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The Neighbor Discovery Protocol (NDP) enables nodes on the same IPv6 link to advertise their existence to their neighbors and learn about their neighbors’ existences in an IPv6 link-local network. Duplicate Address Detection (DAD) on NDP is used to determine whether or not an address requested by a node is already in use by another node. The Neighbor Solicitation (NS) and Neighbor Advertisement (NA) operations are associated to DAD checks in order to ensure that each interface within the transmission session is unique. Unfortunately, NS and NA operations have a significant disadvantage in that they are based on insecure architectures and lack verification procedures for determining whether incoming messages originate from a valid or illegitimate node. This will eventually allow any node in the same link to be manipulated during NS and NA message transmission sessions. Despite some attempts to secure the entire NDP operations, they still suffer from computing resources requirement for their operations. As a result, this study proposes an Initial Neighbor Inspection (INI) on DAD operation. The proposed techniques allow for an initial round of verification of the nodes on the same link before a broadcast request on the existence of neighbors, which is followed by another round of learning about neighbors’ existences. Conclusively, using this idea, as a simple verification will indicate the presence of neighbors, we may restrict solicitation and advertising to only those who are eligible. This means that the computational processing time for NS and NA on DAD operations would not rise.

Semantic approaches present an efficient, detailed and easily understandable representation of knowledge from documents. Al-Quran contains a vast amount of knowledge that needs appropriate knowledge extraction. A semantic based approach can help in designing an efficient and explainable knowledge representation model for Al-Quran. This research aims to propose a semantic-graph knowledge representation model for verses of Al-Quran based on word dependencies. These features are used in the proposed knowledge representation model allowing the semantic graph matching to improve Al-Quran search applications' accuracy. The proposed knowledge representation model is essentially a formalism for generating a semantic graph representation of Quranic verses, which can be applied for knowledge base construction for other applications such as information retrieval system. A set of rules called Semantic Dependency Triple Rules are defined to be mapped into the semantic graph representing the verse's logic. The rules translate word dependencies and other NLP metadata into a triple form that holds logical information. The proposed model has been tested with English translation of Al-Quran on a document retrieval prototype The basic system has been enhanced with anaphoric pronouns correction, which has shown improvement in retrieval performance. The results have been compared with a closely related system and evaluated on the accuracy of the document retrieval in Precision, Recall and F-score measurements. The proposed model has achieved 65%, 60% and 62.4% for the measurements, respectively. It has also improved the overall accuracy of previous system by 43.8%.

Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.

This work is about designing a smartwatch that is used mostly by fishermen. The watch provides the location of the user using the global positioning system (GPS) and a panic button that he can use in case of emergency. It can record the heart rate by using a heart rate sensor. The paper works with a software that is used by the appropriate authorities; they record the information provided by the smartwatch. The third part of the paper is a mobile application used by the rescue team; the application shows the location of the fisherman. This paper aims to help fishermen in case of emergency cases, they can be saved by the rescue team. Based on the heart rate sensor and the panic button provided by the smartwatch, the appropriate authorities can know when a fisherman needs help and send the rescue team.

Cyberbullying affects almost 33% of youth daily. One in five students skips school because of cyberbullying. Neurodiverse youth are more susceptible to cyberbullying than their neurotypical counterparts. The ongoing pandemic has forced an online shift for all activities, with neurodiverse youth being no exception. The online shift makes them prone to increased cyberbullying incidents than pre-pandemic due to increased exposure during mandatory teaching and learning activities, including therapies. This study aims to study the prevalence, experience, and impact of cyberbullying among neurodiverse children during online teaching and learning, especially in the pandemic context, based on parental perceptions. A total of ten parents with neurodiverse children were interviewed to obtain an in-depth understanding of the characteristics mentioned above related to cyberbullying. The analysis revealed that pandemic has adverse effects on their children in general. Cyberbullying seems to be on the rise with minimal support for the neurodiverse community. Surprisingly, safe spaces involving teachers and therapists seem to be involved in bullying as well. Recommendations from the parents include awareness and enforcement of strict laws to curb cyberbullying to the best.

Internet and social media networks have become a defining factor for modern socialization, communication, and collaboration. At the same time, they have also become a massive platform for antisocial behavior such as cyberbullying, hate speech, and aggressive behavior, among others. This paper examines the automatic detection and classification of cyberbullying incidents in social networks. Most of the current work focuses on the detection of cyberbullying as a binary problem. However, some incidents are more severe than others and demand immediate attention and intervention from the stakeholders such as parents, government, and other relevant agencies. This paper presents the detection and classification of cyberbullying events based on their severity using various deep learning techniques – CNN, recurrent neural networks (RNN-BiLSTM), and HLSTM (HAN-BiLSTM). We curated three datasets from different social networks of varying balances to study the impact of class balance in a multi-class classification problem such as cyberbullying severity detection. Experimental results reveal higher efficiency of HLSTM and RNN-BiLSTM over CNN using all significant metrics such as k-score, MAP, MAR, and F1-Score. Similarly, results demonstrate that the deep learning techniques generally work well with moderately balanced datasets. The study reveals that deep learning techniques effectively detect severity in cyberbullying incidents, a multi-class classification problem.

A variety of applications have been built in recent years with the aim to extract knowledge from Al-Quran. Current knowledge representations of Al-Quran give attention primarily on conceptual ontology models that describe the semantic relations between the Quranic concepts or entities. There seems to be minimal effort towards recognizing the semantic relations between words in Quranic text, which is relatively more complex. This paper aims to present a framework for semantic knowledge representation of Al-Quran using dependency relations between words, in an attempt to boost the retrieval accuracy for Al-Quran. The semantic analysis is performed on Quranic verses according to word dependency relations using dependency parsing. Based on parsed dependencies, a set of rules are formulated to build a semantic graph of Surah Ali Imran of Al-Quran. The efficiency of the semantic representation was tested by developing a prototype question answering system. The framework was evaluated using precision and recall, First Hit Success, First Answer Reciprocal Rank and Total Reciprocal Rank by comparing the retrieved and actual answers. The results indicate that the performance of the proposed framework using word dependencies improves the semantic representation of knowledge.

Distributed Denial of Service (DDoS) attack is a major threat impeding service to legitimate requests on any network. Although the first DDoS attack was reported in 1996, the complexity and sophistication of these attacks has been ever increasing. A 2 TBps attack was reported in mid-August 2020 directed towards critical infrastructure, such as finance, amidst the COVID-19 pandemic. It is estimated that these attacks will double, reaching over 15 million, in the next 2 years. A number of mitigation schemes have been designed and developed since its inception but the increasing complexity demands advanced solutions based on emerging technologies. Blockchain has emerged as a promising and viable technology for DDoS mitigation. The inherent and fundamental characteristics of blockchain such as decentralization, internal and external trustless attitude, immutability, integrity, anonymity and verifiability have proven to be strong candidates, in tackling this deadly cyber threat. This survey discusses different approaches for DDoS mitigation using blockchain in varied domains to date. The paper aims at providing a comprehensive review, highlighting all necessary details, strengths, challenges and limitations of different approaches. It is intended to serve as a single platform to understand the mechanics of current approaches to enhance research and development in the DDoS mitigation domain.

Contextual retrieval is a critical component for efficient usage of knowledge hidden behind the data. It is also among the most important factors for user satisfaction. It essentially comprise of two equally important parts – the retrieval mechanism and the knowledge base from which the information is retrieved. Despite the importance, context aware knowledge bases have not received much attention and thereby, limiting the efficiency of precise context aware retrieval. Such knowledge bases would not only contain information that has been efficiently stored but the knowledge contained would be context based. In other words, machines would understand the knowledge and its context rather than just storing data. This would help in efficient and context aware retrieval. The current paper proposes rules and methodologies for construction of such context aware knowledge bases. A case study to demonstrate the application of the methodology and test the efficiency of the proposed methodology has also been presented. The results indicate that knowledge bases built on these principles tend to generate more efficient and better context aware retrieval results.

Speed of decision making, increased operations tempo and enhanced situational awareness are some of the essential characteristics required by United Nations Peacekeeping forces. These can be efficiently achieved by leveraging with enhanced information processing technologies. The process of discovering essential information from the explicit knowledge and its digitization producing web and electronic documents will produce a more effective and efficient decision making environment, enhancing the value of the decision making process. The current work deals with discovering and visualizing useful patterns and knowledge especially in unstructured text, within the domain of United Nations Peacekeeping operations. Text analytics is a powerful technique that helps in achieving the aforementioned goals. Text Analytics of Unstructured Data (TAUD) framework is utilized for analyzing, discovering and visualizing essential patterns and knowledge within the military text documents. The framework focuses on data collection, pre-processing, text analytics and visualization using the hierarchical cluster analysis and K-Means algorithm. The results upon verification indicate that the technique has successfully extracted important knowledge areas or information required by the troops before being deployed in the ground operations. It will substantially help the military commanders and training officers to have an easy and efficient access to all the essential military knowledge before and during deployment on an assignment. Hence, it may lead to an efficient and enhanced situational awareness and decision making during a peacekeeping mission.

The knowledge behind the data is of utmost importance. It is necessary to develop meaningful, expressive and comprehensive knowledge base for every domain in order to use the knowledge. The repositories need to be designed considering natural language interactions for better performance. A knowledge base with such features seems to be non-existent for Al-Qur'an. The current work aims to develop an efficient and comprehensive knowledge base for Al-Qur'an based on principles of logic, linguistics and semantic networks, etc. The developed knowledge base is expressive, comprehensive, meaningful and efficient, establishing and maintaining semantic relations. The developed knowledge base can be used for varied purposes such as expert systems, etc. contributing to the efficiency of these systems based on its unique design.

It is critical to have a knowledge base model for efficient storage of extracted knowledge. This ensures that the knowledge is stored in a meaningful way to be used for different applications. The efficiency of the knowledge base model depends largely on the rules of construction. Knowledge represented using logico-linguistic techniques and semantic networks lack a consistent rule based knowledge model. The current paper deals with the analysis of text from the knowledge extraction, representation and semantic network phase to formulate rules which would lay foundations of a knowledge model. The developed rules seem to be promising providing a comprehensive coverage of different scenarios. The extensive coverage is an indication that the knowledge model will cater to the entire domain knowledge, thereby laying the foundations of automatic construction of efficient knowledge bases.

The knowledge behind the gigantic pool of data remains largely unextracted. Techniques such as ontology design, RDF representations, hpernym extraction, etc. have been used to represent the knowledge. However, the area of logic (FOPL) and linguistics (Semantics) has not been explored in depth for this purpose. Search engines suffer in extraction of specific answers to queries because of the absence of structured domain knowledge. The current paper deals with the design of formalism to extract and represent knowledge from the data in a consistent format. The application of logic and linguistics combined greatly eases and increases the precision of knowledge translation from natural language. The results clearly indicate the effectiveness of the knowledge extraction and representation methodology developed providing intelligence to machines for efficient analysis of data. The methodology helps machines to retrieve precise results in an efficient manner.

The increased stiffness in competition has dramatically increased the risk occurrence in project delivery. As the number of projects grows, enterprises or project managers have to eventually run simultaneous projects. Risk management in such cases becomes extremely necessary as failure of one project may lead to the near failure of all the parallel projects running under the same supervision. Therefore, it becomes necessary to understand the best practices for risk management in a parallel project operation environment. Though the issue is of high importance, yet not much has been discussed. This study was carried out in an enterprise environment whereby professionals of this field with high experience were interviewed and requested to share their experiences. The research results bear witness to the fact that risk is inevitable and leaves a strong negative impact on all the projects operating in parallel. As such there is a high need to understand the strategies and best practices that are being applied in this field to avoid heavy losses. We seek to apply lessons learned to managing the DBrain research project which has multiple collaborators working in parallel.

Information is one of the most crucial assets of current day organizations. However, managing this information has become a never ending challenge. Adequate security and privacy are of great essence for information systems containing valuable data and information. Leakage of such information can facilitate large-scale identity theft and successful impersonation of a victim lets the identity thief commit fraud. Identity theft occurs when someone uses another individual's personal information to pose as that individual. This study highlights the importance of information security management in organizations and identifies various critical security challenges that often lead to information leakage within the organizations. Qualitative method of research has been employed for data collection and analysis. The results obtained are categorized into eleven critical security challenges in order of the number of cases reported for each of them.