Reviewerly/exHarmony huggingface dataset:
TASuRe: Text Aware Super-Resolution

Recognition of text on low-resolution (LR) images is a challenging task. Traditional interpolation methods, as well as general super-resolution approaches, do not recover the shape of text character robustly. In this work, we propose a text-aware super- resolution neural network called TASuRe. Text awareness is interwoven in the proposed network that contains a text rectification part and text recognition auxiliary module. The training procedure is built around character shape restoration by adding a binary mask to the input image and using a specialized loss that penalties the network for missing gradients on the border of characters. Experiments on the real LR images have shown that the proposed network can deal with hard cases better than convolutional competitors.
KARAKTERISTIK KEUANGAN SEKOLAH DAN PENGAWASAN TERHADAP EFEKTIVITAS PENGELOLAAN KEUANGAN SEKOLAH KEJURUAN DI BENGKULU UTARA

This research aims is to determine the school financial characteristics and supervision on the effectiveness of financial management of Vocational School in North Bengkulu. In this study the object of research is the school treasurer, treasurer committee, Chairman of Komita, and principal. Characteristics of school finance at are good or run according to the rules. Monitoring of financial management is also indispensable in the financial management of vocational schools. The results of the study indicate that the respondents' answers tend to agree to some point of activity carried out by the principal, school committee, treasurer, etc. The school financial management process should be efficient, cost-effective and financially appropriate, financial management must be well-directed and controlled in accordance with the original plan, program/activity, be open or transparent, and wherever possible using the capability/revenue outcome of funds which has been targeted through the previous plan is quite possible, so that the effectiveness of financial management can work well.
Impact of Seasonal Variations and Oil Activities on the Total Concentrations, Geochemical Fractions, And Human Health Problems of Trace Metals in Soils Within the Oil-Bearing Communities of South-South Region of Nigeria

The impacts of oil activities and seasonal variations on the total concentrations, geochemical fractions, and human health problems of trace metals in soils within oil-bearing communities of Akwa Ibom State, Nigeria were examined. Top soils were obtained from Eket, Onna, Esit Eket, and Ibeno local government areas between January and December, 2017. Samples were also collected from Etinan local government area of the State and used as the Control. The samples and Control were subjected to standard analytical procedures and analysed for their physicochemical properties and total metal concentrations. The modified BCR methods were employed for the speciation of metals. The findings revealed that the pH levels were higher during the dry season while, the electrical conductivity, organic matter, and cation exchange capacity of the studied soils were higher in the rainy season. The total mean concentrations of Pb, As, Cd, Cr, Fe, and V were higher in the dry season whereas, Ni was higher in the wet season. Though, their concentrations were within the acceptable limits. Generally, the concentrations of all the parameters in the studied soils were higher than at the control site. Pb, As, Ni, and V existed predominantly in the reducible fraction, Cd and Cr in acid extractable while, Fe existed mostly in the residual fraction in the studied soils. However, at the control site all the metals except Cd existed mainly in the residual fraction. The study showed that all the studied locations were highly contaminated with these metals and substantial seasonal variations were also recorded for the pollution indices. It was also noted that, seasonal changes and oil activities were the key factors affecting the quality of the studied soils. Daily intake rates for the metals through exposure to soil for both the young and old populations were within their required oral reference doses. The non-carcinogenic risks recorded for both populations were less than one. However, both populations were exposed to high Pb and the younger ones were more susceptible. The study revealed the influence of seasonal change and oil activities on the mobility and toxicity of metals in the studied soils.
PENGARUH FASILITAS BELAJAR DAN KOMUNIKASI KELUARGA TERHADAP MOTIVASI BELAJAR SISWA TINGKAT MENENGAH ATAS DAN KEJURUAN SE - KECAMATAN BUNTU PEPASAN

The purpose of this study was to determine how much influence learning facilities and family communication have on the learning motivation of upper secondary level students in Buntu Pepasan District. In this study the approach used was a quantitative approach. The sampling technique used purposive sampling technique. The number of samples was 62 people. Data collection techniques were carried out by distributing questionnaires directly to the research site. Instrument testing and data analysis were carried out with the help of SPSS Version 25.&#x0D; In testing the hypothesis, a partial test was carried out with the "t" test, obtained a significant value for the independent variable on the dependent variable, namely learning facilities (X1) of 0.009 &lt;0.05 and family communication (X2) of 0.000 &lt; 0.05. on student learning motivation is 0.384 or 38.4%., the influence of family communication on student learning motivation is 0.208 or 20.8%. The large effect of variables X1 and X2 on variable Y is 0.325 or 32.5%. This influence still needs to be improved by increasing the provision of adequate learning facilities at school and at home as well as increasing communication from the family as a provider of support and enthusiasm for learning to high school students throughout Buntu Pepasan District.
Total nitrogen prediction based on quantum weighted minimal gated unit neural networks in wastewater treatment process

The total nitrogen (TN) in the effluent of wastewater treatment plant is an important index to evaluate the effect of water treatment, and its accurate prediction can provide data support for the optimal operation of wastewater treatment process. The Quantum Weighted Minimal Gated Unit (QWMGU) neural network by introducing a quantum computing mechanism into MGU network is presented to predict the effluent total nitrogen. Firstly, a multi-dimensional single-step time series prediction method was used to construct the TN dataset of wastewater; Then, the above index set was input into QWMGU to complete the TN prediction; Lastly, some comparison experiments with the water outlet data of the actual sewage treatment plant were performed. The results show that the proposed TN prediction model based on QWMGU has higher prediction accuracy than QWGRU/MGU/GRU/LSTM, and faster convergence speed than QWGRU/GRU/LSTM.
Effort To Mitigate Malaria Via Early Detection Using Hybrid Machine Learning Architectures

Malaria is a mosquito-borne disease spread by female Anopheles mosquitos that kills a large number of people every year, despite the fact that it has been around for over a century. The early identification of malaria patients is critical to minimizing the number of deaths caused by the disease. Manual malaria identification takes time and differs from expert to expert, thus an automated approach might save time while also being more efficient. This study proposes utilizing CNN models to identify malaria automatically from images of red blood cells. We utilized Decision Tree, KNN, SVM, AdaBoost, XGB classifier, and Random Forest for training after utilizing DenseNET-121 for feature extraction. The DenseNET-121+XGBClassifier model has the highest accuracy of 96.3% among the seven models suggested in this research.
Using K-means Clustering Ensemble to Improve the Performance in Recommender Systems

Collaborative filtering methods are often utilized in the industry of recommender systems. They work by identifying users with similar tastes and recommending items for each active user. Besides, clustering techniques are extensively utilized to create systems based on collaborative filtering recommendation in the context of big data. Nevertheless, the cluster ensemble has emerged in last years as a powerful technique that can replace single clustering algorithms in enhancing the performance of recommendation and prediction. This paper presents a k-means clustering ensemble-based method to improve the performance in recommender systems. The proposed system incorporates the Cosine Similarity and the Pearson Correlation Coefficient as similarity metrics to form clusters. Moreover, it uses the HyperGraph Partitioning Algorithm (HGPA) to combine the results of the k-means clustering technique. The recommendation algorithm constructs the recommendations based on the clusters obtained earlier by the HGPA ensemble clustering. To this end, it finds the nearest cluster for each active user and selects its top N items. Finally, it recommends these top items to the user‘s favorite list. The experiments on two well-known datasets demonstrate that cluster ensembles by HGPA outperform the baseline methods.
Crystal Plane Anisotropy and the Collection Strengthening of Spodumene

Aspects in Mining & Mineral Science Crystal Plane Anisotropy and the Collection Strengthening of Spodumene Ning Sun, Haoran Chu, Yang Hu, Yuhua Wang*, Dongfang Lu and Xiayu Zheng School of Minerals Processing & Bioengineering, Central South University, China *Corresponding author: Yuhua Wang, School of Minerals Processing & Bioengineering, Central South University, Changsha 410083, China Submission: July 12, 2021;Published: August 05, 2021 DOI: 10.31031/AMMS.2021.07.000658 ISSN 2578-0255Volume7 Issue2
SILURIAN DEPTH-TO-BEDROCK MAP FOR EASTERN WISCONSIN, USA

A fractured carbonate system comprises the Silurian-aged bedrock across eastern Wisconsin, USA, potentially acting as a conduit for contaminated surface water, such as manure-treated field effluent, to reach groundwater. Glacial deposits overlying the Silurian bedrock help filter contaminated surface water with thicker deposits having greater filtration potential. In light of revisions to state administrative codes ATCP50 and NR151 to regulate manure spreading, a current and timely mapping effort to derive a seamless depth-to-bedrock (DTB) map across the Silurian bedrock region in eastern Wisconsin was needed. The most recent DTB map and outline of the Silurian across eastern Wisconsin was created in 1979 at 1:250,000 scale. Advancement of geographic information system (GIS) technologies and 43 years of additional subsurface information are available to generate a more current and precise DTB map. We applied a GIS spatial modeling tool called Empirical Bayesian Kriging with Regression Prediction (EBKRP) in Esri ArcGIS Pro 2.9.1 to first model a bedrock elevation surface, and then derive DTB by subtracting that surface from a ground-surface digital elevation model. We chose EBKRP over other interpolation methods because the calculation of an unknown value at a given location is dependent on the theoretical best fit of the variance of the known values over the interpolated area, rather than strictly distance. The theoretical best fit captures the spatial relationship across an area and provides the ability to coincidently generate a map of model error. Additionally, the map presented here includes data from the novel application of airborne electromagnetic (AEM) data collected via SkyTEM Canada Inc. in collaboration with the United States Geological Survey and the Wisconsin Geological and Natural History Survey in 2021. AEM data was collected along helicopter flight lines and were inverted to produce electrical resistivity data at nearly 300 m depth with 1-2 m near-surface resolution. The resistivity transitions in the shallow subsurface were used to estimate DTB. Modern geostatistical techniques with additional and novel subsurface data has allowed us to generate a DTB map with higher resolution, more accuracy, and error estimates across the Silurian carbonate system in eastern Wisconsin.
ATTRIBUTE-BASED ENCRYPTION SCHEMES FOR BOTH USERSAND CLOUD SERVER

To reduce the operator’s cost of decryption and secure the sensitive data from an un trusted party, we have an approach to outsourcing the decipherment of the attribute- based encryption (ABE) scheme to the cloud server. The cloud server requires repeating the process of outsourced decipherment facility of the same coded text for different users. Green computing is the environment of careful and reusable employ of methods. This particular scheme can decrease its price needs by obtaining its performance and enhancing resource management and services. The technique is inefficient. To extend the explanation of the recyclable technique of methods for the cloud server, here we have a new approach to decrease t h e total upstairs of the cloud server when many operators fulfilling this scheme require the outsourced decipherments for the similar coded text also reducing the decryption calculation price for operators. Compared with the existing previous methods, our entire limitations of the cloud server is independent of the user numbers who will be satisfied with this scheme and request this decipherment process.
GEOCHEMICAL CHARACTERISTICS OF THE MESOZOIC VOLCANIC ROCKS IN ZHANHUA SAG, BOHAI BAY BASIN, EAST CHINA

Located in eastern Bohai Bay Basin， Zhanhua sag is situated on the west side of Tanlu Fault Zone， the largest fault zone in eastern China. Many works have carried out about petrologic, geochemical and chronological characteristics of the rocks in Zhanhua sag, especially the rocks located in oil reservoir. However few studies have been conducted on Mesozoic volcanic rocks developed in this sag. Here, we analyze the geochemical characteristics of Mesozoic volcanic rocks in Guxi area, in the east of Zhanhua sag via better outcrop sections, which contributes to understand geological setting of Bohai Bay Basin in Mesozoic. The volcanic rocks’ lithologies of Zhanhua sag are mainly basaltic, tephrite, rhyolite, trachyandesites and andesite, mainly belong to alkalic series. The trace element features of these volcanic rocks are rich in LILE but lacking in HFSE. High ratio of w(Sr)/w(Y) and low content of w(Y) might suggest these samples are similar to adakite. Due to the characteristic of lacking HREE, the results for rare earth elements might suggest that volcanic rocks in Zhanhua sag came form lithospheric mantle that receives continental crust mixing. Therefore we believe that volcanic rocks in Zhanhua Sag are island-arc volcanic rocks. During the upwelling of magma, it was influenced by the collision of the Yangtse plate in Mesozoic and mixed with crust so that it has geochemical characteristics of the crust compared to typical edakite.
A High Precision Annotation Method for Vision Simulation Data of Autonomous Vehicle

In recent years, with the rise of artificial intelligence technology, autonomous vehicle has also been developed rapidly. Deep learning-based visual perception algorithms for autonomous vehicle have gained a lot of attention. In order to obtain models with more robustness, a large amount of annotationed data is needed for training. Existing autonomous driving datasets are often captured and annotationed by manual means. This takes a lot of time and effort, and the quality of the annotation varies. To solve this problem, we try to use a virtual engine to produce simulated datasets and annotate them by individual semantics to obtain higher efficiency and accuracy. The experiments in this paper show that the data annotated by our method has a large improvement over the existing methods for annotation. It can play a crucial role in the capture and annotation of autonomous driving datasets.
Transformational and Transactional Leadership as a Function of Employee Performance

Purpose: Using responses from 176 employees working in the top leadership role in the FMCG sector of Pakistan, this study aimed to investigate the effect of transformational and transactional leadership on employee performance.&#x0D; Design/Methodology/Approach: A quantitative approach was adopted, while a convenience sampling technique was used for data collection. The results were analyzed using the SmartPLS and SPSS, i.e., Partial Least Square, and the Measurement Model included Confirmatory Factor Analysis and Factor Loadings.&#x0D; Findings: According to the findings of our study, transactional (TRLS) and transformational leadership (TFLS) are positively correlated with employee performance. Thus, transformational and transactional leadership positively affect employee performance in Pakistan’s FMCG sector&#x0D; Implications/Originality/Value: This study shall help the directors, top management, and executives working in the leadership roles to develop better pathways for achieving high objectives in terms of performance.
A Relation-Aware Span-Level Transformer Network for Joint Entity and Relation Extraction

Named entity recognition and relation extraction are two important tasks in information extraction. Many recent works model two tasks jointly and achieve great success. However, these methods still suffer from the relation semantic insufficiency, head entity dependency and nested entity detection problem. To address the challenges, we propose a relation-aware span-level transformer network (RSTN), which contains a span-level encoder for entity recognition and a non-autoregressive decoder for relation extraction. Specifically, we generate explicit represen-tations for possible spans to extract overlapping entities in our span-level encoder. In addition, we encode relation semantics in our non-autoregressive decoder, and exploit copy mechanism to extract head entities and tail entities simultaneously by modifying the casual attention mask. Through span-level multi-head attention mechanism, we enhance the interaction between entity recognition and relation extraction in our model. We evaluate our model on three public datasets: ACE05, ADE and SciERC. Experiment results show that the proposed model outperforms previous strong baseline methods.
An Analysis of Cloud Computing Security Problems

Cloud computing has become modern trend to convert the companies infrastructure into cloud and Cloud offer distinctive efficient services. Data imitation implies abundant Copy of the similar data in various servers. However, some hardships are consisting in it. In general, security, synchronization, adaptability, and duplication are essential issues. Cloud offering many features like shared infrastructure, network access, handle metering and internet plate form to allow for communication among cloud service provider, consumer and also for broker parties. So a comprehensive, systematic and investigative review about security, privacy and virtual resource migration are important challenges in cloud computing environment. The major focus about the study is depending on previous and current literature, which is explaining the strategy or Methodology according to the cloud providers to protect users’ security, privacy, data and information.&#x0D; This paper will highlight Security, and investigative Challenges. Basic object of this research is to understand major component, Security threat, security risk and with newly solution that may be alleviate vulnerabilities among the clouds
Pelatihan Keterampilan Menyajikan Presentasi Menarik Microsoft Power Point pada Guru SD Kecamatan Salo

Current teaching and learning activities must be supported by utilizing Microsoft power point. The purpose of this service is to train teachers to have the skills to use Microsoft power point in learning, assist teachers in designing lessons and instructions on making lessons interactive and interesting using Microsoft power points and teachers are able to optimally utilize the digital era as a support for the learning process. . The implementation of the service training was carried out at the Salo District Elementary School.
Encoding Lifted Classical Planning in Propositional Logic

Planning models are usually defined in lifted, i.e. first order formalisms, while most solvers need (variable-free) grounded representations. Though techniques for grounding prune unnecessary parts of the model, grounding might – nevertheless – be prohibitively expensive in terms of runtime. To overcome this issue, there has been renewed interest in solving planning problems based on the lifted representation in the last years. While these approaches are based on (heuristic) search, we present an encoding of lifted classical planning in propositional logic and use SAT solvers to solve it. Our evaluation shows that our approach is competitive with the heuristic search-based approaches in satisficing planning and outperforms them in a (length-)optimal setting.
LeapConv: An Energy-Efficient Streaming Convolution Engine with Reconfigurable Stride

Convolution is the central computation kernel for various machine learning applications. The convolution stride controls the number of pixels by which the kernel's window moves after each operation, thereby allowing for the reduction of the output's resolution. The streaming computation of strided convolution inherently involves large periods of inactivity interrupted by periods of actual computation. In this work, we propose LeapConv, a new streaming convolution engine that computes convolutions of arbitrary and reconfigurable stride using local buffering and by leveraging efficient data and memory reuse. The organization of LeapConv is based on the decomposition of strided convolutions into a set of parallel unity-stride convolution channels that are implemented by a merged hardware unit. The experimental results show that LeapConv reduces power consumption with increasing stride by eliminating redundant data movement. The incurred area overhead due to the additional multiplexing logic required to support reconfigurability is demonstrated to be marginal.
A Distributed Authentication Mechanism Based on the Transmission Characteristics of WiFi6

With the development of electric Internet of Things (IoT), the security requirements of service transmission and authentication in Internet of Things are increasing. The emergence of WiFi6 can realize low-cost, high-efficiency and high-speed wireless communication coverage for electric IoT. It can ensure the service transmission of terminals. At the same time, the development of blockchain can ensure the data privacy in electric IoT. Blockchain has the characteristics of openness and traceability. Therefore, according to the transmission characteristics of WiFi6, this paper studies the channel resource allocation which combined with service attributes. Also, this paper proposes a distributed authentication mechanism based on blockchain, which combines the situation of channel resource allocation as one of authentication factors in distributed authentication process. It can ensure the security, efficiency and accuracy of authentication, increase the overall throughput and improve the quality of electric service transmission.
Evaluation of Sino-Foreign Cooperative Teaching Project Based on AHP Algorithm

In order to effectively evaluate the teaching programs of sino-foreign cooperative education, this paper will carry out relevant research on the Analytic Hierarchy Process, mainly discuss the basic concepts of the algorithm, establish an evaluation system according to the principles, and finally verify the results by carrying out project evaluation and combining with cases. Through the analysis of this paper, AHP algorithm can be used in the evaluation of sino-foreign cooperative education discipline teaching project, the evaluation results are accurate, the quality is up to standard, so the method in this paper is effective, can help related projects to improve their competitiveness.