Reviewerly/exHarmony huggingface dataset:
A Budget-Constrained Energy-Efficient Scheduling Algorithm on Cloud-Edge Collaborative Workflows

With the development of Internet of Things applications, traditional cloud computing cannot meet the new application requirements, and the mode of cloud edge collaborative processing is becoming more and more important. Cloud computing workflow scheduling algorithm seldom considers the heterogeneity of cloud edge devices, so it is difficult to achieve the balance of completion time, energy consumption, and cost. Cloud-edge collaborative workflow scheduling can reasonably allocate tasks according to the characteristics of dependencies and heterogeneous resources, which can solve the imbalance between performance and cost as well as between performance and energy consumption. Therefore, we propose a heterogeneous earliest completion time scheduling algorithm (W2DG-HEFT) based on a greedy rotation strategy. Considering the differences in computing performance, transmission delay, energy consumption, and cost of cloud edge computing nodes, the candidate queues selected by cloud and edge 2D weights are adjusted and balanced. The HEFT algorithm, which adopts energy consumption awareness and greedy rotation strategy, allocates the appropriate cloud or edge computing nodes according to the task priority in the workflow. Through simulation platform experiment, compared with GPR-HEFT algorithm, when the cost threshold is 2-3 USD per hour, the scheduling completion time on the workflow of 100 tasks is reduced by about 5.4% on average. Compared with MOACS, PSO, and GA, the scheduling completion time on the workflow of 1000 tasks is reduced by 8.28% on average, and the average comprehensive cost is reduced by 23% on the condition of satisfying the time constraints.
Creating Vlog as Media in English Language Teaching (ELT):Teaching Speaking

In modern era, everything can be accessed by technology. One of the famous ones for young generation is YouTube or video blog (vlog) where someone can take a video for specific purposes. Google noted that there are 60% of people all over the world uploaded their activity in vlog and publish it in YouTube. Teaching speaking for students is not easy it needs huge extra to improve their speaking moreover in this global era where everything is measured by the achievement of the studentsThis research used to know the implementation of creating vlog in English Language Teaching (ELT). This research used qualitative descriptive in ilustrating the result. The subject of this research is the students of VI SDN Batang-Batang Daya 1. The instrument of this research are observation sheet, field note, in depth interview with the teacher and questionnaire for students. To analyze the data the researcher used descriptive qualitative. The result of this research described abot the implementation of creating video blogging (vlog) in English langage teaching (ELT) in teaching speaking.
Predicting credit card fraud using multipurpose classification based on evolutionary rules

Abstract Due to advances in Internet technology, credit card transactions are increasing faster than ever before. This has led to a fraud problem that affects businesses, cooperating institutions, and government agencies. Credit card fraud occurs when a third party uses your credit card or credit account for an unauthorized transaction. Given the increased security of credit card transactions, fraudsters are developing new tricks or exploiting new vulnerabilities. Thanks to evolving technology, it is possible to analyze the use of maliciously obtained data by studying the time and cost associated with account switching transactions. In this study, we propose the ENORA and NSGA‐II methods to create rule‐based classifiers that can be easily interpreted using the credit card fraud diagnosis dataset created by analyzing the time intervals of individuals' credit card usage. In this experiment, credit card payments are classified as fraudulent or nonfraudulent based on several variables. The experiments were conducted in full training mode and 10‐fold cross‐validation mode. The performance of the algorithm was measured by accuracy, area under the receiver operating characteristic (ROC) curve, mean square error, proportion of true positives, proportion of false positives, precision, recall, F‐measure, area under the curve (AUC), and Matthews correlation coefficient. The classification performance of the model using the correct classification ratio (ACC) is as follows: NSGA‐II = 94.244%, ENORA = 93.236%. The performance of the other metrics is also discussed in detail in the results section. These results illustrate the significant predictive power of the proposed credit card theft models. After a thorough statistical analysis of our results, we found that the proposed strategy is capable of producing accurate and easy‐to‐understand categorization models.
A Dual-Attention Heterogeneous Graph Neural Network for Expert Recommendation in Online Agricultural Question and Answering Communities

Online Agricultural Question and Answering (Q&A) Communities aim to help farmers obtain useful suggestions in the form of interactive Q&A dialogues. However, with the increasing number of users, a large number of questions cannot be answered in time. Moreover, questions in agricultural Q&A communities often contain limited textual information which also becomes a major challenge for expert recommendation as traditional methods are not applicable. In this article, we leverage heterogeneous graph neural networks to address the issue. To do this, we take a popular agricultural Q&A community in China as an example and we propose DAHGNN, a Dual-Attention Heterogeneous Graph Neural Network model for the expert recommendation problem. Our model considers the influence of both the node type and different neighbors of the same type. Through extensive experimental evaluations, we show that our model can obtain a better performance compared to many state-of-art methods. Our work helps to improve user participation in the community so as to efficiently assist farmers to solve problems.
An improved algorithm of TFIDF combined with Naive Bayes

The TF-IDF algorithm is often used for the extraction of keywords of articles, but it only considers the information of word frequency, which limits the choice of keywords. In order to improve the efficiency of the algorithm, an improved algorithm has been presented, which adds the synonyms of keywords trained by word2vec model to the word vector composed of keywords. Then the improved algorithm is given different weights based on the part of speech and the location information. And combine the improved algorithm with the Naive Bayes algorithm. In order to verify the effectiveness of the improved algorithm, experiments were conducted on a standard data set. The experimental results show that, compared with the traditional method, the accuracy of the improved TFIDF algorithm combined with Naive Bayes is greatly improved.
MSSA‐Net: A novel multi‐scale feature fusion and global self‐attention network for lesion segmentation

Abstract In medical image segmentation tasks, it is typical to adopt convolutional neural networks with a serial encoder‐decoder structure. However, mainstream networks cannot simultaneously achieve sufficient extraction of global features and the fusion of multi‐scale information, which may lead to unpromising results for the segmentation of pathological images. Therefore, this article proposed a novel multi‐scale feature fusion and global self‐attention network (MSSA‐Net) for medical image segmentation. Specifically, we designed a parallel double‐encoder network with a multi‐scale feature fusion encoder (MS‐Encoder) and a self‐attention encoder (SA‐Encoder). The SA‐Encoder introduces the transformer's global self‐attention mechanism to extract global features, and the MS‐Encoder adopts atrous spatial pyramid pooling (ASPP) to realize multi‐scale fusion. We have evaluated the proposed MSSA‐Net using three medical segmentation datasets, covering various imaging modalities such as colonoscopy and magnetic resonance imaging. Experiments on the CVC‐ClinicDC, the 2015 MICCAI subchallenge on automatic polyp detection dataset, and anatomical tracings of lesions after stroke (ATLAS) show that our MSSA‐Net outperforms mainstream methods such as DoubleU‐Net and TransUNet. Moreover, MSSA‐Net can predict more accurate segmentation masks, especially in the case of ATLAS, which has challenging images such as multiple shadow areas and discrete lesions.
End-User License Agreements (EULAs)

Abstract An online survey ( N = 810) examined the impact of design best practices on the perceived usability of End-User License Agreements (EULAs). Redesigning a EULA according to best-practices (without changing the EULA’s terms and conditions) led to higher perceived usability while responses to attitudinal (perceived reasonableness of conditions) and behavioral (anticipated agreement and use) items were unaffected. Readers (who reported reading EULAs frequently) (a) provided more positive evaluations of EULAs and (b) were more likely to anticipate agreeing to EULA terms than non-readers . These results suggest that best practices in document design can improve the usability of End-User License Agreements.
Robust visual tracking algorithm with coattention guided Siamese network

In recent years, Siamese trackers locate the target based on the cross-correlation results of the template and the search branch. Siamese trackers emphasized better features embedding and similarity matching. The latest work focuses on enriching the feature representation of the template. However, it is still difficult for the tracker to eliminate the side effect of the distractors in the search region, resulting in the decline in tracking accuracy. We propose a coattention guided Siamese network (CGS) for visual tracking from two aspects. To mine the inherent correlation between the template and search branch, a coattention block is proposed that can guide the target-aware feature learning during the tracking process. To wisely choose the target-specific features, a self-gated mechanism is exploited on the template features, which can adaptively reallocate the confidence of channels and strengthen the feature representation. We conduct extensive experiments on OTB2015, VOT2016, and GOT-10K benchmarks to evaluate the performance of the proposed method. Experimental results demonstrate that our CGS network performs favorable against other state-of-the-art methods. Moreover, our CGS runs at 79FPS, far above the real-time requirement.
Multispectral image registration based on an improved scale-invariant feature transform algorithm

There are often significant intensity variations between multispectral images, making automatic registration tasks difficult. Traditional feature matching methods, such as the scale-invariant feature transform (SIFT), are often sensitive to nonlinear variations of intensity between multispectral images. To solve this problem, an improved SIFT algorithm is introduced. First, the contrast limited adaptive histogram equalization algorithm is introduced in the feature extraction stage to improve the feature point extraction results. Then, the Sobel operator is used to enhance the main direction consistency between homologous feature point pairs. The experimental results suggest that the method can obtain reliable registration results on unmanned aerial vehicle multispectral images.
Face Mask Detection using Deep Learning and Transfer Learning Models

The fast spread of Coronavirus (COVID-19) has created a global health crisis. The World Health organization (WHO) released several recommendations to help in preventing the spread of coronavirus. Wearing a mask in crowded venues is the most appropriate protective practice against COVID-19, according to the WHO. Keeping a close eye on people in public places is next to impossible, so identifying face masks becomes critical in the fight against COVID-19. Medical image analysis and classification are two areas where deep learning has lately become one of the widely used ways for enhancing performance. It can be utilized quite efficaciously to identify individuals who are not wearing a mask. The utilization of transfer learning models as a deep learning technique is also on the rise, and they work quite well. In our study, we used a total number of 7235 data images from an online dataset for face mask detection, which is done with two deep learning models and one transfer learning model. The deep learning-based CNN model using MaxPooling operation and AveragePooling operation achieved the accuracy of 95.78% and 95.36% respectively. Contradictory to that, the transfer learning-based MobileNetV2 model achieved an accuracy of 99.10%.
Device Action Prediction Based on K-means and Apriori for Smart Home

This paper proposes to use K-means and Apriori to prediction device action based on time in Smart Home System. In the existing methods, the system provides services to human when conditions are met, such as high temperature and low humidity. However, those methods ignoring the correlation between device action and human activity time. In order to solve this problem, we combine K-means and Apriori algorithm to generate association rules with time information. First, calculate the appropriate K-value for K-means. We select consecutive data in weeks. The data contains the time information of device action. Second, run K-means to classify based on device action time information, and run Apriori to mine association rules based on classification. Finally, decode the associate rules and realize device action prediction. In our work, K-means experiments show that clustering according to the difference of working days is an effective way to process user time information; Apriori experiments proved that the association rules mined with the results of K-means can predict device actions in the Smart Home environment.
Research and Application of Data Partition Technology in Distributed Database

Distributed database has the characteristics of high scalability, high availability, low cost and performance improvement. How to build an appropriate data partition is the core problem for distributed database to solve the storage problem and improve the performance at the same time. By studying the data partition technology of distributed database, this paper gives the principles and design methods of database partition in distributed database design, and puts forward formulae of aggregation and balance that needs to be paid attention to in database partition, which provides a new idea for the transformation of database partition design from qualitative analysis to quantitative analysis. Moreover, why aggregation should be considered as one of the principles of data partitioning in distributed database is verified by experiments, and the application of database partition technology is illustrated by an example in telecom business support system.
Classification of Mobile Encryption Services Based on Context Feature Enhancement

Smart phones have become the preferred way for Chinese Internet users currently. The mobile phone traffic is large from the operating system. These traffic is mainly generated by the services. In the context of the universal encryption of the traffic, classification identification of mobile encryption services can effectively reduce the difficulty of analytical difficulty due to mobile terminals and operating system diversity, and can more accurately identify user access targets, and then enhance service quality and network security management. The existing mobile encryption service classification methods have two shortcomings in feature selection: First, the DL model is used as a black box, and the features of large dimensions are not distinguished as input of classification model, which resulting in sharp increase in calculation complexity, and the actual application is limited. Second, the existing feature selection method is insufficient to use the time and space associated information of traffic, resulting in less robustness and low accuracy of the classification. In this paper, we propose a feature enhancement method based on adjacent flow contextual features and evaluate the Apple encryption service traffic collected from the real world. Based on 5 DL classification models, the refined classification accuracy of Apple services is significantly improved. Our work can provide an effective solution for the fine management of mobile encryption services.
APTNER: A Specific Dataset for NER Missions in Cyber Threat Intelligence Field

This paper provides a new dataset for Named Entity Recognition (NER) missions in cyber threat intelligence (CTI) studying. To the best of our knowledge, the proposed dataset is the biggest and challenging one in the field to comply with the STIX 2.1 specification. We collected the APT (Advanced Persistent Threats) reports from different network security companies and manually annotated them. Then we constructed a dataset named APTNER, which can be used for NER joint and multi-task learning tasks in CTI. Apart from common labels like IP, URL, mal-ware, location and so on, APTNER contains 21 categories, which make APTNER more challenging than other NER datasets in CTI field and we have proved the rationality of the dataset. For ease of comparison studies, we realize several state-of-the-art baselines and report their analysis. To facilitate future work on fine-grained NER for CTI, we make APTNER public at https://github.com/wangxuren/APTNER.
Flexible application-aware approximation for modern distributed graph processing frameworks

The interest in the ability of processing data that has an underlying graph structure has grown in the recent past. This has led to the development of many distributed graph processing systems. However, due to rapidly growing amount of data, e.g., web graphs and social graphs, even such distributed graph processing frameworks end up requiring several minutes or even several hours to execute popular graph algorithms. This leads to the question: do we always need to know the exact answer for a large graph?
Intelligent English Writing Automatic Scoring System Based on Data Fusion Algorithm

With the rapid development of science and information technology, computers are increasingly used in high school English teaching. People use network technology to develop an automatic scoring system for English writing, and use it to automate English writing correction and feedback. Automatic English writing correction and feedback are convenient, fast, detailed and relevant. This will also be the direction of feedback education and research in English high schools in the future. This paper studies the intelligent English writing automatic scoring system based on data fusion algorithms. After a general understanding of the intelligent English writing automatic scoring system and the related theories of data fusion algorithms, the intelligent English writing automatic scoring system based on data fusion algorithms is carried out, then design and test the designed system. The test results show that the overall score of the system in this paper is 1-2 points higher than the manual score.
Trusted Healthcare Smart Brain : Innovational Internet Architectures of Intelligent Collaboration of Multi-institution for the Healthcare Service

In the past decade, with the deepening of the aging of the population and the strengthening of the health consciousness of the whole society, the Internet healthcare service has grown to be an inevitable trend of current society. We propose a collaborative adaptive architecture named Trusted Healthcare Smart Brain (THSB) for cross-blockchain intelligent collaboration of multi-institution healthcare services. THSB is an interdisciplinary system with Healthcare Internet of Things (H-IoT), blockchain, Artificial Intelligence, Cloud Computing, Big Data, and Internet. The participants of THSB include patients, rehabilitation institutions, medical service institutions, healthcare content service institutions, medical regulatory institutions, scientific research institutions, and government institutions. In addition, we propose a medical resources service balance method to maximize the utilization of medical service resources to solve the contradiction between random medical events and the normal distribution of medical resources.
Recognition of Bangla Sign Language Characters and Digits Using CNN

Sign language is an elemental way of communication in the mute and hearing-impaired community. Bangla Sign Language (BSL) is the conversation medium for Bengali deaf people. In this paper, a BSL dataset containing 2660 images is presented where 2160 belong to 36 classes of letters and 500 belong to 10 digit classes. A deep learning based system can be a great replacement for human interpreters and motion sensing devices to ensure a cheaper, rapid and accurate BSL recognition. Convolution Neural Network (CNN) can effectively extract features from images and generalize unseen images. Transfer learning involving InceptionV3, VGG19, InceptionResnetV2 and ResNetl52V2 pretrained architectures has been conducted in the experiments. Insignificant image preprocessing was required to train these models and minimal overfitting could be ensured by thoroughly tuning the hyperparameters. The validation accuracy for the best model (ResNetl52V2) is 98.5% when trained on 12k augmented images.
A Budget and Nutrition-aware Mobile Application Featuring Food Menu Generation and Monitoring for the School Feeding Programme in Bangladesh

The school feeding program is regarded as one of the effective solutions to solve the malnutrition problem and primary school educations dropout issue. Hence, to run the school feeding program effectively, a budget and nutrition-aware food menu generation scheme is required along with proper monitoring, students feedback, and real-time attendance facilities. At present, the existing related works related to mobile application development for school feeding programs do not consider a budget and nutrition-aware food menu generation, real-time attendance system, evidence attachment of the distributed foods to ensure monitoring, students feedback regarding the program, and the donation facility at the same time. To tackle the existing challenges, this paper presents a budget and nutrition-aware mobile application featuring food menu generation and proper monitoring for the school feeding program in Bangladesh. By using our proposed application, an authorized teacher can select the best possible food menu for students by verifying budget, nutrition, choices, and cost, and send evidence to the higher authorities regarding distributed food number, food nutrition value, and cost. Moreover, the students can give feedback to the authority regarding the school feeding program. Our evaluation results show that more than 80% of users are satisfied with the proposed system.