Reviewerly/exHarmony huggingface dataset:
Satisfaction Analysis of Smart Teaching System Based on Structural Equation Model

The application of smart teaching system is an effective way to promote the reform and innovation of higher education and improve the quality, and it is the innovation frontier of the development of education informatization. Learning satisfaction and improvement of learning effect are important indicators for testing the application of smart teaching system. The article introduces the structural equation model and improves the technology acceptance model (TAM) to construct a smart teaching system satisfaction that includes 6 latent variables of teaching function, interactive function, learning interest, learning satisfaction, learning effect, and 21 observation variables Analytical model (ITSAM), take the wisdom teaching system of Jinan University as an example to conduct empirical research. The results show that improving the satisfaction and learning effect of the smart teaching system can be achieved by increasing students' interest in learning. Appropriate tools should be selected when applying information systems, and interaction and social functions should be used appropriately. Excessive interaction will affect the learning effect and Satisfaction.
Research on Mixed Teaching Reform and Innovation of Piano Course in Colleges and Universities based on MOOC Technology

In the new era, with the continuous development of network computer technology, the ways for students to obtain knowledge are increasing, and the attraction of piano classroom education to students is weakening. At the same time, the dependence of contemporary college students' learning on computer network is increasing. Network learning has become a learning way more in line with students' learning habits than library and classroom education. The integration of MOOC teaching and piano teaching is easy to stimulate students' interest in learning. This paper introduces in detail the feasibility of integrating piano teaching with MOOC, and explains the specific methods of applying modern educational technology to piano teaching.
Various Applications of Artificial Neural Network and Wavelet Transform

Neural Network is used in various applications like extraction of information, generation of language, text classification, recognition of speech and characters and many more. But there are certain limitations also such as time consumed, large structure, unexplained functions, and number of epochs. The number of epochs and time can be reduced by using neural network with wavelet transform. By implementing these two techniques together, the number of epoch can be minimized to a greater extend and with which less time is consumed. In this paper, such applications are discussed in which ANN-WT (Artificial Neural Network- Wavelet Transform) was implemented and showed better results.
The Impact of ICT in Conflict Management and Peace Building in Nigeria

Information Technology has the potential to not only support and solve some of Nigeria's major concerns, such as conflicts and violence, but also to create new problems. The function of information and communication technology (ICT) in preventing and responding to violence has been studied extensively. These works, on the other hand, cover a broader range of topics. In Nigeria, there is no literature on the impact of ICT in conflict resolution and peacebuilding. The goal of this study was to see if ICT may have a good or harmful impact on a Nigerian challenges situation. The study will explain and illustrate the impact of ICT in conflict resolution in Nigeria, identify techniques for using ICT for peacebuilding in Nigeria, and show how ICT (Social Media) might contribute to violence in Nigeria. More importantly, the study will emphasize ICT strategies for preventing violence and facilitating peacebuilding in Nigeria. The use of mobile phones, social media such as Twitter and Facebook, and other social media such as websites, blogs, and YouTube videos during the EndSars protests in Nigeria, where looting, destruction, and burning government agencies, breaking correctional centers, and breaking banks occurred in several states, has demonstrated how ICT can complement and augment crisis and violence. However, when the research's findings are put to good use, information and communication technology can help people interact, access information, make decisions, and understand each other more effectively in Nigeria.
Dynamic weighted majority based on over-sampling for imbalanced data streams

Imbalanced data stream with concept drift mining have gained the significant popular among researchers in recent years. The complexity of data stream mining algorithm with concept drift and imbalance will be greatly increased. In this paper, dynamic weighted majority based on over-sampling algorithm for imbalanced data stream with concept drift is proposed. Combining the ensemble learning and resampling method, the proposed algorithm is designed based on chunk-based ensemble and under-sampling methods. The previous minority samples are selectively stored to amply the minority samples in current chunk. The method of update the stored minority samples is designed to ensure the diversity among the base-classifiers and the informativity of the selected samples. In addition, in order to improve the performance of ensemble classifier, dynamic weight is calculated for every base-classifier according to a certain evaluation indicator on every data chunk. The efficiency and classification performance of the proposed algorithm have been confirmed on the basis of extensive experimental research carried out on the all kind of the data streams.
Viewpoint estimation of image object based on parameters sharing network

Viewpoint estimation in 3D scene is a cutting-edge subject, which has a strong relationship with 3D recognition, scene understanding, multi-view 3D modeling of objects or scenes, etc. The existing techniques of coding space invariance in deep convolution neural networks only model the 2D transform field, which does not take into account the fact that objects in 2D field are projections of 3D ones. Calculating a high quality 3D viewpoint in scenes will help us get a more comprehensive analysis of 3D field. However, the sample size of the natural image data set with viewpoint annotations is too small, and each kind of target is trained independently by deep learning method, so the parameters of each category model are not universal. In this paper, we proposed a novel framework that joints object detection and viewpoint estimation of image object based on parameter sharing. We control the rendering parameters to expand the number of effective sample images, use the Kernel Density Estimation function, to get close to the viewpoint of each kind of target instance in the natural scene. We optimize the loss function of the viewpoint estimation task, and designs an image target viewpoint estimation method based on parameters sharing, which reduces the network redundant parameters and speeds up the iteration speed. Our experiments on PASCAL 3D + data set demonstrate the effectiveness of our pipeline on joint object detection and viewpoint estimation.
Face Mask Detection Using Convolutional Neural Network

COVID-19 is one of the most dangerous forms of diseases which is caused by corona virus. It is a highly transmissible disease. WHO has already declared it as a pandemic .Therefore at the current scenario, due to outbreak of this pandemic (COVID-19), face masks has become the necessary tool of everyone to avoid spread of disease to some extent. There has been a great demand for the development of software which can easily recognize person who is wearing a mask. Therefore we are going to develop a system which will fulfill the need using Deep Learning . We will use convolutional neural network to train our model. Here two categories of dataset will be used. The one which contains set of images of faces with mask and the other without face masks We will train the program with this data set to learn to decide whether a person’s face is masked or not. OpenCV, tensorflow and keras will be used for the real time face detection with live stream through web camera. After the successful deployment of the product, we will be able to design a software which can be installed at various places such as at the entry gate of colleges, railway stations ,air ports ,temples, hotels and shops etc. This will easily detect the persons who are entering without face masks by using cameras of the systems. Hence this product is the need of the hour for us to develop so as to work for the safety of humans.
bRIGHT – A Framework for Capturing and Adapting to Context for User-Centered Design

The ability to create and maintain a highly accurate model of an end user’s context is an extremely useful feature. Achieving this ability poses many challenges, especially since a great degree of partial information and uncertainty is involved in capturing the user’s context. The bRIGHT human-computer interaction (HCI) framework and workstation address these challenges by creating a highly accurate context model of a user engaging with a computer system. In this paper we discuss the architectural design of bRIGHT, which addresses performance and scalability to build accurate user context models, and the benefits we expect from this improved version. We also discuss technological advances in other related fields that influenced our decision-making.
Deep Learning in Network-Level Performance Prediction Using Cross-Layer Information

Wireless communication networks are conventionally designed in model-based approaches through utilizing performance metrics such as spectral efficiency and bit error rate. However, from the perspectives of wireless service operators, network-level performance metrics such as the 5%-tile user data rate and network capacity are far more important. Unfortunately, it is difficult to mathematically compute such network-level performance metrics in a model-based approach. To cope with this challenge, this work proposes a data-driven machine learning approach to predict these network-level performance metrics by utilizing customized deep neural networks (DNN). More specifically, the proposed approach capitalizes on cross-layer information from both the physical (PHY) layer and the medium access control (MAC) layer to train customized DNNs, which was considered impossible for the conventional model-based approach. Furthermore, a robust training algorithm called weighted co-teaching (WCT) is devised to overcome the noise existing in the network data due to the stochastic nature of the wireless networks. Extensive simulation results show that the proposed approach can accurately predict two network-level performance metrics, namely user average throughput (UAT) and acknowledgment (ACK)/negative acknowledgment (NACK) feedback with great accuracy.
An Elephant Identification by Trunk Using Digital Image Processing in Deep Learning

On this earth Human and animals have friendly relations. As human has entered the home of animal and started misusing them, this relationship has been unpleasant. Elephant and human relations are also been disturbed. Technology has helped a lot in the identification of elephants. Image processing and deep learning are majorly used for Our work is based on elephant identification. This work is related to various images of elephant body parts. We have used CNN Classifier of Deep learning to train and test our dataset. In the first section of our work, we have given the introduction of elephants, digital image processing, and an elephant trunk. In the second section methodology and dataset have been discussed. In the third section, results are given and discussions have been made. Lastly, the conclusion and future work. Our work gives 98% accuracy.
Compressing Embedding Table via Multi-dimensional Quantization Encoding for Sequential Recommender Model

Sequential recommender models have become a research hotspot in the field of current recommender systems due to its excellent ability to describe users’ dynamic preferences. Sequential recommender models based on deep learning have achieved state-of-the-art results. However, with the increasing number of users and items, the traditional item embedding table may consume a huge amount of memory so that the model may be more difficult to deploy to resource-limited devices. In this paper, we propose a multi-dimensional quantization encoding(MDQE) method to resolve this issue. MDQE mainly consists of two compression techniques. We first divide items into several groups according to the interaction frequency of items and assign different dimensions to each group to construct multi-dimensional group-wise embedding tables. Then, we use mapping matrices to transform the multi-dimensional group-wise embedding tables into quantized codebooks for further compressing. The experiments on three real-world datasets demonstrate that the proposed MDQE can achieve up to 13.86x compression ratio with negligible accuracy loss during inference.
Strengthening the Security of Deniable Authentication Scheme Using Zero-Knowledge Proof

In an electronic voting system, authentication is used to ensure that the voter is legitimate without knowing his/her identity, while the vote collectors verify the data is received from a legitimate user without knowing the identity of the voter. One of the authentication schemes that fulfilled this requirement is called a deniable authentication scheme, where the receiver can prove the source of the message while another party cannot identify the source of the message. In 2013, Li-Takagi et al. proposed a deniable authentication scheme. However, Li-Takagi's scheme has weaknesses if the receiver fully cooperates with the third party. In this case, the third party can identify the source of a given message. In the proposed method, zero-knowledge proof is introduced to preserve the anonymity of the deniable authentication scheme when the receiver fully cooperates with the third party. Based on the analysis, the proposed scheme fulfills the requirement of the deniable authentication scheme when the receiver fully cooperates with the third party. However, the proposed scheme has additional computation costs for securing the shared secret key. Two attack schemes that are carried out on both Li-Takagi and the proposed scheme are the MITM attack and the impersonation attack. The probability of breaking the proposed scheme using an MITM attack is lower than when using Li-Takagi's scheme, but the probability of breaking the proposed scheme using an impersonation attack is the same as Li-Takagi's scheme.
Generating Emotional Coherence and Diverse Responses in a Multimodal Dialogue System

The perception of emotion and the diversity of generated response are two key factors considered by researchers in multimodal dialogue generation. However, in the field of multimodal dialogue generation, these two key factors have not been considered at the same time. In our model, we first extract the features of each modal from the multimodal context dialogue, and use the heterogeneous graph neural network to represent the large graph network composed of dialogue history, voice, video, and speaker's emotional state. Then, we use conditional variational autoencoders to generate coherent and diverse responses. A large number of experiments have shown that our model can not only automatically generate reaction emotions in two multimodal datasets, but also has coherence and controllability, which is significantly better than previous more advanced models.
On the Impact of Dataset Size:A Twitter Classification Case Study

The recent advent and evolution of deep learning models and pre-trained embedding techniques have created a breakthrough in supervised learning. Typically, we expect that adding more labeled data improves the predictive performance of supervised models. On the other hand, collecting more labeled data is not an easy task due to several difficulties, such as manual labor costs, data privacy, and computational constraint. Hence, a comprehensive study on the relation between training set size and the classification performance of different methods could be essentially useful in the selection of a learning model for a specific task. However, the literature lacks such a thorough and systematic study. In this paper, we concentrate on this relationship in the context of short, noisy texts from Twitter. We design a systematic mechanism to comprehensively observe the performance improvement of supervised learning models with the increase of data sizes on three well-known Twitter tasks: sentiment analysis, informativeness detection, and information relevance. Besides, we study how significantly better the recent deep learning models are compared to traditional machine learning approaches in the case of various data sizes. Our extensive experiments show (a) recent pre-trained models have overcome big data requirements, (b) a good choice of text representation has more impact than adding more data, and (c) adding more data is not always beneficial in supervised learning.
Semantic Computing Enhancement of Industrial Augmented Reality Solutions with Machine Learning

research-article Share on Semantic Computing Enhancement of Industrial Augmented Reality Solutions with Machine Learning Authors: Juan Izquierdo-Domenech Valencian Research Institute for Artificial Intelligence, Polytechnic University of Valencia, Spain Valencian Research Institute for Artificial Intelligence, Polytechnic University of Valencia, SpainView Profile , Jordi Linares-Pellicer Valencian Research Institute for Artificial Intelligence, Polytechnic University of Valencia, Spain Valencian Research Institute for Artificial Intelligence, Polytechnic University of Valencia, SpainView Profile , Jorge Orta-Lopez Valencian Research Institute for Artificial Intelligence, Polytechnic University of Valencia, Spain Valencian Research Institute for Artificial Intelligence, Polytechnic University of Valencia, SpainView Profile Authors Info & Claims VSIP 2021: 2021 3rd International Conference on Video, Signal and Image ProcessingNovember 2021 Pages 131–138https://doi.org/10.1145/3503961.3503982Published:08 March 2022Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Data Augmentation Based on Pre-trained Language Model for Event Detection

Event detection (ED) is an important task which needs to identify the event triggers in the sentence and classify the event types. For the general fine-grained event detection task, we propose an event detection scheme based on pre-trained model, combined with data augmentation and pseudo labelling method, which improves the event detection ability of the model. At the same time, we use voting for model ensemble, so as to effectively utilize the advantages of multiple models. Our model achieves F1 score of 69.86% on the test set of CCKS2021 general fine-grained event detection task and ranks the third place in the competition.
Time Power Law Mapping of Signal Complexity Measure

The complexity measure is to describe the cognitive cost of a system, such as a one-dimensional time-domain signal in periodic, chaotic, or random state. Its research paradigms depend on the understandings of symbolic dynamics, entropy expansion, FFTs’ combination, and the complex network. The new trends of computing aesthetics show both the group expansion with formula and without artificial selection of parameters. Following the power law of time in gold rate, we give a practical algorithm with a new feature plot and its complex number. The contrast criteria are spectral entropy complexity and approximate entropy. Three chaotic equations are used as test examples. The results reveal new complex patterns and novel complex numerical ranges, obtaining basically consistent recognition effects compared with upper control criteria. The calculation complexities of all running algorithms are reported. This work illustrates new progress in our brainchildren lab on automatic measuring of signal complexity.
Digital Habit Evidence

This Article explores how “habit evidence” will become a catalyst for a new form of digital proof based the explosive growth of smart homes, smart cars, smart devices, and the Internet of Things. Habit evidence is the rule that certain sorts of semi-automatic, regularized responses to particular stimuli are trustworthy and thus admissible under the Federal Rules of Evidence (FRE) 406 Habit; Routine Practices and state equivalents. While well-established since the common law, “habit” has made only an inconsistent appearance in reported cases, and has been under-utilized in trial practice. But intriguingly, once applied to the world of digital trails and the Internet of Things, this long dormant rule could transform our “quantified lives” into a significant new evidentiary power. In fact, habit evidence as quantified fact may become weaponized to reimagine trial practice in the digital age.
Correlation-Based Data Augmentation for Machine Learning and Its Application to Road Environment Recognition

The accuracy of machine learning depends largely on the quantity and quality of the training data. However, it is generally difficult to prepare a large number of high-quality data. To generate diverse image data, image generation techniques using deep learning, such as a generative adversarial network, can be used. However, because these methods require a large number of training data and a significant calculation time, they are unsuitable for generating training data for machine learning. In this article, we propose an image data augmentation model based on the statistical properties of the training data. With the proposed method, each image is divided into sub-regions based on the correlation calculated using a set of images. The image of each sub-region is modeled through a Gaussian mixture, and data augmentation is conducted by generating images based on this model. The proposed method does not require a large number of training data and can generate data within a relatively short calculation time. The proposed method is applied to the task of road environment recognition. The experiment results showed that the accuracy was improved through image augmentation using the proposed model.