Reviewerly/exHarmony huggingface dataset:
Research on Barrage Sentiment Ontology Construction Based on SO-PMI Algorithm

Barrage is the commentary content that pops up when a video playing, which is short and concise and has strong domain characteristics. Because the sentiment analysis method in the field of barrage language is relatively single, this paper presents a method for constructing a library of barrage ontology based on SO-PMI algorithm and the word vector maximum similarity algorithm. The barrage ontology is based on professional knowledge and the barrage sentiment dictionary, which is added into the ontology as an example to construct the knowledge base. The sentiment classification of barrage can be realized and a new way for sentiment analysis of barrage language is provided by this method. The barrage ontology with tendency of sentiment can be better applied to video recommendation and opinion monitoring.
AIM against survey fraud

Although there exists a variety of anonymous survey software, this study aimed to develop an improved system that incentivizes responses and proactively detects fraud attempts while maintaining anonymity.The Anonymous Incentive Method (AIM) was designed to utilize a Secure Hash Algorithm, which deterministically assigned anonymous identifiers to respondents. An anonymous raffle system was established to randomly select participants for a reward. Since the system provided participants with their unique identifiers and passwords upon survey completion, participants were able to return to the survey website, input their passwords, and receive their rewards at a later date. As a case study, the validity of this novel approach was assessed in an ongoing study on vaping in high school friendship networks.AIM successfully assigned irreversible, deterministic identifiers to survey respondents. Additionally, the particular case study used to assess the efficacy of AIM verified the deterministic aspect of the identifiers.Potential limitations, such as scammers changing the entry used to create the identifier, are acknowledged and given practical mitigation protocols. Although AIM exhibits particular usefulness for network studies, it is compatible with a wide range of applications to help preempt survey fraud and expedite study approval.The improvements introduced by AIM are 2-fold: (1) duplicate responses can be filtered out while maintaining anonymity and (2) the requirement for the participant to keep their identifier and password for some time before returning to the survey website to claim a reward ensures that rewards only go to actual respondents.
Contactless Human-Computer Interaction Using a Deep Neural Network Pipeline for Real-Time Video Interpretation and Classification

Nowadays, all applications are developed with the user's comfort in mind. Regardless of the application's objective, it should be as simple as possible so that it is easily accepted by its users. With the evolution of technology, simplicity has evolved and has become intrinsically related to the automation of tasks. Therefore, many researchers have focused their investigations on the interaction between humans and computing devices. However, this interaction is usually still carried out via a keyboard and/or a mouse. We present an essemble of deep neural networks for the detection and interpretation of gestural movement, in various environments. Its purpose is to introduce a new form of interaction between the human and computing devices in order to evolve this paradigm. The use case focused on detecting the movement of the user's hands in real time and automatically interpreting the movement.
A Nugget-Based Concept for Creating Augmented Reality

Creating Augmented Reality (AR) applications can be challenging, especially for persons with little or no technical background. This work introduces a concept for pattern-based AR applications that we call AR nuggets. One AR nugget reflects a single pattern from an application domain and includes placeholder objects and default parameters. Authors of AR applications can start with an AR nugget as an executable stand-alone application and customize it. This aims to support and facilitate the authoring process. Additionally, this paper identifies suitable application patterns that serve as a basis for AR nuggets. We implement and adapt AR nuggets to an exemplary use case in the medical domain. In an expert user study, we show that AR nuggets add a statistically significant value to an educational course and can support continuing education in the medical domain.
Methods of Machine Learning

The article discusses machine learning methods. Methods are taken as an example, ways to study under observation and training, as well as training with partial attraction of a teacher based on experiments and research. Presented options for the main types of machine learning models for each method. An isolated structure of any machine learning model is analyzed. The main component of the main component is considered, which is a statistical operation on orthogonal transformation, translating observation of variables into a set of main component—values. Dana Library “Scikit-Learn”, in which most machine learning algorithms are implemented. Described by the “Random Forest” algorithm, which is used in “Sberbank” to predict the non-payment and bankruptcies of companies and IP. In conclusion, describes what tasks can be solved depending on the choice of learning.
Chapter 2. Swedish FrameNet

This chapter describes the development of Swedish FrameNet. A new framenet project often follows one of two methodological approaches: (1) extension, through translation of a different-language – often English – framenet into the target language, and (2) merging, where the resource is built from scratch in the target language. Both approaches have their pros and cons, which have been extensively discussed in the literature. Swedish FrameNet is mainly developed through the extension approach, although balanced with the merging approach. Drawing on the two approaches simultaneously, we describe how integrated language resources and tools have been exploited to create and develop Swedish FrameNet: how it was constructed, what it contains, and the basic assumptions underlying the annotation of its contents.
A Study of Neuro-Weighted Nearest-Neighbour Classification

Irrelevant features of the dataset have a certain impact on the judgment ability of the classifier. Effectively distinguishing strong and weakly relevant features can improve classification performance. Since neural networks can effectively dig out the underlying impact of the conditional features on the decision, in this work, the knowledge learned and stored in the parameters of neural networks will be applied to weight the features. Furthermore, the weighted features will be used in the similarity-based nearest-neighbour (SNN) classifier to enhance the model’s classification performance. The experimental results demonstrate that the proposed weighted nearest-neighbour approach generally outperforms many popular classification techniques.
Aircraft image recognition in airport flight area based on deep transfer learning

Aircraft recognition is of great significance to the construction of intelligent airport surface video monitoring system. At present, there are few studies on aircraft identification in airport flight area. In order to realize the accurate monitoring of airport surface aircraft, the method of transfer learning is proposed to identify aircraft. By collecting the images of Sichuan Airlines aircraft in Chengdu Shuangliu international airport, a total of 539 data sets composed of five types of AirBus aircraft images are constructed. 80% images are randomly selected as the training set and 20% images as the test set. The method of constructing deep transfer learning network based on AlexNet, ResNet and EfficientNet is used to modify partial convolution layer and full connection layer for the obtained dataset. The accuracy rates of the three deep learning network models on the test set are 88.2 %, 98.1 % and 94.4 % respectively. The experimental results show that using deep transfer learning method to train small or medium sample aircraft image dataset can achieve high recognition accuracy.
Identification of Breast Cancer Through Digital Processing of Mammograms

Breast cancer is one of the main types of cancerous diseases in terms of its mortality rate, although it can normally be cured if the disease is detected early enough. For this reason, it is very important to have computer-assisted diagnosis systems that allow the early detection of breast cancer, especially in underdeveloped countries where women do not have the opportunity to access specialist physicians. Thus, this work proposes a system for the identification of breast cancer on mammograms, whose main objective is to keep the implementation of the system simple, efficient and effective, reducing the need for complex operations or expensive hardware. For this, the current proposal uses digital image processing techniques for the enhancement and segmentation of the regions of interest. Machine learning algorithms (e.g., naive Bayes, artificial neural networks, decision trees, and nearest neighbors) are subsequently used to classify a reduced set of features extracted from the processed mammograms, according to labels that have been validated by specialist clinicians. The evaluation results show that the current proposal is straightforward and, at the same time, precise enough to identify breast cancer on mammograms with accuracy and sensitivity values greater than 99%.
Intelligent Lifebuoy Based on Machine Vision

Abstract This article mainly introduces the intelligent lifebuoy based on machine vision. The design uses the OpenMv camera to identify the drowning person. OpenMV sends the collected position information of the person to the MCU. If the drowning person is on the left or right side of the lifebuoy, the MCU controls the motor drive module. Control the differential rotation of the motor behind the lifebuoy so that the lifebuoy is facing the drowning person. If the drowning person is directly in front of the lifebuoy, the MCU controls the motor drive module to control the rotation of the motor behind the lifebuoy to make the lifebuoy gradually approach the drowning person, which can save life. Blue balloons was used instead of drowning people to conduct rescue experiments on the lake. The smart lifebuoy successfully recognized the blue balloon and swam near the blue balloon. Furthermore, a perfect solution is proposed for the shortcomings of the intelligent lifebuoy.
Identification and Visualization of Zombie Enterprise Portraits - Mining Short-time Series Features from the Perspective of Image

Governance of zombie enterprises is an important means to ensure the healthy, sustained development of the economy. Traditional methods such as identifying zombie enterprises based on expert knowledge suffer from incomplete expert database and increasingly complex economic environment. Thus the proposed data-driven system is implemented to not only identify zombie enterprises, but also visually present the enterprise portraits. In this paper, the three-year data of 50000 enterprise is transformed into N×N×3 image-format-matrix (N×N are the number of features). Afterward, Convolutional Neural Network, namely CNN is applied and result is got in one stage instead of fitting the data of each year and voting. It is also proved that CNN can effectively mine the short-time series features of enterprises by reconstruct the data into image-format-matrix. Considering the imbalance of data, Focal-Loss is implemented as the loss function when applying CNN model to the data. Grad-CAM, a model interpretive method in the image domain, is used to explain the CNN network after the fitting is completed. It is found that the model pays too much attention to salient features. Thus Mutual Channel Loss is further implemented to make the model pay attention to those indistinguishable features. At the same time, CBAM attention module is added to pay selective attention to different characteristics of enterprises in different years. The three-year information of 15050 enterprises collected from the State Administration for Industry and Commerce of China is used as the source data. The results show that comparing with other models, our CNN model reached the state of art in the rate of misjudgment and missed judgment.
Vocabulary: Common or Basic?

Neither linguistics nor psychology offers a single, unified notion of simplicity, and therefore the simplest "core" layer of vocabulary is hard to define in theory and hard to pinpoint in practice. In section 1 we briefly survey the main approaches, and distinguish two that are highly relevant to lexicography: we will call these common and basic. In sections 2 and 3 we compare these approaches, and in section 4 we point the reader to Kolmogorov complexity, unfamiliar as it may be to most working psychologists, lexicographers, and educators, as the best formal means to deal with core vocabulary.
College Exam Allocation Using MongoDB and Python3

A college has a minimum of 4000 students. During examination activities, these students have to be allotted examination rooms in a way that there are students equal or less than the capacity of the rooms and that no student is allotted multiple rooms or no rooms at all. The faculty should be posted as Invigilators to the appropriate rooms and care should be taken such that not a faculty is not posted at multiple rooms and should have the number of duties corresponding to their experience. The Courses should also be taken care of such that no student has 2 examinations on different courses at once and that all the course exams are allotted.This is a replacement for the previous existing system[3] where everything was done manually, considering one or a batch of students at once allocated to one room at a time. It reduces the time taken by the previous system and the resources needed to carry out this task. This requires specific python distributions as well as third party database systems to be working effectively. It uses Microsoft Excel Documents as well as querying operators provided by MongoDB[4] to work efficiently. The goal is to handle the scheduling tasks required during examination activities by a college institution effectively, efficiently and reliably.
User Satisfaction with an AI-Enabled Customer Relationship Management Chatbot

Chatbots' ability to carry out focused, result-oriented online conversations with human end-users impacts user experience and user satisfaction. Using "Chatbots" as key identifiable examples utilized by Electronic Commerce (E-Commerce) firms in Customer Relationship Management (CRM), this study offers a user satisfaction model in the context of Artificial Intelligence (AI) enabled CRM in E-Commerce. The model is based on Expectation Confirmation Theory (ECT) and Uncertainty Reduction Theory (URT) within the Chatbot context. This model will allow us to investigate if chatbots can provide both businesses and consumers the opportunity to complete a journey from normal through abnormal to the new normal in situations similar to a covid pandemic.
Improve Asset Recognition Rate through Network Audit System

The core of the network audit system is to locate and trace the source of network events. The identification of user identity information is the key to the realization of event location and traceability. After a brief introduction to the common identity information recognition technology in network auditing, an innovative intelligent association and recognition technology of user identity information is introduced.
Transfer Bayesian Meta-Learning Via Weighted Free Energy Minimization

Meta-learning optimizes the hyperparameters of a training procedure, such as its initialization, kernel, or learning rate, based on data sampled from a number of auxiliary tasks. A key underlying assumption is that the auxiliary tasks - known as meta-training tasks - share the same generating distribution as the tasks to be encountered at deployment time - known as meta-test tasks. This may, however, not be the case when the test environment differ from the meta-training conditions. To address shifts in task generating distribution between meta-training and meta-testing phases, this paper introduces weighted free energy minimization (WFEM) for transfer meta-learning. We instantiate the proposed approach for non-parametric Bayesian regression and classification via Gaussian Processes (GPs). The method is validated on a toy sinusoidal regression problem, as well as on classification using miniImagenet and CUB data sets, through comparison with standard meta-learning of GP priors as implemented by PACOH.
Chapter 4. A lexical resource for computational historical linguistics

In this chapter we present the diachronic dimension of Swedish FrameNet++. We describe the historical lexical resources currently available for Swedish, linked to the Contemporary Swedish lexicon Saldo. We present a case study of how interlinking the dictionaries simultaneously allows us to study lexical change. We also present a method of linking text words to lexicon entries, facilitating interactive exploration of historical texts. Diachronical language resources present both a high-variation challenge from a wider language technology perspective, and an interesting object of linguistic study. While a number of improvements of the parts of the diachronic lexical macroresource are still needed, this resource is invaluable for analysing and accessing historical texts, as well as for both synchronic historical and diachronic lexical studies.
Incorporation of Autonomous Model Analytics for Avionic System Design into Standard Framework for Integrated Engineering

Several computer tools exist to develop High-Integrity Avionics Systems (HIAS). The plethora and diversity of tools are good to provide variety to support the System Development Life Cycle (SDLC), but they require extra efforts to interconnect tools to make use of tool results in a systematic way. The variety of tools comes along with stakeholders working on diverse models of the same HIAS under development for which developers must wait for others to get notifications of model changes so updates can be considered by other models from tools. Such a deadlock unfortunately increases risks and costs. Some initiatives such as the Open Services for Lifecycle Collaboration (OSLC) standard have made efforts to connect different tools with each other along the SDLC. They provide a framework to facilitate integration and interoperation of computer tools but do not deal with the above deadlock problem. This paper discusses technical aspects and business impact to incorporate model analytics by means of a design process based on autonomously crosschecked models of avionic systems. The approach is meant to make easy the verification of models from the same HIAS. The OSLC initiative provides an attractive chance to explore considerations and investigate technological details to implement a plugin model-analytics capability in the OSLC cloud. This paper also presents a research discussion on challenges and opportunities, including relevant topics such as aviation certification standards. Concluding remarks, recommendations, and future research are presented as well.
An Empirical Comparison of Joint-Training and Pre-Training for Domain-Agnostic Semi-Supervised Learning Via Energy-Based Models

Some semi-supervised learning (SSL) methods heavily rely on domain-specific data augmentations. Recently, semi-supervised learning (SSL) via energy-based models (EBMs) has been studied and is attractive from the perspective of being domain-agnostic, since it inherently does not require data augmentations. There exist two different methods for EBM based SSL - joint-training and pre-training. Joint-training estimates the joint distribution of observations and labels, while pre-training is taken over observations only and followed by fine-tuning. Both joint-training and pre-training are previously known in the literature, but it is unclear which one is better when evaluated in a common experimental setup. To the best of our knowledge, this paper is the first to systematically compare joint-training and pre-training for EBM-based for SSL, by conducting a suite of experiments across a variety of domains such as image classification and natural language labeling. It is found that joint-training EBMs outperform pre-training EBMs marginally but nearly consistently, presumably because the optimization of joint-training is directly related to the targeted task, while pre-training does not.
Classification of XML Documents Using Semantic Resources

In this paper, we investigate the automatic classification of XML documents into predefined categories. We propose to develop a classification model by combining the content and structure of documents. Furthermore, we propose to use semantic resources, specifically WordNet and ontology linked to the terms of the corpus, in order to model the notion of the semantic neighborhood by using a calculation regarding the similarity between terms.To validate the results, we used the INEX 2007 XML corpus.
Student Perspective on Virtual Education in Two Universities in Ecuador: A Survey

Education has always been elusive for some people, but with the pandemic, this reality has deepened. This study investigates what is the perception that students of a public and a private university in Ecuador have about aspects such as connectivity and learning in the digital age. A survey was applied to 571 students to analyze through quantitative and qualitative techniques how the process of adaptation to this modality of studies imposed by the emergence of COVID-19 has been. The results show a clear division of opinions in favor and against virtual education.
Design of security integrated monitoring system based on Internet of things

The development of Internet of things technology has brought about changes in the monitoring industry. The integration of video monitoring and defense monitoring system based on video technology and sensor technology becomes possible. In this paper, combined with the widely used video monitoring equipment and defense monitoring equipment in the current market, as well as the corresponding software interface, a set of integrated security monitoring software system with defense monitoring and video monitoring functions is designed, which can quickly make video response in the defense alarm. At the same time, this paper discusses the key technical problems that need to be solved in the practical application of the system.
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation

Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource—yet widely spoken—languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks—despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese.