Decentralized control, low-complexity, flexible and efficient communications are the requirements of an architecture that aims to scale blockchains beyond the current state. Such properties are attainable by reducing ledger size and providing paralle
l operations in the blockchain. Sharding is one of the approaches that lower the burden of the nodes and enhance performance. However, the current solutions lack the features for resolving concurrency during cross-shard communications. With multiple participants belonging to different shards, handling concurrent operations is essential for optimal sharding. This issue becomes prominent due to the lack of architectural support and requires additional consensus for cross-shard communications. Inspired by hybrid Proof-of-Work/Proof-of-Stake (PoW/PoS), like Ethereum, hybrid consensus and 2-hop blockchain, we propose Reinshard, a new blockchain that inherits the properties of hybrid consensus for optimal sharding. Reinshard uses PoW and PoS chain-pairs with PoS sub-chains for all the valid chain-pairs where the hybrid consensus is attained through Verifiable Delay Function (VDF). Our architecture provides a secure method of arranging nodes in shards and resolves concurrency conflicts using the delay factor of VDF. The applicability of Reinshard is demonstrated through security and experimental evaluations. A practical concurrency problem is considered to show the efficacy of Reinshard in providing optimal sharding.
We focus on the task of future frame prediction in video governed by underlying physical dynamics. We work with models which are object-centric, i.e., explicitly work with object representations, and propagate a loss in the latent space. Specifically
, our research builds on recent work by Kipf et al. cite{kipf&al20}, which predicts the next state via contrastive learning of object interactions in a latent space using a Graph Neural Network. We argue that injecting explicit inductive bias in the model, in form of general physical laws, can help not only make the model more interpretable, but also improve the overall prediction of model. As a natural by-product, our model can learn feature maps which closely resemble actual object positions in the image, without having any explicit supervision about the object positions at the training time. In comparison with earlier works cite{jaques&al20}, which assume a complete knowledge of the dynamics governing the motion in the form of a physics engine, we rely only on the knowledge of general physical laws, such as, world consists of objects, which have position and velocity. We propose an additional decoder based loss in the pixel space, imposed in a curriculum manner, to further refine the latent space predictions. Experiments in multiple different settings demonstrate that while Kipf et al. model is effective at capturing object interactions, our model can be significantly more effective at localising objects, resulting in improved performance in 3 out of 4 domains that we experiment with. Additionally, our model can learn highly intrepretable feature maps, resembling actual object positions.
Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer u
sing wireless mechanics boosts their susceptibility to cyber-attacks. Consequently, the rate of cybercrime is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit the researchers to identify appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, healthcare, etc. Along with the role of advanced technologies like Artificial Intelligence, Machine Learning, Cloud computing, Edge computing, Fog computing, and Blockchain technology in cybercrime investigation are also discussed. At last, various open research challenges in IoT to assist cybercrime investigation are explained, which provide a new direction for further research.
Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has a basic reproductive number R of 2.2-2.7. In March 2020, the World Health Organization declared t
he COVID-19 outbreak a pandemic. COVID-19 is currently affecting more than 200 countries with 6M active cases. An effective testing strategy for COVID-19 is crucial to controlling the outbreak but the demand for testing surpasses the availability of test kits that use Reverse Transcription Polymerase Chain Reaction (RT-PCR). In this paper, we present a technique to screen for COVID-19 using artificial intelligence. Our technique takes only seconds to screen for the presence of the virus in a patient. We collected a dataset of chest X-ray images and trained several popular deep convolution neural network-based models (VGG, MobileNet, Xception, DenseNet, InceptionResNet) to classify the chest X-rays. Unsatisfied with these models, we then designed and built a Residual Attention Network that was able to screen COVID-19 with a testing accuracy of 98% and a validation accuracy of 100%. A feature maps visual of our model show areas in a chest X-ray which are important for classification. Our work can help to increase the adaptation of AI-assisted applications in clinical practice. The code and dataset used in this project are available at https://github.com/vishalshar/covid-19-screening-using-RAN-on-X-ray-images.
Speaker Diarization is the problem of separating speakers in an audio. There could be any number of speakers and final result should state when speaker starts and ends. In this project, we analyze given audio file with 2 channels and 2 speakers (on s
eparate channel). We train Neural Network for learning when a person is speaking. We use different type of Neural Networks specifically, Single Layer Perceptron (SLP), Multi Layer Perceptron (MLP), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) we achieve $sim$92% of accuracy with RNN. The code for this project is available at https://github.com/vishalshar/SpeakerDiarization_RNN_CNN_LSTM
The 5G networks have the capability to provide high compatibility for the new applications, industries, and business models. These networks can tremendously improve the quality of life by enabling various use cases that require high data-rate, low la
tency, and continuous connectivity for applications pertaining to eHealth, automatic vehicles, smart cities, smart grid, and the Internet of Things (IoT). However, these applications need secure servicing as well as resource policing for effective network formations. There have been a lot of studies, which emphasized the security aspects of 5G networks while focusing only on the adaptability features of these networks. However, there is a gap in the literature which particularly needs to follow recent computing paradigms as alternative mechanisms for the enhancement of security. To cover this, a detailed description of the security for the 5G networks is presented in this article along with the discussions on the evolution of osmotic and catalytic computing-based security modules. The taxonomy on the basis of security requirements is presented, which also includes the comparison of the existing state-of-the-art solutions. This article also provides a security model, CATMOSIS, which idealizes the incorporation of security features on the basis of catalytic and osmotic computing in the 5G networks. Finally, various security challenges and open issues are discussed to emphasize the works to follow in this direction of research.
Cellular (C) setups facilitate the connectivity amongst the devices with better provisioning of services to its users. Vehicular networks are one of the representative setups that aim at expanding their functionalities by using the available cellular
systems like Long Term Evolution (LTE)-based Evolved Universal Terrestrial Radio Access Network (E-UTRAN) as well as the upcoming Fifth Generation (5G)-based functional architecture. The vehicular networks include Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), Vehicle to Pedestrian (V2P) and Vehicle to Network (V2N), all of which are referred to as Vehicle to Everything (V2X). 5G has dominated the vehicular network and most of the upcoming research is motivated towards the fully functional utilization of 5G-V2X. Despite that, credential management and edge-initiated security are yet to be resolved under 5G-V2X. To further understand the issue, this paper presents security management as a principle of sustainability and key-management. The performance tradeoff is evaluated with the key-updates required to maintain a secure connection between the vehicles and the 5G-terminals. The proposed approach aims at the utilization of high-speed mmWave-based backhaul for enhancing the security operations between the core and the sub-divided functions at the edge of the network through a dual security management framework. The evaluations are conducted using numerical simulations, which help to understand the impact on the sustainability of connections as well as identification of the fail-safe points for secure and fast operations. Furthermore, the evaluations help to follow the multiple tradeoffs of security and performance based on the metrics like mandatory key updates, the range of operations and the probability of connectivity.
The rapid involution of the mobile generation with incipient data networking capabilities and utilization has exponentially increased the data traffic volumes. Such traffic drains various key issues in 5G mobile backhaul networks. Security of mobile
backhaul is of utmost importance; however, there are a limited number of articles, which have explored such a requirement. This paper discusses the potential design issues and key challenges of the secure 5G mobile backhaul architecture. The comparisons of the existing state-of-the-art solutions for secure mobile backhaul, together with their major contributions have been explored. Furthermore, the paper discussed various key issues related to Quality of Service (QoS), routing and scheduling, resource management, capacity enhancement, latency, security-management, and handovers using mechanisms like Software Defined Networking and millimeter Wave technologies. Moreover, the trails of research challenges and future directions are additionally presented.
For combining different single photon channels into single path, we use an effective and reliable technique which is known as quantum multiple access. We take advantage of an add-drop multiplexer capable of pushing and withdrawing a single photon int
o an optical fiber cable which carries quantum bits from multiusers. In addition to this, spreading spreads the channel noise at receiver side and use of filters stop the overlapping of adjacent channels, which helps in reducing the noise level and improved signal-to-noise ratio. In this way, we obtain enhanced performance of code division multiple access-based QKD links with a single photon without necessity of amplifiers and modulators.
Cellular-Vehicle to Everything (C-V2X) aims at resolving issues pertaining to the traditional usability of Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) networking. Specifically, C-V2X lowers the number of entities involved in vehicula
r communications and allows the inclusion of cellular-security solutions to be applied to V2X. For this, the evolvement of LTE-V2X is revolutionary, but it fails to handle the demands of high throughput, ultra-high reliability, and ultra-low latency alongside its security mechanisms. To counter this, 5G-V2X is considered as an integral solution, which not only resolves the issues related to LTE-V2X but also provides a function-based network setup. Several reports have been given for the security of 5G, but none of them primarily focuses on the security of 5G-V2X. This article provides a detailed overview of 5G-V2X with a security-based comparison to LTE-V2X. A novel Security Reflex Function (SRF)-based architecture is proposed and several research challenges are presented related to the security of 5G-V2X. Furthermore, the article lays out requirements of Ultra-Dense and Ultra-Secure (UD-US) transmissions necessary for 5G-V2X.
