811 resources related to Data Intelligence
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.
Early Vision and Sensors Color, Illumination and Texture Segmentation and Grouping Motion and TrackingStereo and Structure from Motion Image -Based Modeling Physics -Based Modeling Statistical Methods and Learning in VisionVideo Surveillance and Monitoring Object, Event and Scene Recognition Vision - Based Graphics Image and Video RetrievalPerformance Evaluation Applications
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.
2011 First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering, 2011
Digital revolution has truly changed the world. Processing speed, storage capacity, and network bandwidth have been doubling every 18 months for the past 30 years. Every type of consumer device is increasingly being web connected and is becoming an open host to applications and contents. Meanwhile, the World Wide Web has been evolving from centralized, directory based host of static ...
2017 Tenth International Conference on Mobile Computing and Ubiquitous Network (ICMU), 2017
Recently, research of indoor location estimation has been attracting attention for tracking behaviors of human in a store. In the case of the store, indoor location estimation adopts absolute location estimation techniques using RSSI of WiFi or BLE signals from beacons installed in the store. As the RSSI has been influenced by the signal reflection, the diffraction, the shelter and ...
2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017
The growth and diffusion of online social media have been enormously increased in recent years, as well as the research and commercial interests toward these rising sources of information as a direct public expression of the communities. Moreover, the depth and the quality of data that can be harvested by monitoring and analysis tools have evolved significantly. In particular, Twitter ...
2018 International Conference on Platform Technology and Service (PlatCon), 2018
The concept of Internet of Things (IoT) and Internet of Everything (IoE) was proposed to realize intelligent to thing and smart device communications using Web standard technology. To many, the large scale data generated by IoT and IoE environment are considered having highly valuable and useful information. Increasing requirements to generate new values through data analysis and autonomous mash-up have ...
2015 IEEE International Conference on Multimedia Big Data, 2015
The last decade has witnessed an explosive growth in multimedia big data, with the invention of big data analytics technologies, such as Hadoop, Spark, Storm. All these technologies are valuable to mine intelligence from multimedia big data, and further shed new insights into multimedia networks. This paper presents an outlook on the development of future multimedia networks and discuss utilizing ...
Interaction and Experience in Enactive Intelligence and Humanoid Robotics
Big Data Analytics: Tools and Technologies - Big Data Analytics Tutorial Part 2
Playing Games with Computational Intelligence
Some Recent Work in Computational Intelligence for Software Engineering
Keynote Address and Opening Remarks - IEEE AI & Ethics Summit 2016
Time-series Workloads and Implications for Time-series Databases - Michael Freedman - IEEE Sarnoff Symposium, 2019
Data for Good: Data Science at Columbia - Jeannette Wing - IEEE Sarnoff Symposium, 2019
LPIRC: A Facebook Approach to Benchmarking ML Workload
Part 1: Derek Footer and Miku Jah - Agricultural Food Systems Panel - TTM 2018
How to Get Real Benefits from Real Intelligence in the Grid
Collection, Modeling & Interpretation of Mobile Sensor Big Data - Santosh Kumar - IEEE EMBS at NIH, 2019
Artificial Intelligence: Who Does the Thinking? - Recap of the IEEE AI & Ethics Summit, 2016
Interview with Fuzzy Systems Pioneer Jim C. Bezdek
Q&A with Dan Hutcheson: IEEE Rebooting Computing Podcast, Episode 16
Deep Graph Learning: Techniques and Applications - Haifeng Chen - IEEE Sarnoff Symposium, 2019
5G Virtual RAN Network Architectures - Olufemi Adeyemi - IEEE Sarnoff Symposium, 2019
IROS TV 2019- Industry 4.0: The Business of Robotics- Industrial CEO Summit Forum
The Sorites Paradox: Introduction to Fuzzy Logic
Who Should the Car Hit? Javier Gozalvez - Ignite: Sections Congress 2017
Digital revolution has truly changed the world. Processing speed, storage capacity, and network bandwidth have been doubling every 18 months for the past 30 years. Every type of consumer device is increasingly being web connected and is becoming an open host to applications and contents. Meanwhile, the World Wide Web has been evolving from centralized, directory based host of static documents to distributed, collaborative, "smart" place of integrated services. Data Intelligence (DI) is a newly establishing field that intelligently and in real-time, tries to manage and analyze such raw, streaming, irregular, and heterogeneous data to support better business decision making. I will talk about some of the key technical aspects of Data Intelligence in the age of CE Centric Life Web, and if done well, how they can greatly enhance our lives.
Recently, research of indoor location estimation has been attracting attention for tracking behaviors of human in a store. In the case of the store, indoor location estimation adopts absolute location estimation techniques using RSSI of WiFi or BLE signals from beacons installed in the store. As the RSSI has been influenced by the signal reflection, the diffraction, the shelter and others, an estimated values can cause errors during the process of converting the RSSI values into distances. In this paper, we propose dynamical and optimal beacon selection method to minimize affections of wireless signal shielding by the static and movable obstructions. As the result of a simulation, we found that the average of the location estimation errors using our proposed method is reduced by 1.2m against a method only considering static obstructions and our method keeps a constant error regardless of the number of selected BLE beacons.
The growth and diffusion of online social media have been enormously increased in recent years, as well as the research and commercial interests toward these rising sources of information as a direct public expression of the communities. Moreover, the depth and the quality of data that can be harvested by monitoring and analysis tools have evolved significantly. In particular, Twitter has revealed to be one of the most widespread microblogging services for instantly publishing and sharing opinions, feedbacks, ratings etc., contributing in the development of the emerging role of users as sensors. However, due to the huge amount of data to be collected and analyzed and limitations on data access imposed by Twitter public APIs, more efficient requirements are needed for analytics tools, both in terms of data ingestion and processing, as well as for the computation of analysis metrics, to be provided for deeper statistic insights and further investigations. In this paper, the Twitter Vigilance platform is presented, realized by the DISIT Lab at University of Florence. Twitter Vigilance has been designed as a cross- domain, multi-user tool for collecting and analyzing Twitter data, providing aggregated metrics based on the volume of tweets and retweets, users' influence network, Natural Language Processing and Sentiment Analysis of textual content. The proposed architecture has been validated against a dataset of about 270 million tweets showing a high efficiency in recovering Twitter data. For this reason it has been adopted by a number of researchers as a study platform for social media analysis, early warning, etc.
The concept of Internet of Things (IoT) and Internet of Everything (IoE) was proposed to realize intelligent to thing and smart device communications using Web standard technology. To many, the large scale data generated by IoT and IoE environment are considered having highly valuable and useful information. Increasing requirements to generate new values through data analysis and autonomous mash-up have resulted in active studies on integration with semantic web technologies in the IoT and IoE. Particularly, in order to utilize IoE data in real environment, connectivity and mutual cooperation technology suitable for various services are required. In this paper, we proposed a semantic thing retrieval system in IoT and IoE that can perform efficient thing retrieval operation with various thing metadata and things with states information in IoT and IoE.
The last decade has witnessed an explosive growth in multimedia big data, with the invention of big data analytics technologies, such as Hadoop, Spark, Storm. All these technologies are valuable to mine intelligence from multimedia big data, and further shed new insights into multimedia networks. This paper presents an outlook on the development of future multimedia networks and discuss utilizing the big data intelligence into the design and optimization. We start with an end-to-end view for future multimedia networks, including contents, multimedia networks, and consumers. For each part, we enumerate several representative applications and technologies that bring new challenges and opportunities. After that, we discuss the common idea of utilizing the big data intelligence to benefit the whole ecosystem, and highlight some open research issues for the future.
This paper proposes an approach to computational knowledge analytics. Identification and measurement of knowledge - a meta-knowledge of who knows what - is a crucial foundation for decision-making. The availability of information sources and know-how is a valuable production factor. In this paper, the authors propose a framework to capture, visualize, and analyze organizational knowledge by extracting granular, hierarchical entity- relationship models from unstructured text data that are mapped to individual users and organizational units. With a granular computing approach, the framework can be applied to model, represent, and visualize knowledge profiles that show who in the organization knows what. The framework can also be applied in big data management to enhance data intelligence - the competence, knowledge, and skills necessary to analyze and utilize big data. We present a prototype implementation that assesses and harnesses data sources and metrics to deliver a meaningful analysis of user and community knowledge, based on text analytics.
Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved scalability. The large data set is clustered into subsets using K-means algorithm, then the obtained subsets are used to train the convolutional neural network. A real-world power industry data set containing more than 1.4 million of load records is used in this study and the experimental results demonstrate the effectiveness of the proposed method.
The explosion in the variety and volume of video services makes bandwidth and latency performance of networks more critical to the user experience. The media industry's response, HTTP-based Adaptive Streaming technology, offers media players the possibility to dynamically select the most appropriate bitrate according to the connectivity performance. Moving forward, the telecom industry's move is 5G. 5G aims efficiency by dynamic network optimization to make maximum use of the resources to get as high capacity and Quality of Service (QoS) as possible. These networks will be based on software defined networking (SDN) and network function virtualization (NFV) techniques, enabling self-management functions. Here, machine learning is a key technology to reach this 5G vision. On top of machine learning, SDN and NFV, this paper provides a network resource allocator system as the main contribution which enables autonomous network management aware of quality of experience (QoE). This system predicts demand to foresee the amount of network resources to be allocated and the topology setup required to cope with the traffic demand. Furthermore, the system dynamically provisions the network topology in a proactive way, while keeping the network operation within QoS ranges. To this end, the system processes signals from multiple network nodes and end-to-end QoS and QoE metrics. This paper evaluates the system for live and on-demand dynamic adaptive streaming over HTTP and high efficiency video coding services. From the experiment results, it is concluded that the system is able to scale the network topology and to address the level of resource efficiency, required by media streaming services.
Recently, there is a growing trend towards machine learning models that run on client devices such as smartphones, with constraints such as model size and time. Often, the user data on client devices is considered private and so cannot be uploaded to an external server for processing and running the model. Blockchain provides a new way of sharing data that is secure and decentralized. In this paper, we provide a way to use blockchain to run a machine learning model in a decentralized way using various nodes to compute part of the learning task. We apply our system in a smart home IoT setting to generate user customizations based on user activity prediction for IoT devices. We use distributed association rule mining to generate the rules of user activity from the device logs. We describe the system architecture and simulate the system using Ethereum based tools.
Short-term load forecasting in smart grid is key to electricity dispatch scheduling, reliability analysis, and maintenance planning for the generators. In this paper, we present a convolutional neural networks (CNN) based bagging model for forecasting hourly loads. We employ CNN to train forecasting models on big load data sets. Then, we segment a real industry load data set into many subsets, fine-tune the forecasting models on these subsets to learn weak forecasting models, and assemble these weak forecasting models to conduct a bagging forecasting model, where the learning and assembling procedures are implemented on Spark. Specifically, all load samples in those data sets are reorganized as images with respect to similarities between relations of pixels in images and those of features in load samples. Experimental results indicate the effectiveness of the proposed method.