Machine Translation has become one of the major application areas of Artificial Intelligence (AI) and Natural Language Processing (NLP), especially in multilingual countries like India. Although recent Neural Machine Translation systems have shown good performance for several language pairs, translation quality is still inconsistent for many Indian languages because of linguistic and structural differences between English and Indian language families. Most Indian languages are morphologically rich and contain flexible word order, complex agreement patterns, compound constructions, and context-dependent grammatical forms. Because of this, direct translation from English often produces structurally incorrect or semantically weak output. In many existing systems, the source sentence is passed to the translation model without sufficient linguistic analysis. As a result, ambiguity present in the source text propagates further during translation. This work focuses on the importance of linguistic enrichment before the translation stage. The proposed framework, named Unified Linguistic-Aware Pre-Parsing Framework, introduces a coordinated pre-processing layer for English-to-Indian Machine Translation (MT). A key contribution of this research is the development of a novel linguistically enriched intermediate representation that extends beyond conventional text normalization. By transforming noisy input text into linguistically enriched translation-ready representation, the proposed approach facilitates effective knowledge transfer to machine translation models, leading to improve contextual adequacy, linguistic fidelity, and overall translation performance. The framework combines multiple linguistic processing stages including POS tagging, NE detection, clause boundary analysis, contextual token handling, syntactic structure preparation, and morphology-related processing. Instead of executing these modules independently, the proposed system allows interaction between lexical, syntactic, and morphological information during analysis. This helps reduce structural ambiguity and improves sentence-level interpretation before translation begins. The need for such a framework becomes more relevant in the context of Indian languages where morphology and grammatical relations carry significant semantic information. This framework is especially relevant for Indian languages, where semantic information is often encoded through morphological variations and grammatical dependencies. The proposed framework can be effectively integrated with both conventional machine translation architectures and modern large language models. The overall study highlights how classical linguistic analysis can still play an important role in improving multilingual AI systems for Indian languages.

This paper presents a novel probabilistic parsing framework for Tree-Adjoining Grammar (TAG) that integrates part-of-speech (POS) information to enhance syntactic disambiguation and improve parsing accuracy. While TAG remains a linguistically expressive formalism for modelling complex syntactic phenomena such as long-distance dependencies and recursive structures, conventional statistical TAG parser predominantly relies on lexical information, limiting their ability to resolve structural ambiguities inherent in Natural Languages (NL). To address this limitation, we extend the probabilistic TAGformalism by conditioning derivation decisions jointly on lexical anchors and their associated POS tags. Our model supports both generative and discriminative formulations, incorporating POS-based feature representations into the derivation scoring mechanism. The training process is adapted to align POS-tagged lexical items with elementary tree structures, allowing the parser to learn syntactic patterns with greater accuracy and robustness. Empirical evaluations across multiple languages demonstrate that POS- augmented approach yields significant gains in parsing accuracy, particularly in the presence of syntactic ambiguity. The POS-Augmented Statistical Parser was evaluated on a dataset of 12,000 sentences, resulting a 30% reduction in parsing time compared to the conventional TAG Parser. The integration of POS not only enhances parsing speed but also provides structural advantages. In contrast, Tree Adjoining Grammar (TAG) often struggles to fully capture the complexity of linguistic phenomena, especially in crosslinguistic transfer between English and Indian languages. The proposed framework of ers a scalable and linguistically informed enhancement to TAG-based systems, bridging symbolic grammatical representations with data-driven statistical learning. This POS-augmented approach of ers a lightweight yet ef ective extension to existing TAG-based systems, enhancing their linguistic expressiveness and robustness for Natural Language Processing (NLP) applications.

The emergence of quantum computing introduces unprecedented risks to classical cryptographic schemes, particularly digital signatures that safeguard authentication, confidentiality, and data integrity across digital ecosystems. To address these concerns, this paper investigates the adoption of Post-Quantum Cryptography (PQC) within Public Key Infrastructure (PKI) frameworks, emphasizing its role in strengthening electronic signature (eSign) systems against quantum threats. Traditional PKI implementations rely on algorithms such as RSA and ECC in combination with Hardware Security Modules (HSMs) for secure key management; however, these methods are vulnerable to quantum algorithms like Shor's. We examine how quantum-resistant alternatives-including lattice-based, hash-based, and multivariate polynomial-based schemes standardized by NIST-can be integrated into PKI without compromising interoperability and compliance. A comparative evaluation highlights trade-offs in performance, storage, and computational complexity while demonstrating the feasibility of embedding PQC into existing eSign infrastructures. Our findings underscore the urgency of transitioning towards quantum-safe digital signatures to ensure resilience, regulatory alignment, and long-term trust in national-scale digital services.

Time-based One-Time Password (TOTP), as specified in RFC 6238, is a widely used algorithm in multi-factor authentication (MFA) systems for generating time-synchronized, one-time codes. While effective against many conventional threats, TOTP relies on classical cryptographic primitives that are vulnerable to future quantum attacks. Quantum algorithms such as Shor's and Grover's pose significant risks to the shared secrets used in TOTP, potentially undermining the security of the entire MFA process once scalable quantum computing becomes practical. To address these vulnerabilities, this paper proposes a post-quantum enhancement of the TOTP mechanism by integrating CRYSTALS-Dilithium-a lattice-based digital signature algorithm standardized by the National Institute of Standards and Technology (NIST). Dilithium offers strong resistance to quantum attacks and is designed for practical use in modern cryptographic systems. Our approach embeds Dilithium-based signatures into the TOTP lifecycle, enabling secure key exchange, tamper-proof validation, and forward secrecy, while maintaining compatibility with existing TOTP infrastructure. We present a detailed analysis of TOTP's mathematical framework, assess Dilithium's security properties and performance, and propose a formal integration model. Experimental and theoretical evaluations demonstrate that the Dilithium-enhanced TOTP system introduces minimal computational overhead while significantly increasing resistance to both classical and quantum threats. This work provides a scalable and future-proof framework for securing MFA systems in the post-quantum era.

In the age of Artificial Intelligence (AI), a significant breakthrough occurred as machines demonstrated their ability to communicate in human languages. This marked the beginning of a ground-breaking era in Natural Language Processing. defined by unparalleled computational capabilities. Amidst this evolution, parsers stand as an indispensable component, facilitating syntactic comprehension and empowering various NLP applications, from Machine Translation to sentiment analysis. Parser plays a crucial role in deciphering the complex syntactic structures inherent in human languages. With the use of a parser, machines can comprehend human language, extract meaning, and facilitate a variety of natural language processing (NLP) applications, such as information retrieval, sentiment analysis, and machine translation. This research paper presents the implementation of Bi-Directional Head-Driven Parser, aiming to expand the horizons of NLP beyond the constraints of traditional early-type L-TAG (Lexicalized Tree Adjoining Grammar) Parsing. While effective, conventional Parsers encounter inherent limitations in grappling with the intricacies and subtleties of natural language. Through the utilization of Bi-Directional principles, Head-Driven techniques offer a revolutionary breakthrough in computational frameworks for large-scale grammar parsing, enabling complex NLP tasks such as discourse analysis and semantic parsing, and guaranteeing reliable linguistic analysis for practical applications. The performance of the Bi-Directional Parser has been examined on the data set of 15000 sentences and observed a reduction in the variation of derivations for sentences of the same length compared to the conventional TAG Parser, this research showcases how Head-Driven Parser facilitates breakthrough in language processing, syntactic analysis, semantic comprehension, and beyond. Moreover, it underscores the structural implications of integrating Head-Driven Parsing. Traditional approaches, such as Tree Adjoining Grammar (TAG), while valuable, often encounter limitations in capturing the full spectrum of linguistic phenomena, particularly in the context of cross-linguistic transfer between English and Indian languages. In light of the significance of natural language processing (NLP) in addressing these issues, this research introduces a Bi-Directional Head-Driven Parser implementation. Drawing upon the rich foundation of TAG and acknowledging its constraints, our approach transcends these limitations by harnessing advanced parsing traversal techniques and linguistic theories. By bridging the gap between theory and application, our approach not only enhances our understanding of syntactic parsing across language families but also surpasses the performance of an ‘Early-type Parser’ in terms of time and memory. Through rigorous experimentation and evaluation, this research contributes to the ongoing discourse on expanding the frontiers of Tree Adjoining Grammar-based research and shaping the trajectory of Machine Translation.

In the era of Artificial Intelligence (AI), significant progress has been made by enabling machines to understand and communicate in human languages. Central to this progress are parsers, which play a vital role in syntactic analysis and support various Natural language Processing (NLP) applications, including Machine Translation and sentiment analysis. This paper introduces a robust implementation of an optimized Head-Driven Parser designed to advance NLP capabilities beyond the limitations of traditional Lexicalized Tree Adjoining Grammar (L-TAG) based Parser. Traditional parser, while effective, often struggle with the capturing complexities of natural languages, especially translation between English to Indian languages. By leveraging Bi-directional approach and Head-Driven techniques, this research offers a revolutionary enhancement in parsing frameworks. This method not only improves performance in syntactic analysis but also facilitates complex tasks such as discourse analysis and semantic parsing. This research involves experimentation the Bi-Directional Parser on a dataset of 15,000 sentences, resulting a reduction in derivation variations compared to conventional TAG Parsers. This advancement highlights how Head-Driven Parsing can overcome traditional constraints and provide more reliable linguistic analysis. The paper demonstrates how this new implementation not only builds on the strengths of L-TAG but also addresses its limitations and contributes to expanding the scope of Tree Adjoining Grammarbased methodologies and advancing the field of Machine Translation.

Due to concerns over the evolving quantum computing scenario and its potential threat to existing cryptosystems, including Public-Key Infrastructure (PKI) used in electronic signatures, there is a need to enhance the eSign components with quantum-resistant algorithms. One such algorithm is CrystalsDilithium, based on lattice cryptography, which can perform PKI operations in the eSign system. In addition to being resistant to quantum attacks, Long-Term Validation (LTV) is also utilized to ensure the validity of a document for an extended period. This is achieved by referring to PAdES/CAdES profiles and a time stamping service, which provides an accurate and trustworthy record of when a particular electronic document, file, or transaction was created, modified, or sent. Given the significance of eSign as a crucial digital service worldwide, migrating to post-quantum components will strengthen the system’s security. Therefore, the proposed research provides implementation, analysis and experimentation to ensure the eSign system components are robust against quantum computing threats.

This paper presents a state-of-the-art virtual research lab (vTAG) for creating, updating, analyzing, and maintaining multilingual large-scale tree-adjoining grammar for natural languages (NL). vTAG has designed to be language independent and tested its performance by constructing grammar of many Indian and European languages with significant reductions in grammar development time by auto rule creation mechanism with the help of a supervised machine learning algorithm. vTAG provides an integrated development environment (IDE) that can generate grammar of any natural language based on tree-adjoining grammar (TAG) formalism by using a specially designed graphical user interface without focusing on the programming aspect. vTAG also contains an advanced age experimental workspace to evaluate and improvise TAG-based parsers, part-of-speech tagger, sematic text analyzer, and associated NLP tools. vTAG can be utilized as an ‘interactive training kit’ for upcoming young talent to empower them for learning and developing advanced tools solving practical problems in NLP field. Furthermore, it can serve as foundation for understanding and building end-to-end machine translation solutions. vTAG uses object oriented model (OOM) for handling the NLP resources so every entity within the environment is in form of an object that can be encrypted which makes linguistic resources convenient for maintenance and secure to exchange and distribution, thus vTAG consists of a workbench, development kit, a graphical user interface, experimental workspace, and language resources on a single platform by combining major concepts from modern computer science, artificial intelligence, and linguistics.

The Revolution of the Artificial Intelligence (AI) has started when machines could decipher enigmatic symbols concealed within messages. Subsequently, with the progress of Natural Language Processing (NLP), machines attained the capacity to understand and comprehend human language. Tree Adjoining Grammar (TAG) has become powerful grammatical formalism for processing Large-scale Grammar. However, TAG mostly rely on Grammar which is created by Languages expert and due to structural ambiguity in Natural Languages computation complexity of TAG is very high o(n^6). We observed that rules-based approach has many serious flaws, firstly, language evolves with time and it is impossible to create grammar which is extensive enough to represent every structure of language in real world. Secondly, it takes too much time and language resources to develop a practical solution. These difficulties motivated us to explore an alternative approach instead of completely rely on the rule-based method. In this paper, we proposed a Statistical Parsing algorithm for Natural Languages (NL) using TAG formalism where Parser makes crucial use of data driven model for identifying Syntactic dependencies of complex structure. We observed that using probabilistic model along with limited training data can significantly improve both the quality and performance of TAG Parser. We also demonstrate that the newer parser outperforms previous rule-based parser on given sample corpus. Our experiment for many Indian Languages, also provides further support for the claim that above mentioned approach might be an awaiting solution for problem that require rich structural analysis of corpus and constructing syntactic dependencies of any Natural Language without much depending on manual process of creating grammar for same. Finally, we present result of our on-going research where probability model will be applying to appropriate selection of adjunction of any given node of elementary trees and state chart representations are shared across derivation.

In the era of ever-changing technology, we people are adopting new technologies/domains to make a better world. In recent times, Blockchain technology is known for its distributed, permissioned/permission-less and immutability nature. Likewise, the Hyperledger fabric is a permission-based framework that can be used in cross-industry applications for Blockchain technology. The logic behind the cross-industry applications is written in a specific language called a smart contract. Though Hyper ledger fabric is permissioned and is considered immutable, detecting architectural threats on the security and privacy mechanisms of Hyperledger Fabric is a challenge. In this paper, we are proposing the Security Assessment Integrated (SC-SIF) Framework for smart contract assessment in the Hyperledger fabric. This can be used to analyze, scan, and evaluate the smart contracts security vulnerability written in NodeJS/Go/script language. Furthermore, framework provide an integration platform to define and configure multiple security tools including communication protocol, exchange policy and security rules to obtain the security vulnerability on provided smart contracts. It also provides an option to evaluate the tools security assessment report and generate smart contract assessment report in human readable form (JSON/XML) that can help research community, developer, and analyst to improve their smart contract. This paper also briefs literature survey of existing security assessment smart contract and incorporates the working of the ledger, channels, endorsement policy roles, and the transaction flow.

In recent years, there has been ample research on quantum computing technology that uses quantum mechanics to solve complex mathematical problems that are difficult for classical computers to solve. Currently, digital communication has been secured by many of the public-key cryptosystems that could be compromised by quantum computers. The objective of post-quantum cryptography (also called quantum-resistant cryptography) in cryptographic systems is to protect information against quantum and classical computers. This paper proposes the use of post-quantum cryptographic (PQC) algorithms in digital signature applications over classical cryptographic algorithms to obtain confidentiality and integrity against quantum computers. To promote PQC in electronic signature applications (e-Sign), we developed a platform (QEsign) to illustrate and analyze the available PQC algorithms to evaluate the performance of the signing time required for a digital signature certificate (DSC) generation in the eSign system. Furthermore, QEsign also provides a provision to generate the CSR (Certificate Signing Request) by providing certificate subject information, which will be exploited to issue the DSC signed by post-quantum algorithms, and the evaluation report will be visualized using a graphical representation.

In the 21st century, natural language processing (NLP) has obtained much prominence for human–machine interaction (HMI). With this interest in natural language processing (NLP) has grown significantly, numerous NLP tools (e.g., morphology, the tagger, and a parser, etc.) have been developed all over the world. Despite having huge importance and requirements, we have noticed gaps for having a comprehensive single framework or platform, which encompass all NLP-related tools and technologies for promoting the research in NLP and sharing the knowledge and resources among NLP researchers required for understanding and building the solution for HMI. Our objective is to apply Software engineering in natural language processing with the concept of an object-oriented model by using a collection of reusable objects by defining the communication protocol, consisting of a set of rules that must be applied to exchange data between two NLP modules. We proposed state of art ivrE—A virtual environment for creating, modifying, executing, and analyzing various NLP solutions and technology. The proposed idea is broadly based on to define own ivrE-NLP object framework model that permits the developer to create, modify, and execute the application and analyze their outcomes by operations on visual representations of the modules. A variety of NLP-based applications (tools, modules, and plugins) already exist, they can publish into store available with environment so it can be used by research community at large. To develop complete NLP framework or platform, we require much more than just assembling or collecting these tools or modules at one place, no matter how good any tool or module is working individually. It requires not only the standards and a set of protocols, but also requires a compliant composition than a pre-defined algorithms and their implementation. In brief, we require a comprehensive open framework to bundle, manage, and integrate set of NLP tools, modules, components, applications, algorithms, and define their associated rules, comprehensive data structures, and knowledge.

This paper proposes language independent natural language generator for Tree Adjoining Grammar (TAG) based Machine Translation System. In this model, the TAG based parsing and generation approach considered for the syntactic and semantic analysis of a source language. This model provides an efficient and a systematic way of encapsulating language resources with engineering solution to develop the machine translation System. A TAG based Generator is developed with existing resources using TAG formalism to generate the target language from TAG based parser derivation. The process allows syntactic feature-marking, the Subject-Predicate Agreement marking and multiple synthesized generated outputs in complex and morphological rich language. The challenge in applying such approach is to handle the linguistically diversified features. It is achieved using rule-based translation grammar model to align the source language to corresponding target languages. Nevertheless, this paper also describes the process of lexicalization and explain the state charts, TAG based adjunction and substitution function and the complexity and challenges beneath parsing-generation process.

The main objective of this paper is to introduce an electronic mail service in Indian languages. Electronic mail is an exchange of computer-stored messages by communication. E-mail is prominently used on the Internet and is still the most popular means of communication. At present these services are available in English like Gmail, Yahoo, Rediffmail, and so on. Language barriers are one of the biggest problems remaining for intercultural communication using the Internet. Here, proposed mail service is available in Indian languages. This paper shows how to send mail / receive mail and provide online chatting in all regional languages in INDIA.

Machine Translation System (MTS) that uses the Tree Adjoining Grammar (TAG) is considered. To improve the response time of our online MTS, we propose the use of a translation memory (TM). The integrated architecture of MTS with TM is outlined. Several examples of language dependent TM tools and translation process are given. To further speedup the translation process, we port MTS on a computing cluster that uses the Hadoop framework and carry out distributed execution. The computational experiments demonstrate that substantial speedups could be obtained by using the Hadoop framework.

The main objective of this paper is to introduce a high performance natural language processing (NLP) service to fulfill the needs of researchers and users in the area of natural language computing. We consider various NLP components developed at Applied Artificial Group of C-DAC Pune, and carry out parallelization on the GARUDA grid. We demonstrate that almost linear speedup is achieved with good efficiencies. With 32 processors, we have achieved a speedup of more than 19. This allows us to offer high performance scalable NLP Web services. Further, the GARUDA grid offers high availability.

In this paper we consider a machine translation (MT) system based on the tree adjoining grammar (TAG) formalism. We have successfully carried out sentence level parallelization and its parallel implementations on a multicore machine with varying number of cores and a computing cluster with multicore nodes. Since our code is in Java, we use MPJ Express for parallel implementations. We have carried out experiments with these parallel implementations and their performance is analysed.