Completely Automated Public Physical test to tell Computers and Humans Apart: A usability study on mobile devices

A very common approach adopted to fight the increasing sophistication and dangerousness of malware and hacking is to introduce more complex authentication mechanisms. This approach, however, introduces additional cognitive burdens for users and lowers the whole authentication mechanism acceptability to the point of making it unusable. On the contrary, what is really needed to fight the onslaught of automated attacks to users data and privacy is to first tell human and computers apart and then distinguish among humans to guarantee correct authentication. Such an approach is capable of completely thwarting any automated attempt to achieve unwarranted access while it allows keeping simple the mechanism dedicated to recognizing the legitimate user. This kind of approach is behind the concept of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), yet CAPTCHA leverages cognitive capabilities, thus the increasing sophistication of computers calls for more and more difficult cognitive tasks that make them either very long to solve or very prone to false negatives. We argue that this problem can be overcome by substituting the cognitive component of CAPTCHA with a different property that programs cannot mimic: the physical nature. In past work we have introduced the Completely Automated Public Physical test to tell Computer and Humans Apart (CAPPCHA) as a way to enhance the PIN authentication method for mobile devices and we have provided a proof of concept implementation. Similarly to CAPTCHA, this mechanism can also be used to prevent automated programs from abusing online services. However, to evaluate the real efficacy of the proposed scheme, an extended empirical assessment of CAPPCHA is required as well as a comparison of CAPPCHA performance with the existing state of the art. To this aim, in this paper we carry out an extensive experimental study on both the performance and the usability of CAPPCHA involving a high number of physical users, and we provide comparisons of CAPPCHA with existing flavors of CAPTCHA

Towards a power consumption estimation model for routers over TCP and UDP protocols

Due to the growing development in the information and communication technology (ICT) industry, the usage of routers has increased rapidly. Meanwhile, these devices that are produced and developed today consume a definite amount of power, Furthermore, with limited focus on power estimation techniques and the increased demands of networking devices, it led to an increase of the vitality consumption as a result. While new high capacity router components are installed, energy intake in system elements will be rising due to the higher capability network consuming larger component of the vitality. This study considers providing estimating power model in different traffic settings over TCP and UDP protocols, this study is mainly concerned about the transport protocols power consumption. Isolating the power consuming components within an electronic system is a very precise process that requires deep understanding of the role of each component within the system and a thorough study of the component datasheet. The study started by simulating the protocols mechanism then followed by protoclos power measurements, a simple simulation has been provided for Xilinx Virtex-5, it is very complicated to simulate the whole system due to the need of an external devices, so the simulation focused on wavelengths, frequencies and traffic types. This study found that the estimated power stokes was high when the 1480nm, 1580nm, and 1750nm power source increase. while there were differrence in the consumed power while transiting different types of traffic such as CBR and HTTP through UDP and TCP. The effect of different frequencies has been noticed also while applying different frequencies to the protocols. So it is believed that this study may enhance the power scenarios in the network and routers throug applying different techniques to UDP and TC

Data Transmissions using Hub Nodes in Vehicular Social Networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Vehicular Social Networks (VSNs) consist of groups of individuals (i.e., people) who may share common interests, preferences and needs in the context of temporal spatial proximity on roads. In this environment, the impact of human social factors, such as mobility, willingness to cooperate and personal preferences, on vehicular connectivity is taken under consideration, thus extending the concept of Vehicular Ad-hoc Networks. In VSNs, vehicles are classified based on their social degree, a vehicle considered to be a ¿social¿ one if it accesses the vehicular social network and posts messages with a frequency higher than a given threshold. Therefore, to speed up the data dissemination process within a vehicular social network, a packet should be forwarded to those vehicles showing high social activity. In a previous paper, we introduced a new probabilistic-based broadcasting scheme called SCARF (SoCial-Aware Reliable Forwarding Technique for Vehicular Communications), and we analytically demonstrated its effectiveness in packet transmission reduction while guaranteeing network dissemination. In this paper, we assess SCARF in more realistic scenarios with real traffic traces, and we compare it with other similar techniques. We show that SCARF outperforms other approaches in terms of delivery ratio, while guaranteeing acceptable time delay values and average number of forwardings.Vegni, AM.; Souza, C.; Loscrí, V.; Hernández-Orallo, E.; Manzoni, P. (2020). Data Transmissions using Hub Nodes in Vehicular Social Networks. IEEE Transactions on Mobile Computing. 19(7):1570-1585. https://doi.org/10.1109/TMC.2019.2928803S1570158519

Deep Learning for Mobile Multimedia: A Survey

Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments