of MC-dropout on the obtained posterior predictive distribution (PPD), the value of MC-dropout should be chosen carefully because if the MC-dropout value is too large, the predictive distribution (PD) will be very diverse. Whereas if the MC-dropout value is very small, the PD will be very similar. Therefore, an optimization algorithm known as the slime mould algorithm is utilized to set the optimal value of the MC-dropout. During inference, the proposed OBCNN runs for T forward passes to configure the PPD. The mean, standard deviation (SD) and entropy are then calculated over the obtained PD to estimate the predictive uncertainty (PU) of the OBCNN. The proposed OBCNN achieved an accuracy of 93.83%, a precision of 96.14%, a G-mean of 93.04%, False Negative Rate of 4.72%, and False Positive Rate of 9.96 %. The proposed OBCNN has also been compared with recent related work, the comparison’s results demonstrated that OBCNN performed better in the diagnosis of IDC.
Enhanced lightweight and secure certificateless authentication scheme (ELWSCAS) for Internet of Things environment

The Internet of Things (IoT) is a fast-growing technology that enable existing systems to communicate with one another by using new devices such as sensors and other smart devices. The IoT devices such as wireless sensors and other smart devices are often very cheap, very small, and have limited power sources, memories, and processing capabilities. These devices are used in numerous critical applications, in which confidential information is sent across wireless channels among other devices participating in the system. This communication is vulnerable to multiple cyber-attacks due to the insecure wireless channel between them and without strong security mechanism, the important credential can be stolen by network attackers. One of the most important aspects of securing IoT communication is the authentication mechanism, used to validate the identity of authorized devices and users in IoT networks and to ensure data confidentiality, integrity, and authenticity for secure communication. Several authentication schemes for IoT environment have been proposed in the literature, none of these approaches entirely fulfil the necessary security and lightweight feature requirements. Some schemes fulfill the required security features but are unable to provide lightweight features. Similarly, other schemes provide lightweight features, but their security features are unsatisfactory. Therefore, it is necessary to design an effective security mechanism to ensure secure communication in IoT environments. In this paper, we propose Authenticated Encryption with Associated Data (AEAD) and Elliptic Curve Cryptography (ECC) based Enhanced Lightweight and Secure Certificateless Authentication Scheme (ELWSCAS) for IoT environment to fulfill the required security and lightweight performance features. The security of the proposed solution is evaluated using formal and informal security analysis. For formal security analysis we used RoR model and AVISPA tool. We have implemented the proposed solution in network simulator (NS3.35) by using Python and C++ to measure the network parameters such as throughput and packet delivery ratio (PDR). The computational and communication cost of the proposed scheme shows that in comparison to the existing state-of-the-art, our approach is considerably less costly and is a viable option for constrained IoT environment.
Media and Information Literacy in the Prescribed Curriculum: A Systematic Review on its Integration

Abstract The curricular integration of Media and Information Literacy (MIL) is crucial for cultivating informed, critical, and engaged citizens in contemporary society. It assists in addressing the challenges of the digital era and capitalizing on the opportunities presented by the ever-changing media landscape. Thus, the present systematic literature review uses the PRISMA guidelines to examine three dimensions in the process of integration of Media and Information Literacy (MIL) in the prescribed curriculum: formulation, implementation, and evaluation and challenges. Starting with the search criteria, 131 studies were found in the Web of Science, Scopus, ERIC, Dialnet and Google Scholar databases, published between January, 2013, and March, 2023, written in English, Spanish, Portuguese, and Turkish. The findings suggest that the will of the political sphere and the activism of the triad composed by passionate teachers, civil society, and academia, are key factors for promoting the introduction of MIL in formal education. Likewise, it is underlined that the evaluation of this education policy requires special attention, in order to guarantee the analysis of its reach, effectiveness, and capacity to adapt against the challenges that emerge in the media ecosystem. Thus, the intention is to provide up-to-date information for the creation of policies, research studies, and curricular content on this subject.
Experimental investigation of the tire wear process using camera-assisted observation assessed by numerical modeling

This paper presents a novel experimental method to study the abrasion mechanism of car tires. It is based on the detection of microscopic movements associated with material damage (cracking) on the rubber tread. This is referred to as degrading layer relaxation. It correlates with the wear rate and, interestingly, the direction of the pattern’s movement is opposite to the lateral forces during cornering. To measure and analyze the microscopic movements, a new camera-based method with feature point matching using video stabilization was developed. Besides extensive experimental investigation, the formation and propagation of microcracks are investigated using a simplified numerical model in which a phase field approach coupled with a viscoelastic constitutive behavior is implemented in a finite element framework.
U-MLP: MLP-based ultralight refinement network for medical image segmentation

The convolutional neural network (CNN) and Transformer play an important role in computer-aided diagnosis and intelligent medicine. However, CNN cannot obtain long-range dependence, and Transformer has shortcomings in computational complexity and a large number of parameters. Recently, compared with CNN and Transformer, the Multi-Layer Perceptron (MLP)-based medical image processing network can achieve higher accuracy with smaller computational and parametric quantities. Hence, in this work, we propose an encoder-decoder network, U-MLP, based on the ReMLP block. The ReMLP block contains an overlapping sliding window mechanism and a Multi-head Gate Self-Attention (MGSA) module, where the overlapping sliding window can extract local features of the image like convolution, then combines MGSA to fuse the information extracted from multiple dimensions to obtain more contextual semantic information. Meanwhile, to increase the generalization ability of the model, we design the Vague Region Refinement (VRRE) module, which uses the primary features generated by network inference to create local reference features, thus determining the pixel class by inferring the proximity between local features and labeled features. Extensive experimental evaluation shows U-MLP boosts the performance of segmentation. In the skin lesions, spleen, and left atrium segmentation on three benchmark datasets, our U-MLP method achieved a dice similarity coefficient of 88.27%, 97.61%, and 95.91% on the test set, respectively, outperforming 7 state-of-the-art methods.
Efficient dynamic feature adaptation for cross language sentiment analysis with biased adversarial training

Fine-tuning a large multi-lingual pretrained language model demonstrates impressive results in cross-language understanding. However, it still suffers when the training and test data have different distributions owing to various languages and domains. On one hand, annotating target data for different languages or domains is time-consuming or infeasible. On the other hand, fine-tuning a large language model often incurs high computational costs. In this paper, we aim to develop an efficient and effective adaptation framework for cross-language sentiment analysis based on a fixed pretrained multi-lingual model. Specifically, we propose a Dynamic Feature Adaptation (DFA) module to fully leverage the features from different layers of the pretrained model such that its large backbone is not involved during adaptation training. Furthermore, we observe that traditional adversarial domain adaptation training could compromise the discriminative information of the model by pushing source and target features towards each other. The source features obtained with supervised training preserved the discriminability of the model, which should be less affected. Therefore, we propose a novel Biased Adversarial Training (BAT) method, that encourages only the target features towards source features. Extensive experimental results on various cross-lingual and cross-lingual-and-domain sentiment analysis tasks demonstrate the superiority of the proposed framework. Additionally, several ablation studies are conducted to validate the effectiveness of each proposed module.
Enhanced dynamic feature representation learning framework by Fourier transform for domain generalization

Domain generalization is an active research topic for mitigating distribution shifts and improving model generalization by transferring domain-invariant knowledge learned from source domains to a target domain. Most existing methods that use static parameters attempt to obtain domain-invariant knowledge by aligning all the sample distributions. However, such static models lack adaptability, and it is difficult to resolve cross-domain conflicts. Moreover, these approaches align all representations to a latent space, where the aligned non-transferable features are prone to cause negative transfer and reduce the discriminability of the algorithms. To solve these problems, we develop an enhanced dynamic feature representation learning framework (DFRL) using Fourier transform for domain generalization. The framework consists of two parts: Fourier-based dynamic residual feature representation learning (DRFR) and dynamic factor. Specifically, DRFR is implemented by developing a dynamic residual module based on Fourier-based sample amplitude mixing to capture the coarse-grained and fine-grained dynamic feature knowledge. Then, we construct a dynamic factor to quantitatively trade-off the alignment and discriminability, preventing model performance degradation due to excessive of pursuit of any aspects. Extensive experiments have been carried on several standard public benchmarks, including Digits-DG, VLCS, PACS and Office-Home, which demonstrate that our framework achieves significant results.
Improving Differentiable Architecture Search via self-distillation

Differentiable Architecture Search (DARTS) is a simple yet efficient Neural Architecture Search (NAS) method. During the search stage, DARTS trains a supernet by jointly optimizing architecture parameters and network parameters. During the evaluation stage, DARTS discretizes the supernet to derive the optimal architecture based on architecture parameters. However, recent research has shown that during the training process, the supernet tends to converge towards sharp minima rather than flat minima. This is evidenced by the higher sharpness of the loss landscape of the supernet, which ultimately leads to a performance gap between the supernet and the optimal architecture. In this paper, we propose Self-Distillation Differentiable Neural Architecture Search (SD-DARTS) to alleviate the discretization gap. We utilize self-distillation to distill knowledge from previous steps of the supernet to guide its training in the current step, effectively reducing the sharpness of the supernet’s loss and bridging the performance gap between the supernet and the optimal architecture. Furthermore, we introduce the concept of voting teachers, where multiple previous supernets are selected as teachers, and their output probabilities are aggregated through voting to obtain the final teacher prediction. Experimental results on real datasets demonstrate the advantages of our novel self-distillation-based NAS method compared to state-of-the-art alternatives.
Shortest path planning via the rapid Dhouib-Matrix-SPP (DM-SPP) method for the autonomous mobile robot

Planning the shortest path for an autonomous mobile robot is a challenging problem. It consists in designing for the robot the optimal path to join the ending location from a starting position with avoiding the obstacle spaces. In this paper, the innovative greedy Dhouib-Matrix-SPP (DM-SPP) method is proposed to address this problem in a static environment with a grid model representation. At first, the grid model is codified as a graph using the eight movement directions where the maximal time complexity of DM-SPP for this problem is O(9*n) with n is the number of nodes in the graph. Then, DM-SPP uses this graph to rapidly search for the shortest path between the initial and the goal nodes. Hence, DM-SPP is developed with Python language and the planning path is illustrated using the Matplotlib library. Finally, experiments on eleven grid models with comparison on twelve metaheuristics (such as Particle Swarm Optimization integrated with Gray Wolf Optimization, Ant Colony Optimization combined with A* method and other metaheuristics) are studied and the results demonstrated the rapidity and the effectiveness of DM-SPP in designing the shortest path for the autonomous mobile robot. Clearly, DM-SPP improves massively the running time and the quality compared to all the other techniques and it can be concluded that DM-SPP is the fastest Artificial Intelligence method for the Mobile Robot Path Planning Problem.
A novel method for resampling detection in highly compressed JPEG images through BAR using a deep learning technique

As JPEG has become a prevalent compression standard, identifying manipulations in JPEG images has become increasingly necessary. However, many of the inspected images are highly compressed, making it challenging for forensics tools and methodologies to detect clues of resampling. To overcome this issue, a new method has been proposed that uses a deep convolutional neural network (CNN) to detect image resampling by removing blocking artifacts (BAR) from recompressed images without altering resampling clues. The proposed framework has three stages: noise residual extraction, feature extraction, and classification. Initially, the image content and BAR are suppressed, and noise residuals from recompressed images are extracted using ten convolutional layers without pooling layers. The feature extraction phase then learns resampling features from the noise residuals in an adaptive manner. Deep resampling characteristics are processed through the fully connected layer to identify whether the input image is double compressed and resized (DCR) or double compressed and unresized (DCUR). The proposed deep CNN has been tested on heterogeneous datasets such as RAISE and DRESDEN, identifying resampling with a mean accuracy of 91.85% and 90.21% in cross-dataset settings. In addition, detecting downscaling with more than an accuracy of 87.98% in highly recompressed images indicates that it outperforms state-of-the-art approaches. Furthermore, the proposed method yields notable results in estimating the resampling factor in recompressed images.
ATSA: An Adaptive Tree Seed Algorithm based on double-layer framework with tree migration and seed intelligent generation

The Tree Seed Algorithm (TSA) is a popular meta-heuristic algorithm that excels in solving optimization problems. However, TSA has some structural deficiencies and certain limitations manifested as limited population diversity, inadequate information utilization, and local optima stagnation. This paper proposes the Adaptive Tree Seed Algorithm (ATSA) to solve these mentioned shortcomings with three enhancements. First, a double-layer framework is designed to achieve a more effective balance between exploration and exploitation with the feedback mechanism. Second, based on this framework, an evolutionary classifier is designed to generate seeds intelligently for enriching population diversity. Third, a migration mechanism is proposed to avoid falling into local optima. The performance of ATSA is tested by 30 benchmark functions on IEEE CEC 2017 in comparison with 15 algorithms (TSA, STSA, TSASC, fb_TSA, EST-TSA, GWO, PSO, ABC, BOA, RSA, HBA, SMA, HHO, INFO, RUN). In addition, 5 engineering design problems are evaluated to illustrate applicability. All experimental results demonstrate that the proposed ATSA significantly outperforms other algorithms, especially in solving high-dimensional and complex problems, as verified by the Wilcoxon Signed-Rank test. The outstanding performance of ATSA makes it a promising candidate for addressing challenges in the field of swarm intelligence.
Modular DFR: Digital Delayed Feedback Reservoir Model for Enhancing Design Flexibility

A delayed feedback reservoir (DFR) is a type of reservoir computing system well-suited for hardware implementations owing to its simple structure. Most existing DFR implementations use analog circuits that require both digital-to-analog and analog-to-digital converters for interfacing. However, digital DFRs emulate analog nonlinear components in the digital domain, resulting in a lack of design flexibility and higher power consumption. In this paper, we propose a novel modular DFR model that is suitable for fully digital implementations. The proposed model reduces the number of hyperparameters and allows flexibility in the selection of the nonlinear function, which improves the accuracy while reducing the power consumption. We further present two DFR realizations with different nonlinear functions, achieving 10× power reduction and 5.3× throughput improvement while maintaining equal or better accuracy.