Reviewerly/exHarmony huggingface dataset:
A probabilistic approach towards source level inquiries for forensic soil examination based on mineral counts

Forensic soil examination has a well-established foundation in forensic science, this is in part due to the widely varied and complex nature of soil. Within this domain, mineral suite studies are a commonly utilized tool in soil examination. However, statistical or probabilistic approaches towards the interpretation of results from such analysis are lacking and this study aims to fill that gap. Soil samples from four different locations in the city of Lausanne, Switzerland were sampled and their mineral fractions, light and heavy of size between 90 and 180 µm, were studied utilizing microscopical methods. First, the light minerals were identified and counted by employing scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). Second, the heavy minerals were identified and counted manually under a polarized light microscope (PLM). The resulting count data were subjected to various multivariate statistical treatments such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and linear discriminant analysis (LDA). These methods assist in identifying pertinent variables and subsequently in building various classification models. The validities of these models were then tested and evaluated using blind tests. Finally, these methods demonstrate how a probabilistic approach can be taken in the interpretation of the results to answer source level questions.
Stochastic Heuristic Algorithms for Multi-UAV Cooperative Path Planning

Multi-UAV collaborative path planning (MUCPP) is a complex multi-objective optimization problem. Compared with the traditional path planning algorithms, stochastic heuristic algorithm (SHA) can solve this kind of problem more efficiently. In this paper, we first introduce several widely used UAV path planning algorithms. Then, recent works on MUCPP based on SHA are reviewed, where the feature, improvement and application, merits and demerits of those proposed algorithms are discussed. Finally, the challenges of path planning for multiple UAVs are put forward and the future development trend is prospected from the aspects of theoretical and applied research.
Doctor Patient Assistance System Using Artificial Intelligence

The world is digitizing, and many chores, such as bank transactions and internet payments, are being digitized as a result. With the advancement of technology, we may also consider digitizing doctor-patient interactions in order to avoid personally consulting a doctor. This paper suggests a diagnostic conversation by utilizing artificial intelligence to better identify the illness before a doctor's appointment and include specific knowledge regarding the disease. This would increase the insurance expenses, improve the exposure of a larger population to treatment, and expand the exposure of a broader community to medical assistance. Some chatbots function like a medical guidebook and will help patients/doctors search for knowledge about any illness, but to profit entirely from a chatbot, all necessary tools must be used in order to support them identify and include the details required for disease detection. Both remedies and signs for the actual disease are not known by the average layman. With certain things that do not create any pain, this chatbot does not travel to a hospital directly with checks, because it might be a waste of time. The chatbot uses NLP to master the human language, which aims to grasp patients' health issues. The patient will raise questions dependent on healthcare without needing to travel to a doctor or clinic.
Service Relationship Orchestration: Lessons Learned From Running Large Scale Smart City Platforms on Kubernetes

Smart cities aim to make urban life more enjoyable and sustainable but their highly heterogeneous and distributed context creates unique operational challenges. In such an environment, multiple companies work together with government on applications and data streams spanning several management domains. Deploying these applications, each of which consists of several connected services, and maintaining an overview of application topologies remains difficult. Even though cloud modelling languages have been proposed to solve similar issues, they are not well fit for such a heterogeneous environment because they often require an “all or nothing” approach. Moreover, cloud modelling languages add an additional abstraction layer which rarely supports all features of the underlying platform and make it harder to reuse existing knowledge and tools. This research defines service relationships as the key element to modelling applications as topologies of services. We use this definition to pinpoint what is lacking in the state of the art Kubernetes orchestration tools and provide a blueprint for how relationship support can be added to any orchestrator. We present “orcon”, a proof of concept orchestrator which extends the Kubernetes API to allow managing relationships between services by adding metadata to service definitions. Our evaluation shows this orchestrator enables lifecycle synchronization and configuration change propagation with an overhead of only 0.44 seconds per service.
Ant-Based Hyper-Heuristics for the Movie Scene Scheduling Problem

The paper provides a study of the use of hyper-heuristics on the movie scene scheduling problem. In particular, the paper extends the definition of the movie scene scheduling problem to include a new method of calculating the solution quality. The study is also a novel application of hyper-heuristics to the movie scene scheduling problem and demonstrates one potential method for using hyper-heuristics as a solution method for the given problem. This includes the development of new low-level heuristics for the problem that are presented as well. The study showed that hyper-heuristics could be applied to the problem doing better than a random approach but that work would need to be done on improving the low-level perturbative heuristics. The study also showed that the new formulation would be tenable as a problem definition with little change to the underlying problem itself.
DESIGN OF WIRELESS SENSOR NETWORK USING STATISTICAL FRACTAL MEASUREMENTS

To further expand the application range of fractal theory and promote the application of sensors in the era of intelligent interconnection, we design a wireless sensor network (WSN) using statistical fractal measurements in simulation. Fractal features and network complexity of the WSN are analyzed. Besides, the related influencing factors of the WSN transmission success rate are explored. When the box size is smaller than 100, the relationship between the box size and the number of boxes tends to be a straight line, the relationship between them is more stable when the number of nodes increases gradually. Moreover, the degree distribution of nodes presents more obvious symmetry with the increase of network nodes. The simulation results of the network transmission success rate show that under certain other conditions, with the increase of node hops, the network transmission success rate can be promoted significantly. When the transmission failure probability [Formula: see text] is 0.1 and hop count is 6, the transmission success rate can reach more than 90%. This exploration can provide a scientific and effective reference for the follow-up research of the fractal theory.
Comparative Study of Five Metaheuristic Algorithms for Team Formation Problem

This paper presents a comparative study of five metaheuristic algorithms, namely, salp swarm algorithm (SSA), owl search algorithm (OSA), sooty tern optimization algorithm (STOA), squirrel search algorithm (SqSA), and crow search algorithm (CSA) adopted in the Covid19 team formation (CTF) problem. The performance comparison of these algorithms is conducted by executing each algorithm twenty times to ensure the statistical significance. The study considers the minimum number of experts and the minimum team formation cost in defining the objective function. The CSA was found to be the more effective metaheuristic algorithm for the Covid19 team formation problem from the optimal results in terms of overall solution quality and runtime efficiency.
Image Stitching Method of Aerial Image Based on Feature Matching and Iterative Optimization

This paper studies the mismatch of the overlap areas during the stitching process of Unmanned Aerial Vehicle (UAV) remote sensing images. For solving this problem, the feature points are filtered first by combining double matching with the Random sampling consensus (RANSAC) method. Secondly, to ensure the consistency of the projection relationship between each image and panoramic photograph, we propose a local stitching method. In order to avoid the mistake of image tilt caused by perspective changes accumulate as the number of images increases, we have established an image rotation coordinate system and limited the relationship between images to translation and rotation. The relative position of the coordinate origin is used to represent the translation distance, and the optimal rotation angle is solved by iteration. Finally, the overlapping parts of the image are fused by linear weighting. It is verified through the experimental results that the method proposed in this paper can guarantee faster processing speed and higher processing accuracy under the condition of a large number of images, thereby achieving an ideal stitching effect.
A new method for ensemble combination based on adaptive decision making

Ensemble classifiers methods are considered as one of the strongest classification approaches in different applications. A key factor in creating a successful ensemble is to design an optimal combiner that minimizes classification errors. Unfortunately, finding the best fusion methodology that maximizes ensemble performance is a challenge. Most popular ensemble classifiers systems use fixed fusion methods such as average and majority voting. For a given application, there is no clear understanding of why a fusion method might work better than other methods. In this work, decision-making based on Adaptive Power Mean Combiner (APMC) is investigated. A mathematical and experimental framework for ensemble classifier systems based on APMC is proposed. The theoretical results explain why a given algebraic combiner such as average, product, maximum, minimum, and others works best for a given dataset while providing poor results on other datasets. It was shown that selecting an optimal fusion method is strongly dependent on the statistics of base learners’ outputs. Theoretical results for a given ensemble condition show that an improvement in classification accuracy of ∼8% compared to the average fusion method is achieved. Guided by theoretical results, we propose an experimental ensemble-learning algorithm based on surrogate optimization method for searching fusion methods for a given ensemble condition. The performance trend predicted by the mathematical model is validated through several datasets and compared against different fusion methods. Experimental results show the proposed algorithm achieves notable classification results compared to several fusion methods.
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood Filters

Graph convolutional neural networks (GCNNs) are popular deep learning architectures that, upon replacing regular convolutions with graph filters (GFs), generalize CNNs to irregular domains. However, classical GFs are prone to numerical errors since they consist of high-order polynomials. This problem is aggravated when several filters are applied in cascade, limiting the practical depth of GCNNs. To tackle this issue, we present the neighborhood graph filters (NGFs), a family of GFs that replaces the powers of the graph shift operator with k-hop neighborhood adjacency matrices. NGFs help to alleviate the numerical issues of traditional GFs, allow for the design of deeper GCNNs, and enhance the robustness to errors in the topology of the graph. To illustrate the advantage over traditional GFs in practical applications, we use NGFs in the design of deep neighborhood GCNNs to solve graph signal denoising and node classification problems over both synthetic and real-world data.
Shifting Capsule Networks from the Cloud to the Deep Edge

Capsule networks (CapsNets) are an emerging trend in image processing. In contrast to a convolutional neural network, CapsNets are not vulnerable to object deformation, as the relative spatial information of the objects is preserved across the network. However, their complexity is mainly related to the capsule structure and the dynamic routing mechanism, which makes it almost unreasonable to deploy a CapsNet, in its original form, in a resource-constrained device powered by a small microcontroller (MCU). In an era where intelligence is rapidly shifting from the cloud to the edge, this high complexity imposes serious challenges to the adoption of CapsNets at the very edge. To tackle this issue, we present an API for the execution of quantized CapsNets in Arm Cortex-M and RISC-V MCUs. Our software kernels extend the Arm CMSIS-NN and RISC-V PULP-NN to support capsule operations with 8-bit integers as operands. Along with it, we propose a framework to perform post-training quantization of a CapsNet. Results show a reduction in memory footprint of almost 75%, with accuracy loss ranging from 0.07% to 0.18%. In terms of throughput, our Arm Cortex-M API enables the execution of primary capsule and capsule layers with medium-sized kernels in just 119.94 and 90.60 ms, respectively (STM32H755ZIT6U, Cortex-M7 @ 480 MHz). For the GAP-8 SoC (RISC-V RV32IMCXpulp @ 170 MHz), the latency drops to 7.02 and 38.03 ms, respectively.
Differentially Private Federated Learningfor Anomaly Detection in eHealth Networks

Increasing number of ubiquitous devices are being used in the medical field to collect patient information. Those connected sensors can potentially be exploited by third parties who want to misuse personal information and compromise the security, which could ultimately result even in patient death. This paper addresses the security concerns in eHealth networks and suggests a new approach to dealing with anomalies. In particular we propose a concept for safe in-hospital learning from internet of health things (IoHT) device data while securing the network traffic with a collaboratively trained anomaly detection system using federated learning. That way, real time traffic anomaly detection is achieved, while maintaining collaboration between hospitals and keeping local data secure and private. Since not only the network metadata, but also the actual medical data is relevant to anomaly detection, we propose to use differential privacy (DP) for providing formal guarantees of the privacy spending accumulated during the federated learning.
Reinforcement Learning for the Privacy Preservation and Manipulation of Eye Tracking Data

In this paper, we present an approach based on reinforcement learning for eye tracking data manipulation. It is based on two opposing agents, where one tries to classify the data correctly and the second agent looks for patterns in the data, which get manipulated to hide specific information. We show that our approach is successfully applicable to preserve the privacy of a subject. For this purpose, we evaluate our approach iterative to showcase the behavior of the reinforcement learning based approach. In addition, we evaluate the importance of temporal, as well as spatial, information of eye tracking data for specific classification goals. In the last part of our evaluation we apply the procedure to further public data sets without re-training the autoencoder nor the data manipulator. The results show that the learned manipulation is generalized and applicable to other data too.
Integrating federated learning with IoMT for managing obesity in smart city

Wearable and ambient sensors have been increasingly used for unique remote health monitoring applications. In this work, we propose an Internet of Medical Things (IoMT) architecture based on automated sensing of physiological and ambient parameters, to detect the risk of obesity in individuals. Timely assessment of obesity risk could improve the quality of life for patients in future as it would help to reduce the probability of chronic diseases such as diabetes. The architecture uses Body Mass Index (BMI) of patient as an input parameter which is a key measure to indicate obesity. Furthermore, we also propose to integrate the proposed IoMT architecture with the emerging scheme of Federated Learning (FL). FL has been used to recommend the appropriate fitness routine for the users. MATLAB simulations for the proposed FL algorithm has been performed and the variations in the loss functions have been observed.
Asset Tracking System Using Blockchain

Asset tracking here refers to the process of tracking a product or a batch of products throughout the supply chain to make sure that the products that reach the customers are tamperproof and original. Depending upon the type of products, every product has either some kind of packaging or a body of it's own which can be used for the purpose of tracking it throughout the supply chain. The ultimate goal of asset tracking system is to trace the route of the products in the supply chain, making sure it is not tampered and if it is, then point out exactly where the tampering took place in the supply chain.
DSGNN: A dynamic and static intentions integrated graph neural network for session-based recommendation

Session-based recommendation, which aims to predict subsequent user actions based on anonymous sessions, plays a significant role in many online services. Existing methods construct each session as a digraph and then capture the rich transition relationship of items by using graph neural networks. However, their ability to obtain the user’s static intentions is insufficient and they suffer from improper combinations of different user intentions. In this study, we propose a dynamic and static intentions integrated graph neural network (DSGNN) for session-based recommendation, in which the user’s intentions captured by a digraph and an undigraph are comprehensively considered to enhance the recommendation performance. The prediction results from the dynamic and static intentions are combined using a lightweight gating network, which reduces the conflict between the two kinds of information. Furthermore, the weighted inner product is designed to alleviate the impact of the value of the item representation vectors. Extensive experiments on three real-world datasets show that the DSGNN outperforms other state-of-the-art methods and demonstrates that the components in our model are effective.
Has knowledge improved economic growth? Evidence from Nigeria and South Africa

This study examines whether knowledge causes economic growth in Africa's two leading economies, Nigeria and South Africa. Using the Vector Autoregressive and Vector Error Correction approaches, the findings show cointegration among the variables. The speed of convergence of the variables to their long‐term mean values is relatively higher for South Africa than for Nigeria. In the short run, it is observed that knowledge unidirectionally Granger causes growth for Nigeria, whereas bidirectional causality is observed for South Africa. The higher correlation between knowledge and growth in South Africa reflects the success of greater investment in education. Nigeria must increase investment in education and modern infrastructure to converge to South Africa's growth trajectory. Moreover, for Nigeria, (1) knowledge unidirectionally Granger cause growth, (2) evidence of bidirectional causality flow is apparent between trade, the economic incentive, and growth, and (3) health unidirectionally Granger cause knowledge. As for South Africa (1) there is bidirectional causality between knowledge, trade openness, and growth, whereas investment and economic incentive, unidirectionally Granger causes growth, (2) investment, trade openness, and health unidirectionally Granger cause knowledge, and (3) economic incentive unidirectionally Granger cause trade openness. In conclusion, this paper argues that a transformed education system can provide the knowledge base essential for promoting and sustaining economic growth.
Efficient experimental characterization of quantum processes via compressed sensing on an NMR quantum processor

We employ the compressed sensing (CS) algorithm and a heavily reduced data set to experimentally perform true quantum process tomography (QPT) on an NMR quantum processor. We obtain the estimate of the process matrix $$\chi $$ corresponding to various two- and three-qubit quantum gates with a high fidelity. The CS algorithm is implemented using two different operator bases, namely the standard Pauli basis and the Pauli-error basis. We experimentally demonstrate that the performance of the CS algorithm is significantly better in the Pauli-error basis, where the constructed $$\chi $$ matrix is maximally sparse. We compare the standard least square (LS) optimization QPT method with the CS-QPT method and observe that, provided an appropriate basis is chosen, the CS-QPT method performs significantly better as compared to the LS-QPT method. In all the cases considered, we obtained experimental fidelities greater than 0.9 from a reduced data set, which was approximately 5–6 times smaller in size than a full data set. We also experimentally characterized the reduced dynamics of a two-qubit subsystem embedded in a three-qubit system and used the CS-QPT method to characterize processes corresponding to the evolution of two-qubit states under various J-coupling interactions.
Gaussian Mixture Model for the Estimation of Multiyear Solar Irradiance Probability Density

The increasing popularity of photovoltaic resources and the connection of solar farms in larger sizes to power distribution networks make it imperative for network designers to assess the variability of available solar resources at a given location. This is normally achieved by attempting to obtain an accurate estimation of the probability density function (pdf) of solar irradiance at the given site. The parametric beta distribution has long been a popular choice in such studies because of its ease of use. However, pdf estimation using parametric functions can lead to inaccurate models and suboptimal decisions being made about the suitability of potential farm site. In this article, a more robust estimation of solar irradiance pdf than that given by the popular beta distribution is obtained by using a Gaussian mixture model (GMM). Using multiyear solar data, the GMM estimate is also compared with a widely used nonparametric kernel density estimation model that employs a common rule-of-thumb bandwidth selection method. Assessments are carried out using a goodness-of-fit test, three error measures, and the coefficient of determination index. Results demonstrate the improved accuracy and robustness of the GMM, which consistently achieves better performance metrics compared with the kernel density estimation (KDE) model and the beta distribution.