Reviewerly/exHarmony huggingface dataset:
[Spatial Distribution Characteristics and Assessment of Heavy Metal Pollution in Cultivated Soil of Agricultural Small Watershed in Typical Mining Area in South China].

In order to clarify the spatial distribution characteristics and the present situation of heavy metal pollution in cultivated soil of a typical mining basin in southern China, the contents of Cd, As, Mn, Cu, Zn, and Pb in cultivated soil in the selected small watershed were determined, and the spatial distribution characteristics of heavy metals were analyzed using the spatial interpolation method. Further, the ecological risk was evaluated using the geo-accumulation index and potential ecological risk coefficient (PERC). The results showed that the average contents of Pb, Zn, Cu, As, and Cd in the topsoil (0-20 cm) in the small watershed were approximately 11.9, 3.8, 8.2, 4.7, and 14.2 times that of the background value in Hunan soil, respectively. Compared with the soil risk screening value of agricultural land, the over-standard rates of Pb, Zn, Cu, As, and Cd were approximately 24%, 56%, 75%, 44%, and 48%, respectively. Zn and Mn showed medium variation, whereas Pb, As, Cu, and Cd showed strong variation, which indicated that there was an obvious enrichment of heavy metals in the small watershed. The spatial analysis results showed that there was an obvious consistent characteristic of six heavy metals in the topsoil, that is, there was enrichment in the mining activity area and its downstream and the township streets in the west of the small watershed. Additionally, there were high value points of some heavy metals on both sides of the main river, indicating that the main pollution sources in the study area were mining and metallurgy activities and sewage irrigation. The results of the geo-accumulation index and potential ecological risk index showed that there was compound pollution of heavy metals, in which the degree of Cd pollution was the highest, and the soil of both sides of the river could pose the strongest potential ecological risk.
An Improved Tobit Kalman Filter with Adaptive Censoring Limits

This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements which belong to some interval with given censoring limits, is considered. Two improvements of the standard TKF process are proposed, in order to estimate the hidden state vectors. Firstly, the exact covariance matrix of the censored measurements is calculated by taking into account the censoring limits. Secondly, the probability of a latent (normally distributed) measurement to belong in or out of the uncensored region is calculated by taking into account the Kalman residual. The designed algorithm is tested using both synthetic and real data sets. The real data set includes human skeleton joints' coordinates captured by the Microsoft Kinect II sensor. In order to cope with certain real-life situations that cause problems in human skeleton tracking, such as (self)-occlusions, closely interacting persons etc., adaptive censoring limits are used in the proposed TKF process. Experiments show that the proposed method outperforms other filtering processes in minimizing the overall Root Mean Square Error (RMSE) for synthetic and real data sets.
Comparative Study of Quantum and Classical Error Correction for Future Quantum-6G Networks

&lt;p&gt;The paper compares quantum and classical error correction scheme with the aim for a pathway to 5G beyond networks where there will be a notion of quantum computation in the current classical communication architecture. &lt;/p&gt;
Direct image classification using an on-chip photonic deep neural network

Artificial neural networks have been a major driving force in a wide variety of technologies from pattern recognition and natural language reprocessing to medical diagnosis. Neural networks can be trained to perform a certain set of tasks and are typically formed by the interconnection of multiple layers of neurons to perform linear and nonlinear computations. Highly reconfigurable clock-based digital signal processors have been the main hardware platform to implement such networks. However, clock frequency and memory access time are two main contributors in limiting the processing speed. Integrated photonics benefits from the large available bandwidth at optical frequencies and low-loss interconnects, and has proved to be a promising candidate to address the challenges of electronic systems. Most of the demonstrated photonic neural networks have been either using bench-top setups, or the partial integration of some of the linear and/or nonlinear computation blocks. A scalable solution that can integrate all computation blocks has been an open problem. Here, we demonstrate the first end-to-end photonic deep neural network (PDNN) for direct image classification. On-chip image formation of the target object using a 2D array of grating couplers and direct processing of the received optical signals through linear (optical) and nonlinear (opto-electronic) computation, enable an end-to-end classification time of 570 ps. Using a uniformly distributed supply light enables scaling the PDNN to networks with more layers of neurons. The PDNN chip is used to demonstrate classification of 2- and 4-class datasets of handwritten letters and the system achieves accuracies higher than 93.8% and 89.8%, respectively.
A Randomized Optimal k-mer Indexing Approach for Efficient Parallel Genome Sequence Compression

&lt;p&gt;We evaluate the performance of RGCOK on eight benchmark human genomes and other benchmark data commonly used to assess the performance of various state-of-the-art algorithms for referential genome compression.&lt;/p&gt;
Edge-view Dual Message Carriers GCN for Sequential Recommendation

&lt;p&gt;Sequential Recommendation (SR) aims to predict the most likely coming user-item interaction based on their interaction history. Since bipartite graphs can easily represent SR records, GCN-based models proved to be very effective in SR. However, 1) directly importing high-order sequential patterns (SP) would improve performance but bring a huge complexity burden; 2) current GCNs are to learn nodes’ interaction characteristics in SR by transporting unary message carriers between adjacent nodes, which neglects that users and items are different. Thus, we proposed an Edge-view Dual message carriers GCN for sequential Recommendation (EDG4Rec), which regards users and items as dual heterogeneous message carriers delivering their influences on SR bipartite graph independently. Specifically, an interaction and its user’s 1-order SP and its item’s 1-order SP are joined directly as a basic domain, where the dual message carriers transported by our proposed DMT mechanism with three variants, i.e., CD-DMT, JSP-DMT, CI-DMT. Finally, an edge-based negative sampling mechanism (E-NSM) is proposed to make training faster. Extensive experiments on dense and sparse real-world datasets demonstrate the superior performance of our model. It shows that supplementary information is no longer necessary for our design. Besides, our model can work well in cold-start situations.&lt;/p&gt;
Implementing Secure Bridges: Learnings from the Secure Asset Transfer Protocol

&lt;p&gt;Securely transferring tokenised assets between largely non-interoperable blockchain networks is a great challenge to take on. Implementations must coordinate transactions on multiple blockchain networks which have inherently different characteristics and functionality. Moreover, implementations must coordinate transactions in strict order, in an atomic, synchronised way so that owners do not risk their assets becoming lost, while also mitigating the age-old problem of “avoiding double spend”. Several protocols for cross-network asset transfer, i.e. the bridging of assets, exist, and several production bridge implementations exist. However, frequent high-profile security breaches have arguably given bridges a bad reputation. Therefore, there is a clear need for standardised bridge protocols to re-establish trust. One initiative for standardising cross-network bridges is the Secure Asset Transfer Protocol (SATP) developed by the Internet Engineering Task Force (IETF). This protocol establishes four distinctive phases for the asset transfer process, driven by a standardised messaging cycle between specialised bridge orchestration applications, known as gateways. In this paper, we demonstrate a reference framework with functions needed to implement SATP. We describe our implementation of this reference framework, why SATP is unique and why SATP is likely to be an important contribution to powering future bridges.&lt;/p&gt;
INSA National Report on Seismological Research in India: 2019 – 2022 

the National Adhering body representing India in the International Union of Geodesy and Geophysics (IUGG), submits a National Report to the IUGG during its General Assembly, held every four years.The present chapter is a compilation for the International Association of Seismology and Physics of the Earth's Interior (IASPEI), one of the eight Associations of the IUGG, encompassing the scientific activities carried out in the country during 2019-2022, in the field of seismology.The chapter is arranged to categorize the contributions under internal structure of the Indian lithosphere and sub-lithospheric mantle, crust and upper mantle deformation, seismic attenuation, seismic hazards, triggered seismicity, environmental seismology, paleoseismology and seismological networks.In addition, the chapter includes activities related to societal projects and outreach programs.It is not an exhaustive review of the entire Indian contribution to seismology and allied fields during this period but a window to the topics covered within the framework of IASPEI.During the reporting period, researchers from several Indian R&D and Academic Institutions continued adding new results on the crustal and upper mantle structure of the Indian shield, both at regional and geological province scale.The Himalaya has been a major focus of seismological research for many Institutes who operated broadband seismological networks from Kashmir to Arunachal along the Himalayan arc.The data from these networks have facilitated in enhancing our understanding of the structure of the Main Himalayan Thrust, crustal and upper mantle structure as well as the dip of the underthrusting Indian plate, seismicity monitoring, and estimation of attenuation.Some of these networks also include accelerographs for strong ground motion studies.A few studies also focused on mapping of the mantle transition zone beneath the Indian Ocean Geoid Low.The studies on anthropogenic seismicity added near-field investigations in the Koyna -Warna region through borehole seismology.Organization of the Joint Scientific Assembly of IAGA and IASPEI during 21-27 August 2021 (JSA-2021) was a major event during this period related to the IUGG activities.Originally, the event was planned to be hosted at Hyderabad, India, but due to the COVID-19 pandemic it was organized in virtual mode.Initiatives have been taken to nucleate the field of environmental seismology in the country.
Masakhane Web: A Machine Translation Platform for African Languages

Abstract Low-resource languages pose a particularly difficult challenge to neu-ral machine translation (NMT), and there appears to be insufficient machine translation (MT) systems to support African language accessibility. Masakhane Web, an NMT system for African languages, is proposed in this paper. Our approach is an open-source platform that is free, flexible, and produces reasonably accurate translations for African languages. The platform makes use of Masakhane community-trained MT models. It enables users to generate new data by providing feedback on translations, which is then used to retrain the models to improve them. Ultimately, our goal is to create a platform that can provide accurate translations for African languages and make the process of creating MT models easier for those who lack the technical expertise. Furthermore, we include strategies for domain experts to evaluate the system and explain how the platform can be used as a data collection source to improve MT for African languages.
Multimodal Image Local Registration by Attention Gauge Fields with Robust Adaptive Probability Distributions

&lt;p&gt;Image registration is a technique of matching and superimposing of features, which can be better applied to high-level vision tasks. However, high-level vision tasks often pay more attention to analysis of target areas. In summary, we rethink the collaborative relationship between image registration and high-level vision tasks, and propose a local registration strategy for constructing a robust adaptive attention gauge fields, and construct robust adaptive attention gauge fields to register images focus more on important target areas. In order to improve the robustness and timeliness of the algorithm for feature matching of target areas in complex environments, we propose a robust adaptive probability distribution(RA), and construct robust adaptive mixed model expectation maximum attention(RAMM-EMA). In order to make robust adaptive parameters to reach the global optimum in deep learning, we designed a simulated annealing method to optimize the robust adaptive parameters during training. In order to focus on important regions in the image as much as possible, propose a feature fusion map with weak boundaries and probabilistic properties, called Robust Adaptive Attention Gauge Fields(RAA-Gauge Fields). High-level vision tasks experiments on image fusion, object delect and 3D-reconstruction, the method presented in this paper is the most helpful, and registration effect of the target areas perform best under more extreme conditions. &lt;/p&gt;
DISCRETE-TIME HOPFIELD NEURAL NETWORK: CONVERGENCE THEOREM: PERTURBATION ANALYSIS

&lt;p&gt;In this research paper, we show that the convergence theorem of Hopfield neural network hods true without the condition that the diagonal elements must be non-negative (using epsilon perturbation of diagonal elements). More general version of this result is also proved. It is also shown that the eigenvalue vector of a symmetric matrix and the diagonal element vector are related through a linear system of equations with the coefficient matrix being a doubly stochastic matrix. Also, Perturbation analysis of Hopfield neural network is discussed.&lt;/p&gt;
Gestão da informação e da comunicação em saúde: intersecções e inter-relações entre os dois campos

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THE RULING of WATER WHICH USED For PURITY WHEN MIXING With The PERFUMES

&#x0D; &#x0D; &#x0D; &#x0D; Water is the basis of purity that cannot be worshiped except by it, and condition of this water is required to be pure in itself and cleansed to others things, if mixture with other things is caused to change its feature and lost its pure .&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; If mix the water with perfume caused a great impact on its quality, and that led to change in the feature water (color, taste, or smell) sothat the scientists disagreed in this water, some of them said: , the water lost its pure And others said opposite opinion that the water keeps its features
Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications

&lt;p&gt;This paper is more about feature extraction and also based on applied machine learning.&lt;/p&gt;
A GUI Based Application for Lung Cancer Diagnosis Using Comprehensive Analysis Deep Learning Approach

Abstract: Lung cancer is the third most terrible cancer in the world. It has been among the leading causes of death in both adults and children from recent years. Early cancer detection can lead to better treatment options for patients. The focus of conventional feature extraction methods is either on low -level features or high-level features, with some manually created features being utilized to fill in the gaps. A feature extraction framework does not require handcrafted features can be created to close this gap via encoding/combining low-level and high-level characteristics. Due to its ability to fully explain both low-level and high-level information and its integration of the feature extraction stage into the self-learning process, deep learning is incredibly effective for feature representation. Consequently, in this study, we use deep learning algorithms to detect the presence of lung cancer without the need for several doctor consultations. A web application is created as a healthcare application where lung cancer is detected using an input x-ray image. To identify cancer, the implementation uses the VGG-16 classification algorithm. As a result, the presence of the disease can be predicted early. We can then take quick action to stop any additional repercussions, which saves time, money, and human error. Lung cancer and its presence are identified in this investigation.
Intuitionistic Hesitant Fuzzy UP (BCC)-Filters of UP (BCC)-Algebras

The concepts of intuitionistic hesitant fuzzy UP (BCC)-subalgebras, UP (BCC)-ideals, and UP (BCC)-filters of UP (BCC)-algebras are presented, some of their features are explained, and their extensions are demonstrated using the theory of hesitant fuzzy sets as a foundation. The necessary conditions for those intuitionistic hesitant fuzzy sets are provided and include their relation to their complement. The concept of prime and weakly prime of intuitionistic hesitant fuzzy sets was also introduced and studied. We also talk about the connections between intuitionistic hesitant fuzzy UP (BCC)-subalgebras (UP (BCC)-ideals, UP (BCC)-filters) and their level subsets. The homomorphic pre-images of intuitionistic hesitant fuzzy UP (BCC)-filters in UP (BCC)-algebras are also studied and some related properties are investigated.
Review of Automatic Language Identification System in Indian languages from the Non-Uniform Region

Abstract—The process of language identification involves au- tomatically detecting which natural language(s) are given in a speech sample. Using automatic language recognition, an audio clip can be recognised as being spoken in a specific language (LID). Identifying the language correctly from a given speech sample is the language identification system’s fundamental purpose. There are two types of systems they are explicit language identification and implicit language identification. The phoneme sequence used in the explicit language recognition system is produced from a speech sample. Language is determined using the phoneme sequence that is retrieved. There is no need to identify the phoneme in the implicit language recognition method, which determines using certain speech properties. The language identification process involves training and a classifier model. the first step is to extract the speech sample using phonemes then it is trained using classifiers. Data are randomly collected from male and female peoples in society. Index Terms—Language identification (LID), Mel-frequency cepstral coefficients (MFCC), broad phoneme classifier (BPC),Neural network, support vector machine (SVM), LDA, CNN,i-vector,GMM Super Vector(GSV),Factor Analysis(FA),Native Language Identification (NLI),Stylistically Related Text Samples (SSTs),Deep Dumb Multi Layer Perceptron (DDMLP), Deep Convolutional Neural Network (DCNN), and Semi-supervised Generative Adversarial Network(SSGAN),Gated Recurrent Units (GRUs),Hidden Markov models (HMMs),phone error rate (PER),Convolutional and Long Short Term Memory Recurrent (CLSTM), Neural Networks ,Singing Language Identification (SLID),Automatic Speech Recognition (ASR).
Role of Motivation in Learning English Foreign Language on the Academic Achievements of Students in China

Purpose: The purpose of this study is to investigate the role of motivation in learning English foreign language on the academic achievements of students in China.&#x0D; Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings revealed that there exists a contextual and methodological gap relating to the role of motivation in learning English foreign language on the academic achievements of students in China. Preliminary empirical review reveals that motivation is a component that is both very crucial and effective in the process of picking up a new language. Therefore, it is important for instructors and lecturers of languages to learn about, appreciate, and pay attention to the personalities of the pupils in their classes. In addition, they need to have an understanding of motivation, the significant value it holds, and the different forms it might take.&#x0D; Unique Contribution to Theory, Practice and Policy: The Gardner’s Motivation theory, the Acculturation model and the Self Determination theory may be used to anchor future studies relating to the role of motivation in learning English foreign language. The study will be beneficial to EFL teachers and other relevant stakeholders in establishing policies and strategies to promote motivation in learning English foreign language.&#x0D;
Transdisciplinary Information and Analytical Platform Supporting Evaluation Processes

With the aim of identifying and developing gifted students, providing them with assistance in choosing a profession and involving them in scientific research and research activities, the National Center “Junior Academy of Sciences of Ukraine” has initiated and annually holds a number of All-Ukrainian competitions of creative and intellectual direction: “Junior Erudite”, “Junior Researcher”, “Future of Ukraine”, “Ecoview” and many others. Among them, the most popular and representative in terms of the composition of its participants is the “Contest-presentation of scientific research projects”. The competition takes place in several stages, about 100,000 high school students from all over Ukraine take part in it, of which more than 1,000 of the best make it to the finals. The rules of the competition provide that a jury is created for each scientific section (of which there are 65), in which the corresponding stage of the competition is held. The members of the jury independently evaluate the research works of schoolchildren. Winners are determined in each scientific section separately by the sum of points scored by participants in all sections of the program. The final result (rating score) of each participant is calculated taking into account the weight of each component of the factor-criterion model, according to which the contestants’ achievements are evaluated, that is, the participants (alternatives) are actually ranked according to a set of indicators of different importance, which have a hierarchical structure. In order to ensure the access of the jury members to all the materials of scientific and research achievements of the contestants, as well as to carry out their effective evaluation and calculation of the final results, the information and analytical platform (IAP) POLYHEDRON-Competition was created.This article talks about the created computer system – an information and analytical platform that ensures the effective work of experts (jury members) in reviewing and evaluating scientific research materials submitted for defense by participants of intellectual contests. The system is deployed on the basis of an interactive document, which is a variant of the ontology-controlled system, and its work is illustrated on the example of the contest-presentation of scientific research projects.