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Advanced applications of NLP and deep learning in social media data
\"The primary objective of this book is to build a better and safer social media space by making human language available on different social media platforms intelligible for machines with the blessings of AI. This book bridges the gap between Natural Language Processing (NLP), Advanced Machine(AML) and Deep Learning (DL), and Online Social Media. This book connects various interdisciplinary domains related to Natural Language Understanding, Deep machine Leaning Technology and will be highly beneficial for the students, researchers, and academicians working in this area as this book will cover state-of-the-art technologies around NLP and DML techniques and their role in Social Media Data Analysis. Furthermore, the OSN service providers will take the advantage of this book to update, modify and make better social platforms for its users. Psychiatrists and clinicians will also be beneficial as this book's main focus are to analyze the user behavior in Online Social networks which play a key ingredient in several psychological tests\"-- Provided by publisher.
Book
Hybrid model of general fuzzy automata and semantic computing: an application to transportation e-service
The computing models such as crisp automata, fuzzy automata and general fuzzy automata (GFA) are used to represent complex systems for predefined input alphabets or symbols. A framework that can process words rather than symbols is needed to simulate applications based on the natural language. Semantic computing (SC) offers a technique to accommodate semantically similar words instead of predefined words, thus extends the applicability and flexibility of GFA. In present work, a hybrid model of GFA and SC is proposed to deal with a situation where input can be user-dependent or related to words that have semantically similar meanings. In traditional theory of automata, if input symbols are changed one must define a new automata, whereas in the proposed work instead of defining a new GFA, existing GFA can process the semantically similar external words. An application related to transportation e-service is further discussed to understand the enhanced flexibility and applicability of the proposed models.
Journal Article
Linked data management
\"With the growing popularity of the Semantic Web, more and more semantic data and data sources become available and accessible for everyone. By establishing semantic links between the data, answers to (complex) queries can be evaluated based on the data on multiple providers instead of just one. This book motivates, introduces, and details techniques for processing heterogeneous structured data on the Web by providing a comprehensive overview for database researchers and practitioners about this new publishing paradigm on the web, and show how the abundance of data published as Linked Data can serve as a fertile ground for database research and experimentation\"-- Provided by publisher.
Book
A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response
The growth of IoT networks has led to significant security issues, especially in areas of real-time threat detection and response. This research paper presents a hybrid deep learning and semantic reasoning framework that enhances threat intelligence and autonomous response. The proposed research framework integrates Convolutional Neural Networks for spatial anomaly detection and Recurrent Neural Networks for sequential pattern recognition. Concurrently, a semantic contextualization layer utilizes knowledge graphs for context-aware threat detection. The model is highly computational and energy efficient, incorporating path-breaking Edge Computing and Real-Time Stream Processing paradigms, facilitating low-latency identification of highly dynamic advanced attacks like APTs and DDoS. During this research study, extensive statistical validation was performed using the CICIoT 2023 dataset and a custom Internet of Things testbed, demonstrating high accuracy, scalability, and adaptability across diverse IoT environments. The paper also outlines privacy, ethical considerations, and regulatory compliance (GDPR, CCPA) to ensure responsible deployment. This research contributes to next-generation autonomous IoT security solutions, bridging deep learning, semantic reasoning, and real-world security challenges, with future work focusing on real-world deployments and adaptive threat intelligence.
Journal Article
Knowledge representation of the state of a cloud-native application
Cloud Computing has revolutionized the way applications are developed, deployed, and maintained. Over the past decade, we have observed dynamically growing interest in Cloud Computing. The benefits of the cloud approach caused the increasing popularity of Cloud-native applications. Cloud-native is an approach to developing and deploying applications according to the concepts of DevOps, Continuous Integration/Continuous Delivery (CI/CD), containers and microservices. The knowledge about Cloud Computing has become extensive and complex. Fortunately, before Cloud-native applications development, there was a great deal of effort to develop tools for effective knowledge representation. Ontologies are a convenient way to show the relations between domain-specific concepts. In this paper, we propose an ontology named CNOnt that describes the state-of-the-art of Cloud-native applications. CNOnt covers aspects from the clusterization perspective. First, this paper presents the engineering perspective of building the CNOnt ontology. Second, we demonstrate a use case of our ontology that proves the correctness of CNOnt development. This ontology is exhausted in CNOnt Broker. It is a system that applies the information in the OWL file into the Kubernetes cluster and in reverse. The knowledge representation makes Cloud-native applications understandable to third-party systems and increases interoperability between different microservices.
Journal Article
Allocation of applications to Fog resources via semantic clustering techniques: with scenarios from intelligent transportation systems
The fast development in IoT and Cloud technologies has propelled the emergence of a variety of computing paradigms, among which Fog and Edge computing are salient computing technologies. Such new paradigms are opening up new opportunities to implement novel application scenarios, not possible before, by supporting features of mobility, edge intelligence and end-user support. This, however, comes with new computing challenges. One such challenge is the allocation of applications to Fog and Edge nodes. Indeed, for some application scenarios larger computing capacity might be needed. Therefore, due to co-existence of computing devices of different computing granularity, techniques for grouping up and clustering resources into virtual nodes of larger computing capacity are required. In this paper we present some clustering techniques for creating virtual computing nodes from Fog/Edge nodes by combining semantic description of resources with semantic clustering techniques. Then, we use such clusters for optimal allocation (via heuristics and Liner Programming) of applications to virtual computing nodes. Simulation results are reported to support the feasibility of the model and efficacy of the proposed approach. First Fit Heuristic Algorithm (FFHA) outperformed ILP method for medium and large size instances. Likewise, FFHA performed more consistently than ILP on various experimental setting. Finally, the results showed that the proposed clustering techniques deliver relatively fast response times, while enabling the service of a larger number of applications, with more demanding requirements.
Journal Article
Formalizing Natural Languages
This book is at the very heart of linguistics. It provides the theoretical and methodological framework needed to create a successful linguistic project.
Potential applications of descriptive linguistics include spell-checkers, intelligent search engines, information extractors and annotators, automatic summary producers, automatic translators, and more. These applications have considerable economic potential, and it is therefore important for linguists to make use of these technologies and to be able to contribute to them.
The author provides linguists with tools to help them formalize natural languages and aid in the building of software able to automatically process texts written in natural language (Natural Language Processing, or NLP).
Computers are a vital tool for this, as characterizing a phenomenon using mathematical rules leads to its formalization. NooJ – a linguistic development environment software developed by the author – is described and practically applied to examples of NLP.
eBook
CustRE: a rule based system for family relations extraction from english text
Relation extraction is an important information extraction task that must be solved in order to transform data into Knowledge Graph (KG), as semantic relations between entities form KG edges of the graph. Although much effort has been devoted to solve this task during the last three decades, but the results achieved are not as good yet. For instance, winner at Text Analysis Conference’s (TAC) Knowledge Base Population (KBP) 2015 slot filling task, the Stanford’s system, achieves F1 score of 60.5% on standard Relation Extraction (RE) dataset (Zhang et al., in: Position-aware attention and supervised data improve slot_lling. In: EMNLP 2017-Conference on Empirical Methods in Natural Language Processing, Proceedings, (2017). https://doi.org/10.18653/v1/d17-1004). The RE task therefore needs better solutions. This paper presents our system, CustRE, for better identification and classification of family relations from English text. CustRE is a rule based system, that uses regular expressions for pattern matching to extract family relations explicitly mentioned in text, and uses co-reference and propagation rules to extract family relations implicitly implied in the text. The proposed system, its implementation and the results obtained are presented in this paper. The results show that our approach makes a great improvement over existing methods by achieving F1 scores of 79.7% and 76.6% on TACRED family relations and CustFRE datasets respectively, which are 6.3 and 18.5 points higher than LUKE, the best score reporter on TACRED.
Journal Article
Generalized rough and fuzzy rough automata for semantic computing
The classical automata, fuzzy finite automata, and rough finite state automata are some formal models of computing used to perform the task of computation and are considered to be the input device. These computational models are valid only for fixed input alphabets for which they are defined and, therefore, are less user-friendly and have limited applications. The semantic computing techniques provide a way to redefine them to improve their scope and applicability. In this paper, the concept of semantically equivalent concepts and semantically related concepts in information about real-world applications datasets are used to introduce and study two new formal models of computations with semantic computing (SC), namely, a rough finite-state automaton for SC and a fuzzy finite rough automaton for SC as extensions of rough finite-state automaton and fuzzy finite-state automaton, respectively, in two different ways. The traditional rough finite-state automata can not deal with situations when external alphabet or semantically equivalent concepts are given as inputs. The proposed rough finite-state automaton for SC can handle such situations and accept such inputs and is shown to have successful real-world applications. Similarly, a fuzzy finite rough automaton corresponding to a fuzzy automaton is also failed to process input alphabet different from their input alphabet, the proposed fuzzy finite rough automaton for SC corresponding to a given fuzzy finite automaton is capable of processing semantically related input, and external input alphabet information from the dataset obtained by real-world applications and provide better user experience and applicability as compared to classical fuzzy finite rough automaton.
Journal Article
The handbook of computational linguistics and natural language processing
This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies
eBook