Academic credentials play an immensely significant role in the career of a person and in the progress of society. The system in place currently used for the issuance, storage & sharing of academic credentials is quite inefficient in its operations, due to being paper based in large. There is also widespread fraud committed every year in terms of academic credentials, ranging from diploma mills to plain forgery. There presents a need for a total digital transformation in this field, which ensures complete independent authenticity of credentials that could be proven in a fool-proof manner. Blockchain technology promises to alleviate the above identified problems by ensuring complete transparency & verifiable proofs of ownership using decentralized storage of credentials & Public Key Cryptography (PKC) in the form of Digital Signatures. This project implements the proposed solution by using the ECDSA algorithm to (i) sign the accreditation of institute by governing body, such as The Ministry of Education (MOE) (ii) sign the academic credentials by the issuing party (iii) sign the issued credentials by the learner to claim iv) sign the credentials by some governing body like the MOE. The learner can share the signed credential with any third party, such as a potential employer. The ECDSA algorithm can then authenticate the credential by programmatically verifying the signature proof by any party using the corresponding Public Key without going through tedious channels.

The usage of Large Language Models (LMMs) and their exponential progress has created a Cambrian Explosion in the development of new tools for almost every field of science and technology, but also presented significant concerns regarding the AI ethics and creation of sophisticated malware and phishing attacks. Moreover, several worries have arisen in the field of dataset collection and intellectual property in that many datasets may exist without the license of the respective owners. Triple-Entry Accounting (TEA) has been proposed by Ian Grigg to increase transparency, accountability, and security in financial transactions. This method expands upon the traditional double-entry accounting system, which records transactions as debits and credits in two separate ledgers, by incorporating a third ledger as an independent verifier via a digitally signed receipt. The utilization of a digital signature provides evidentiary power to the receipt, thus reducing the accounting problem to one of the presence or absence of the receipt. The integrity issues associated with double-entry accounting can be addressed by allowing the parties involved in the transaction to share the records with an external auditor. This manuscript proposes a novel methodology to apply triple-entry accounting records on a publicly accessed distributed ledger technology medium to control the queries of LLMs in order to discourage malicious acts and ensure intellectual property rights.

The address resolution protocol (ARP) is used by computers to map network addresses (IP) to physical addresses [MAC] and we can't imagine a communications between networks without the support of ARP protocol. However, ARP had been misused by many malicious hosts for illegitimate penetration; ARP Spoofing is one example for such illegal access. ARP Spoofing can enable malicious hosts to perform man-in-the-middle attacks [MiM] as well as a denial of service attacks [DoS]. Unfortunately, ARP Spoofing has not been focused by security experts or solutions, e.g. intrusion detection systems or intrusion protection systems [IDS/IPS]. In this research we evaluate the most famous & expensive detection and prevention [IDS/IPS] systems for detecting all types of ARP spoofing attacks and introduce an algorithm which can be implemented in IDS/IPS systems to enhance it's security.

Most Artificial Intelligence (AI) implementations so far are based on the exploration of how the human brain is designed. Nevertheless, while significant progress is shown on specialized tasks, creating an Artificial General Intelligence (AGI) remains elusive. This manuscript proposes that instead of asking how the brain is constructed, the main question should be how it was evolved. Since neurons can be understood as intelligent agents, intelligence can be thought of as a construct of multiple agents working and evolving together as a society, within a long-term memory and evolution context. More concretely, we suggest placing Multiple Neighborhood Cellular Automata (MNCA) on a blockchain with an interaction protocol and incentives to create an AGI. Given that such a model could become a “strong” AI, we present the conjecture that this infrastructure is possible to simulate the properties of cognition as an emergent phenomenon.

Global demographic trends clearly point out that the world population is ageing owing to a combination of dropping mortality rates and increasing life expectancy. The global community is looking for ways to address the pressing societal challenge of providing effective and efficient healthcare to the elderly. It is difficult to achieve satisfactory results merely by relying on scaling up of conventional healthcare infrastructure as the conventional techniques will not be sufficient to assist the elderly to independently live in house especially if they are suffering from chronic diseases, thus require continuous health monitoring. It is imperative to exploit the advances in emerging technologies such as biosensors, mobile devices, and communication networks to provide remote health monitoring services along with the physical infrastructural facilities. Remote/continuous monitoring of patients with chronic diseases is being considered as an efficient and cost-effective solution, which will reduce the burden on the elderly as well as on the health authorities and the government's expenses. While considerable research and development is being undertaken in this field, most of the current state of the art reflects a lack of a concerted and cohesive approach to develop an integrated remote health monitoring system. The present paper proposes a novel healthcare monitoring system based on an integrated and scalable architecture which provides flexibility and enables interoperability between myriads of healthcare monitoring devices and products.