Reviewerly/exHarmony huggingface dataset:
Fine-Grained Text-to-Video Temporal Grounding from Coarse Boundary

Text-to-video temporal grounding aims to locate a target video moment that semantically corresponds to the given sentence query in an untrimmed video. In this task, fully supervised works require text descriptions for each event along with its temporal segment coordinate for training, which is labor-consuming. Existing weakly supervised works require only video-sentence pairs but cannot achieve satisfactory performance. However, many available annotations in the form of coarse temporal boundaries for sentences are ignored and unexploited. These coarse boundaries are common in streaming media platform and can be collected in a mechanical manner. We propose a novel approach to perform fine-grained text-to-video temporal grounding from these coarse boundaries. We take dense video captioning as base task and leverage the trained captioning model to identify the relevance of each video frame to the sentence query according to the frame participation in event captioning. To quantify the frame participation in event captioning, we propose event activation sequence , a simple method that highlights the temporal regions which have high correlations to the text modality in videos. Experiments on modified ActivityNet Captions and a use case demonstrate the promising fine-grained performance of our approach.
BiLSTM-based Federated Learning Computation Offloading and Resource Allocation Algorithm in MEC

Mobile edge computing (MEC) driven by 5G cellular systems has recently emerged as a promising paradigm, enabling mobile devices (MDs) with limited computing resources to offload various computation-intensive tasks (such as autopilot, online game) to edge servers to enhance the data processing capabilities of MDs. However, the uncertainty of wireless channel state and data volume of offloading tasks, as well as the data security privacy of offloading tasks, bring serious challenges to computation offloading in MEC. In this article, we consider a time-varying MEC scenario and formalize the delay and energy consumption during the computation offloading process as a joint optimization problem. Then the optimization problem is decomposed into two sub-problems: intelligent task prediction and resource allocation. Different from traditional methods, we improve the federated learning (FL) algorithm and propose a thoughtful cloud-edge-client FL task prediction mechanism based on Bidirectional Long Short-Term Memory. Each participating MD trains the model locally without uploading data to the server, and periodically aggregates the model in the edge and in the cloud. The algorithm both eliminates the need to solve complex optimization problems and ensures user privacy security. Finally, experimental results show that our proposed algorithm significantly outperforms other benchmark algorithms in energy efficiency.
Domination Based Federated Learning Algorithm

Traditional centralized federated learning has only one server, which causes other clients to send their models to the server in order to participate in federated learning, no matter how far away they are. In this article, we design a novel domination based federated learning algorithm in terms of graph theory, where clients only need to send the model to the nearest neighbor. We give the execution steps of the specific algorithm.
Deep Knowledge Tracing with GRU and Learning State Enhancement

With the rapid development of artificial intelligence + education, knowledge tracing, as the core technology of adaptive education system, has gradually become a challenging research hotspot in the field of intelligent education. In recent years, the deep knowledge tracing model (DKT), which successfully applied neural network in knowledge tracing field for the first time, has made a great breakthrough in prediction accuracy, and has aroused the wave of knowledge tracing using neural network since then. In DKT, the recurrent neural network (RNN) stores the previous information of students in the hidden layer parameters. However, due to the continuous accumulation of hidden layer information, it is difficult to re-extract the important information at the earlier time, resulting in the deviation of prediction results. Meanwhile, the model does not consider the role of students’ recent state. That often has a more important impact on students’ current level of doing problems. Inspired by the above questions, we improved the DKT model and used the gate units of GRU model to determine the retention and forgetting of previous information, so as to solve the problem that the important information at the early time was difficult to use due to the continuous accumulation of hidden layer information. At the same time, the module of enhancing students’ learning state is added in the model, and the recent learning information of students is effectively used to enhance students’ recent learning state. The experimental results of the Assistment2009 and Assistment2017 public datasets show that the model proposed in this paper can effectively improve the accuracy of model prediction.
Recognition Method of Wa Language Isolated Words Based on Convolutional Neural Network

Speech recognition technology is a popular research direction in artificial intelligence, especially with the development of deep learning technology, speech recognition gradually shifts from traditional recognition methods to end-to-end recognition based on deep learning. Most of the current speech recognition models have achieved high recognition accuracy for mainstream languages, but these models are relatively complex in structure and have many model parameters, which are not suitable for recognizing isolated words in low-resource languages. Based on the deep learning approach, we use a simple and effective model to recognize isolated words in Wa language of minority languages. The encoder includes a simplified deep convolutional neural network VGG and BiLSTM, where the VGG network is used to extract depth features of the audio signal and BiLSTM is further encoded. The decoder includes two decoding methods, CTC and Attention, which can be decoded individually or jointly, which is an end-to-end speech recognition model. We use this model to conduct experiments on our Wa isolated words speech dataset, and the experimental results show that the model has a good recognition effect. The WER is below 20% whether it is decoded alone or jointly.
Design of Active Defense System for Railway Communication Network Based on Deep Neural Network

In order to improve the security of railway communication network, an active defense system of railway communication network based on deep neural network is proposed and designed. Through the design of railway communication network monitoring terminal, railway communication network alarm, railway communication network intrusion data parser and railway communication network active defense circuit, the hardware design of the system is completed. In the software part, based on the collected spatial data of railway communication network, the intrusion data characteristics of railway communication network are extracted by using deep neural network, and the active defense of railway communication network is realized by constructing the active defense model of railway communication network. The experimental results show that the function of the designed system meets the needs of users for the system function, and improves the performance of the system by reducing the memory occupancy, false alarm rate and packet loss rate.
Visual Analysis of Facial Expression Recognition Research Based on Knowledge Graph

The change of facial expression is the external form of human inner activity, and the change of facial expression in the process of interaction is also a medium of communication. The same is true in teaching. Researchers have conducted a lot of research on how to obtain facial expressions, and how to identify and analyze them after obtaining them. In future research, it is very important how to quickly find the facial recognition methods and technologies suitable for the scene, how to quickly find the existing problems on the basis of existing research, and to optimize and carry out follow-up research. This paper mainly analyzes the research literature on facial expression recognition collected by CNKI in the past 10 years, and uses the knowledge graph technology to display it visually, so as to provide reference and theoretical support for users to choose methods when conducting research on facial expression recognition.
Machine Learning Based Abnormal Flow Analysis of University Course Teaching Network

Due to the influence of abnormal traffic of university course teaching network, the accuracy of analysis results is low, this paper presents a method for analyzing the network traffic of university course teaching based on machine learning. According to the process of encapsulating user data into Ethernet data frames, a network traffic identification model based on machine learning is constructed. The screening strength is controlled by the screening coefficient, and the model deviation is reduced by measuring the task. After data collection and sorting, data cleaning and pretreatment, model evaluation and unknown traffic detection, the network traffic analysis steps are designed to analyze the abnormal situation of teaching network traffic. The experimental results show that the highest F1 score is 98%, and the accuracy of the analysis results is high, which provides sufficient network traffic for college course teaching.
Optimization of Data Transmission Efficiency of Wireless Communication Network for Chemical Energy Monitoring

Due to the large amount of data in the wireless communication network, and the data transmission is easily affected by environmental factors, resulting in low data transmission efficiency. In order to reduce the data transmission delay and packet loss rate of wireless communication network and improve the data transmission efficiency, an optimization method of data transmission efficiency of wireless communication network for chemical energy monitoring is designed. In chemical energy monitoring, a perfect HTTP traffic model is established to simulate the data traffic behavior in the existing real monitoring process, so as to design a multicast wireless resource scheduling algorithm to implement wireless communication network data resource scheduling. Combined with the scheduling structure, an optimization model for data transmission efficiency of wireless communication network for chemical energy monitoring is established. The model can select the best link according to the different user experience requirements of upper layer applications and the current network environment. It can deal with the network interruption caused by network switching and realize the optimization of data transmission efficiency. The experimental results show that this method achieves low data transmission delay and packet loss rate in wireless communication network, and improves the data transmission efficiency.
An Unsupervised Surface Anomaly Detection Method Based on Attention and ASPP

It is the main task of visual anomaly detection to find local regions whose saliency is inconsistent with normal appearance. However, existing mainstream anomaly detection models suffer from low detection accuracy and poor generalization performance. Therefore, this paper designs an unsupervised surface anomaly detection model based on attention and atrous spatial pyramid pooling. The proposed model learns anomaly images and their normal reconstruction and simultaneously learns the decision boundaries of normal and anomaly images. The method utilizes a squeeze-and-excitation block to assign attention to feature channels to improve the sensitivity of related favorable features, thus enhancing the model’s ability to learn normal and anomaly boundaries. In addition, atrous spatial pyramid pooling is introduced in the discriminative sub-network to obtain the multi-scale semantic information of the training image, which improves the detection ability of defects of different sizes and enhances the universality of the model. The superiority of our method is demonstrated in the anomaly detection benchmark MVTec dataset.
Encrypted Transmission Method of Network Speech Recognition Information Based on Big Data Analysis

Voice is the medium of language. With the development and popularization of network technology, voice has also become the main carrier of network information. Because the network is open to a certain extent, it poses a great threat to the security of network information transmission. Therefore, a research on encrypted transmission method of network speech recognition information based on big data analysis is proposed. Preprocess network speech signals (sampling, quantization, filtering, pre emphasis, windowing and framing, endpoint detection, etc.), extract network speech signal features based on big data analysis technology, apply hidden Markov model to obtain network speech recognition information, build information encryption model PKI, effectively combine symmetric encryption algorithm and asymmetric encryption algorithm, and formulate encryption and decryption steps of speech recognition information. Thus, the encrypted transmission of network speech recognition information is realized. The experimental data show that the minimum value of the network speech recognition information acquisition time of the proposed method is 10.09s, and the maximum proportion of successful speech recognition information encryption is 97%, which fully proves that the proposed method has better information encryption transmission effect.
Visualized Analysis of the Emerging Trends of Automated Audio Description Technology

Automated audio description has provided groundbreaking solutions to audio description production and delivery and has been one of the core topics of audio description studies. Based on the data collected from the Web of Science database, this article uses the visualized analysis software of CiteSpace to analyze the progress and development of automated audio description technology. It is found that the main keywords include computer vision, natural language processing, video captioning and deep learning, among which video captioning is at the core of the emerging trend of automated audio description, facilitated by deep learning, such as CNN, RNN, LSTM and their improvements. Knowledge-guided and data-driven learning methods may be possible solutions for computer understanding and generation of human-like description of videos.
Differential Privacy Protection Algorithm for Data Clustering Center

With the rapid development of the information era, data mining is widely used in various fields as a technique that can discover the interrelationship between data, but it also inevitably brings some privacy leakage problems. Differential privacy as an emerging privacy protection technology, because of its strict provability it has received great attention and been widely studied. By analyzing the privacy leakage problem in the traditional K-means algorithm, this paper proposes an improved differential privacy K-means algorithm to improve and protect for the selection of data clustering centers. When improvement, the degree of privacy protection and the availability of the clustering algorithm are analyzed. The initial centroids are selected by using the within-cluster sum of squared errors, and the different privacy intensity assigned to each cluster is calculated by using the silhouette coefficient. Finally, the experimental results demonstrate the feasibility of the differential privacy protection algorithm for data clustering center proposed in this paper.
DAS: Efficient Street View Image Sampling for Urban Prediction

Street view data is one of the most common data sources for urban prediction tasks, such as estimating socioeconomic status, sensing physical urban changes, and identifying urban villages. Typical research in this field consists of two steps: acquiring a dataset with a street view image sampling algorithm and designing a prediction algorithm for urban prediction tasks. However, most of the previous research focuses on the prediction algorithms, leaving the sampling algorithms underexplored. To fill this gap, we set out to investigate how different street view image sampling algorithms affect the performance of the follow-up tasks and develop an effective street view image sampling algorithm for urban prediction. Through a comprehensive analysis of the performance of different sampling algorithms in three of the most common urban prediction tasks, including commercial activeness prediction, urban liveliness prediction, and urban population prediction, we provide solid empirical evidence that the sampling algorithm significantly affects the performance of the prediction model. Specifically, the performance differences of different sampling algorithms can reach over 25%. Further, we revealed that the sampling step size and the sampling quality are two important factors that affect the performance of a sampling algorithm, while the sampling angle has little influence. Inspired by our analysis results, we propose an effective street view image sampling algorithm, DAS, which contains a denoising module and an adaptive sampling module. It can dynamically adjust the sampling step size to adapt to the optimal size for each region and get rid of the impact of noise images in the meantime. Experiments on three large-scale datasets demonstrate its superior performance over multiple state-of-the-art baselines, and further ablation study shows the effectiveness of each module. Finally, through a thorough discussion of our findings and experimental results, we provide insights into the street view image sampling algorithm design, and we call for more researches in this blank area.
Effectiveness of digital modules with recitation programs to improve students' conceptual understanding and critical thinking skills in learning work and energy

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Alvi Hasanati, Edi Supriana, Nandang Mufti; Effectiveness of digital modules with recitation programs to improve students' conceptual understanding and critical thinking skills in learning work and energy. AIP Conf. Proc. 10 January 2023; 2569 (1): 050015. https://doi.org/10.1063/5.0114390 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The effectiveness of online platform-based RICOSRE in improving information literacy skills

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Mahdiyani Nur Fadhilah, Susriyati Mahanal, Siti Zubaidah, Deny Setiawan; The effectiveness of online platform-based RICOSRE in improving information literacy skills. AIP Conf. Proc. 10 January 2023; 2569 (1): 020010. https://doi.org/10.1063/5.0112363 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Profile of student’s digital literacy on LMS-based RICOSRE

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Deny Setiawan, Susriyati Mahanal, Siti Zubaidah; Profile of student’s digital literacy on LMS-based RICOSRE. AIP Conf. Proc. 10 January 2023; 2569 (1): 020039. https://doi.org/10.1063/5.0112462 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Research on the Application of Generative Adversarial Networks in Aerial Image Generation

Computer vision is one of the important areas and directions of deep learning research, which requires different approaches to be chosen for different fields due to the complexity and diversity of vision tasks. In the field of aviation, the existing image resources are still far from the real needs due to the influence and constraints of realistic scenes and difficulties of image acquisition. More detailed and comprehensive images can better provide reliable technical support and basis for applications, and then make more accurate decisions on problems, which requires generating more effective images to expand the data. Generative Adversarial Networks (GAN) are the fastest growing and most effective generation method in recent years, so this experiment investigates the application of GAN on aviation data, taking images of airplanes, cars and ships as examples to conduct quantitative research. The effect on the effect of GAN is studied from the perspective of image size, number of images, number of iterations, and different categories of images, in order to obtain better parameter settings for generating effective images, which provides a theoretical and experimental basis for the subsequent application of GAN in the aviation field to generate more images with similar characteristics and solve the problem of insufficient data.
Video-Based 3D Human Pose Estimation Research

At present, most of the video-based 3D human pose estimation methods process the video frame by frame, ignoring the timing information of the upper and lower frames of the video. When there is a single-frame estimation error caused by self-occlusion, the occlusion part will be Some strange movements appear due to information loss, and the estimated results make it difficult to produce accurate motion sequences. Therefore, considering the continuity of the upper and lower frames of the video, this paper adopts the HMR algorithm and introduces a bidirectional long-term memory network (BI-LSTM) in the encoding part to process the video sequence, and then outputs the parameters of the human body model through the SMPL parameters and inputs them into the discriminator for confrontation, train. In order for the discriminator to also take into account the semantic information of the temporal context, the discriminator in this paper also introduces a BI-LSTM network to improve the authenticity of the generated poses. Experimental results show that the accuracy of the model is improved when self-occlusion occurs.
Fostering Student Engagement in E-Learning Using Mobile Technology

The pandemic has induced significant changes in the higher education sector with the shift to e-learning. The range of e-learning possibilities in higher education has increased due to mobile learning. The present study attempts to understand the role of mobile technology in the higher education sector in India. Using a qualitative exploratory approach, the authors obtained the responses of 100 educators by conducting semi-structured interviews. Various advantages associated with the application of mobile technology in e-learning in higher education that can boost student engagement and participation have been identified. The authors also propose a 4 E Process that educators can adopt to augment student engagement when using mobile learning technology in the e-learning process.