Reviewerly/exHarmony huggingface dataset:
[Hydrochemical Characteristics and Transformation Relationship of Surface Water and Groundwater in the Plain Area of Bortala River Basin, Xinjiang].

The geological conditions of the Bortala River basin are complex, and the transformation of surface water and groundwater is frequent. It is great significance to study the hydrochemical characteristics and transformation relationship of surface water and groundwater for the reasonable development and allocation of water resources in the basin. Based on 15 surface water samples and 39 groundwater samples collected in April and May 2021, the APCS/MLR model and the combination of stable hydrogen and oxygen isotopes with hydrochemistry were used in this study to analyze the hydrochemical types and the distribution characteristics of hydrogen and oxygen isotopes, the source contribution of chemical components, and the transformation relationship between surface water and groundwater. The results showed that the surface water was mainly HCO3·SO4-Ca type and HCO3-Ca type, and the groundwater was mainly HCO3·SO4-Ca·Na type, HCO3-Ca type, and HCO3·SO4-Ca type. The contribution rates of the dissolution-migration-enrichment factor, human activities factor, environment factor, and native geological factor to the chemical components were 28.8%, 17.7%, 12.0%, and 6.5%, respectively. Bortala River water D and 18O was enriched along the distance, and groundwater D and 18O in the north bank was generally more enriched than groundwater in the south bank as a whole. Under the control of geological conditions such as geological structure and hydrogeological conditions, the transformation of river water and groundwater was concentrated in the middle reaches of the Bortala River, which was generally manifested in both unconfined groundwater and spring overflow in the sides of the recharge river water, and the recharge proportion ranged from 1.0% to 70.9%.
On the Robustness of Quantum Algorithms for Blockchain Consensus

Blockchain has revolutionized many fields, such as distributed sensor networks, finance, and cryptocurrency. Consensus between distributed network nodes is at the core of such blockchain technologies. The three primary performance measures for any consensus algorithm are scalability, security, and decentralization. This paper evaluates the usefulness and practicality of quantum consensus algorithms for blockchain-enhanced sensor, and computing networks and evaluates them against the aforementioned performance measures. In particular, we investigate their noise robustness against quantum decoherence in quantum processors and over fiber-optic channels. We observe that the quantum noise generally increases the error rate in the list distribution. However, the effect is variable on different quantum consensus schemes. For example, the entanglement-free scheme is more affected than entanglement-based schemes for the local noise cases, while in the case of noisy optical fiber links, the effect is prominent on all quantum consensus schemes. We infer that the current quantum protocols with noisy intermediate-scale quantum devices and noisy quantum communication can only be employed for modular units in intraenterprise-level blockchain, such as Zilliqa, for sensor, and computing networks.
Genetic Algorithm: A study survey

Genetic Algorithms (GA) is a based population approach. It belongs to a metaheuristic procedure that uses population characteristics to guide the search. It maintains and improves multiple solutions which may produce a high-quality solution to an optimization problem. This study presents a comprehensive survey of the GA. We provide and discuss genetic algorithms for new researchers. We illustrate which components build up the GAs and view the main results on complexity time.
HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity

This paper describes our system designed for SemEval-2022 Task 8: Multilingual News Article Similarity.We proposed a linguisticsinspired model trained with a few task-specific strategies.The main techniques of our system are: 1) data augmentation, 2) multi-label loss, 3) adapted R-Drop, 4) samples reconstruction with the head-tail combination.We also present a brief analysis of some negative methods like two-tower architecture.Our system ranked 1st on the leaderboard while achieving a Pearson's Correlation Coefficient of 0.818 on the official evaluation set.
ADAPTIVE MORPHOLOGICAL FRAMEWORK FOR 3D DIRECTIONAL FILTERING

Engineering materials often feature lower dimensional and directed structures such as cracks, fibers, or closedfacets in foams. The characterization of such structures in 3D is of particular interest in various applications inmaterials science. In image processing, knowledge of the local structure orientation can be used for structureenhancement, directional filtering, segmentation, or separation of interacting structures. The idea of usingbanks of directed structuring elements or filters parameterized by a discrete subset of the orientation space isproven to be effective for these tasks in 2D. However, this class of methods is prohibitive in 3D due to the highcomputational burden of filtering on a sufficiently fine discretization of the unit sphere.This paper introduces a method for 3D pixel-wise orientation estimation and directional filtering inspiredby the idea of adaptive refinement in discretized settings. Furthermore, an operator for distinction betweenisotropic and anisotropic structures is defined based on our method. This operator utilizes orientationinformation to successfully preserve structures with one or two dominant dimensions. Finally, feasibilityand effectiveness of the method are demonstrated on 3D micro-computed tomography images in three usecases: detection of a misaligned region in a fiber-reinforced material, crack detection in concrete, and facetdetection in partially closed foams.
Building Chatbot Using Amazon Lex and Integrating with A Chat Application

Engaging in hotel room reservations has remained a hassle even though there are few applications which provide their own services. This study aims at building a voice chatbot device that can be used for hotel reservation using Amazon Lex Service with Facebook Messenger as the communication platform. The chatbot is built by leveraging on Amazon Web Services (AWS) in the form of a service called Amazon Lex for configuring the bot with utterances and responses and Lambda Functions to validate the responses while carrying out the operations by using Facebook Messenger service. The lambda function runs a script that collects input in the form of plain text or by using voice recognition using the microphone connected to it, which is sent to the Amazon Lex to be processed using various services provided by Amazon Web Services. Then the chatbot sends back a suitable response to the user through the speaker connected to the device or in the form of plain text. This bot is also integrated with a Facebook page and can be implemented in the real-world applications as well. The admin of the page has access to entire messages that are received through the chatbot and can do the appropriate arrangements as required.
Research on a Novel Clutter Map Constant False Alarm Rate Detector Based on Power Transform

A power transform-based clutter map constant false alarm rate (CM/PT-CFAR) algorithm is proposed to improve the detection performance to the weak target in multiple persisting targets situations.In the CM/PT-CFAR detector, the radar dataset obtained at each scan is normalized and multiplied by a scale factor, and then fed to the power transform operation.The transformed dataset is divided into two parts with a numerical value of 1 as the boundary.The part exceeding 1 is fed to update the scale factor, while the other is used for updating the detection threshold.Because transcendental integrals are produced in the derivation process, an accurate analytical expression of detection probability for CM/PT-CFAR is non-existent.Hence a third-order Taylor expansion operation is introduced to approximate the result.The detection performance of CM/PT-CFAR under various conditions is evaluated and compared with those of other CM/-CFAR detectors.The advantage of CM/PT-CFAR is the detection for weak targets, especially in complex detection situations.The proposed algorithm remains a relatively stable computation load in different cases, which is beneficial for practical application.
Twitter Sentiment Analysis Using Deep Learning Techniques

The World Wide Web has taken a serious look at new ways for individuals to express their viewpoints and conclusions on a variety of topics, models, and concerns. Clients provide material for a variety of media, such as web gatherings, discussion groups, and weblogs, and provide a robust and open foundation for gaining clout in areas such as promoting and research. Strategy, justification research, market estimations, and a business perspective are all important considerations. Theory study eliminates derivations from publicly available data and organizes the sentiments that the author associates with a given object into one of two specified categories (positive and negative). Make a distinction between the two problems. This follows a Twitter speculation audit cycle for quickly seeking unstructured news. Furthermore, we're looking at several ways to present an itemized positive assessment on Twitter News. It also shows a parametric relationship between operations that are influenced by perceived boundaries. The qualities conveyed in them address the tweets: positive, negative, or fair. This work will in general present the defense appreciate exploring on Twitter; the qualities conveyed in them address the tweets: positive, negative, or fair. Twitter is a web-based application that integrates with a blog and a wide range of contacts, allowing users to send brief 140-character messages. It's a rapidly growing partnership with over 200 million endorsers, 100 million of whom are active clients, and a large portion of them follow Twitter on a regular basis, sending out over 250 million tweets. This study aims to perform Sentimental analysis using deep learning with bigrams and trigrams to classify the tweets accurately.
THE IMPORTANCE OF GUIDANCE IN UNDERSTANDING CULTURAL DISCOURSE IN THINKING AND SPEAKING FOR FOREIGN STUDENTS IN BIPA PROGRAM

The lack of understanding of cultural discourse in speech is the key to international failure success in understanding language concepts, especially for BIPA learners in Indonesia. This research aims to reveal the importance of understanding the concepts of speech that must be considered in communicating. The method used is qualitative descriptive. The object of this study is an international student from Taiwan named Xing-Qian, who is participating in the Indonesian program for Foreign Speakers (BIPA) at PGRI University of West Sumatra. This research is naturalistic. The data in this study is in the form of video that is the result of recording in the field directly, then using the technique of seeing the record to transcribe the research results into writing form. The results showed that the international students used a form of guidance on the instructions for begging, asking, welcoming, advising, and inviting.
A new pedestrian recognition system based on edge detection and different census transform features under weather conditions

&lt;span lang="EN-US"&gt;Pedestrian detection has so far achieved great success in normal illumination, while pedestrians captured in extreme weather are often ignored. This paper investigates the importance of studying the effects of weather conditions on the recognition task, such as blurring and low contrast. Many image restoration techniques have recently been proposed, but are still insufficient to remove weather effects from images. We present our strong new pedestrian recognition system against climate situations, which is based on locating contours cues by applying multiple edge filters and extracting multiple features from images such as census transform (CT), modified census transform (MCT), and local gradient pattern (LGP) without performing any image restoration algorithm. The next stage involves finding the most discriminative characteristics using feature selection (FS) techniques. Finally, we use the final feature vector as an input to a radial basis function-based support vector machine classifier (RbfSVM) for pedestrian recognition. Experiments are performed on the daimler pedestrian classification benchmark dataset. Results show that the area under the curve (AUC) and the detection rate of our model are less affected by weather conditions compared to other common models like histogram of oriented gradients (HOG) and gabor filter bank (GFB) detectors.&lt;/span&gt;
Experimental Analysis of Quantum Annealers and Hybrid Solvers Using Benchmark Optimization Problems

This paper studies the Hamiltonian cycle problem (HCP) and the traveling salesman problem (TSP) on D-Wave quantum systems. Motivated by the fact that most libraries present their benchmark instances in terms of adjacency matrices, we develop a novel matrix formulation for the HCP and TSP Hamiltonians, which enables the seamless and automatic integration of benchmark instances in quantum platforms. We also present a thorough mathematical analysis of the precise number of constraints required to express the HCP and TSP Hamiltonians. This analysis explains quantitatively why, almost always, running incomplete graph instances requires more qubits than complete instances. It turns out that QUBO models for incomplete graphs require more quadratic constraints than complete graphs, a fact that has been corroborated by a series of experiments. Moreover, we introduce a technique for the min-max normalization for the coefficients of the TSP Hamiltonian to address the problem of invalid solutions produced by the quantum annealer, a trend often observed. Our extensive experimental tests have demonstrated that the D-Wave Advantage_system4.1 is more efficient than the Advantage_system1.1, both in terms of qubit utilization and the quality of solutions. Finally, we experimentally establish that the D-Wave hybrid solvers always provide valid solutions, without violating the given constraints, even for arbitrarily big problems up to 120 nodes.
Customer Relationship Management Based on SPRINT Classification Algorithm under Data Mining Technology

Under the advance of computational intelligence, customer relationship management system based on data mining technology can not only bring more economic benefits to an enterprise but also improve the management and decision-making level of Chinese enterprises. In this paper, the application of data mining technology in customer relationship management (CRM) is analyzed, and four data mining modes are realized: customer classification, cross-marketing, customer acquisition, and customer retention. In the data mining module, SPRINT classification algorithm is used in customer classification. At the same time, FP-growth, an association rule algorithm without candidate set, is applied in cross-marketing, which enhances the practicability of the system. The algorithm of optimal customer retention strategy under digital intelligence technology is adopted in customer retention, which makes up for the shortcomings of traditional CRM system and helps enterprises to better operate and adjust marketing strategies.
A Novel Hybrid Binary Farmland Fertility Algorithm with Naïve Bayes for Diagnosis of Heart Disease

One of the essential aims of intelligent algorithms concerning the diagnosis of heart disease is to achieve accurate results and discover valuable patterns. This paper proposes a new hybrid model based on Binary Farmland Fertility Algorithm (BFFA) and Naïve Bayes (NB) to diagnose heart disease. The BFFA is used for Feature Selection (FS) and the NB for data classification. FS can be employed to discover the most beneficial features. Four valid and universal UCI datasets (Heart, Cleveland, Hungary and Switzerland) were used to diagnose heart disease. Each dataset included 13 main features. The evaluation of the proposed model is simulated in MATLAB 2017b. The number of features in four datasets of Heart, Cleveland, Hungary and Switzerland is equal to 13, which was reduced to six for each dataset through the BFFA to better the efficiency of the proposed model. For evaluation, the accuracy criterion, the criterion of accuracy in the proposed model for all features in the four datasets Heart, Cleveland, Hungary and Switzerland, is equal to 82.25%, 86.91%, and 89.32% 89.24%, respectively. Results of the proposed model showed appropriateness in comparison to some other methods. In this paper, the proposed model was compared with other methods, and it was found out that the proposed model possessed a better accuracy percentage.
Cloud resources modelling using smart cloud management

Cloud computing complexity is growing rapidly with the advancements that it is witnessing. It has created a requirement to simplify the process of configuring cloud and re-configuring it when required, it also involves tasks like auto scaling of infrastructure, elastic computing and maintaining the health of the servers. The proposed method introduces a smart cloud management using knowledge base, which models the resources of cloud; it handles service level agreement and its evaluations. The proposed knowledge base supports representational state transfer (REST/RESTful) services to store and manipulate different cloud aspects like type of application, business configuration, and metrics value and its type; it also implements the strategy for efficient resource management for smart clouds. The proposed architecture consists of smart cloud engine (which provides autonomous services, which help to exploit cloud resources for service optimization and to perform service automation), knowledge base (KB) (provide a cloud ontology which will help in the management of resources and provides intelligence to the smart cloud), server and cloud enrolment, designated monitoring tool and moderator. The resulted module is easy to integrate with any of the existing cloud management tool or orchestrator. As It is developed using REST protocol and extensible markup language (XML) language it is also easy to integrate with existing monitoring tool or application programming interface (APIs).
Survey on Software code clone detection

Software code clones refer to the same part of code that appears in different places. Software clones (with or without edits) may occur during the software development for the same requirements. This copy and paste may be done on different levels (file, class, and method). Despite this process reduces the time and effort required to build the software but on the other hand, it may increase the cost and effort required to maintain the software. Also, may lead to bugs propagation when the bug occurs in the original copy of code. For this reason, many tools for code clone detection are implemented in the last 20 years to detect different kinds of clones (kind1, kind 2, kind 3, and kind 4) using different methods for different programming languages (Java, C, C++…etc). This paper explains: what are software clones, their kinds, and the methods used to detect them. It contains also a list of researches in this field till Jan 2022.
The buildings gallery: visualizing buildings

Buildings are beautiful mathematical objects tying a variety of subjects in algebra and geometry together in a very direct sense. They form a natural bridge to visualizing more complex principles in group theory. As such, they provide an opportunity to talk about the inner workings of mathematics to a broader audience, but the visualizations could also serve as a didactic tool in teaching group and building theory, and we believe they can even inspire future research. We present an algorithmic method to visualize these geometric objects. The main accomplishment is the use of existing theory to produce three-dimensional, interactive models of buildings associated with groups with a BN-pair. The final product, an interactive web application called The Buildings Gallery, can be found at https://buildings.gallery/ [Bekker, B. (2021, June). The Buildings Gallery].
A STACKED ENSEMBLE TECHNIQUE WITH GLOVE EMBEDDING MODEL FOR DEPRESSION DETECTION FROM TWEETS

With the increasing volume of web content on social network sites like Facebook, Twitter, etc. identifying the attitude of people becomes an easy task.That attitude can be used as an input to find the mental status of that person through their texts.According to WHO, depression is a general mental disorder, which has already affected more than 264 million people.With the help of sentiment analysis, it is possible to detect depression at an early stage from their tweets as they represent their attitude.Machine Learning Classification algorithms help to classify the texts as Depressed or non-depressed, but their accuracy is limited when researchers are using only traditional Bag of Words vectorizers to extract features.Instead of this, word embedding models can be used which represent words as real-valued vectors in a distinct vector space that is already defined and provides better accuracy.In this paper, we try to implement eight machine learning techniques for depression detection from tweets namely SVM, Logistic Regression, ExtraTree, Bagging, Random Forest, AdaBoost, XG boosting, and Gradient Boosting which employs different word embeddings like Word2Vec, FastText, and Glove on the dataset which is used to train the classifiers and performed a comparative evaluation of word embedding models with different classifiers.This study proposes a new Stacked ensemble technique with a glove embedding model for Depression detection from Tweets which provides higher accuracy than standalone models.
Virtual Management of Students' Unrest During the COVID-19 New Normal: The Need for an Innovative Approach

COVID-19 has changed the face of universities in terms of their operation, which include but are not limited to teaching, learning, researching, and management system. This does not exclude the management of students’ unrest. Despite the COVID-19 inversion, students’ unrest did not stop; instead, it took a more sophisticated dimension. Such dimension includes virtual protest and virtual meetings with the use of social media, among others. This significantly affects the culture of teaching and learning and also attempt to blackmail the image of the universities. Therefore, this conceptual paper aimed to respond by proffering an up-to-date management style suitable to managing student unrest during COVID-19. The argument is located within Diffusion of Innovation Theory to understand the university stakeholders' behaviour and adaptability to new ways of doing things during the COVID-19 inflicted change. The study argued the need for a change in the management styles towards managing students’ unrest. The challenges that hinder the university towards effective management of virtual protest/unrest and the possible solutions to the new methods of students’ protest/unrest was also presented. The study recommends, among others, the incorporation of strategic leadership and communication in the universities' management styles to meet the unavoidable dynamics of human behaviours in the system.