Reviewerly/exHarmony huggingface dataset:
The response of benthic foraminifera to environmental changes in Epie creek, Yenagoa, Bayelsa State

Epie creek, located at the Southern part of Nigeria has been known for its use as sink for the disposal of untreated sewage and industrial effluents. Benthic foraminifera are increasingly been used as environmental bio-indicators of pollution. Their munity structure provides information on the general characteristics of the environment and some species are sensitive to specific environmental parameters. This study investigates recent foraminifera from sediment samples collected from ten (10) stations (ranging in depths from 1m to 4.2m) in the Epie creek from Igbogene to Tombia- Etegwe. The environmental factors that necessitate their existence such as temperature, pH, salinity and dissolved oxygen were measured during the sampling time. Metals were determined using atomic absorption spectrophotometer. Recognized benthic foraminifera species belonging to four (4) genera of three (3) families and four(4) suborder were identified. Five (5) benthic foraminifera taxa were identified from sediment samples which include Lenticulina stellata, Saccammina sp, Marginulina costata, Marginulina cf. planata and Trochammina sp. The stress tolerant specie, Trochammina ,sp. was common in the study area confirming a stressed environment.
DeFi: data-driven characterisation of Uniswap v3 ecosystem &amp; an ideal crypto law for liquidity pools

Uniswap is a Constant Product Market Maker built around liquidity pools, where pairs of tokens are exchanged subject to a fee that is proportional to the size of transactions. At the time of writing, there exist more than 6,000 pools associated with Uniswap v3, implying that empirical investigations on the full ecosystem can easily become computationally expensive. Thus, we propose a systematic workflow to extract and analyse a meaningful but computationally tractable sub-universe of liquidity pools. Leveraging on the 34 pools found relevant for the six-months time window January-June 2022, we then investigate the related liquidity consumption behaviour of market participants. We propose to represent each liquidity taker by a suitably constructed transaction graph, which is a fully connected network where nodes are the liquidity taker's executed transactions, and edges contain weights encoding the time elapsed between any two transactions. We extend the NLP-inspired graph2vec algorithm to the weighted undirected setting, and employ it to obtain an embedding of the set of graphs. This embedding allows us to extract seven clusters of liquidity takers, with equivalent behavioural patters and interpretable trading preferences. We conclude our work by testing for relationships between the characteristic mechanisms of each pool, i.e. liquidity provision, consumption, and price variation. We introduce a related ideal crypto law, inspired from the ideal gas law of thermodynamics, and demonstrate that pools adhering to this law are healthier trading venues in terms of sensitivity of liquidity and agents' activity. Regulators and practitioners could benefit from our model by developing related pool health monitoring tools.
A Study on the Effects of Track System-based Convergence Education Support on University Student’s Creative Confluence Competency

The purpose of this study was to understand the effects of track system-based convergence education support on university students’ creativity and interdisciplinary abilities. Among the students of H university, which operates the track system, 1,229 students who participated in the core competencies assessment in the second semester of 2021 were the subject of the study. For analysis, t-test, ANOVA and multiple regression analysis were conducted. Factors were explored in creative confluence competency as dependent variables, and the results are as follows. First, there were significant differences in creative confluence competency according to participation on convergence education. Second, GPA, capstone design course attendance, creativity convergence based extracurricular activities, satisfaction of Sangsang Park, satisfaction of major courses were statistically significant in explaining creative confluence competency. Based on the results, suggestions and policies were discussed, along with limitations.
Logistic Regression Analysis of Learner Traits and Satisfaction Influencing Levels of D igital Literacy and Self-regulation in University Distance Learning

Purpose: This study was conducted with the purpose of predicting the learner traits and satisfactionfactors that affect the digital literacy and self-regulation levels of students who have taken a universitycourse with distance learning.&#x0D; Methods: This study was conducted through a survey given to all students who took a distance learningcourse in the second semester of 2020 at C University in Chungbuk province. Of the 419 valid surveys332 subjects were analyzed excluding the average values of digital literacy and self-regulation. Followingthis the 332 subjects were again divided into upper and lower groups by variables. Binary logisticregression was then performed. Moreover, multinominal logistic regression was performed by dividingthe group into four groups lower-lower, upper-lower, lower-upper and upper-upper.&#x0D; Results: The main research results are as follows. First, the possibility that the lower digital literacygroup would not think it was convenient because they could take the course any place they wanted,and the possibility that thought the class method was appropriate increased. Second, the self-regulationlower group was found to be more likely to think that the guidance for academic affairs and distancelearning was not appropriate. Third, compared to the digital literacy and self-regulation the upper-uppergroup, the lower-lower group was more likely to think that the class method was appropriate, andthe belief that the guidance for academic affairs was not appropriate increased. The lower-upper classwas more likely to think that the attendance check recognition method was not clear. Furthermore,the upper-lower groups were found to be more likely to have lower grades.&#x0D; Conclusion: In conclusion, it is necessary to apply a differentiated class management strategy reflectingthe group characteristics according to the level of digital literacy and self-regulation.
A Study of English Dialogue Activity Based on Flipped Learning

The purpose of this paper is to look into the effects of flipped learning with English dialogue activity for EFL English speaking proficiency, and self-efficacy. For this study, 40 college students were chosen as the participants and asked to participate in creating dialogue and practicing the dialogue in the form of group activity. It was implemented for 12 weeks. In order to measure the students’ speaking proficiency, pre and post speaking tests were performed before and after dialogue activity based on flipped learning. In addition, questionnaires of self-efficacy were conducted for data collection. The following results have been showed in this study; first, this study showed that there was significant improvement in students’ speaking evaluation. Especially, there was the greatest improvement in fluency. Second, there was positive change in the self-efficacy. Theses imply that dialogue activity based on flipped learning had a positive effect on improving students' speaking achievement, and self-efficacy.
Energy transport and optimal design of noisy Platonic quantum networks

Abstract Optimal energy transport is one of the primary goals for designing efficient quantum networks. In this work, the maximum energy transport is investigated for three-dimensional quantum networks with Platonic geometries affected by dephasing and dissipative Markovian noise. The network and the environmental characteristics corresponding the optimal design are obtained and investigated for five Platonic networks with 4, 6, 8, 12, and 20 number of sites that one of the sites is connected to a sink site through a Markovian dissipative process. Such optimal designs could have various applications like switching and multiplexing in quantum circuits.
Application of natural language processing technology in text classification

With the development of science and technology and the arrival of the era of big data, artificial intelligence is constantly developing, and related theories are maturing. One of the important branches of artificial intelligence, that is, natural language processing technology, is also constantly developing. In the Internet era, there are more and more text information. Therefore, we should seek a good way to effectively obtain and manage this information. Through experiments, this paper tests the feasibility and efficiency of using natural language processing technology for text classification and draws a conclusion that natural language processing technology is efficiently applied to text classification.
Depth Invariant Real-time Fixed/Random Valued Impulse Noise Removal Algorithm for Back-end of Ultrasonography Systems

Ultrasound images often get distorted by impulse noise during data acquisition and processing in the Back-end of the system, which overlay the finer details of the scanned body parts. Generally, a portable low-cost USG system doesn't have an impulse noise-cleaning module which hinders detections of smaller details in the images. A Depth Invariant Impulse Noise Removal (DIINoR) algorithm for real-time impulse noise removal from the corrupt USG image is proposed in this paper. In this decision-based algorithm, the corrupt pixel is first detected depending on the homogeneity of the processing window and is restored with the median of the window or previous pixel value. Testing of the DIINoR algorithm on different USG images establishes that the denoised images have superior quantitative performance compared to those of existing schemes which proves its suitability for the real-time fixed and random valued impulse noise cleaning in the Back-end of the portable USG system.
Training and Assistance in the Preparation of Promotional Media for Junior High Schools and MTs Muhammadiyah Malang City

The large number of junior high schools, zoning system, and pandemic raises problems regarding the decline in the number of junior high school students and MTs Muhammadiyah in Malang City. There are quite a lot of factors behind the number of registrants. However, as an effort to solve the problem, the preparation of promotional media with the right language and media is expected to help increase the number of registrants. This service activity aims to provide training and assistance in the preparation of school promotion media for Muhammadiyah Junior High Schools and MTs in Malang City. Devotion is carried out by the method of 4 stages of the creative process, namely preparation, incubation, illumination, and verification. Based on the activities that have been carried out, it was found that one of the problems in promoting the registration of new students was the lack of ability of teachers to determine the superior activities that would be offered to prospective students. In addition, the visual communication design in the poster has also not been able to provide clear information to the reader. Through intensive training and mentoring, satisfactory results are obtained. Teachers can create interesting and targeted school promotional media.
An Error Analysis on the Use of Simple Present Tense in Descriptive Text

The aims of this research include: (1) to identify the types of errors made by 12th grade of the students SMAN 13 Bone in their writing descriptive text, (2) to find out the dominant type of error, (3) to discover the source of error. This research used mix method. The instrument used in collecting the data was writing test. The test was given twice to the students. The population of this research was the 12th grade of the students SMAN 13 Bone. The sample was the 10% of the whole population which consisted of 42 students. In analyzing the data, the researcher found that the students made errors in using simple present tense in writing a descriptive text. The types of errors discovered, namely omission errors, addition errors, misformation errors, and misordering errors. The omission and misformation errors found to be the dominant errors in the students’ writing descriptive text. The sources of errors include Inter-lingual and Intra-lingual transfer.
Flux-controllable and fast state inversion in a Laudau–Zener system of superconducting charge qubit

Abstract Coherent control of quantum systems in an optimized manner is of significance to information processing and state engineering. In this paper, an effective scheme is proposed for implementing rapid state inversion in a Laudau–Zener (LZ) system of superconducting charge qubit. By linearly adjusting time-dependent gate charge, the system with a given tunneling splitting of energy can be described by the LZ model. By means of the applied flux capable of inducing desired level spacing, qubit state inversion with high probability can be performed in a short time. We further address the criterion to ensure system evolution with negligible non-adiabatic excitation. With the accessible decoherence rates, high-fidelity operations can be obtained numerically. Without adding auxiliary driving, the present strategy could perform the shortcut-like accelerated operation, which paves a promising avenue towards optimized information processing with superconducting qubits.
Sparse Trend Estimation

The low-frequency movements of many economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes, which is guided by a judicious choice of priors and is characterized by sparsity. We present some novel stylized facts from longer-run survey expectations that inform the structure of the estimation procedure. The general version of the proposed Bayesian estimator with a slab-and-spike prior accounts explicitly for cyclical dynamics. The practical implementation of the method is discussed in detail, and we show that it performs well in simulations against some relevant benchmarks. We report empirical estimates of trend growth for U.S. output (and its components), productivity, and annual mean temperature. These estimates allow policymakers to assess shortfalls and overshoots in these variables from their economic and ecological targets.
FNR-GAN: Face Normalization and Recognition with Generative Adversarial Networks

The normalization of faces in the wild is an interesting process which can improve face recognition performances and avoid the complex computation of face generation in cross different variations. In this paper, we propose a multi-objective approach that generates and recognizes normalized faces while preserving their identities. An unsupervised Face Normalization and Recognition framework using discriminant normalized features is presented. This latter is based on an optimized combination of Generative Adversarial Network (GAN) generators and Convolutional Neural Network (CNN) classifiers. The main power of our approach is to generate optimized features representing normalized faces finding a trade-off between improving identity preservation and minimizing the architecture complexity. Additionally, it can be adapted to impaired and unlabeled datasets which can respond to real-world face variations and available data. Experimental results show that the proposed method outperforms other models on face normalization and achieves state-of-the-art frontal-frontal face verification in CFP protocol and face recognition in LFW. The code and results are available at github/FNR-GAN .
Joint-Confidence-Guided Multi-Task Learning for 3D Reconstruction and Understanding From Monocular Camera

3D reconstruction and understanding from monocular camera is a key issue in computer vision. Recent learning-based approaches, especially multi-task learning, significantly achieve the performance of the related tasks. However a few works still have limitation in drawing loss-spatial-aware information. In this paper, we propose a novel Joint-confidence-guided network (JCNet) to simultaneously predict depth, semantic labels, surface normal, and joint confidence map for corresponding loss functions. In details, we design a Joint Confidence Fusion and Refinement (JCFR) module to achieve multi-task feature fusion in the unified independent space, which can also absorb the geometric-semantic structure feature in the joint confidence map. We use confidence-guided uncertainty generated by the joint confidence map to supervise the multi-task prediction across the spatial and channel dimensions. To alleviate the training attention imbalance among different loss functions or spatial regions, the Stochastic Trust Mechanism (STM) is designed to stochastically modify the elements of joint confidence map in the training phase. Finally, we design a calibrating operation to alternately optimize the joint confidence branch and the other parts of JCNet to avoid overfiting. The proposed methods achieve state-of-the-art performance in both geometric-semantic prediction and uncertainty estimation on NYU-Depth V2 and Cityscapes.
A Survey on the Online Shopping Customer Review Data using Association Rule Mining

Revealing complex associations between entities is of vast significance for business optimization, prediction and decision making. Such associations include not only co-occurrence-based explicit relations but also non co-occurrence-based implicit ones. Associative rule mining (ARM) is used to study these implicit and explicit relationships. Online shopping customer review (OSCR) data has become a major information resource for consumers and has extremely important implications for a wide range of management activities. Consumer reviews examine the bond between service quality and customer purchase behaviour in online shopping context. Apriori is a key algorithm for mining frequent item sets for Boolean association rules. To develop the efficiency of the level-wise generation of frequent itemsets in online customer shopping customer review data, Apriori property is used to reduce the search space .The detection of interesting patterns in this collection of data can guide to important marketing and management strategic decisions. In this survey paper, some of the research work carried out on customer online shopping data is discussed. Also, the use of Apriori algorithm for the same type of data set is analyzed.
A Framework for Hybrid Risk Analysis

Changes within the worldwide security environment proceed to challenge our ability to comprehend and react to the constantly changing hybrid threats that are becoming more diverse, emanating from a wide range of actors who are enabled by technology. Actors can wield an array of means and ways to further their security interests at the expense of a target and are able to do so without being identified.Developing proper situational awareness is a first and crucial step on the road to achieving better protection against hybrid threats. Here we propose a novel framework for hybrid risk analysis that enables the better understanding of operations of the adversary before their taking place.The idea of the framework is based on the model of hybrid operations, which combine the elements of space, time, objects at risk, goals, and actors into a single structure - a hyper-forest of multi-trees.Taking into account that hybrid operations are carried out according to certain scenarios characterized by repeatability of tools in relation to certain goals, we propose using case-based reasoning approach based on calculating the dynamic similarity of the information structure of ongoing attack to retrospective sequences of hybrid attacks for which the goals, tools, and methods are known. Retrospective data is stored in the case base.The proposed framework combines several models and methods, the main of which are the multi-tree model of hybrid attack representation, spatially distributed model of hybrid attack distribution, and the method for hybrid risk analysis. The method for hybrid risk analysis is based on two additional models such as vulnerability model and consequences assessment model that are developed for each type of object at risk.The suggested framework for hybrid risk analysis offers a better comprehension of adversary operations prior to them occurring and aids in formulating an appropriate reaction to the changing scenario.
Overview of erythrocyte sedimentation rate examination results of farmers aged 30-40 years in Manunggal Karya Village Sub-district of Randangan District of Pohuwato

This research aimed to give an overview of the erythrocyte sedimentation rate (ESR) examination results of farmers aged 30-40 years in Manunggal Karya Village, sub-district of Randangan, Pohuwato.&#x0D; This research method used a quantitative research approach with the type of research being descriptive research. Samples in this study were 29 samples using the simple random sampling technique as well as conducting interview and examination of erythrocyte sedimentation rate of the farmers.&#x0D; The results of this study concerning the erythrocyte sedimentation rate examination of farmers aged 30-40 years in Manunggal Karya Village, sub-district of Randangan showed that the percentage of normal ESR results was 62.1% and the percentage of abnormal ESR results was 37.9%. Based on the risk factors that affect the ESR value, by the medical history of the farmers, the percentage of normal ESR results by 62.1% and the percentage of abnormal ESR results was 37.9%; by the length of work, farmers with a working period of &gt;10 years had normal ESR results of 62.1% and the abnormal ESR results of 37.9%.&#x0D;
The online major-related nursing home volunteering in nursing students and its effects on attitudes toward dementia

The purposes of this study were to introduce a case of online major-related nursing home volunteering during the COVID-19 Pandemic because face-to-face volunteer work was difficult, and also to identify the effects on improving attitudes toward dementia among nursing students.&#x0D; This study adopted a single group pre and post-test, a quasi-experimental design. The participants, third-year nursing students, had the Dementia Partner online education and were given orientation on the cognitive enhancement activities program. The participants planned a cognitive enhancement activities program for the elderly in nursing homes. Also they made a video clip to let the elderly know how to conduct the programs. The elderly in nursing home participated in the cognitive enhancement program together with the caregiver while watching the video clip made by nursing students. To identify the effect of the online major-related nursing home volunteering, attitudes toward dementia of students were measured before, and a month after the volunteering.&#x0D; The mean scores of attitudes toward dementia measured after the online major-related nursing home volunteering were significantly improved compared to the baseline. By subcategory, it was identified that the score of ‘social comfort’ related to behaviors and feelings of comfort around people with dementia increased significantly. However, there was no significant change in ‘dementia knowledge’.&#x0D; This study introduced specifically the case of online major-related nursing home volunteering during the COVID-19 pandemic and confirmed there was effects on improving attitudes toward dementia of nursing students even though online volunteering. The education and experience of dementia could make professionals who will care for dementia patients in the future have a positive attitude toward dementia. As a result, it could play an important role in providing quality and safe services to people with dementia. The results of this study could provide a fundamental and practical strategy for online volunteering to contribute to solving social problems in the situation when online volunteering increases in quantity.