scholarly journals Optimization of an Intelligent Music-Playing System Based on Network Communication

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liaoyan Zhang
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Topic Model ◽  
User Interaction ◽  
Interaction Data ◽  
Music Recommendation ◽  
System Testing ◽  
The Real ◽  
Wide Range ◽  
And Performance

Streaming media server is the core system of audio and video application in the Internet; it has a wide range of applications in music recommendation. As song libraries and users of music websites and APPs continue to increase, user interaction data are generated at an increasingly fast rate, making the shortcomings of the original offline recommendation system and the advantages of the real-time streaming recommendation system more and more obvious. This paper describes in detail the working methods and contents of each stage of the real-time streaming music recommendation system, including requirement analysis, overall design, implementation of each module of the system, and system testing and analysis, from a practical scenario. Moreover, this paper analyzes the current research status and deficiencies in the field of music recommendation by analyzing the user interaction data of real music websites. From the actual requirements of the system, the functional and performance goals of the system are proposed to address these deficiencies, and then the functional structure, general architecture, and database model of the system are designed, and how to interact with the server side and the client side is investigated. For the implementation of data collection and statistics module, this paper adopts Flume and Kafka to collect user behavior data and uses Spark Streaming and Redis to count music popularity trends and support efficient query. The recommendation engine module in this paper is designed and optimized using Spark to implement incremental matrix decomposition on data streams, online collaborative topic model, and improved item-based collaborative filtering algorithm. In the system testing section, the functionality and performance of the system are tested, and the recommendation engine is tested with real datasets to show the discovered music themes and analyze the test results in detail.

Comparing and Combining Interaction Data and Eye-tracking Data for the Real-time Prediction of User Cognitive Abilities in Visualization Tasks

2020 ◽  
Vol 10 (2) ◽  
pp. 1-41 ◽  
Author(s):  
Cristina Conati ◽  
Sébastien Lallé ◽  
Md Abed Rahman ◽  
Dereck Toker
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Cognitive Abilities ◽  
Interaction Data ◽  
Tracking Data ◽  
The Real ◽  
Time Prediction

scholarly journals Phone-Based Restaurant Recommendation System by Using Position Information and Association Rules

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Association Rules ◽  
Recommendation System ◽  
Rapid Development ◽  
Smart Phone ◽  
Smart Phones ◽  
Location Information ◽  
Position Information ◽  
The Real

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.

A Survey of Real-Time Industrial Wireless LAN Research

2015 ◽  
Vol 738-739 ◽  
pp. 1105-1110 ◽  
Author(s):  
Yuan Qing Qin ◽  
Ying Jie Cheng ◽  
Chun Jie Zhou
Keyword(s):  
Real Time ◽  
Future Development ◽  
Wireless Lan ◽  
State Of The Art ◽  
The State ◽  
Local Networks ◽  
The Real ◽  
Time Requirements ◽  
And Performance ◽  
Fundamental Mechanism

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.

An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm

2021 ◽  
pp. 101-107
Author(s):  
Mohammad Alshehri ◽  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Initial Position ◽  
Swarm Optimization ◽  
The Real ◽  
Assisted Navigation ◽  
Glowworm Swarm Optimization ◽  
Tracking Process ◽  
Wide Range ◽  
Localization And Tracking

Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.

scholarly journals An improved rapid quantitative detection and identification method for a wide range of fungi

2009 ◽  
Vol 58 (8) ◽  
pp. 1037-1044 ◽  
Author(s):  
Nobutoshi Soeta ◽  
Masanori Terashima ◽  
Mitsukazu Gotoh ◽  
Shuichi Mori ◽  
Kyoko Nishiyama ◽  
...  
Keyword(s):  
Real Time ◽  
Real Time Pcr ◽  
Diagnostic Technique ◽  
Fungal Species ◽  
Quantitative Detection ◽  
Its2 Region ◽  
The Real ◽  
Detection And Identification ◽  
Wide Range ◽  
Pcr Products

To develop a rapid and quantitative diagnostic technique for the detection and identification of a wide range of fungi, an improved molecular method based on real-time PCR and the analysis of its products that targets the internal transcribed spacer (ITS) 2 region was established. The real-time PCR could quantitatively and specifically detect the ITS2 region from all 24 tested pathogenic fungal species at between 101 and 107 copies per test without amplification of bacterial or human DNA. The sequences of the primer-binding sites are conserved in the registered sequences of 34 other pathogenic fungal species, suggesting that the PCR would also detect these species. The hyperpolymorphic nature of the ITS2 region between fungal species in terms of length and nucleotide sequence provided valuable information for the determination of species. By labelling the 5′ end of the reverse primer with NED fluorescent dye, the fragment lengths of the real-time PCR products and their 3′-terminal fragments, derived using restriction enzyme ScrFI digestion, were easily evaluated by capillary electrophoresis. Using this analysis, the number and species of fungi present in samples could be estimated. Moreover, sequence analysis of the real-time PCR products could accurately determine species in samples containing a single species. This diagnostic technique can estimate a wide range of fungi from various clinical samples within 1 day and accurately identify them in 2 days. Quantitative results for fungal titre in samples can also provide useful information for understanding the progression of disease and the efficacy of antifungal chemotherapy.

Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data

2012 ◽  
Vol 253-255 ◽  
pp. 1365-1368
Author(s):  
Ge Qi Qi ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Traffic Simulation ◽  
Rapid Development ◽  
Mobile Phone Data ◽  
Simulation Platform ◽  
Traffic Data ◽  
The Real ◽  
Real Time Traffic ◽  
Wide Range

With the rapid development of the society, the transportation system has become more complicated and vulnerable. For simulating the real-time traffic condition of the whole city, a wide range of OD matrix data are needed which are hard to collect in whole based on the present conventional methods. The paper raises a feasible design of the traffic simulation platform based on the real-time mobile phone data. The popularity and development of mobile phones make the vast amounts of real-time traffic data can be collected and usable. With the help of the GIS module, dynamic OD traffic generation module and other related modules, the real-time mobile phone data will be converted to the valuable traffic data and applied to the traffic simulation platform.

scholarly journals The Design and Performance of the Real-Time Software Architecture for the ITER Radial Neutron Camera

2019 ◽  
Vol 66 (7) ◽  
pp. 1310-1317 ◽  
Author(s):  
N. Cruz ◽  
B. Santos ◽  
A. Fernandes ◽  
P. F. Carvalho ◽  
J. Sousa ◽  
...  
Keyword(s):  
Software Architecture ◽  
Real Time ◽  
The Real ◽  
And Performance

Recommendation System based on user interaction data applied to intelligent electronic books

2011 ◽  
Vol 27 (4) ◽  
pp. 1445-1449 ◽  
Author(s):  
Rubén González Crespo ◽  
Oscar Sanjuán Martínez ◽  
Juan Manuel Cueva Lovelle ◽  
B. Cristina Pelayo García-Bustelo ◽  
José Emilio Labra Gayo ◽  
...  
Keyword(s):  
Recommendation System ◽  
User Interaction ◽  
Interaction Data ◽  
Electronic Books

scholarly journals Designing Musical Games for Electroacoustic Improvisation

2021 ◽  
Vol 26 (1) ◽  
pp. 78-88
Author(s):  
Christos Michalakos
Keyword(s):  
Real Time ◽  
Performance Practice ◽  
Game Design ◽  
Music Composition ◽  
Live Electronics ◽  
Design Decisions ◽  
Future Directions ◽  
Wide Range ◽  
Game Engines ◽  
And Performance

This paper describes the background and motivations behind the author’s electroacoustic game-pieces Pathfinder (2016) and ICARUS (2019), designed specifically for his performance practice with an augmented drum kit. The use of game structures in music is outlined, while musical expression in the context of commercial musical games using conventional game controllers is discussed. Notions such as agility, agency and authorship in music composition and improvisation are in parallel with game design and play, where players are asked to develop skills through affordances within a digital game-space. It is argued that the recent democratisation of game engines opens a wide range of expressive opportunities for real-time game-based improvisation and performance. Some of the design decisions and performance strategies for the two instrument-controlled games are presented to illustrate the discussion; this is done in terms of game design, physical control through the augmented instrument, live electronics and overall artistic goals of the pieces. Finally, future directions for instrument-controlled electroacoustic game-pieces are suggested.

scholarly journals PlayMyMood: Personalized Hybrid Music Recommendation Engine based on Body Monitoring Parameters

2021 ◽  
Author(s):  
Aditya Vinod Kumar
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Hierarchical Model ◽  
Recommendation System ◽  
Music Recommendation ◽  
Body Parameters ◽  
Music Recommendation System

In this paper, we build a hybrid personalized music recommendation system that takes into account the user's previous listening history, real-time body parameters such as heart rate and interest of similar users. We build a unique unsupervised hierarchical model that combines all these attributes and computes the best song recommendation to the user.

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