IEEE 1073 Standard implementation to manage and storage corporal variables using mobile devices

2015 ◽  
Vol 13 (3) ◽  
pp. 835-842
Author(s):  
David Alejandro Martinez Vasquez
Keyword(s):  
Author(s):  
Weixiang Xu ◽  
Xiangyu He ◽  
Tianli Zhao ◽  
Qinghao Hu ◽  
Peisong Wang ◽  
...  

Large neural networks are difficult to deploy on mobile devices because of intensive computation and storage. To alleviate it, we study ternarization, a balance between efficiency and accuracy that quantizes both weights and activations into ternary values. In previous ternarized neural networks, a hard threshold Δ is introduced to determine quantization intervals. Although the selection of Δ greatly affects the training results, previous works estimate Δ via an approximation or treat it as a hyper-parameter, which is suboptimal. In this paper, we present the Soft Threshold Ternary Networks (STTN), which enables the model to automatically determine quantization intervals instead of depending on a hard threshold. Concretely, we replace the original ternary kernel with the addition of two binary kernels at training time, where ternary values are determined by the combination of two corresponding binary values. At inference time, we add up the two binary kernels to obtain a single ternary kernel. Our method dramatically outperforms current state-of-the-arts, lowering the performance gap between full-precision networks and extreme low bit networks. Experiments on ImageNet with AlexNet (Top-1 55.6%), ResNet-18 (Top-1 66.2%) achieves new state-of-the-art.


Author(s):  
Ihssan Alkadi

There are many steps involved with securing a cloud system and its applications (SaaS) and developed ones in (PaaS). Security and privacy issues represent the biggest concerns to moving services to external clouds (Public). With cloud computing, data are stored and delivered across the Internet. The owner of the data does not have control or even know where their data are being stored. Additionally, in a multi-tenant environment, it may be very difficult for a cloud service provider to provide the level of isolation and associated guarantees that are possible with an environment dedicated to a single customer. Unfortunately, to develop a security algorithm that outlines and maps out the enforcement of a security policy and procedure can be a daunting task. A good security algorithm presents a strategy to counter the vulnerabilities in a cloud system. This chapter covers the complete overview, comparative analysis of security methods in Cloud Applications in STEM Education and the introduction of a new methodology that will enforce cloud computing security against breaches and intrusions. Much light will be shed on existing methodologies of security on servers used for cloud applications in STEM education and storage of data, and several methods will be presented in addition to the newly developed method of security in cloud-based servers, such as the MIST (Alkadi). Not only can cloud networks be used to gather sensitive information on multiple platforms, also there are needs to prevent common attacks through weak password recovery, retrieval, authentication, and hardening systems; otherwise hackers will spread cyber mayhem. Discussion of current security issues and algorithms in a real world will be presented. Different technologies are being created and in constant competition to meet the demands of users who are generally “busy”. The selling point of these technologies is the ability to address these demands without adding more to any workloads. One of the demands often discussed is that users want to have their digital information accessible from anywhere at any time. This information includes documents, audio libraries, and more. Users also demand the ability to manage, edit and update this information regardless of physical location. Somewhat recently, mobile devices such as laptops, tablets, and smartphones have provided these abilities. This is no small feat as vendors and providers have reduced the size of these devices to increase mobility. However, as the amount of personal information that users are wanting to access has grown exponentially, manipulation and storage of it require more capable devices. To meet increased demands, increasing the capabilities of mobile devices may be impractical. Making mobile devices more powerful without technological advancement would require that the device be larger and use more resources such as battery life and processing power to function properly. Storing all of a user's information on a mobile device that travels everywhere also adds vulnerability risks. The best technical solution to having a user's information accessible is some sort of online storage where there is the convenience to store, manipulate and retrieve data. This is one of the most practical applications for the concept of cloud computing in STEM education. As storage capabilities and Internet bandwidth has increased, so has the amount of personal data that users store online. And today, the average user has billions of bytes of data online. Access is everywhere and whenever is needed. As everyone started doing so, people want their data safe and secure to maintain their privacy. As the user base grew in size, the number of security issues of the personal data started to become increasingly important. As soon as someone's data are in the remote server, unwanted users or “hackers” can have many opportunities to compromise the data. As the online server needs to be up and running all the time, the only way to secure the cloud server is by using better passwords by every user. By the same token, the flaws in the password authentication and protection system can also help unwanted users to get their way to other people's personal data. Thus, the password authentication system should also be free from any loopholes and vulnerabilities.


2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Paulo A. L. Rego ◽  
Fernando A. M. Trinta ◽  
Masum Z. Hasan ◽  
Jose N. de Souza

Mobile cloud computing is an approach for mobile devices with processing and storage limitations to take advantage of remote resources that assist in performing computationally intensive or data-intensive tasks. The migration of tasks or data is commonly referred to as offloading, and its proper use can bring benefits such as performance improvement or reduced power consumption on mobile devices. In this paper, we face three challenges for any offloading solution: the decision of when and where to perform offloading, the decision of which metrics must be monitored by the offloading system, and the support for user’s mobility in a hybrid environment composed of cloudlets and public cloud instances. We introduce novel approaches based on machine learning and software-defined networking techniques for handling these challenges. In addition, we present details of our offloading system and the experiments conducted to assess the proposed approaches.


Author(s):  
Chi-Sheng Shih ◽  
Joen Chen ◽  
Yu-Hsin Wang ◽  
Norman Chang

The number and variety of applications for mobile devices continue to grow. However, the resources on mobile devices including computation and storage do not keep pace with the growth. How to incorporate the computation capacity on cloud servers into mobile computing has been desired and challenge issues to resolve. In this work, we design an elastic computation framework to take advantage the heterogeneous computation capacity on cloud servers, which consist of CPUs and GPGPUs, to meet the computation demands of ever growing mobile applications. The computation framework extends OpenCL framework to link remote processors with local mobile applications. The framework is flexible in the sense that the computation can be stopped at any time and gains results, which is called imprecise computation in real-time computing literature. The framework has been evaluated against OpenCL benchmark and physical computation engine for gaming. The results show that the framework supports OpenCL benchmark, RODINIA, without modifying the codes with few exceptions. The elastic computation framework allows the cloud servers to support more mobile clients without sacrificing their QoS requirements. The experiment results also show that IO intensive applications do not perform well when the network capacity is insufficient or unreliable.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liang Xie ◽  
Xi Fang

With the advance of mobile technologies, mobile devices such as unmanned aerial vehicle (UAV) become more important in video surveillance. By applying mobile person re-identification (re-id), mobile devices can monitor pedestrians in the transportation system from complex environments. Since the computing and storage resources of mobile devices are limited, traditional person re-id methods are not appropriate for mobile condition. Besides, mobile person re-id task also requires real-time processing. In this paper, we propose a novel hashing method: online discrete anchor graph hashing (ODAGH) for mobile person re-id. ODAGH integrates the advantages of online learning and hashing technology. In ODAGH, we propose an online discrete optimization algorithm to improve the efficiency of anchor graph learning in the online scenario. Experimental results demonstrate the superiority of ODAGH in terms of both effect and efficiency.


Author(s):  
Fragkiskos Sardis ◽  
Glenford Mapp ◽  
Jonathan Loo

Advances in Mobile and Cloud technologies have redefined the way we perceive and use computers. Mobile devices now rely on Cloud technology for storage and applications. Furthermore, recent advances in network technology ensure that mobile devices in the future will have high-bandwidth connectivity at all times. This drives the incentive of doing all the processing and storage in the Cloud and using mobile devices to access the services. In this chapter, the authors argue that always-on connectivity along with increased demand of Cloud services will contest the Internet backbone and create problems in the management of Cloud resources. Client mobility is also a factor that should be taken into account when providing Cloud services to mobile devices. The authors therefore propose a new service delivery architecture that takes into account client mobility as well as the distance between clients and services in order to manage Cloud and network resources more efficiently and provide a better Quality of Experience for the user.


Author(s):  
Dr. Suma V

The mobile devices are termed to highly potential due to their capability of rendering services without being plugged to the electric grid. These device are becoming highly prominent due to their constant progress in computing as well as storing capacities and as they are very much closer to the users. Despites its advantages it still faces many problems due to the load balancing and energy consumption due to its limited battery limited and storage availability as some applications or the video downloading requires high storage facilities consuming majority of the energy in turn reducing the performance of the mobile devices. So as to improve the performance and the capability of the mobile devices the mobile cloud computing that integrates the mobile devices with the cloud paradigm has emerged as a promising paradigm. This enables the augmentation of the local resources for the mobile devices to enhance its capabilities in order to improve its functioning. This is basically done by proper offloading and resource allocation. The proposed method in the paper utilizes the optimal offloading strategy (Single and double strand offloading) and follows an Ant colony optimization based resource allocation for improving the functioning the mobile devices in terms of energy consumption and storage.


Author(s):  
Mohamed El Ghmary ◽  
Tarik Chanyour ◽  
Youssef Hmimz ◽  
Mohammed Ouçamah Cherkaoui Malki

<span>With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes.</span>


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yuting Li ◽  
Qingfeng Cheng ◽  
Jinzheng Cao

As a component of mobile communication, the pay-TV system has attracted a lot of attention. By using mobile devices, users interact with the head end system in service providers to acquire TV services. With the growth of mobile users, how to protect the privacy of users while improving efficiency of the network has become an issue worthy of attention. Anonymous authentication schemes for mobile pay-TV systems came into being. In this paper, we analyze the shortcomings of the existing authentication protocol and then propose an improved one, which is secure against stored set attack and user traceability attack. The proposed scheme is proved to be secure. Moreover, our new scheme performs better in efficiency and storage, compared with several other schemes.


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