Reviewerly/exHarmony huggingface dataset:
Methodological approach and general guidelines to geochemical mapping and background-baseline analysis for environmental assessment: a case study in the Carajás Mineral Province, Brazil

We present this manuscript as a methodological approach and general guidelines for geochemical mapping and background/baseline projects for environmental assessment in tropical areas. A case study was carried out in the Itacaiúnas River watershed (IRW), Eastern Amazon, to fill in a gap in knowledge on the distribution of chemical elements, particularly those potentially toxic, in the near-surface environment of the area. The high-impact results of this research project revealed the need for similar scientific investigation across the globe with the implementation of a systematic methodology. The study shows, for example, the importance of well-planned field activities, multi-medium sampling, analytical methods, laboratory procedures, database construction, and general aspects of data processing and statistical treatment. The importance of this contribution is that it can be used as a reference in support of geospatial analysis in research within the scope of geochemical mapping and background-baseline projects. The database is accessible through a web-based geographic information system front-end; a Geochemical Atlas of the IRW will be available as soon as possible.
Functional classification of bitcoin addresses

A classification model for predicting the main activity of bitcoin addresses based on their balances is proposed. Since the balances are functions of time, methods from functional data analysis are applied; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. The proposed approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.
Augmenting DSM-5 diagnostic criteria with self-attention-based BiLSTM models for psychiatric diagnosis

Most previous studies make psychiatric diagnoses based on diagnostic terms. In this study we sought to augment Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) diagnostic criteria with deep neural network models to make psychiatric diagnoses based on psychiatric notes.We augmented DSM-5 diagnostic criteria with self-attention-based bidirectional long short-term memory (BiLSTM) models to identify schizophrenia, bipolar, and unipolar depressive disorders. Given that the diagnostic criteria for psychiatric diagnosis include a certain symptom profile and functional impairment, we first extracted psychiatric symptoms and functional features with two approaches, including a lexicon-based approach and a dependency parsing approach. Then, we incorporated free-text discharge notes and extracted features for psychiatric diagnoses with the proposed models.The micro-averaged F1 scores of the two automatic annotation approaches were greater than 0.8. BiLSTM models with self-attention outperformed the rule-based models with DSM-5 criteria in the prediction of schizophrenia and bipolar disorder, while the latter outperformed the former in predicting unipolar depressive disorder. Approaches for augmenting DSM-5 criteria with a self-attention-based BiLSTM outperformed both pure rule-based and pure deep neural network models. In terms of classification of psychiatric diagnoses, we observed that the performance for schizophrenia and bipolar disorder was acceptable.This DSM-5-augmented deep neural network models showed good performance in identifying psychiatric diagnoses from psychiatric notes. We conclude that it is possible to establish a model that consults clinical notes to make psychiatric diagnoses comparably to physicians. Further research will be extended to outpatient notes and other psychiatric disorders.
Towards Bilateral Client Selection in Federated Learning Using Matching Game Theory

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several devices. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality and resources across the participants. To overcome this problem, we propose an intelligent client selection approach for federated learning on IoT devices using matching game theory. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several criteria such as accuracy and price, and (2) intelligent matching algorithms that take into account the preferences of both parties in their design. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.
A SYSTEMATIC VIDEO INDEXING APPROACH USING DECISION TREE

A systematic categorization approach for video indexing is presented in this paper.The amount of multimedia information that can be accessed over the internet continues to expand exponentially.Due to this growth and development of multimedia on the internet, particularly videos, there has been a rise in the need for video retrieval.The goal of this work is to discover subsets of characteristics that are suitable for indexing or categorizing video content.The features chosen from the frames of the video have dominant texture characteristics.Several statistical features have been applied for better performance with decision tree classification.The investigation included 1000 videos from different video contents, such as news, cartoon, advertisements, movies, and sports categories.Results show that the overall misclassification rate percentage was below 3%.The capability of indexing the video contents indicates the real power of the proposed system, which can enhance existing indexing services, thereby enriching the tools that are available for video indexing.
A New Data Deletion Scheme for a Blockchain-based De-duplication System in the Cloud

Almost all Cloud Service Providers (CSP) takes a principled approach to the storage and deletion of Customer Data. Most of them have engineered their cloud platform to achieve a high degree of speed, availability, durability, and consistency. Their systems are designed to be optimized for these performance attributes and must be carefully balanced with the necessity to achieve accurate and timely data deletion.many researchers have turn their focus toward data storage and how it will be a challenging task for CSPs in term of storage capacity, data management and security, a considerable number of papers has been published containing new models and technique that will allow data De-duplication in a shared environment but few of them have discussed data deletion.In this paper we will be discussing a new approach that will allow a smart deletion of data stored in the file system as well as its reference in the Blockchain since, by its nature, Blockchains does not allow deletion without violating the Blockchain’s consistency, a preexisting de-duplication system will be our base platform on which we will be working to achieve an accurate and secure data deletion using Blockchain technology while preserving its consistency.
Classification of Wall Following Robot Movements Using Genetic Programming Symbolic Classifier

The navigation of mobile robots throughout the surrounding environment without collisions is one of the mandatory behaviors in the field of mobile robotics. The movement of the robot through its surrounding environment is achieved using sensors and a control system. The application of artificial intelligence could potentially predict the possible movement of a mobile robot if a robot encounters potential obstacles. The data used in this paper is obtained from a wall-following robot that navigates through the room following the wall in a clockwise direction with the use of 24 ultrasound sensors. The idea of this paper is to apply genetic programming symbolic classifier (GPSC) with random hyperparameter search and 5-fold cross-validation to investigate if these methods could classify the movement in the correct category (move forward, slight right turn, sharp right turn, and slight left turn) with high accuracy. Since the original dataset is imbalanced, oversampling methods (ADASYN, SMOTE, and BorderlineSMOTE) were applied to achieve the balance between class samples. These over-sampled dataset variations were used to train the GPSC algorithm with a random hyperparameter search and 5-fold cross-validation. The mean and standard deviation of accuracy (ACC), the area under the receiver operating characteristic (AUC), precision, recall, and F1−score values were used to measure the classification performance of the obtained symbolic expressions. The investigation showed that the best symbolic expressions were obtained on a dataset balanced with the BorderlineSMOTE method with ACC¯±SD(ACC), AUC¯macro±SD(AUC), Precision¯macro±SD(Precision), Recall¯macro±SD(Recall), and F1−score¯macro±SD(F1−score) equal to 0.975×1.81×10−3, 0.997±6.37×10−4, 0.975±1.82×10−3, 0.976±1.59×10−3, and 0.9785±1.74×10−3, respectively. The final test was to use the set of best symbolic expressions and apply them to the original dataset. In this case the ACC¯±SD(ACC), AUC¯±SD(AUC), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1−score¯±SD(F1−Score) are equal to 0.956±0.05, 0.9536±0.057, 0.9507±0.0275, 0.9809±0.01, 0.9698±0.00725, respectively. The results of the investigation showed that this simple, non-linearly separable classification task could be solved using the GPSC algorithm with high accuracy.
Tools for Implementing Distance Learning during the War: Experience of Uzhhorod National University, Ukraine

The article is devoted to describing tools for implementing distance learning in the educational process of higher educational institutions in Ukraine in the war condition. Due: to the introduction of martial law in Ukraine and the need for the digitalization of modern education, distance learning tools contribute to better students knowledge acquisition. The current state of research on distance education in Ukraine is presented, and various kinds, types, methods, and means of training are clarified. Different types of tasks are described, and special attention is paid to test lessons, which is an effective way to check the level of mastering the educational material by students. The analysis is based on the characteristics of Moodle, Learning Apps, and Kahoot! used in the learning process at the Faculty of Philology of Uzhhorod National University. The advantages and disadvantages of using web resources in synchronous and asynchronous learning modes are determined. Distance learning tools are an integral part of achieving educational success under computer literacy and a sufficient level of motivation for all participants in the educational process. The prospects and improvement of the remote learning process by developing strategies using new information and communication technologies in distance learning are determined
IDHUP: Incremental Discovery of High Utility Pattern

&lt;p&gt;As a sub-problem of pattern discovery, utility-oriented pattern mining has recently emerged as a focus of researchers&amp;rsquo; attention and offers broad application prospects. Considering the dynamic characteristics of the input databases, incremental utility mining methods have been proposed, aiming to discover implicit information/ patterns whose importance/utility is not less than a user-specified threshold from incremental databases. However, due to the explosive growth of the search space, most existing methods perform unsatisfactorily under the low utility threshold, so there is still room for improvement in terms of running efficiency and pruning capacity. Motivated by this, we provide an effective and efficient method called IDHUP by designing an indexed partitioned utility list structure and employing four pruning strategies. With the proposed data structure, IDHUP can not only dynamically update the utility values of patterns but also avoid visiting non-occurred patterns. Moreover, to further exclude ineligible patterns and avoid unnecessary exploration, we put forward the remaining utility reducing strategy and three other revised pruning strategies. Experiments on various datasets demonstrated that the designed IDHUP algorithm has the best performance in terms of running time compared to state-of-the-art algorithms.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Resonant Multilevel Amplitude Damping Channels

We introduce a new set of quantum channels: resonant multilevel amplitude damping (ReMAD) channels. Among other instances, they can describe energy dissipation effects in multilevel atomic systems induced by the interaction with a zero-temperature bosonic environment. At variance with the already known class of multilevel amplitude damping (MAD) channels, this new class of maps allows the presence of an environment unable to discriminate transitions with identical energy gaps. After characterizing the algebra of their composition rules, by analyzing the qutrit case, we show that this new set of channels can exhibit degradability and antidegradability in vast regions of the allowed parameter space. There we compute their quantum capacity and private classical capacity. We show that these capacities can be computed exactly also in regions of the parameter space where the channels aren&amp;apos;t degradable nor antidegradable.
The influence of creative self-efficacy, creative self-identity, and creative process engagement on innovative behaviour

Given that creativity and innovation is a sine qua non for any living organism, the calls for increasing research has got momentum in this landscape. The present study attempts to scrutinise the impact of creative self-efficacy (CSE) and creative self-identity (CSI) on employees' creative process engagement (CPE) as well as innovative behaviour (IB). Deductive reasoning approach was followed and data were collected using self-administered and other administered survey methods from small and medium enterprises listed in the SME foundation of Bangladesh. The current study used structural equation modelling, SmartPLS3, to estimate the results. The results reveal that the association of CSE and CSI with IB is not supported (p > 0.05). In contrast, the relationship between CSI and CPE, CSE and CPE, CPE and IB are statistically significant. The study advances the current literature by providing additional insights into the impact of CSE and CSI on employees' CPE and on IB.
Fitness and Distance Based Local Search With Adaptive Differential Evolution for Multimodal Optimization Problems

Local search has been regarded as a promising technique in multimodal algorithms to refine the accuracy of found multiple optima. However, how to execute the local search operations precisely on the found global optima and avoid the meaningless local search operations on local optima or found similar areas is still a challenge. In this paper, we propose a novel local search technique based on the individual information from two aspects, termed as fitness and distance based local search (FDLS). The fitness information can avoid the ineffective local search operations on the local optima, while the distance information can avoid the meaningless local search operations on the similar areas. These two kinds of information act in different roles and complement each other, which ensures that the local search is executed in different (ensured by distance information) and promising (ensured by fitness information) areas, leading to successful local search. Based on this, we design an adaptive DE (ADE) with adaptive parameters scheme and apply FDLS to ADE, termed as FDLS-ADE. Experimental results on the CEC2015 multimodal competition show the effectiveness and superiority of the FDLS-ADE, including comparisons with the winner of the CEC2015 multimodal competition. Furthermore, compared with other multimodal algorithms, the performance of the FDLS-ADE is seen relatively insensitive to niching parameters. Besides, experiments conducted also show that the FDLS can be applied to other multimodal algorithms easily and can further improve their performance. Finally, an application to a real-world nonlinear equations system further illustrates the applicability of the FDLS-ADE.
ResSKNet-SSDP: Effective and Light End-To-End Architecture for Speaker Recognition

In speaker recognition tasks, convolutional neural network (CNN)-based approaches have shown significant success. Modeling the long-term contexts and efficiently aggregating the information are two challenges in speaker recognition, and they have a critical impact on system performance. Previous research has addressed these issues by introducing deeper, wider, and more complex network architectures and aggregation methods. However, it is difficult to significantly improve the performance with these approaches because they also have trouble fully utilizing global information, channel information, and time-frequency information. To address the above issues, we propose a lighter and more efficient CNN-based end-to-end speaker recognition architecture, ResSKNet-SSDP. ResSKNet-SSDP consists of a residual selective kernel network (ResSKNet) and self-attentive standard deviation pooling (SSDP). ResSKNet can capture long-term contexts, neighboring information, and global information, thus extracting a more informative frame-level. SSDP can capture short- and long-term changes in frame-level features, aggregating the variable-length frame-level features into fixed-length, more distinctive utterance-level features. Extensive comparison experiments were performed on two popular public speaker recognition datasets, Voxceleb and CN-Celeb, with current state-of-the-art speaker recognition systems and achieved the lowest EER/DCF of 2.33%/0.2298, 2.44%/0.2559, 4.10%/0.3502, and 12.28%/0.5051. Compared with the lightest x-vector, our designed ResSKNet-SSDP has 3.1 M fewer parameters and 31.6 ms less inference time, but 35.1% better performance. The results show that ResSKNet-SSDP significantly outperforms the current state-of-the-art speaker recognition architectures on all test sets and is an end-to-end architecture with fewer parameters and higher efficiency for applications in realistic situations. The ablation experiments further show that our proposed approaches also provide significant improvements over previous methods.
Topology optimization using super-resolution image reconstruction methods

This paper proposes a new topology optimization method to obtain super-resolution images without increasing mesh refinement by using various methods. For traditional process, low-resolution (LR) images are fed into the Solid Isotropic Material with Penalization (SIMP) and Optimality Criteria (OC) methods. Here, the trained super-resolution images are added to the inner loops to reconstruct the topology and used to obtain high-resolution (HR) images from the LR images at the end of each iteration. After finishing the reconstruction process, the main topology optimization method recovers the original size images from the HR images for the next iteration. Several examples are presented to demonstrate the effectiveness of the proposed method. The final topologies provide noticeably improvement over those of typical SIMP method and create a much sharper and higher contrast images. Moreover, the proposed strategy using the super-resolution image reconstruction methods can give valuable innovation for conventional topology optimization process.
Speed Tracking Performance for a Coreless Linear Motor Servo System Based on a Fitted Adaptive Fuzzy Controller

Fuzzy control is widely used in linear motor servo systems. However, simple fuzzy rules reduce the control accuracy of the servo system, while complex fuzzy rules reduce the speed of its decision making. This paper proposes a fitted adaptive fuzzy controller (FAFC) to improve the speed tracking performance of a coreless linear motor servo system. The FAFC took a planned curve as a motion target. The planned curve is compounded by multiple performance curves of fuzzy control under the same given input. These multiple performance curves cover the variation range of motor parameters, which are offline-obtained. The performance of the planned curve is ensured by the multiple offline performance curves. The FAFC only needs simple fuzzy rules to fit the planned curve, and achieves high control accuracy without affecting the decision speed. The experimental results verified the feasibility of an FAFC. This research shows that an FAFC can effectively shorten the online calculation time of complex algorithms and keep the consistency of performance.
WIT-Greedy

Large error rates of quantum bits (qubits) are one of the main difficulties in the development of quantum computing. Performing quantum error correction (QEC) with surface codes is considered the most promising approach to reduce the error rates of qubits effectively. To perform error correction, we need an error-decoding unit, which estimates errors in the noisy physical qubits repetitively, to create a robust logical qubit. While complicated graph-matching problems must be solved within a strict time restriction for the error decoding, several hardware implementations that satisfy the restriction at a large code distance have been proposed.
Invariant Factor Graph Neural Networks

Graph neural networks (GNNs) have achieved significant success in numerous fields under settings where training and testing graphs are identically distributed. However, this setting is rarely satisfied in real life. Due to the lack of out-of-distribution (OOD) generalization abilities, existing GNNs methods perform disappointingly when there exist distribution shifts between testing and training graphs. Though several attempts have been made to deal with the issue, they mainly focus on structural properties while overlooking rich graph feature information. To this end, we propose an Invariant Factor GNN (IFGNN), which utilizes causal factor graphs to achieve invariant performances across different environments. Specifically, we dissect the graph generalization problem in a causal view, and argue that the key of graph generalization lies in discovering causal factors. Thus we extract the latent factors in the graph through disentanglement, and the causal ones are discovered with the invariant learning mechanism. We conduct extensive experiments on both synthetic and real-world datasets with distribution shifts to validate the OOD generalization abilities. The results demonstrate that our proposed IFGNN significantly outperforms the state-of-the-art baselines.