Network Information Retrieval Research Articles

Artificial Intelligence on the Identification of Beiguan Music

This research determines an identification system for the types of Beiguan music – a historical, nonclassical music genre – by combining artificial neural network (ANN), social tagging, and music information retrieval (MIR). Based on the strategy of social tagging, the procedure of this research includes: evaluating the qualifying features of 48 Beiguan music recordings, quantifying 11 music indexes representing tempo and instrumental features, feeding these sets of quantized data into a three-layered ANN, and executing three rounds of testing, with each round containing 30 times of identification. The result of ANN testing reaches a satisfying correctness (97% overall) on classifying three types of Beiguan music. The purpose of this research is to provide a general attesting method, which can identify diversities within the selected non-classical music genre, Beiguan. The research also quantifies significant musical indexes, which can be effectively identified. The advantages of this method include improving data processing efficiency, fast MIR, and evoking possible musical connections from the high-relation result of statistical analyses.

Effective cooperative caching techniques for information retrieval in mobile ADHOC networks (2020)

Effective cooperative caching techniques for information retrieval in mobile ADHOC networks (2020)

Display Design Model Based on the Internet of Things Prototype System

The display design of the Internet of Things prototype system is the top priority of the design of the display space, and it is also a problem to be studied in the future development of the display space. However, the current research on the display design direction of IoT prototype system is not deep enough. This article mainly studies the display design model based on the Internet of Things prototype system. This article mainly uses the related technology of IoT heterogeneous protocol to analyze the structure of the IoT prototype system and proposes to use the heterogeneous network to speculate the future development trend of the IoT. In the aspect of display design, applications such as network information retrieval and data mining at the level of missions are accelerated in depth so that the arrangement of multiple factors is more reasonable. Finally, the data conversion function is used to design each port in the IoT accessor architecture. The protocol is adapted and converted, and the parsed protocol is uniformly encapsulated and uniformly transmitted on the platform. The model in this article integrates various heterogeneous detection networks, realizes the standardized operation of top-level applications, and completes the decoupling of top-level applications. It is encapsulated in a unified protocol and meets the standard operation requirements of web applications. The experimental data in this article show that the frequency of the audio signal designed by the system under normal working conditions is mostly concentrated at about 180 Hz, and the highest frequency value is less than 800 Hz; when the system has a partial discharge failure, the frequency of the audio signal is mostly concentrated between 1400 Hz and 1800 Hz. The experimental results of this article show that the prototype system of the Internet of Things can identify and judge the state of the display design model. Applying it in practice can effectively solve practical problems such as large-scale data storage of the Internet of Things.

Arabic Twitter Conversation Dataset about the COVID-19 Vaccine

The development and rollout of COVID-19 vaccination around the world offers hope for controlling the pandemic. People turned to social media such as Twitter seeking information or to voice their opinion. Therefore, mining such conversation can provide a rich source of data for different applications related to the COVID-19 vaccine. In this data article, we developed an Arabic Twitter dataset of 1.1 M Arabic posts regarding the COVID-19 vaccine. The dataset was streamed over one year, covering the period from January to December 2021. We considered a set of crawling keywords in the Arabic language related to the conversation about the vaccine. The dataset consists of seven databases that can be analyzed separately or merged for further analysis. The initial analysis depicts the embedded features within the posts, including hashtags, media, and the dynamic of replies and retweets. Further, the textual analysis reveals the most frequent words that can capture the trends of the discussions. The dataset was designed to facilitate research across different fields, such as social network analysis, information retrieval, health informatics, and social science.

Analysis of Network Information Retrieval Method Based on Metadata Ontology

In order to solve the problem that people can accurately search for the network information they need, the research on network information retrieval methods becomes more important. This article is mainly about the research of network information retrieval methods based on metadata ontology calculations. This article constructs an LDA three-layer Bayesian model with a three-layer structure of document, topic, and single order. The three-layer structure obeys a random polynomial distribution and can be calculated the joint distribution probability of all variables in the LDA model greatly increases the calculation efficiency. Using a cross-modal information retrieval method, it can mine the common data features between different modal data and analyze the semantic correlation between different modal data, improve the accuracy of search, and solve the existence of different modal data. There is a gap in the expression semantics between heterogeneous and different modal data. The experimental results in this paper show that the text feature extraction of the network information retrieval method based on the metadata ontology calculation has a good performance in terms of accuracy, and the accuracy of the extraction and clustering results is as high as about 90%. The improved CCA algorithm used is better than the traditional CCA and the accuracy is improved by 23%, which is 12% higher than the LDA-CCA algorithm.

Fuzzy matching algorithm of network information retrieval based on discrete mathematics

Fuzzy matching algorithm of network information retrieval based on discrete mathematics

Graph Convolutional Networks for Cross-Modal Information Retrieval

In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.

Semantic-Based Sports Music Information Fusion and Retrieval in Wireless Sensor Networks

The wireless sensor network has developed rapidly in recent years. It is formed by the intersection of multiple disciplines. It integrates embedded technology, sensor technology, distributed technology, wireless communication technology, and modern networks. It is a brand new information acquisition platform. The characteristics of sensor networks determine that information fusion technology is a hot spot in the research of wireless sensor networks. Information fusion can achieve high performance and low cost in terms of energy and communication, which is of great significance to the research of sensor networks. This paper is aimed at studying the semantic-based sports music information fusion and retrieval research in wireless sensor networks. WSNs may face various attacks including eavesdropping attacks, replay attacks, Sybil attacks, and DOS attacks. Therefore, they are designing sensor network solutions. It is necessary to consider the network security issues. This article summarizes and analyzes the existing WSN security data fusion solutions for this issue and compares them by classification. This paper proposes methods and theories such as the spatial correlation detection algorithm, CBA algorithm, FTD algorithm, and DFWD algorithm, which enriches the research of information fusion and retrieval in wireless sensor networks, which is of exploratory significance, and it also establishes this problem. The model was studied, and reliable data was obtained. The experimental results of this paper show that when using these methods to diagnose faults in WSN, the correct rate of model diagnosis is higher than 77%.

Sensing ecosystem dynamics via audio source separation: A case study of marine soundscapes off northeastern Taiwan.

Remote acquisition of information on ecosystem dynamics is essential for conservation management, especially for the deep ocean. Soundscape offers unique opportunities to study the behavior of soniferous marine animals and their interactions with various noise-generating activities at a fine temporal resolution. However, the retrieval of soundscape information remains challenging owing to limitations in audio analysis techniques that are effective in the face of highly variable interfering sources. This study investigated the application of a seafloor acoustic observatory as a long-term platform for observing marine ecosystem dynamics through audio source separation. A source separation model based on the assumption of source-specific periodicity was used to factorize time-frequency representations of long-duration underwater recordings. With minimal supervision, the model learned to discriminate source-specific spectral features and prove to be effective in the separation of sounds made by cetaceans, soniferous fish, and abiotic sources from the deep-water soundscapes off northeastern Taiwan. Results revealed phenological differences among the sound sources and identified diurnal and seasonal interactions between cetaceans and soniferous fish. The application of clustering to source separation results generated a database featuring the diversity of soundscapes and revealed a compositional shift in clusters of cetacean vocalizations and fish choruses during diurnal and seasonal cycles. The source separation model enables the transformation of single-channel audio into multiple channels encoding the dynamics of biophony, geophony, and anthropophony, which are essential for characterizing the community of soniferous animals, quality of acoustic habitat, and their interactions. Our results demonstrated the application of source separation could facilitate acoustic diversity assessment, which is a crucial task in soundscape-based ecosystem monitoring. Future implementation of soundscape information retrieval in long-term marine observation networks will lead to the use of soundscapes as a new tool for conservation management in an increasingly noisy ocean.

How to Construct a Power Knowledge Graph with Dispatching Data?

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process.

Opinion dynamics with emergent collective memory: A society shaped by its own past

In order to understand the development of common orientation of opinions in the modern world we propose a model of a society described as a large collection of agents that exchange their expressed opinions under the influence of their mutual interactions and external events. In particular we introduce an interaction bias which results in the emergence of a collective memory such that the society is able to store and recall information coming from several external signals. Our model shows how the inner structure of the society and its future reactions are shaped by its own history. We provide an analytical explanation of such mechanism and we study the features of external influences with higher impact on the society. We show the emergent similarity between the reaction of a society modelled in this way and the Hopfield-like mechanism of information retrieval in Neural Networks.

Automatic retrieval of network hotspot information based on improved DBSCAN clustering algorithm

With the continuous development of the Internet in the world today, the proliferation of network information makes it difficult for people to effectively screen out current hotspot information. In order to solve the problem of how to retrieve the current hot information quickly and accurately under the massive network information, an automatic network information retrieval method based on the improved DBSCAN clustering algorithm is proposed. The retrieved related keywords are combined to reduce the feature terms, which effectively solves the problem of repeated acquisition of public resource object neighbourhoods, greatly improves the accuracy and efficiency of the clustering algorithm, and realizes automatic retrieval of network information hot spots. The results show that the automatic network information retrieval method based on the improved DBSCAN clustering algorithm proposed in this paper can quickly and accurately find the current information hotspots on the Internet, help users to obtain the hotspot information of their own most interest, and promote the progress and development of the Internet.

QR Label for Handicapped Exhibition Visitors Queue Built-Up Avoidance

Most exhibitions such as in museums items are provided with printed labels that contain information of interest. A queue of visitors will be formed both to view the items and read the labels for information of interest. Visitors have limited time to read and view as a queue will built-up in due time. Handicapped visitors on wheel chair are very disadvantaged in enjoying exhibits due to the possibility of queue built-up that could cause inconvenience to other visitors. Current visitors most likely possess smartphones as communication device. Smartphones can be exploited to satisfy the need of information. QR labelling together with private cloud or network for information retrieval is presented as a viable solution. The system could cater the need of handicapped visitors. Visitors can conveniently collect information and study it at their convenience using their smartphones. Excessive queue forming can be avoided. This research is to find the position where a handicapped or any person can reliably scan the QR code label and the advisable size of label. The result is beneficial both for the exhibition organizer and the visitor. The parameters of interest are the distance and angle where a QR code label of certain can be reliably scanned.

Research and Construction of Yunnan Plant Vertical Retrieval System

The plant richness of Yunnan Province ranks first in the country, and scientific and rational protection and utilization of Yunnan plant resources are of great significance for maintaining China’s biodiversity. In view of the current distribution patterns and physiological characteristics of plants in Yunnan Province, as well as related geography, climate and wild habitats, this paper constructs a set of vertical search system for Yunnan plants through the research of vertical search architecture, network data collection, semantic analysis of network information and the pattern of network information retrieval. The system can effectively solve the technical obstacles of Yunnan plant network resource utilization, and provide new technical support and theory for the macro-level and deep research of plants in Yunnan Province.

Inferring mechanisms for global constitutional progress.

Constitutions help define domestic political orders, but are known to be influenced by international mechanisms that are normative, temporal and network based. Here we introduce the concept of the 'provision space'-the set of all legal provisions existing across the world's constitutions, which grows over time. We make use of techniques from network science and information retrieval to quantify and compare temporal and network effects on constitutional change, which have been the focus of previous work. Furthermore, we propose that hierarchical effects-a set of mechanisms by which the adoption of certain constitutional provisions leads to or facilitates the adoption of additional provisions-are also crucial. These hierarchical mechanisms appear to play an important role in the emergence of new political rights, and may therefore provide a useful roadmap for advocates of those rights.

Detecting trends in academic research from a citation network using network representation learning.

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth.

Second-level degree-based entity resolution in online social networks

Abundance of online social platforms allows users to create more than one profile in different social networks. Several issues arise due to multiple user identities including data integration and information retrieval in social networks. Identifying user profiles across social platforms is known as entity resolution. In this paper, we propose a degree-based method to attack entity resolution problems. More precisely, we utilize the degree of users and their friends to identify user profiles. Our results show that, without help of critical information such as e-mail addresses, the proposed method can outperform existing string matching-based solutions as well as popular classifiers such as SVM and Naive Bayes.

Practical k-agents search algorithm towards information retrieval in complex networks

Spanking information retrieval in large-scale Web and network has attracted increasing interest in the research community, many typical approaches have been recalled such as greedy, random-walk and high degree seeking since the search capabilities of complex networks are proved by Kleinberg in 2000. Unfortunately, the retrieval efficiency of these classic approaches is not ideal, and they are only suitable for the specific networks due to their defects. The motivation of this paper is to increase the retrieval efficiency, and we thus proposed a novel k-agents search approach for different types of networks which searches the networks with k-agents, simultaneously. Besides, to better test the efficiency of algorithms, a new evaluation method which considers search steps and query information both is put forward to measure the cost of the search algorithm. The complexity analysis also will be discussed, and the comparison with other algorithms will be displayed in detail to show its superiority. In the end, for the purpose of displaying a universe application of our algorithm, the simulations in WS (proposed by Watts and Strogatz), NW (proposed by Newman and Watts) small-world and BA (proposed by Barabai and Albert) scale-free network model are carried out to illustrate the performance of the proposed method.

Functions of Uni- and Multi-citations: Implications for Weighted Citation Analysis

Abstract Purpose (1) To test basic assumptions underlying frequency-weighted citation analysis: (a) Uni-citations correspond to citations that are nonessential to the citing papers; (b) The influence of a cited paper on the citing paper increases with the frequency with which it is cited in the citing paper. (2) To explore the degree to which citation location may be used to help identify nonessential citations. Design/methodology/approach Each of the in-text citations in all research articles published in Issue 1 of the Journal of the Association for Information Science and Technology (JASIST) 2016 was manually classified into one of these five categories: Applied, Contrastive, Supportive, Reviewed, and Perfunctory. The distributions of citations at different in-text frequencies and in different locations in the text by these functions were analyzed. Findings Filtering out nonessential citations before assigning weight is important for frequency-weighted citation analysis. For this purpose, removing citations by location is more effective than re-citation analysis that simply removes uni-citations. Removing all citation occurrences in the Background and Literature Review sections and uni-citations in the Introduction section appears to provide a good balance between filtration and error rates. Research limitations This case study suffers from the limitation of scalability and generalizability. We took careful measures to reduce the impact of other limitations of the data collection approach used. Relying on the researcher’s judgment to attribute citation functions, this approach is unobtrusive but speculative, and can suffer from a low degree of confidence, thus creating reliability concerns. Practical implications Weighted citation analysis promises to improve citation analysis for research evaluation, knowledge network analysis, knowledge representation, and information retrieval. The present study showed the importance of filtering out nonessential citations before assigning weight in a weighted citation analysis, which may be a significant step forward to realizing these promises. Originality/value Weighted citation analysis has long been proposed as a theoretical solution to the problem of citation analysis that treats all citations equally, and has attracted increasing research interest in recent years. The present study showed, for the first time, the importance of filtering out nonessential citations in weighted citation analysis, pointing research in this area in a new direction.

Relational Deep Learning: A Deep Latent Variable Model for Link Prediction

Link prediction is a fundamental task in such areas as social network analysis, information retrieval, and bioinformatics. Usually link prediction methods use the link structures or node attributes as the sources of information. Recently, the relational topic model (RTM) and its variants have been proposed as hybrid methods that jointly model both sources of information and achieve very promising accuracy. However, the representations (features) learned by them are still not effective enough to represent the nodes (items). To address this problem, we generalize recent advances in deep learning from solely modeling i.i.d. sequences of attributes to jointly modeling graphs and non-i.i.d. sequences of attributes. Specifically, we follow the Bayesian deep learning framework and devise a hierarchical Bayesian model, called relational deep learning (RDL), to jointly model high-dimensional node attributes and link structures with layers of latent variables. Due to the multiple nonlinear transformations in RDL, standard variational inference is not applicable. We propose to utilize the product of Gaussians (PoG) structure in RDL to relate the inferences on different variables and derive a generalized variational inference algorithm for learning the variables and predicting the links. Experiments on three real-world datasets show that RDL works surprisingly well and significantly outperforms the state of the art.