Reviewerly/exHarmony huggingface dataset:
Identification of Rare Earth Elements (REEs) distribution patterns in the soils of Campania region (Italy) using compositional and multivariate data analysis

A big monitoring programme was carried out in Italy from 2015 to 2018 with the aim to assess the environmental conditions of the Campania region territory. Among the different environmental media, 7300 samples of topsoil (10–15 cm) were collected and analysed for 52 elements through an analytical methodology combining ICP-MS and ICP-ES. Specifically, this paper focuses on the geochemical distribution of La, Ce, Sc and Y with implications for the whole REEs group. Machine learning techniques, Compositional Data Analysis (CoDA) and multivariate statistics were used in order to understand the behaviour of these elements in different geogenic and anthropogenic conditions and their interaction with other elements. Lasso regression (LR) was performed to select the variables most related with REEs. Subsequently, robust Principal Component Analysis (rPCA) was performed, using 20 elements selected through the LR to identify the main geochemical associations. Finally, using rPCA scores and additive log-ratio (alr) coordinates, distribution maps were generated to assess the patterns of the REEs sources identified. The results show that median concentration values of the investigated elements (Ce = 80.3, La = 41.3, Y = 15.4 and Sc = 3.3 mg/kg) are significantly higher than those of European and Italian soils. The type of lithologic substrate and the degree of soil alteration are the main factors responsible of the REEs concentrations. Cerium and La have the same behaviour and show higher concentrations in volcanic soils, especially the more altered ones, which are associated with high values of low-mobile elements like Th, Fe, Hf, Zr, Mn, or where the pyroclastic cover lies on limestone rocks. Otherwise, Sc is enriched in soils formed on siliciclastic and calcareous rocks while Y shows an intermediate behaviour between Sc and, La and Ce. This study has shown that Th, Fe and Mn are the elements most closely related to REEs in soils of Campania region, therefore they can be used to estimate their natural concentration in soil.
Multitask learning for multilingual intent detection and slot filling in dialogue systems

Dialogue systems are becoming an ubiquitous presence in our everyday lives having a huge impact on business and society. Spoken language understanding (SLU) is the critical component of every goal-oriented dialogue system or any conversational system. The understanding of the user utterance is crucial for assisting the user in achieving their desired objectives. Future-generation systems need to be able to handle the multilinguality issue. Hence, the development of conversational agents becomes challenging as it needs to understand the different languages along with the semantic meaning of the given utterance. In this work, we propose a multilingual multitask approach to fuse the two primary SLU tasks, namely, intent detection and slot filling for three different languages. While intent detection deals with identifying user’s goal or purpose, slot filling captures the appropriate user utterance information in the form of slots. As both of these tasks are highly correlated, we propose a multitask strategy to tackle these two tasks concurrently. We employ a transformer as a shared sentence encoder for the three languages, i.e., English, Hindi, and Bengali. Experimental results show that the proposed model achieves an improvement for all the languages for both the tasks of SLU. The multi-lingual multi-task (MLMT) framework shows an improvement of more than 2% in case of intent accuracy and 3% for slot F1 score in comparison to the single task models. Also, there is an increase of more than 1 point intent accuracy and 2 points slot F1 score in the MLMT model as opposed to the language specific frameworks.
Attention-guided dynamic multi-branch neural network for underwater image enhancement

Underwater images often suffer from quality deteriorations such as color deviation, reduced contrast, and blurred details due to wavelength-dependent light absorption and scattering in water media. Recently, convolutional neural networks (CNNs) have achieved impressive success in underwater image enhancement (UIE). However, there is still room for improvement in terms of representational capability and receptive field (RF)/channel variant ability in almost all CNN-based UIE networks. To treat these problems, we propose an attention-guided dynamic multibranch neural network (ADMNNet) to obtain high-quality underwater images. Different from existing CNN-based UIE networks that generally share a fixed RF size of artificial neurons in one feature layer, we propose an attention-guided dynamic multibranch block (ADMB) to boost the diversity of feature representations by merging the properties of different RFs into a single-stream structure. Concretely, ADMB includes two main components, namely, a dynamic feature selection module (DFSM) and a multiscale channel attention module (MCAM). Inspired by the selective kernel mechanism in the visual cortex, we incorporate a nonlinear strategy into the DFSM that allows the neurons to adjust their RF sizes reasonably by using soft attention to achieve dynamic fusion of multiscale features. In the MCAM, channel attention is designed to exploit the interdependencies among the channelwise features extracted from different branches. Our ADMNNet can obtain better visual quality of underwater images captured under diverse scenarios and achieves superior qualitative and quantitative performance compared to state-of-the-art UIE methods.
A novel minimum generalized cross entropy-based multilevel segmentation technique for the brain MRI/dermoscopic images

One of the challenging and the primary stages of medical image examination is the identification of the source of any disease, which may be the aberrant damage or change in tissue or organ caused by infections, injury, and a variety of other factors. Any such condition related to skin or brain sometimes advances in cancer and becomes a life-threatening disease. So, an efficient automatic image segmentation approach is required at the initial stage of medical image analysis.To make a segmentation process efficient and reliable, it is essential to use an appropriate objective function and an efficient optimization algorithm to produce optimal results.The above problem is resolved in this paper by introducing a new minimum generalized cross entropy (MGCE) as an objective function, with the inclusion of the degree of divergence. Another key contribution is the development of a new optimizer called opposition African vulture optimization algorithm (OAVOA). The proposed optimizer boosted the exploration, skill by inheriting the opposition-based learning.The experimental work in this study starts with a performance evaluation of the optimizer over a set of standards (23 numbers) and IEEE CEC14 (8 numbers) Benchmark functions. The comparative analysis of test results shows that the OAVOA outperforms different state-of-the-art optimizers. The suggested OAVOA-MGCE based multilevel thresholding approach is carried out on two different types of medical images - Brain MRI Images (AANLIB dataset), and dermoscopic images (ISIC 2016 dataset) and found superior than other entropy-based thresholding methods.
GloVe-CNN-BiLSTM Model for Sentiment Analysis on Text Reviews

Nowadays, social media networks generate a tremendous amount of social information from their users. To understand people’s views and sentimental tendencies on a commodity or an event timely, it is necessary to conduct text sentiment analysis on the views expressed by users. For the microblog comment data, it is always mixed with long and short texts, which is relatively complex. Especially for long text data, it contains a lot of content, and the correlation between words is more complex than that in short text. To study the sentiment classification of these mixed texts composed of long-text and short-text, this research proposes an optimized GloVe-CNN-BiLSTM-based sentiment analysis model. In this model, GloVe is used to vectorize words, and CNN is given to represent part space character. BiLSTM is used to build temporal relationship. Twitter’s comment data on COVID-19 is used as an experimental dataset. The results of the experiments suggest that this method can effectually identify the sentimental tendency of users’ online comments, and the accuracy of sentiment classification on complete-text, long-text, and short-text can achieve to 0.9565, 0.9509, and 0.9560, respectively, which is obviously higher than other deep learning models. At the same time, experiments show that this method has good field expansion.
Ensuring Security and Energy Efficiency of Wireless Sensor Network by Using Blockchain

With the advancement of new technology, security is the biggest issue nowadays. To solve security problems, blockchain technology will be used. In recent work, most of the work has been done on homogeneous systems, but in our research, the primary focus is on the security of wireless sensor networks using blockchain. Over the last few decades, the Internet of Things (IoT) has been the most advancing technology due to the number of intelligent devices and associated technologies that have rapidly grown in every field of the world, such as smart cities, education, agriculture, banking, healthcare, etc. Many of the applications are developing by using IoT technologies for real-time monitoring. Because of storage capacity or low processing power, smart devices or gadgets are vulnerable to attack as existing cryptography techniques or security are insufficient. In this research work, firstly, we review and identify the privacy and security issues in the IoT system. Secondly, there is a solution for the security issues, which is resolved by blockchain technology. We will check the wireless sensor network to see how data work on distributed or decentralized network architecture. Wireless sensor network clustering technique was introduced by researchers for network efficiency because when the workload spreads, the system will work faster and more efficiently. A cluster comprises a number of nodes, and the cluster head manages the local interactions between the nodes in the cluster (CH). In general, cluster members connect with the cluster head, and the cluster head aggregates and fuses the data acquired in order to save energy. Before approaching the sink, the cluster heads may additionally create another layer of clusters among themselves. The clustering concept divides data traffic into several groups similar to the other data points in the same data point. In contrast, this data point is dissimilar from other data points in another group. All results are presented at the end of this study paper, in which we will see the network or nodes’ performance in the specific area of the network, how it works, and how efficient it is. Likewise, Blockchain also works in a distributed manner.
IoT-Based Decentralized Energy Systems

In traditional energy production at large-scale, conventional methods are being used, including fossil fuels. This in turn leads to greenhouse gas emissions (e.g., carbon dioxide or CO2) that cause environmental concerns, but also those traditional methods rely on traditional distribution systems, which are burdened with high transmission losses. This paper focuses on a new concept in the energy sector that undergoes transformation from a traditional centralized system to a decentralized one. In reaching sustainability goals, such as net-zero emissions, the energy sector is incorporating renewable energy sources into the energy system. This requires transformation that combines big conventional energy producers with multiple small- and large-scale energy producers (rooftop photovoltaic panels, wind farms and solar plants) in one system. This enormous transformation is a difficult task, but with recent advancements in information and communication technologies, digitalization, the Industry 4.0 paradigm and Internet of Things technology, it is feasible to achieve. This paper provides a review based on keyword bibliometric analysis, and although it cannot be considered exhaustive or conclusive, it provides a picture of the current international research.
Learning Material Selection for Metaverse-Based Mathematics Pedagogy Media Using Multi-Criteria Recommender System

One of the challenges of learning mathematics is making students like the subject to understand the material being taught more efficiently.Therefore, several studies propose the use of gamification elements in the mathematics learning process.Another important consideration in learning mathematics is the accuracy of the subject matter given to students.This study proposes a metaverse-based mathematics pedagogy media (MMPM) equipped with learning material selection (LMS) to adaptively select subject matter according to the level of student knowledge.We use the multi-criteria recommender system (MCRS) to support LMS in providing recommendations for determining the choice of subject matter.The method used to calculate similarity is cosine-based, while the ranking is based on average, worst-case, and aggregate.We built the MMPM system using the unity game engine in the experimental stage.At the same time, the subject matter that becomes the focus of scenario visualization is about beam, cube, prism, and pyramid.The test results show that the LMS system works well in carrying out its duties by adaptively choosing the appropriate subject matter scenarios for students based on their pre-test results.In this study, MCRS-based LMS produces the highest accuracy of 92% for two to three input items and the lowest 90% for four input items.
Mobile computer-supported collaborative learning for mathematics: A scoping review

This study conducted a scoping review of publications in mobile Computer-Supported Collaborative Learning for mathematics. Papers published between 2007 and 2021 inclusive were retrieved from research databases to achieve this goal. Twenty-eight papers met the inclusion-exclusion criteria of the study. It was shown that two papers were published on average over the last 15 years. The majority of the papers were published in peer-reviewed journals. Intending to improve mathematics pedagogy, the two most popular math mCSCL contents were general elementary mathematics and geometry. The review also revealed that math mCSCL benefited elementary students the most. The majority of math mCSCL software was custom-built and designed for synchronous sharing. The research designs were consistent with the existing reviews. The effects on social and attitude skills, as well as mathematics competency, were the most frequently mentioned benefits of math mCSCL. Usability issues, device unfamiliarity, inability to track students' activities, synchronization, and coordination concerns were among the problems highlighted during the implementation of math mCSCL. The implications for future research are discussed.
Adversarial training of LSTM-ED based anomaly detection for complex time-series in cyber-physical-social systems

With the development and maturity of smart cities, more and more Cyber-Physical-Social Systems (CPSSs) need to monitor a variety of time-series data from sensors and network transmissions to ensure the quality and reliability of the Cyber-Physical-Social Services. Time-series anomaly detection is a common search problem in the field of pattern recognition. Existing approaches and models of anomaly detection have solved the problem of simple smooth time-series and perform ideal recognition performance. However, in real scenarios, complex time-series with non-Gaussian noise and complex data distributions are prevalent. Compared to smooth and simple time-series, complex time-series occur more frequently in real-world settings and are difficult to model and label. To address these challenges, this paper proposes an unsupervised anomaly detection algorithm based on Long Short-Term Memory Encoder-Decoder (LSTM-ED) via an adversarial training method for complex time-series in cyber-physical-social systems with high performance. This is a novel method that incorporates adversarial learning to improve the robustness of encoder-decoder architecture, enabling it to obtain good anomaly detection results for complex time-series. In addition, LSTM is employed as the network unit of encoder-decoder architecture, which is also able to extract temporal correlation in time-series to a greater extent. We have conducted extensive experiments on four datasets from real scenarios and the results show that the accuracy of the proposed adversarial training of LSTM-ED is significantly better than that of the state-of-the-art methods, including other unsupervised methods and traditional supervised methods.
BioKnowPrompt: Incorporating imprecise knowledge into prompt-tuning verbalizer with biomedical text for relation extraction

Domain tuning pre-trained language models (PLMs) with task-specific prompts have achieved great success in different domains. By using cloze-style language prompts to stimulate the versatile knowledge of PLMs, which directly bridges the gap between pre-training tasks and various downstream tasks. Large unlabelled corpora in the biomedical domain have been created in the last decade(i.e., PubMed, PMC, MIMIC, and ScienceDirect). In this paper, we introduce BioKnowPrompt, a prompt-tuning PLMs model that has been incorporating imprecise knowledge into verbalizer for biomedical text relation extraction. In particular, we use learnable words and learnable relation words to infuse entity and relation information into quick creation, and we use biomedical domain knowledge constraints to synergistically improve their representation. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks such as relation extraction. BioKnowPrompt has a lot of significant potential in few-shot learning, which outperforms the previous models and achieves state-of-the-art on the 5 datasets.
Adaptive local landscape feature vector for problem classification and algorithm selection

Fitness landscape analysis is a data-driven technique to study the relationship between problem characteristics and algorithm performance by characterizing the landscape features in the search space of an optimization problem. However, most of the existing landscape features still face poor in classifying the problems and low accuracy in selecting the most appropriate algorithm for a given problem. In this study, an adaptive local landscape feature vector (ALLFV) is proposed for problem classification and algorithm selection. Specifically, an adaptive discretization scheme is designed to calculate adaptive related parameters and construct the sequence between the fitness values of the search point and its nearest neighbors. By considering the frequencies of the same sequence values, the spatial structural information for the fitness landscape is computed as a feature vector according to the feature vector calculation mechanism. The experimental results tested on various problems demonstrate the excellence of ALLFV in terms of accuracy, stability, and computational cost. Moreover, ALLFV has shown superior practicality and reliability in the application of algorithm selection for numerical optimization problems. Consequently, ALLFV is well suited as an alternative for problem classification, as well as algorithm selection under excessive candidate optimization algorithms and limited prior knowledge of problems.
Tracking blobs in the turbulent edge plasma of a tokamak fusion device

The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak à Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. By making a dataset and benchmark publicly available, we aim to lower the entry barrier to tokamak plasma research, thereby greatly broadening the community of scientists and engineers who might apply their talents to this endeavor.
Adaptive multi-feature fusion via cross-entropy normalization for effective image retrieval

Multi-feature fusion has achieved gratifying performance in image retrieval. However, some existing fusion mechanisms would unfortunately make the result worse than expected due to the domain and visual diversity of images. As a result, a burning problem for applying feature fusion mechanism is how to figure out and improve the complementarity of multi-level heterogeneous features. To this end, this paper proposes an adaptive multi-feature fusion method via cross-entropy normalization for effective image retrieval. First, various low-level features (e.g., SIFT) and high-level semantic features based on deep learning are extracted. Under each level of feature representation, the initial similarity scores of the query image w.r.t. the target dataset are calculated. Second, we use an independent reference dataset to approximate the tail of the attained initial similarity score ranking curve by cross-entropy normalization. Then the area under the ranking curve is calculated as the indicator of the merit of corresponding feature (i.e., a smaller area indicates a more suitable feature.). Finally, fusion weights of each feature are assigned adaptively by the statistically elaborated areas. Extensive experiments on three public benchmark datasets have demonstrated that the proposed method can achieve superior performance compared with the existing methods, improving the metrics mAP by relatively 1.04% (for Holidays), 1.22% (for Oxf5k) and the N-S by relatively 0.04 (for UKbench), respectively. • We propose an efficient multi-feature fusion image retrieval method. • Our method uses low-level and high-level semantic features. • We use cross-entropy technique to improve the complementarity of features. • This mechanism fuses the result of multi-feature in an unsupervised manner. • Experimental results show that our method is highly competitive.