Reviewerly/exHarmony huggingface dataset:
Efficient path planning for automated guided vehicles using A* (Astar) algorithm incorporating turning costs in search heuristic

The path planned for an automated guided vehicle in, for example, a production facility is often the lowest-cost path in a (weighted) geometric graph. The weights in the graph may represent a distance or travel time. Sometimes turning costs are taken into account; turns (and decelerations before and accelerations after turning) take time, so it is desirable to minimise turns in the path. Several well-known algorithms can be used to find the lowest-cost path in a geometric graph. In this paper, we focus on the A∗ algorithm, which uses an (internal) search heuristic to find the lowest-cost path. In the current literature, generally, either turning costs are not taken into account in the heuristic or the heuristic can only be used for specific graph structures. We propose an improved heuristic for the A∗ algorithm that can be used to find the lowest-cost path in a geometric graph with turning costs. Our heuristic is proven to be monotone and admissible. Moreover, our heuristic provides a higher lower bound estimate for the actual costs compared to other heuristics found in the literature, causing the lowest-cost path to be found faster (i.e. with less iterations). We validate this through an extensive comparative study.
Adaptive barrier Lyapunov function-based obstacle avoidance control for an autonomous underwater vehicle with multiple static and moving obstacles

This study focuses on addressing the waypoints-based path-following problem of an autonomous underwater vehicle (AUV) within multiple static and moving obstacles environment. In order to guarantee the safety of AUV obstacle avoidance in complex marine environment, it requires an effective guidance law to plan a safe path and a robust control method to force the AUV to follow safe path to avoid obstacles. Based on the dynamical virtual ship principle, an improved guidance law is developed with the dynamic virtual AUV (DVA) principle. This guidance law generates real-time commands for AUV to follow reference path and avoid obstacle. Furthermore, by combining adaptive laws and barrier Lyapunov function, an effective robust control algorithm is proposed to drive the AUV to follow the DVA. The adaptive law is used to approximate the dynamic uncertainties and external disturbances. The barrier Lyapunov function (BLF) is employed to guarantee the tracking errors within the preset bounds and thus ensure the safety of the AUV during the obstacle avoidance process. Finally, simulations are performed to verify the effectiveness and feasibility of the proposed control strategy.
Fusion of visible-to-near-infrared and mid-infrared spectroscopy to estimate soil organic carbon

Spectral techniques such as visible-to-near-infrared (VIS–NIR) and mid-infrared (MIR) spectroscopies have been regarded as effective alternatives to laboratory-based methods for determining soil organic carbon (SOC). Research to explore the potential of the fusion of VIS–NIR and MIR absorbance for improving SOC prediction is needed, since each individual spectral range may not contain sufficient information to yield reasonable estimation accuracy. Here, we investigated two data fusion strategies that differed in input data, including direct concatenation of full-spectral absorbance and concatenation of selected predictors by optimal band combination (OBC) algorithm. Specifically, continuous wavelet transform (CWT) was adopted to optimize the spectral data before and after data fusion. Prediction models for SOC were developed using partial least squares regression. Results demonstrated that estimations for SOC using MIR absorbance (i.e., validation R2 = 0.45–0.64) generally outperformed those using VIS–NIR (i.e., validation R2 = 0.20–0.44). Compared to the raw absorbance counterparts, CWT decomposing could improve the prediction accuracy for SOC, for both the individual absorbance and the fusion of VIS–NIR and MIR absorbance. Among all the models investigated, the combinational use of VIS–NIR and MIR using OBC fusion at CWT scale of 1 yielded the optimal prediction, providing the highest validation R2 of 0.66. This model with 10 selected spectral parameters as input is of small total data volume, large processing speed and efficiency, confirming the potential of OBC in fusing both types of spectral data. In summary, CWT decomposing and OBC strategy are powerful algorithms in analyzing the spectral data, and allow the VIS–NIR and MIR spectral fusion models to improve the SOC estimation.
Revealing the solar energy potential by integration of GIS and AHP in order to compare decisions of the land use on the environmental plans

Due to the depletion of fossil fuel resources, interest in renewable energy sources is steadily increasing in the world. Turkey, is very rich in terms of its solar energy potential enabling it to use the sun as a means of generating energy. Thanks to marginal lands such as non-residential areas, agricultural areas and industrial areas, electricity production is provided by means of renewable energy sources. Since land is a scarce resource, optimal use of it is a spatial necessity. In developed societies its usage is determined in the environmental plan based on criteria related to industry, agriculture etc. and presented to decision makers for their approval. However, in these plans, natural energy potentials such as the sun and wind can often be ignored. This lack of planning hinders the effective utilization of the solar energy potential. A case study has been carried out to better understand this issue. The most commonly used Solar Power Plant criteria have been determined with the literature review, an informal interview with SPP experts and the authors’ experiences and these criteria have been weighted with the Analytical Hierarchy Process method. A cost-surface map has been produced for the study area and the environmental plan has been associated with it. It has been determined that areas that have a very high solar energy potential in the cost-surface map have been planned for urban, commercial or different purposes in the environmental plans. In spite of the fact that the solar energy potential atlas shows the same solar potential for the study area, there are still huge differences in terms of solar energy potential among some regions in the study area. For this reason, it is not logical to build SPP facilities based only on the solar energy potential atlas. It is suggested to determine natural energy potential spatially throughout the country and consider it before the environmental plans are prepared. In addition, those wanting to research this subject are recommended to conduct studies in areas promoting alternative methods according to alternative site selection criteria including geographical and soil characteristics.
Titanite as a petrogenetic indicator

Abstract The trace element composition of titanite reflects the temperature, pressure and bulk‐rock composition from which it crystallized. Two geochemical discriminators are identified by applying a support vector matrix (a machine learning classifier) to a global compilation of titanite trace element data. The compilation comprises more than 7,400 analyses of igneous and metamorphic titanite from a wide range of bulk‐rock compositions. First, igneous and metamorphic titanite are differentiated on the basis of Al/Fe and ∑LREE content. Variation in Th/U aids differentiation in composite settings, such as igneous rocks overprinted by metamorphism. Second, titanite from felsic host rocks is distinguished by low Zr/Y and high Fe content. For titanite from igneous rocks, this effectively discriminates titanite from mafic and felsic rocks. Together, these geochemical discriminators may be used to characterize the crystallization setting and host‐rock of an unknown titanite, a valuable tool with applications including petrochronology and detrital provenance analysis.
A comprehensive review of federated learning for COVID‐19 detection

The coronavirus of 2019 (COVID-19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID-19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID-19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID-19 detection using chest X-ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID-19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.
Weakly-supervised deep learning for ultrasound diagnosis of breast cancer

Conventional deep learning (DL) algorithm requires full supervision of annotating the region of interest (ROI) that is laborious and often biased. We aimed to develop a weakly-supervised DL algorithm that diagnosis breast cancer at ultrasound without image annotation. Weakly-supervised DL algorithms were implemented with three networks (VGG16, ResNet34, and GoogLeNet) and trained using 1000 unannotated US images (500 benign and 500 malignant masses). Two sets of 200 images (100 benign and 100 malignant masses) were used for internal and external validation sets. For comparison with fully-supervised algorithms, ROI annotation was performed manually and automatically. Diagnostic performances were calculated as the area under the receiver operating characteristic curve (AUC). Using the class activation map, we determined how accurately the weakly-supervised DL algorithms localized the breast masses. For internal validation sets, the weakly-supervised DL algorithms achieved excellent diagnostic performances, with AUC values of 0.92-0.96, which were not statistically different (all Ps > 0.05) from those of fully-supervised DL algorithms with either manual or automated ROI annotation (AUC, 0.92-0.96). For external validation sets, the weakly-supervised DL algorithms achieved AUC values of 0.86-0.90, which were not statistically different (Ps > 0.05) or higher (P = 0.04, VGG16 with automated ROI annotation) from those of fully-supervised DL algorithms (AUC, 0.84-0.92). In internal and external validation sets, weakly-supervised algorithms could localize 100% of malignant masses, except for ResNet34 (98%). The weakly-supervised DL algorithms developed in the present study were feasible for US diagnosis of breast cancer with well-performing localization and differential diagnosis.
Insulator detection and damage identification based on improved lightweight YOLOv4 network

Aiming at the problem that the detection and damage recognition algorithm of insulator aerial images is difficult to be applied to the embedded platform, this paper proposed an improved Tiny-YOLOv4 lightweight target detection network algorithm. This algorithm combined the Self-Attention mechanism and ECA-Net (Efficient Channel Attention Neural Networks), which could greatly reduce the complexity of the original YOLOv4 algorithm, and the model size is 24.9 MB. Under the condition of ensuring the detection accuracy (>91%), the detection speed is as high as 94FPS. It is transplanted to the Jetson Xavier NX embedded platform, and the average detection speed reaches 22FPS, which effectively meets the real-time detection requirements in the power inspection.
Tracking knowledge proficiency of students with calibrated Q-matrix

With the emergence of intelligent educational systems, numerous research works are dedicated to Knowledge Tracing (KT), which refers to the issue of diagnosing students’ changing knowledge proficiency in exercises. Recent developments in KT have yielded immense success on this task and they mainly use sophisticated and flexible deep neural network-based models to fully exploit the interaction information between students and response logs. However, these models either ignore the significance of Q-matrix associated exercises with knowledge concepts (KCs) or fail to avoid the subjective tendency of experts within the Q-matrix. To tackle these problems, in this paper, we devise a novel Calibrated Q-matrix-based Knowledge Tracing (CQKT) framework to track knowledge proficiency of students dynamically in KT. To be specific, for the original Q-matrix, we primarily strive to capture the high-order connectivity between exercises and KCs to obtain potential KCs of each exercise by utilizing graph convolution network. Then, three Q-matrix calibration methods based on a pairwise Bayesian treatment equipped with potential KCs are adopted to refine and calibrate the raw Q-matrix so that the subjective tendency of the Q-matrix defined by domain experts can be weakened. After that, the embedding of each exercise aggregated the calibrated Q-matrix with historical student interactions is injected into the Long Short-Term Memory (LSTM) network to trace students’ knowledge states. Extensive experiments are conducted on three real-world benchmark datasets and the results show the superiority of CQKT. In particular, we demonstrate its practicability via applying it to three fundamental educational tasks, including score prediction, knowledge state estimation, and diagnosis result visualization.
A one-layer recurrent neural network for nonsmooth pseudoconvex optimization with quasiconvex inequality and affine equality constraints

As two important types of generalized convex functions, pseudoconvex and quasiconvex functions appear in many practical optimization problems. The lack of convexity poses some difficulties in solving pseudoconvex optimization with quasiconvex constraint functions. In this paper, we propose a one-layer recurrent neural network for solving such problems. We prove that the state of the proposed neural network is convergent from the feasible region to an optimal solution of the given optimization problem. We show that the proposed neural network has several advantages over the existing neural networks for pseudoconvex optimization. Specifically, the proposed neural network is applicable to optimization problems with quasiconvex inequality constraints as well as affine equality constraints. In addition, parameter matrix inversion is avoided and some assumptions on the objective function and inequality constraints in existing results are relaxed. We demonstrate the superior performance and characteristics of the proposed neural network with simulation results in three numerical examples.
DeepASLR: A CNN based human computer interface for American Sign Language recognition for hearing-impaired individuals

Sign language is an essential means of communication for hearing-impaired individuals. We aimed to develop an American sign language recognition dataset and use it in the deep learning model which depends on neural networks to interpret gestures of sign language and hand poses to natural language. In this study, we developed a dataset and a Convolutional Neural Network-based sign language interface system to interpret gestures of sign language and hand poses to natural language. The neural network developed in this study is a Convolutional Neural Network (CNN) which enhances the predictability of the American Sign Language alphabet (ASLA). This research establishes a new dataset of the American Sign Language alphabet which takes into consideration various conditions such as lighting and distance. The dataset created in this study is a new addition in the field of sign language recognition (SLR). This dataset may be used to develop SLR systems. Furthermore, our research compares the results of our dataset with two different datasets from other studies. The other datasets have invariant scene conditions, but our suggested CNN model demonstrated high accuracy for all the tested datasets. Despite the different conditions and volume of the new dataset, it achieved 99.38% accuracy with excellent prediction and small loss (0.0250). The proposed system may be considered a promising solution in medical applications that use deep learning with superior accuracy. Moreover, our dataset was created under variable conditions which increases the number of contributions, comparisons, results and conclusions in the field of SLR and may enhance such systems.
CES Blocks—A Novel Chaotic Encryption Schemes-Based Blockchain System for an IoT Environment

With the advent of the Internet of Things (IoT), smart devices have now changed their dimensions to provide applications in different domains such as medical, agriculture, and Industry 4.0.Although IoT provides more diversified applications, enhancing the security in IoT remains on the darker side of the research.Traditional IoT systems involve a third party to secure sensitive data during transmission in an IoT environment which can lead to complex and serious problems.To overcome security issues and eradicate third-party involvement, Blockchain technology is the modern-day solution in an IoT environment.In the context of a Secured IoT system, we proposed a novel chaotic encryption-based blockchain-IoT architecture to clinch the security and privacy of data.Since smart sensors and image sensors are used widely in an IoT environment, the proposed scheme was tested with different image sets to evaluate performance metrics such as Number of Pixel Change Rate (NPCR), Unified Averaged Changed Intensity (UACI), Correlation Coefficients, and entropy under different attack scenarios.We obtained an NPCR of 99.65%, a UACI of 34%, and an entropy value close to 8.These values incite that the novel chaotic encryption-based blockchain-IoT architecture will be safe from IoT attacks.Results showed that integrating chaotic encrypted blockchain architecture with IoT could be more effective in defending attacks.
Women empowerment and environmental sustainability in Africa

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Attacking the trust machine: Developing an information systems research agenda for blockchain cybersecurity

Blockchain-based systems become increasingly attractive targets for cybercrime due to the rising amount of value transacted in respective systems. However, a comprehensive overview of existing attack vectors and a directive discussion of resulting research opportunities are missing. Employing a structured literature review, we extract and analyze 87 relevant attacks on blockchain-based systems and assign them to common attack vectors. We subsequently derive a research framework and agenda for information systems research on the cybersecurity of blockchain-based systems. We structure our framework along the users, developers, and attackers of both blockchain applications and blockchain infrastructure, highlighting the reciprocal relationships between these entities. Our results show that especially socio-technical aspects of blockchain cybersecurity are underrepresented in research and require further attention.
One-step multi-view spectral clustering with cluster label correlation graph

Recently, one-step clustering methods have shown good performance. However, very few one-step approaches have addressed the multi-view case, where an instance may have multiple representations. Data can be represented with multiple heterogeneous views. Clustering with multiple views faces the challenge of how to combine all the different views. A general scheme is to represent the views by view-based graphs and/or a consensus graph. Graphs can be well suited for clustering problems since they can capture the local and global structure of the data. In this paper, we present a novel approach to one-step graph-based multi-view clustering. In contrast to existing graph-based one-step clustering methods, our proposed method introduces two key innovations. First, we build an additional graph by using the cluster label correlation to the graphs associated with the data space. Second, a smoothing constraint is exploited to constrain the cluster-label matrix and make it more consistent with the original data graphs as well as with and label graphs. Experimental results on several public datasets show the efficiency of the proposed approach. All cluster evaluation metrics show significant improvement by applying our method to different types and sizes of datasets. The average improvement (across all datasets) is the difference between the indicator obtained by our approach and the indicator obtained by the most competitive method. The average improvement is approximately 4%, 2%, 3%, and 2% for the Accuracy indicator, the Normalized Mutual Information indicator, the Purity indicator, and the Adjusted Rand index, respectively.
Decomposed POMDP Optimization-Based Sensor Management for Multi-Target Tracking in Passive Multi-Sensor Systems

This paper presents an efficient information-theoretic sensor management method to maximize the performance of the passive multi-sensor system for multi-target tracking. We model the multi-target state as a generalized labeled multi-Bernoulli (GLMB) random finite set and formulate the dynamic sensor selection process as a partially observable Markov decision process (POMDP). The optimization objective is to maximum the information gain obtained from the observed data, which is measured by the Cauchy-Schwarz divergence. This is accomplished with two main technical innovations. The first is a tractable decomposed POMDP based sensor selection solution, in which the informative sensors are selected sequentially from the candidates based on the Cauchy-Schwarz divergence. The second is a novel dual-stage multi-sensor fusion strategy based on the iterated-corrector GLMB filter. Since the uncertainty of the passive multi-sensor system is generally large, the performance of the iterated-corrector scheme can be greatly influenced by the order of sensor updates. To fix this problem, the selected sensors are ranked in order of the Cauchy-Schwarz divergence obtained in sensor selection, followed by the iterated-corrector update. For the multi-sensor fusion, the effect of poor performance sensors is weakened and that of better performance sensors is enhanced. Simulation studies demonstrate the effectiveness and efficiency of the proposed method in challenging passive multi-target tracking scenarios.