Table of Contents

This work proposes the first scaler designed specifically for the three-moduli set $M_1=\{2^{n+1}-1,2^n,2^n-1\}$ . Hence, there is no other functionally similar scaler to compare the proposed scaler with. However, when compared with the latest published scalers for a different moduli set, $M_2=\{2^n+1,2^n,2^n-1\}$ , the proposed scaler has a better area and power performance, while it requires a longer time delay. As demonstrated in earlier publications, replacing the $(2^n+1)$ channel in the $M_2$ moduli set by the $(2^{n+1}-1)$ channel, to form the $M_1$ moduli set, considerably improves the overall time performance of residue-based multiply–accumulate arithmetic units. Full article

Education values such as knowledge sharing, and the linked data (LD) abilities such as interoperability are in perfect harmony. Much research has exploited that and provided important contributions and improvements in education through LD. International universities, large open education repositories, OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs) initiatives, educational search engines, blending and adaptive learning, learning analysis and other various areas were the targets of many works on leveraging LD. However, this research exists in a scattered way without any type of categorization or organization. In this paper, we present a survey on the current works in educational linked data (ELD) to provide a starting point and a comprehensive roadmap to help researchers in recognizing the main tracks in ELD area. In addition, the paper extracted the common life cycle, outcome datasets and vocabularies from the overall presented works. The paper also provides samples of applications that exhibit the practical benefit of adopting LD in the various tracks and highlights the challenges that each track faced during the utilization of LD. Pioneer ELD’s projects, other existing overviews and landscapes and the most used tools based on the stages and prevalent are presented. Lastly, discussion and recommendations were provided based on the overall study. Full article

The paper discusses the use of parallel computation to obtain rough set approximations from large-scale information systems where missing data exist in both condition and decision attributes. To date, many studies have focused on missing condition data, but very few have accounted for missing decision data, especially in enlarging datasets. One of the approaches for dealing with missing data in condition attributes is named twofold rough approximations. The paper aims to extend the approach to deal with missing data in the decision attribute. In addition, computing twofold rough approximations is very intensive, thus the approach is not suitable when input datasets are large. We propose parallel algorithms to compute twofold rough approximations in large-scale datasets. Our method is based on MapReduce, a distributed programming model for processing large-scale data. We introduce the original sequential algorithm first and then the parallel version is introduced. Comparison between the two approaches through experiments shows that our proposed parallel algorithms are suitable for and perform efficiently on large-scale datasets that have missing data in condition and decision attributes. Full article

The scale of modern neural networks is growing rapidly, with direct hardware implementations providing significant speed and energy improvements over their software counterparts. However, these hardware implementations frequently assume global connectivity between neurons and thus suffer from communication bottlenecks. Such issues are not found in biological neural networks. It should therefore be possible to develop new architectures to reduce the dependence on global communications by considering the connectivity of biological networks. This paper introduces two reconfigurable locally-connected architectures for implementing biologically inspired neural networks in real time. Both proposed architectures are validated using the segmented locomotive model of the C. elegans, performing a demonstration of forwards, backwards serpentine motion and coiling behaviours. Local connectivity is discovered to offer up to a 17.5× speed improvement over hybrid systems that use combinations of local and global infrastructure. Furthermore, the concept of locality of connections is considered in more detail, highlighting the importance of dimensionality when designing neuromorphic architectures. Convolutional Neural Networks are shown to map poorly to locally connected architectures despite their apparent local structure, and both the locality and dimensionality of new neural processing systems is demonstrated as a critical component for matching the function and efficiency seen in biological networks. Full article

Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other individuals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for individuals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner. Full article

Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed. Full article

A Ring Oscillator Physical Unclonable Function (RO PUF) is an application-constrained hardware security primitive that can be used for authentication and key generation. PUFs depend on variability during the fabrication process to produce random outputs that are nevertheless stable across multiple measurements. Though industry has a growing need for PUF implementations on Field Programmable Gate Arrays (FPGA) and Application-Specific Integrated Circuits (ASIC), the bit errors in PUF responses become a bottleneck and limit the usage. In this work, we comprehensively evaluate the RO PUF’s stability on FPGAs, and we propose a phase calibration process to improve the stability of RO PUFs. We also make full use of the instability of PUFs to provide a novel solution for authentication. The results show that the bit errors in our PUFs are reduced to less than 1%. Full article

While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems. Full article

The end of Dennard scaling led to the use of heterogeneous multi-processor systems-on-chip (MPSoCs). Heterogeneous MPSoCs provide a high efficiency in terms of energy and performance due to the fact that each processing element can be optimized for an application task. However, the evolution of MPSoCs shows a growing number of processing elements (PEs), which leads to tremendous communication costs, tending to become the performance bottleneck. Networks-on-chip (NoCs) are a promising and scalable intra-chip communication technology for MPSoCs. However, these technological advances require novel and effective programming methodologies to efficiently exploit them. This work presents a novel router architecture called application-specific instruction-set router (ASIR) for field-programmable-gate-arrays (FPGA)-based MPSoCs. It combines data transfers with application-specific processing by adding high-level synthesized processing units to routers of the NoC. The execution of application-specific operations during data exchange between PEs exploits efficiently the transmission time. Furthermore, the processing units can be programmed in C/C++ using high-level synthesis, and accordingly, they can be specifically optimized for an application. This approach enables transferred data to be processed by a processing element, such as a MicroBlaze processor, before the transmission or by a router during the transmission. Moreover, a static mapping algorithm for applications modeled by a Kahn process network-based graph is introduced that maps tasks to the MicroBlaze processors and processing units. The mapping algorithm optimizes the communication cost by allocating tasks to nearest neighboring PEs. This complete methodology significantly simplifies the design and programming of ASIR-based MPSoCs. Furthermore, it efficiently exploits the heterogeneity of processing capabilities inside the routers and MicroBlaze processors. Full article

Biometrics play an avid role in today’s mobile security realm. Security experts have attempted to implement different forms of biometrics, from finger, hand, and signature to voice, retina, and iris. Recently, facial biometrics has been added to this list and has introduced another method for a more secure form of authentication. Various organizations believe that by using something you are, like biometrics, their system would be strongly secured. As this may be true, applications used for facial biometrics have lost their credibility when easily defeated. This may be through printed photographs, electronic images, and even look alike. This paper explores the scientific background as to why facial biometrics have become a trusted form of authentication, a user-friendly method, and explores the security of mobile device applications available for Android and iOS systems. We test several applications with our developed methods and discuss the results. Full article