The study of systems and architectures for ambient assisted living (AAL) is undoubtedly a topic of great relevance given the ageing of the world population. On the one hand, AAL technologies are designed to meet the needs of the ageing population in order to maintain their independence as long as possible. On the other hand, internet of things (IoT) proposes that various “things,” which include not only communication devices but also every other physical object on the planet, are going to be connected and will be controlled across the internet. The continuous technological advancements turn possible to build smart objects with great capabilities for sensing and connecting turn possible several advancements in AAL and IoT systems architectures. Advances in networking, sensors, and embedded devices have made it possible to monitor and provide assistance to people in their homes. This chapter reviews the state of art on AAL and IoT and their applications for enhanced indoor living environments and occupational health.

Ambient Assisted Living and Internet of Things

In Big data computing domains with a huge network of connected devices involved in various internet and social network concerns mainly for security, integrity, authentication and data privacy. Allocation and efficient usage of containers provided by cloud service providers has huge impact over efficient data processing and data handling. Batch processing method emphasizes huge databases by letting it into the programmable domains and segregate in accordance with their size, reliability, processing speed and required memory space. Whereas, Stream processing involves scrutinizing data promptly before entering into the stream and scrutinizing will be done accordingly. Container selection plays a major role in such processing methodologies and promptly makes the effective resource scheduling possible and efficient in cloud service providing. In our proposed method, the Guided Container Selection (GCS) process eradicates the bottle neck problem by selecting an efficient and optimal container which satisfies all requirements like required size, reliability, processing speed etc. Implementing either batch processing or stream processing to analyze solutions for multiple domain container selection which will be analyzed and resolved through Deep Neural Learning (DNL). The novel DNL method successfully ranks and handles optimal container selection according to the dynamic data involvement and provides efficient solutions for data processing.In future it also helps selecting appropriate containers for similar requirements for cloud service providers and also for its consumers.

Guided container selection for data streaming through neural learning in cloud

Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area.

Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

The contemporary innovations in financial technology (fintech) serve society with an environmentally friendly atmosphere. Fintech covers an enormous range of activities from data security to financial service deliverables that enable the companies to automate their existing business structure and introduce innovative products and services. Therefore, there is an increasing demand for scholars and professionals to identify the future trends and directions of the topic. This is why the present study conducted a bibliometric analysis in social, environmental, and computer sciences fields to analyse the implementation of environment-friendly computer applications to benefit societal growth and well-being. We have used the ‘bibliometrix 3.0’ package of the r-program to analyse the core aspects of fintech systematically. The study suggests that ‘ACM International Conference Proceedings’ is the core source of published fintech literature. China leads in both multiple and single country production of fintech publications. Bina Nusantara University is the most relevant affiliation. Arner and Buckley provide impactful fintech literature. In the conceptual framework, we analyse relationships between different topics of fintech and address dynamic research streams and themes. These research streams and themes highlight the future directions and core topics of fintech. The study deploys a co-occurrence network to differentiate the entire fintech literature into three research streams. These research streams are related to ‘cryptocurrencies, smart contracts, financial technology’, ‘financial industry stability, service, innovation, regulatory technology (regtech)’, and ‘machine learning and deep learning innovations’. The study deploys a thematic map to identify basic, emerging, dropping, isolated, and motor themes based on centrality and density. These various themes and streams are designed to lead the researchers, academicians, policymakers, and practitioners to narrow, distinctive, and significant topics.

Trends and Directions of Financial Technology (Fintech) in Society and Environment: A Bibliometric Study

We propose a considerably faster approximation of the well known SIFT method. The main idea is to use efficient data structures for both, the detector and the descriptor. The detection of interest regions is considerably speed-up by using an integral image for scale space computation. The descriptor which is based on orientation histograms, is accelerated by the use of an integral orientation histogram. We present an analysis of the computational costs comparing both parts of our approach to the conventional method. Extensive experiments show a speed-up by a factor of eight while the matching and repeatability performance is decreased only slightly.

Fast Approximated SIFT

Quantum is an emerging technology in future computers. Reversibility is the main advantage of quantum computers. In conventional computers, the computation is irreversible i.e. the input bits are lost once the logic block generates the output and input bits cannot be restored but it can be done in reversible computation because in reversible computation the inputs and outputs have a one-to-one correspondence. Therefore, a reversible gate input could even be uniquely determined from their output which leads to less power consumption. Hence the complexity of the digital circuits can be reduced by using reversible computing. In quantum computer to perform reversible operations, we need to implement the reversible gates using quantum gates. In this paper, we discussed various reversible logic gates like Feynman, Toffoli, R, Peres and TR gates using basic quantum gates like CNOT, Pauli, Swap gates and their implementation using IBM quantum experience.

Implementation of Reversible Logic Gates with Quantum Gates

Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task.

Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain

In a DNS Tunneling attack, data or other useful information is embedded within a DNS query and exfiltrated. Such attacks are difficult to detect because DNS is a fundamental protocol and blocking legitimate domain names can lead to an unpleasant experience for the users. Thus, detecting whether the DNS query is exfiltrating data or not is a challenging task. Mimicking genuine queries by the attacker makes this even more difficult. This research work presents two different methods for detecting the DNS Tunneling query and later they are combined to build a DNS Tunneling Attack Detector that can inform the client about a potential attack going on in real time. The first method uses cache misses in a DNS cache server and the second method utilizes machine learning techniques to classify a given DNS query. Overall, with around 93% accuracy of certain Machine Learning classifiers on classifying on a per packet basis along with extra validation from the cache-miss approach, a detector has been developed to accurately report DNS tunneling traffic

DNS Tunneling Detection using Machine Learning and Cache Miss Properties

With the rise in digitization of documents stored online, it is important to have a document verification process. It involves customized verification and authentication of a document based on the content of the document. Among all the certificates, the educational certificate is one of the most important certificates, especially for students. Unfortunately, it is very easy to fake documents that are hard to identify nowadays and are often considered original. Blockchain has recently emerged as a potential alternative to manual verification of certificates. It provides a distributed ledger that is verifiable with cryptographic mechanisms. Also, it provides a common platform for easily sharing, storing, and accessing documents. The identity of the students can be verified using government authorized identity proofs. This paper proposes the use of such unique identity number and secret phrase provided by the student to further improve the security of the certificate verification system. The student's identity and document are both verified by matching the hashes already present in the Blockchain. Also, in the proposed method the documents are linked to the student to add another layer of verification. The implementation of this proposed platform can be used to issue, receive and verify the certificates.

Blockchain based Framework for Student Identity and Educational Certificate Verification

Entanglement plays a major role in quantum information process and is a principal resource for various quantum applications like quantum teleportation, quantum key distribution, quantum communication, etc. Strong entangled pairs are required for efficient information process but system impurities during the transportation diminishes the entanglement by reducing the fidelity of the entangled pair. In order to reduce this, purification techniques can be used. In this paper, we propose an efficient entanglement purification method to distill the entanglement using entanglement swapping. The proposed method increases the fidelity of the entanglement and can be a proficient for various applications of quantum computing.

Bhawana Rudra

Papers

Implementation of Reversible Logic Gates with Quantum Gates

Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain

DNS Tunneling Detection using Machine Learning and Cache Miss Properties

Blockchain based Framework for Student Identity and Educational Certificate Verification

An efficient approach for quantum entanglement purification