Context-aware Data Analysis.
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2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
FUZZ-IEEE 2021 will represent a unique meeting point for scientists and engineers, both from academia and industry, to interact and discuss the latest enhancements and innovations in the field. The topics of the conference will cover all the aspects of theory and applications of fuzzy sets, fuzzy logic and associated approaches (e.g. aggregation operators such as the Fuzzy Integral), as well as their hybridizations with other artificial and computational intelligence techniques.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
IEEE INFOCOM solicits research papers describing significant and innovative researchcontributions to the field of computer and data communication networks. We invite submissionson a wide range of research topics, spanning both theoretical and systems research.
Artificial Intelligence, Control and Systems, Cyber-physical Systems, Energy and Environment, Industrial Informatics and Computational Intelligence, Robotics, Network and Communication Technologies, Power Electronics, Signal and Information Processing
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.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...
Each tutorial reviews currents communications topics in network management and computer and wireless communications. Available tutorials, which are 2.5 to 5 hours in length contains the original visuals and voice-over by the presenter. IEEE Communications Surveys & Tutorials features two distinct types of articles: original articles and reprints. The original articles are exclusively written for IEEE Communications Surveys & Tutorials ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
2009 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2009
Data as a Service (DaaS) emerges as a new trend for exchanging data between independent data owners and data users so that data can be acquired on demand through standard protocols across heterogeneous platforms. It is usually a user-interactive and iterative process to compose the services into various data-driven business scenarios of data acquisition, analysis, and other processing activities. Context ...
2016 2nd International Conference on Intelligent Energy and Power Systems (IEPS), 2016
In this work we present a context-aware framework for energy management system (CAEMS). The CAEMS is a context awareness framework that aims to provide a comprehensive solution to reason about the context from the level of sensor data to the high-level situation awareness (actuator or devices). The paper describes these challenges and presents data management solutions as a module of ...
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009
Enormous uncertainties in unconstrained environments lead to a fundamental dilemma that many tracking algorithms have to face in practice: Tracking has to be computationally efficient, but verifying whether or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to ...
2014 International Conference on Service Sciences, 2014
With the development of big data, the data size becomes bigger and bigger, which makes users consume enormous time to find the items that they might like from abundant options. Recommender systems are expected to help users find interested items. However, most existing recommendation methods do not take into account any additional contextual information with a reasonable complexity. This paper ...
2009 Eighth IEEE/ACIS International Conference on Computer and Information Science, 2009
With rapid development of computer networks, users need a new solution for network security management, aiming at integration. This paper focuses on context-aware alert analysis, which is one of its key functionalities. A practical and efficient approach to guarantee unified representation of context information, background knowledge and attack knowledge for security alerts is still lacking these days. This paper applies ...
IEEE Themes - Efficient networking services underpin social networks
Photo Verification Technology for Radiology Images - Srini Tridandapani - IEEE EMBS at NIH, 2019
TechNews: Big Data
Highly Dynamic, Energy-Aware, Biomimetic Robots
IMS 2012 Microapps - Integrated Electrothermal Solution Delivers Thermally Aware Circuit Simulation Rick Poore, Agilent EEsof
Michael Johnson: Big Data in Healthcare
The eXtensible Event Stream (XES) standard
"What is Big Data Analytics and Why Should I Care?" - Big Data Analytics Tutorial Part 1
Q&A with Dr. K. J. Ray Liu: IEEE Big Data Podcast, Episode 11
Raffaele Giaffreda: Solving IoT Interoperability and Security Problems in an eHealth Context: WF-IoT 2016
Classifying attention in Pivotal Response Treatment Videos - Corey Heath - LPIRC 2019
V-Big Data: An Introduction
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
NEREID: Systems Design & Heterogeneous Integration: Danilo Demarchi at INC 2019
An Analysis of Phase Noise Requirements for Ultra-Low-Power FSK Radios: RFIC Interactive Forum 2017
Part 1: Derek Footer and Miku Jah - Agricultural Food Systems Panel - TTM 2018
Piero P Bonissone - Lazy Meta-Learning - Creating Customized Model Ensembles on Demand
A Bayesian Approach for Spatial Clustering - IEEE CIS Webinar
A 28nm, 475mW, 0.4-to-1.7GHz Embedded Transceiver Front-End Enabling High-Speed Data Streaming Within Home Cable Networks: RFIC Industry Showcase
Data as a Service (DaaS) emerges as a new trend for exchanging data between independent data owners and data users so that data can be acquired on demand through standard protocols across heterogeneous platforms. It is usually a user-interactive and iterative process to compose the services into various data-driven business scenarios of data acquisition, analysis, and other processing activities. Context information can facilitate the recommendation and selection of the services based on the runtime service behavior, user preferences, and historical results. The paper proposes a context-aware approach for integrating data in dynamic changing business processes following the DaaS architecture, as an extension of our previous work on the service- based framework for pharmocogenomics data integration. A context model is defined in three dimensions including the tasks, the services, and the users, from both static and dynamic perspectives. The monitoring and feedback mechanism is introduced to enable continuous profiling of the runtime behavior and updating of the context properties. The paper takes the biomedical research as the motivating scenario to illustrate and evaluate the effectiveness of the proposed approach.
In this work we present a context-aware framework for energy management system (CAEMS). The CAEMS is a context awareness framework that aims to provide a comprehensive solution to reason about the context from the level of sensor data to the high-level situation awareness (actuator or devices). The paper describes these challenges and presents data management solutions as a module of context data analysis for the energy (Microgrid) control system. These solutions include sensor data acquisition and time series forecasting, ontology model and context prediction model for analytical query processing past and future context data.
Enormous uncertainties in unconstrained environments lead to a fundamental dilemma that many tracking algorithms have to face in practice: Tracking has to be computationally efficient, but verifying whether or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either effective but computationally intensive by using sophisticated image observation models or efficient but vulnerable to false alarms. This greatly challenges long-duration robust tracking. This paper presents a novel solution to this dilemma by considering the context of the tracking scene. Specifically, we integrate into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties, at least in a short time interval: 1) persistent co-occurrence with the target, 2) consistent motion correlation to the target, and 3) easy to track. Regarding these auxiliary objects as the context of the target, the collaborative tracking of these auxiliary objects leads to efficient computation as well as strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.
With the development of big data, the data size becomes bigger and bigger, which makes users consume enormous time to find the items that they might like from abundant options. Recommender systems are expected to help users find interested items. However, most existing recommendation methods do not take into account any additional contextual information with a reasonable complexity. This paper aims to propose a context-aware recommender system by incorporating context-aware technology into recommendation. The context-aware approach is based on ontology and Gaussian Mixture Model. The recommendation analysis is implemented by trust aided probabilistic matrix factorization approach. The evaluation shows that the proposed approach has a good effect in recommendation quality.
With rapid development of computer networks, users need a new solution for network security management, aiming at integration. This paper focuses on context-aware alert analysis, which is one of its key functionalities. A practical and efficient approach to guarantee unified representation of context information, background knowledge and attack knowledge for security alerts is still lacking these days. This paper applies security ontology by means of OWL+SWRL+OWL-S based on CIM schema to describe context information and security knowledge in a unified manner. We argue that, our proposed approach improves existing alert analysis techniques by providing formal representations with the use of security ontology, which may possibly be an important stage for implementation of unified network security management.
The unlimited growth of supply chain finance data will inevitably lead to a situation in which it is increasingly difficult to access the desired information. Supply chain finance is often necessary to analyze large data sets, maintained over geographically bank, supplier and customer distributed sites by using cooperative context-aware distributed data mining (DDM) systems. The study of the existing approaches shows that no single solution fulfills all requirements identified for the cooperative context-aware DDM systems. One of the basic obstacles is the lack of context-aware and supporting some of the computational resources - such as data and information bases, computational models, compute power to execute these models, specialized data mining algorithms - required to develop a new compound is not available locally, but accessible via the global computing network infrastructure. This paper proposes a multi-agent-based architecture for supply chain finance cooperative distributed data mining systems. The use of multi-agent-systems (MAS) creates a framework which allows the inter-operation of a vast set of heterogeneous solutions to carry out the complex supply chain finance context-aware distributed data mining tasks, many data mining tasks to connect heterogeneous resources, as data sources, processing nodes and end user applications. It considers a data warehousing that supports context-aware OLAP queries, ensuring the interoperability of all data sources, and then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem solving scenarios. Finally, we outline the implementation of a prototype for shoes manufacturing.
Context-Aware Algorithm has been developed for Human Physical Movement Activity Recognition. This Algorithm internally takes the data from the different sensor devices as an input and provides classified data as an output to the application layer. The Algorithm shall have the capability to contain different context aware algorithms. This Context-Aware Algorithm is flexible and modular enough to be ported to any platform with minimal efforts. The Algorithm has been tested and developed on TI Sensor Tag which is based on CC2650 wireless MCU. In this paper, we have made this algorithm to be able to run on Android Platform as nowadays smart-phone platforms are equipped with a diverse and powerful set of sensors. This work is significant because the Human Movement activity recognition permits us to gain useful knowledge about the habits of millions of users passively just by having them carry cell phones in their pockets. Our work has a wide range of applications enabled by activity recognition, like automatic customization of the mobile devices behavior based upon a user's activity (e.g., sending calls directly to voice- mail, if a user is running) and generating a daily activity profile to determine if a user is performing a healthy amount of exercise.
Internet of Vehicles (IoV) is the evolution of vehicular ad-hoc networks and intelligent transportation systems focused on reaping the benefits of data generated by various sensors within these networks. The IoV is further empowered by a centralized cloud and distributed fog-based infrastructure. The myriad amounts of data generated by the vehicles and the environment have the potential to enable diverse services. These services can benefit from both variety and velocity of the generated data. This paper focuses on the data at the edge nodes to enable fog-based services that can be consumed by various IoV safety and non-safety applications. This paper emphasizes the challenges involved in offering the context-aware services in an IoV environment. In order to overcome these challenges, this paper proposes a data analytics framework for fog infrastructures at the fog layer of traditional IoV architecture that offers context-aware real time, near real-time and batch services at the edge of a network. Finally, the appropriateness of the proposed framework is verified through different use cases in the IoV environment.
We present a family of three interactive Context-Aware Selection Techniques (CAST) for the analysis of large 3D particle datasets. For these datasets, spatial selection is an essential prerequisite to many other analysis tasks. Traditionally, such interactive target selection has been particularly challenging when the data subsets of interest were implicitly defined in the form of complicated structures of thousands of particles. Our new techniques SpaceCast, TraceCast, and PointCast improve usability and speed of spatial selection in point clouds through novel context-aware algorithms. They are able to infer a user's subtle selection intention from gestural input, can deal with complex situations such as partially occluded point clusters or multiple cluster layers, and can all be fine-tuned after the selection interaction has been completed. Together, they provide an effective and efficient tool set for the fast exploratory analysis of large datasets. In addition to presenting Cast, we report on a formal user study that compares our new techniques not only to each other but also to existing state-of-the- art selection methods. Our results show that Cast family members are virtually always faster than existing methods without tradeoffs in accuracy. In addition, qualitative feedback shows that PointCast and TraceCast were strongly favored by our participants for intuitiveness and efficiency.
Recent studies have shown that several government and business organizations experience huge data breaches. Data breaches increase in a daily basis. The main target for attackers is organization sensitive data which includes personal identifiable information (PII) such as social security number (SSN), date of birth (DOB) and credit card /debit card (CCDC). The other target is encryption/decryption keys or passwords to get access to the sensitive data. The cloud computing is emerging as a solution to store, transfer and process the data in a distributed location over the Internet. Big data and internet of things (IoT) increased the possibility of sensitive data exposure. Most methods used for the attack are hacking, unauthorized access, insider theft and false data injection on the move. Most of the attacks happen during three different states of data life cycle such as data-at-rest, data-in-use, and data-in-transit. Hence, protecting sensitive data at all states particularly when data is moving to cloud computing environment needs special attention. The main purpose of this research is to analyze risks caused by data breaches, personal and organizational weaknesses to protect sensitive data and privacy. The paper discusses methods such as data classification and data encryption at different states to protect personal and organizational sensitive data. The paper also presents mathematical analysis by leveraging the concept of birthday paradox to demonstrate the encryption key attack. The analysis result shows that the use of same keys to encrypt sensitive data at different data states make the sensitive data less secure than using different keys. Our results show that to improve the security of sensitive data and to reduce the data breaches, different keys should be used in different states of the data life cycle.
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