Reviewerly/exHarmony huggingface dataset:
RETRACTED: Research on the Characteristic Model of Survival and Development Behavior of Math Teachers in Higher Vocational Colleges Based on Big Data Technology

Abstract With the implementation of the national "double high education program", the living environment and training conditions of teachers in various disciplines have become an important field of education system reform. At present, due to the imperfect construction of teachers’ salary system and training mechanism, teachers in higher vocational colleges still face many difficulties in their survival and development, which seriously limits the implementation effect of the "double high education program". Therefore, it is particularly urgent to study the survival and development of teachers in vocational colleges. This article takes mathematics teachers in vocational colleges as the research object, and describes the current situation of the survival and development of mathematics teachers in vocational colleges. On this basis, this paper uses the refined computer big data technology to construct the survival and development behaviour characteristic model of mathematics teachers in vocational colleges, and discusses the feasible strategies to ensure the quality of survival and development of mathematics teachers, which provides a solid theoretical support for the smooth implementation of the national "double high program".
PENGARUH PENGGUNAAN MODEL PICTURE AND PICTURE TERHADAP KETERAMPILAN MENULIS TEKS CERITA FANTASI SISWA KELAS VII SMP NEGERI 7 PARIAMAN

ABSTRACT The purpose of this study consists of three. First, it describes the writing skills of fantasy text for grade VII Junior High School 7 Pariaman before using the picture and picture models. Secondly, it describes the writing skills of fantasy text of grade VII Junior High School 7 Pariaman using a picture and picture models. Third, describe the effect of using picture and picture models against writing fantasy text skills grade VII Junior High School 7 Pariaman. This type of research is quantitative research with experimental methods. The design of this research is one group pretest-posttest design. The results of this study are three. First, the fantasy text writing skills of grade VII Junior High School 7 Pariaman before using the picture and picture model are in the Almost Enough qualification with an average score of 52.99. Secondly, the skill of writing fantasy text for grade VII Junior High School 7 Pariaman after using the picture and picture model is in the qualification More Than Enough with an average count of 69.53. Third, based on the t-test, the alternative hypothesis (H1) is at a significant level of 95% and the degree of freedom (dk) = n-1 because tcount &gt; t table, which is 4.59 &gt; 1.70. Kata Kunci: pengaruh, model picture and picture, menulis teks cerita fantasi
The Architecture of VisiOn Privacy Platform

The VisiOn Privacy Platform consists of a set of components and tools, which will work and collaborate in a single platform. The overview of the VisiOn architecture is provided in Fig. 1.
Spatial frequencies for detection of pain facial expressions revealed by reverse correlation

The ability to detect pain facial expressions is a crucial step before help can be provided. Because of the biological importance of this skill, it is plausible to expect that an observer can detect that expression even from a relatively large distance. Accordingly, in VSS2019, we presented a study showing that pain facial expression detection relies on low spatial frequencies (SF; Guérette et al., 2019); low SF are available from farther away than high SF. These results were obtained using posed facial expressions, with a method that involves repeating the same stimuli. In the present study, we used Reverse Correlation (Mangini & Biederman, 2004) to verify in which SF the mental representation of pain facial expressions are encoded. This method has the advantage of revealing the expectations about the appearance of an expression, and the latter may be closer to spontaneous expressions encountered in day-to-day social interactions. On each trial, a neutral face was used as background stimulus, on which sinusoidal white noise was added. Participants were asked to choose which of two noisy faces better represented a target emotion. Three target emotion conditions were used: pain, fear, and happiness. Fear and happiness are respectively considered the most similar and dissimilar expressions to pain (Wang et al., 2015). Mental representations of pain involved SF ranging from 1.13 to 12.3 cycles per face (cpf), peaking at 3.78 cpf. Fear and happiness relied on a similar range of SF (4.17 and 3.63 cpf, respectively). These results show that low SF are encoded in mental representations of pain facial expressions. This finding is congruent with previous findings that accurate detection of pain relies on low SF, and add evidence to the idea that pain expressions are communicated in a way to be detected from far away and using coarse visual information.
A PRESENÇA DAS TEORIAS ADMINISTRATIVAS NA ENFERMAGEM NOS DIAS ATUAIS

It refers to a summary of descriptive and exploratory content. The participation of nurses in health management and the use of administrative and organizational theories in the midst of the pandemic was observed as a way to improve the service provided to the patient. Articles worked on in class in the curriculum component of Management of primary care services were used as a research source.
Extract and Merge: Merging Extracted Humans from Different Images

Selecting human objects out of the various type of objects in images and merging them with other scenes is manual and day-to-day work for photo editors. In this work, we proposed an application, utilizing Mask R-CNN (for object detection and mask segmentation), to group people in a single image with a new background after extracting them from different images. At first, it extracts the full body of a person only without any obstacles, such as a dog in front of the person, from different image, such as three person from three different images. Then, it will merge those extracted human instances together as a single image and place them in a new background. This application does not add any overhead to Mask R-CNN, running at five frames per second. It can extract human instances from any number of images for merging them together as well as it can extract more than one person from a single picture. It also works with video and process it frame by frame. We also structured the code to accept videos of different lengths as input and length of the output video will be equal to the longest input video. We wanted to create a simple yet effective application that can serve as a base for photo editing and do most time-consuming work automatically, so, editors can focus more on the design part. Other application could be to group friends (who cannot be physically together), after extracting each friend from different images, in a single picture. We are showing one-person and two-person extraction, and then placement in two different backgrounds. Also, we are showing a video example with single-person extraction.
Research on Cultivation of Computer-aided Translation Talents under the Background of “Internet+”

Abstract In recent years, with the rapid development of language service industry, the original simple service has been far from adapting to the new development of the industry. Under the background of "Internet+", the scope of language services has expanded from simple interpretation and translation to the whole language service outsourcing, such as technical writing, website localization, multilingual information processing and other fields. Then we should start to cultivate computer-aided translation talents. At the same time, the requirements for language service practitioners are not only to master oral and written translation skills, but also to master it skills, project experience, market awareness, cross-cultural communication, professional quality, industry knowledge and localization skills, terminology management, translation technology, translation tools, typesetting and printing, translation management and other skills on this basis.
Research on Multilingual Intelligent Translation System Based on Artificial Intelligence Technology

After the multilingual intelligent translation system is transplanted from DOS to the Windows environment, it aims at problems such as inconvenient dictionary management, troublesome tool use, unfriendly translation and editing interfaces, and imperfect log maintenance during the process of further expanding resources and debugging development. We have developed an intelligent translation support environment subsystem for multiple languages. The subsystem implements functions such as dictionary resource management, translation interface, development tool integration, and system log maintenance. It is compatible with Unicode in coding, and uses dynamic link library technology when calling the original system translation function and development tool, in order to become a multi-language-oriented the development and debugging support environment that can be used for different multilingual intelligent translation systems.
Implementation of Famine Relief in Chungcheong-do Region and Conditions of Relief Provision in Jeongsan-hyeon during Imgye Hyunghwang(the Famine of 1832-1833)

In this article, focusing on famine relief policy in Chungcheong provincial region during 1832∼1833, actual conditions and characteristics of famine relief are examined, on its way to the mid-19th century. In 1832, as the famine caused by the flood was expected, the dynastic government prosecuted the wide range of famine relief services.<BR> First, each organization focused on securing grains necessary for devasted areas. The central government supported resources needed for the relief by transporting grains and issuing Gongmyeongcheop(空名帖; Blank Certificate for Appointment). The relief resources were distributed to each Counties and Prefectures, under the lead of Chungcheong Gamsa, the Governor and the supervisor of the provincial region. At the same time, Suryeong, the Magistrates, tried to arrange grains in his own precinct.<BR> Each Counties and Prefectures started to provide relief. Prefecture of Jeongsan was the one among the severest. From the 11th to the 12th months of the lunar calendar, the Magistrate of Jeongsan procured resources to relieve the starved residents and sorted the relief recipients. To recipients, the relief grains were dispensed 13 times from the late 12th month of 1832 to the 4th month of the following year.<BR> After the relief, the reports and the evaluations were followed. Since the roles of the Magistrates and the rich people were the core of the famine relief, assessments were made, and rewards were granted for them. The famine relief of 1832∼1833 was completed with this process.<BR> Jinhyul of Chungcheong province during 1832∼1833 showed a different aspect compared with previous ones. In previous famine relieves, Gonggok (公穀; Public grain), or grains of central government was the main source, while in this period, grains of local government played the key role. It was a state-wide phenomenon, and so was after late 1830s or 1840s. It cannot be denied that this form of famine relief involved several problems, nevertheless, it must not be ignored that numerous farmers could make their living by what were implemented in the local precincts.
Developing a Network Security Plan

This chapter reviews the changing role of audit and security in distributed environments, the types of threats present in these environments, the range of methodologies available for securing a sample Novell network, and some of the current technologies and tools available for maintaining network security. Developing countermeasures to security threats can be a daunting task if attempted without a well-thought-out methodology. In addition to a strong security plan, an organization must look at its particular environment and technology to develop specific approaches for countering possible security threats inherent within that technology. Firewalls are becoming the method of choice for organizations determined to maintain security from outside intruders. By developing a network security policy that becomes the framework for a proactive plan, Intermediate System executives and managers can integrate the skills and resources of audit and security personnel in maintaining the plan.
Fanaticism Analysis of Social Media Using Machine Learning

Sentiment and emotion analysis on social media is an interesting study because it reveals the emotional state of the public in a domain. The challenges in sentiment analysis research in Indonesian are inefficient preprocessing, inaccurate feature extraction methods, and low classification accuracy by machine learning. One aspect of sentiment analysis is fanaticism. Fanaticism contains an emotional element in sentiment analysis. This article discusses how to detect opinions that contain political fanaticism, then categorize them into several polarities of  political  fanaticism. Feature extraction is done by processing sentiment, anger, happiness, disgust, surprise, fear, and hate speech analysis. Knowledge for classification is K-NN, Naive Bayes, Random Forest, and Decision Tree. The aim is to find out the best combination of machine learning methods for feature extraction and finally used for fanaticism categorization. The best method is Random Forest with an accuracy of 81% and will be used as a final method for monitoring fanaticism on social media.
Homomorphic Interpolation Network for Unpaired Image-to-image Translation

Generative adversarial networks have achieved great success in unpaired image-to-image translation. Cycle consistency, a key component for this task, allows modeling the relationship between two distinct domains without paired data. In this paper, we propose an alternative framework, as an extension of latent space interpolation, to consider the intermediate region between two domains during translation. It is based on the assumption that in a flat and smooth latent space, there exist many paths that connect two sample points. Properly selecting paths makes it possible to change only certain image attributes, which is useful for generating intermediate images between the two domains. With this idea, our framework includes an encoder, an interpolator and a decoder. The encoder maps natural images to a convex and smooth latent space where interpolation is applicable. The interpolator controls the interpolation path so that desired intermediate samples can be obtained. Finally, the decoder inverts interpolated features back to pixel space. We also show that by choosing different reference images and interpolation paths, this framework can be applied to multi-domain and multi-modal translation. Extensive experiments manifest that our framework achieves superior results and is flexible for various tasks.
A Numerical Measure of the Instability of Mapper-Type Algorithms

Mapper is an unsupervised machine learning algorithm generalising the notion of clustering to obtain a geometric description of a dataset. The procedure splits the data into possibly overlapping bins which are then clustered. The output of the algorithm is a graph where nodes represent clusters and edges represent the sharing of data points between two clusters. However, several parameters must be selected before applying Mapper and the resulting graph may vary dramatically with the choice of parameters. We define an intrinsic notion of Mapper instability that measures the variability of the output as a function of the choice of parameters required to construct a Mapper output. Our results and discussion are general and apply to all Mapper-type algorithms. We derive theoretical results that provide estimates for the instability and suggest practical ways to control it. We provide also experiments to illustrate our results and in particular we demonstrate that a reliable candidate Mapper output can be identified as a local minimum of instability regarded as a function of Mapper input parameters.
Metric Study: Use of Memory Corresponding to the ISO/IEC25010 Standard in Augmented Reality Applications

Today there is a wide variety of applications that use the emerging technology of augmented reality in different areas, especially in education with a wide variety of uses, in tourism and even in the military field and art. Each one of them tries to satisfy some need or provide a specific service; however, they do not always meet user expectations. The aim of this research was to analyze the memory usage metric based on the ISO/IEC25010 standard, experimenting in two tourist applications with the same characteristics developed in two RA tools: Vuforia and Wikitude. The research method used was experimentation using the Indicator CPU application running it on three smartphones (two with high features and capabilities and one with low features and capabilities) as a measurement tool. As a result, there are significant differences in memory use between the two devices of the same brand (Samsung J1 and S8) with mean values in Vuforia 0.2420 and 0.5800 respectively and 0.2840 and 0.5200 in Wikitude. Furthermore, it is evident that the differences in the memory usage metric between the high features and capabilities device (Sony Z5) and the low features and capabilities device (Samsung J1) are insignificant.
Multimodal Learning Based Spatial Relation Identification

Spatial Relation identification is one of the integral parts of Spatial Information Retrieval. It deals with identifying the spatially related objects in view of their physical orientation or placement with respect to each other. The concept is widely used in many fields such as Robotics, Image Caption Generation and many more such areas. In this work the focus is to gather information from multiple modalities such as Image and its corresponding Text so as to strengthen the learning process for the identification of Spatial Relation pairs from a given text. Two different multimodal approaches are proposed in this work. In the first approach, information is explored as a sequential learning process where the individual Spatial Roles are identified as connected entities, which makes the Spatial Relation retrieval easy and efficient enough. To counter the small size of the dataset along with necessity to avoid overfitting, an efficient backward propagation based Neural Network was used to classify the candidate roles and the relations. The feature selection was different for all the classification tasks. Building on the selected feature from the first approach, the second approach uses a transfer learning method that utilizes an existing image caption generation model to retrieve the vital topic based information from image which is then used for the task. Thereby both approaches used information from two modalities which are further used to train the system in the respective approach. The model achieves state-of-the-art performance in terms of Precision for two of the Spatial Roles identification. This validates the advantage of using multimodal learning when compared with other partial-multimodal processes.
Applications of New Technologies to Recognition of Financial Statement Fraud

Financial statement fraud is one of common means of false accounting. Authenticity and reliability of financial statements are doubted due to fraud. Recently, it is very frequent to see financial statement fraud of listed companies in China and there might be more frauds which have not been exposed yet. It is urgent to develop technologies for supervision of financial statements and recognition of financial statement fraud. Using new technologies to recognition of financial statement fraud will bring a fresh reform to the auditing industry. More importantly, using new technologies will surely improve auditing quality despite of decreasing labor cost and time cost.
CWOM: a lightweight cloud-oriented workflow optimisation middleware

In recent years, more and more workflow applications have been developed to execute on various kinds of cloud platforms. Although many cloud-based workflow management and enactment systems have been proposed, there are still many challenges when deploying and executing workflows on various kinds of real-world cloud platform. In this paper, we present a lightweight framework, namely cloud-oriented workflow optimisation middleware (CWOM), which is designed for easing the work of optimising the workflow execution efficiency as well as the work of realising fine-grained workflow management. The proposed CWOM is implemented as a plug-in middleware that can interact with the other workflow services and therefore provide more desirable services for workflow applications. Another advantage of our CWOM is that its services are loosely coupled with other cloud services, which makes it more flexible and configurable in most of the real-world cloud platforms. To investigate the effectiveness of the proposed system, we deploy the prototype implementation of CWOM in a campus-based cloud platform, and the experimental results indicate that it can significantly improve the delivered QoS and enhance the service level offered by conventional workflow solutions.
Multi-task Learning based Voice Verification with Triplet Loss

Human voice verification is an essential component in the speech-based biometric signature as its comfortable-in-use and applicability in security systems. Previous studies on voice verification often need a large amount of annotated training data, hence the high cost. In this paper, we propose a multi-task learning model combined with triplet loss to address the voice-based speaker verification task. Our model consists of two main parts: encoder and classifier. The encoder utilizes the features being learned and represent in the DeepSpeaker model [1] for transfer learning to our encoder; while the classifier utilizes the weighted combination of triplet loss and softmax loss for the training process. A rigorous experiment is conducted to verify the proposed model. The experimental results have demonstrated that our proposed method has achieved the good verification accuracies with the equal error rate (EER) of 2.3% on the Librispeech dataset and 4.06% on the Vivos dataset, which are significantly improved compared to the triplet loss baseline method. In addition, we also highlight the results of the fine-tuning pre-trained model for a new language with a small dataset.