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International journal of engineering issues
International journal of engineering issues vol 2015 - no 2 - paper4
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International journal of engineering issues
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This is a set of slides from the Claremont Report on Database Research, see http://db.cs.berkeley.edu/claremont/ for more details. These particular slides are from a "Research Directions" talk by "Anastasia Ailamaki." (Uploaded for discussion at the Stanford InfoBlog, http://infoblog.stanford.edu/.)
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Information security is becoming increasingly important in the modern world. Secure Image Transmission has a potential of being adopted for mass communication. Several stenographic techniques for transmitting information without raising suspicion are found in [8]-[12]. However A new secure image Transmission technique is proposed, known as secret fragment visible mosaic image which allows the user to securely transmit an image under the cover of another image of same size, This paper presents an approach where mosaic image generation has done by dividing the secret image into fragments and transforming their respective colour characteristics into corresponding blocks of the target image. Usage of the Pixel colour transformations helps to yield the lossless recovered image based on the untransformed colour space values. Generation of the key plays an important role to recover the secret image from the mosaic image in lossless manner. Finally the same approach can be performed on videos also which helps to eliminate the flickering artefact to achieve the lossless data recovery in motion related videos. The experimental results show good robust behaviour against all incidental and accidental attacks and compare to the conventional algorithms.
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COGITO INTRODUCTION on LinkedIn
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Kyunam Cho
For further details contact: N.RAJASEKARAN B.E M.S 9841091117,9840103301. IMPULSE TECHNOLOGIES, Old No 251, New No 304, 2nd Floor, Arcot road , Vadapalani , Chennai-26. www.impulse.net.in Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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Machine Learning and Internet-of-Things: opposites attract in the age of data...
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OSGi Community Event 2015 A new world of applications emerges in the home from the growing variety of things – devices, sensors, actuators – potentially available. Several application domains are considered, e.g., security, energy efficiency, comfort, ambient assisted living, multimedia communication. The Smart Home is slowly taking off.</p> Several actors exploit a new technical and economic opportunity to catalyze this market. This opportunity is based on the re-use of the infrastructure that telecom operators have deployed for today classic Internet and TV services. It raises technical and business challenges: Telecom operators have to open their home infrastructure to third-party applications while guaranteeing application security and consistency to all home business actors using this infrastructure. Telecom operators have to open APIs at least two levels of their architecture: APIs in the cloud and APIs on an embedded device environment. This end-to-end infrastructure between the home network and service platforms has also to provide security at several levels, especially a consistent access right management. The presentation will provide a vision of an open end-to-end architecture providing APIs in the cloud and in a home box to host any application and connect to any device in the Home. Among the standard organizations and industrial alliances, oneM2M standard specifications are making a reference architecture emerge. The implementation of oneM2M standard features in OSGi technology will be detailed, especially the end-to-end access right management discriminating both applications and users when accessing devices. This infrastructure is currently prototyped thanks to the integration of open source software bricks provided by <a>Open the Box</a>, <a>Eclipse SmartHome</a> and <a>Eclipse OM2M</a> open initiatives.
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John Thorpe, Head of Product, BreezeML Machine learning (especially deep learning) is becoming increasingly complex and expensive. Many companies build their core businesses (e.g., self-driving, credit card fraud detection, item recommendation, etc.) upon continuous model training and/or inferencing, which is typically performed with dozens or even hundreds of GPU machines on a (public or on-premise) cloud. While a cloud-based environment makes it possible for these jobs to dynamically scale with load changes (e.g., user requests), running these jobs under the cloud's pay-as-you-go pricing model incurs large monetary costs, which would rapidly grow with the model size/complexity, the size of datasets, and the number of users. BreezeML democratizes AI/ML by helping AI companies significantly increase their performance-per-dollar by making effective use of preemptible GPU instances. Rooted in years of research at UCLA and Princeton, BreezeML provides (1) a preemption-resilient software system that allows users to reliably run ML training/inference jobs on preemptible instances (such as spot instances) and (2) a virtual cloud interface that performs intelligent selection and scheduling of (spot and on-demand) instances to minimize the monetary costs with strong SLA guarantees. Currently, BreezeML provides two services: 1. An API server (http://windmill.breezeml.ai/apis/) that allows ML engineers to upload batch jobs for free trails. It also allows customers to use their own cloud (e.g., AWS) credential to log in and use BreezeML to run jobs under their own cloud configurations. 2. We provide a docker image of the Breeze runtime, which includes the Breeze-enhanced Pytorch/Tensorflow/XGBoost as well as a new K8S-based orchestration system that can be easily deployed in the user's local environment (compliant with the user's local security policies). Our runtime allows the user to (a) use cheap spot instances in the cloud or (b) sharing resources between (low-priority) training and (high-priority) inference jobs in their on-premise cluster, thereby significantly improving GPU resource utilization. Experiments across a wide range of vision, language, and classification models demonstrate that BreezeML improve the performance-per-dollar by an average of 3 times. Our approach also eliminates the need of resource over-provisioning in on-premise clusters by allowing (high-priority) inference jobs to safely preempt (low-priority) training jobs.
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BDIx (initially introduced in \cite{Ioannou2020}) is a BDI agent that is extended to utilise in Beliefs any other AI/ML techniques (e.g., Fuzzy Logic, Deep Learning Neural Networks, etc.) that gives, among others, to the agent a better understanding of the surrounding environment and the ability to prioritise the order the Desires will be executed. More specifically, as some Desires must conclude before the execution of others (i.e., because the output of one Desire can be an input to another), we allow the Desires to be assigned with priority values, ranging from 0 (lowest) to 100 (highest). In our DAI framework which utilises BDIx agents, this priority value is estimated by using fuzzy logic (as shown in Figure \ref{fig:flowchart}) considering in its "IF-THEN" rules the current Beliefs, the values measured by the sensors of the D2D Device, any raised events (e.g., see Table \ref{Events}) and cases where the pre-specified threshold values (e.g., Data Rate Drop less than 60\%, Signal Quality Drop less than 30\%) are exceeded. Based on the assigned priority value, Desires become Intentions which are adopted for active pursuit by the agent (referred as a Goal). When the Intention is accomplished, the priority value of the associated Desire is set to zero. In addition, a Desire that will become an Intention can have multiple plans associated with it and the Desire can select an appropriate plan based on a utility function. For simplicity, but without loss of generality, in our DAI Framework we consider each Desire, and indirectly each Intention, to be associated with only one plan. It is also important to highlight here that the Beliefs and the Desires of the BDIx agent comprising the DAI framework have been extracted from the D2D Requirements/Challenges that should be realised in order to implement 5G D2D communication.
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Smart home system is very popular in current days that give many kind of application that make all simple and easy to control. In modern day, home machines are using wireless equipment and can be retrieved by internet that will make populations life easier and organized. It based Home Computerization System is designed to assist the people with physical debilities and elderly to provide support as well as to control the electrical usages and monitor the room infection using mobile application. The design is using surrounded controller board and the home appliances are physically associated to output ports of this board via relays. The Home Automation is a wireless home computerization system that is supposed to be executed in existing home environments, without any variations in the infrastructure. Arun Kumar. N | Sathiyabama. T "Smart Security System (IOT)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29337.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-security/29337/smart-security-system-iot/arun-kumar-n
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the project is aimed to develop a crime file for maintain a computerized record of all the F.I.R against crime .The system is desktop application that can be access throughout the police department. This system can be used as an application for the crime file of the police department to manage the records of different activity of related to first information report .In such desktop Crime file system we will manage all such activities (like registration of the complaint updating information, search of particular viewing of the respective reports of crimes) that will save time, manpower. This software is for police station which provides facility for reporting crimes, complaints, FIR, charge sheet, prisoner records, and show most wanted criminal’s details. This system will provide better prospective for the enhancement of organization regarding to quality and transparency
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BDCaM: Big Data for Context-aware Monitoring - A Personalized Knowledge Disco...
kitechsolutions
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BDCAM: big data for context-aware Monitoring
BDCAM: big data for context-aware Monitoring
kitechsolutions
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chapter 5.docx
chapter 5.docx
Sami Siddiqui
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BDCaM: Big Data for Context-aware Monitoring - A Personalized Knowledge Disco...
BDCaM: Big Data for Context-aware Monitoring - A Personalized Knowledge Disco...
BDCAM: big data for context-aware Monitoring
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chapter 5.docx
chapter 5.docx
Smarthome Presentation
1.
Development of Smarthome
application for remote access to a ZigBee Network By: Neil Higginbotham Supervisor: Dr. Fred Japhet Mtenzi
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Design – Three
Tire & Distributed
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