Mention detection is an important component of the Coreference Resolution (CR) system, where mentions such as name, nominal, and pronominals are identified. These mentions can be purely coreferential mentions or singleton mentions (non-coreferential mentions). Coreferential mentions are those mentions in a text that refer to the same entities in the real world. Whereas, singleton mentions are mentioned only once in the text and do not participate in the coreference as they are not mentioned again in the following text. Filtering of these singleton mentions can substantially improve the performance of a CR process. This article proposes a singleton mention detection module based on a Fully Connected Network (FCN) and a Long Short-Term Memory (LSTM) for Hindi text and model identifying singleton mentions so that these mentions can be filtered out to reduce the search space for CR. A CR system can look for the previous reference of that mention in the text and if these mentions are removed from the list of mentions, then it reduces the searching time and also space-time. This model utilizes a few handcrafted features, context information, and embedding for words from Word2vec and a multilingual Bidirectional Encoder Representations from Transformers (mBERT) language model. The coreference annotated Hindi dataset comprising 3.6K sentences, and 78K tokens are used for the task. The singleton mention detection model is analyzed extensively by experimenting with various lengths of context windows for each mention. The performance of the model is significant with two window sizes of context as compared to other various window sizes of contexts such as 2, 3, 4, 5, etc., and all previous and all next words of each mention. The Precision, Recall, and F-measure of the LSTM-FCN model with mBERT (Word + Context + Syntactic) with two window sizes of context for identifying the singleton mentions are 63%, 71%, and 67%, respectively.

The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid's susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL's indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.

The problem of space layout planning, constrained by a number of functional and non-functional requirements, not only challenges architects in coming up with a good solution, but is more difficult to give an alternative. Genetic algorithms (GAs) have been found suitable for solving the problem of providing alternative solutions. However, GAs have been found to be susceptible to the problem of local maxima and plateau conditions. To overcome these problems, the multi-population genetic algorithm (MPGA) improves the diversity of the population, thereby improving the quality of the solution. Algorithms are employed to automatically generate layout designs in best-connected ways, either rectangular or square. The area of the floor plans is optimized to minimize the extra area in the layout. The layouts are divided into four groups and these groups are related to each other based on highest proximity. Layout designs have been simulated using GA and MPGA algorithms and MPGA has shown significant improvement in computation time as well as quality over alternative solutions. In addition, the algorithm also provides the architect with the facility to interactively modify the dimensions and adjacent criteria during the design phase. The system works on clouds and shows the result for inputs passed by an architect.

Abstract The recent increase in Android's popularity has resulted in a swamp of attacks faced by the platform. Several researchers have come out with various static malware detection tools using opcodes as features since opcodes provide the details of intrinsic patterns of application raw data. This article presents a new malware detection approach CogramDroid based on opcode ngrams. The approach classifies the applications based on the relative frequency patterns of the opcode ngrams using the concept of word cooccurrence of natural language processing. The objective of the article is to develop a malware detection approach with high accuracy and time efficiency. The article also presents an analysis of the number of opcodes required for effective malware detection. In this study, an accuracy rate of 96.22% and an F1‐score of 96.69% is achieved using seven core opcodes and three grams.

Wireless networks are used by everyone for their convenience for transferring packets from one node to another without having a static infrastructure. In WSN, there are some nodes which are light weight, small in size, having low computation overhead, and low cost known as sensor nodes. In literature, there exists many secure data aggregation protocols available but they are not sufficient to detect the malicious node. The authors require a better security mechanism or a technique to secure the network. Data aggregation is an essential paradigm in WSN. The idea is to combine data coming from different source nodes in order to achieve energy efficiency. In this paper, the authors proposed a protocol for worm hole attack detection during data aggregation in WSN. Main focus is on wormhole attack detection and its countermeasures.

Abstract In recent years, Mobile Ad hoc NETworks (MANETs) have generated great interest among researchers in the development of theoretical and practical concepts, and their implementation under several computing environments. However, MANETs are highly susceptible to various security attacks due to their inherent characteristics. In order to provide adequate security against multi‐level attacks, the researchers are of the opinion that detection‐based schemes should be incorporated in addition to traditionally used prevention techniques because prevention‐based techniques cannot prevent the attacks from compromised internal nodes. Intrusion detection system is an effective defense mechanism that detects and prevents the security attacks at various levels. This paper tries to provide a structured and comprehensive survey of most prominent intrusion detection techniques of recent past and present for MANETs in accordance with technology layout and detection algorithms. These detection techniques are broadly classified into nine categories based on their primary detection engine/(s). Further, an attempt has been made to compare different intrusion detection techniques with their operational strengths and limitations. Finally, the paper concludes with a number of future research directions in the design and implementation of intrusion detection systems for MANETs. Copyright © 2016 John Wiley & Sons, Ltd.