162,187 research outputs found
A roadmap to integrated digital public health surveillance: The vision and the challenges
The exponentially increasing stream of real time big data produced by Web 2.0 Internet and mobile networks created radically new interdisciplinary challenges for public health and computer science. Traditional public health disease surveillance systems have to utilize the potential created by new situationaware realtime signals from social media, mobile/sensor networks and citizens' participatory surveillance systems providing invaluable free realtime event-based signals for epidemic intelligence. However, rather than improving existing isolated systems, an integrated solution bringing together existing epidemic intelligence systems scanning news media (e.g., GPHIN, MedISys) with real-time social media intelligence (e.g., Twitter, participatory systems) is required to substantially improve and automate early warning, outbreak detection and preparedness operations. However, automatic monitoring and novel verification methods for these multichannel event-based real time signals has to be integrated with traditional case-based surveillance systems from microbiological laboratories and clinical reporting. Finally, the system needs effectively support coordination of epidemiological teams, risk communication with citizens and implementation of prevention measures. However, from computational perspective, signal detection, analysis and verification of very high noise realtime big data provide a number of interdisciplinary challenges for computer science. Novel approaches integrating current systems into a digital public health dashboard can enhance signal verification methods and automate the processes assisting public health experts in providing better informed and more timely response. In this paper, we describe the roadmap to such a system, components of an integrated public health surveillance services and computing challenges to be resolved to create an integrated real world solution
X-ware: a proof of concept malware utilizing artificial intelligence
Recent years have witnessed a dramatic growth in utilizing computational intelligence techniques for various domains. Coherently, malicious actors are expected to utilize these techniques against current security solutions. Despite the importance of these new potential threats, there remains a paucity of evidence on leveraging these research literature techniques. This article investigates the possibility of combining artificial neural networks and swarm intelligence to generate a new type of malware. We successfully created a proof of concept malware named X-ware, which we tested against the Windows-based systems. Developing this proof of concept may allow us to identify this potential threat’s characteristics for developing mitigation methods in the future. Furthermore, a method for recording the virus’s behavior and propagation throughout a file system is presented. The proposed virus prototype acts as a swarm system with a neural network-integrated for operations. The virus’s behavioral data is recorded and shown under a complex network format to describe the behavior and communication of the swarm. This paper has demonstrated that malware strengthened with computational intelligence is a credible threat. We envisage that our study can be utilized to assist current and future security researchers to help in implementing more effective countermeasure
Computer Technology: State of the Art and Future Trends
Computer technology, and more broadly information technology, is invigorating a fundamental transformation in our society form an industrial economy to an information economy. A review of the short history and present state of information technology identifies two major undercurrents: the miniaturization of computer components which has produced a million-fold increase in the complexity possible in a single chip of silicon and the integration of four previously separate areas of information technology: computation, communication, databases and the user interface. Microelectronics, computer networks, data storage and user amenities are the basic technologies that support these four areas and stimulate their progress. Future trends in speech recognition, voice synthesis, artificial intelligence, expert systems, computational imaging and scientific workstations are also examined
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
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