Author
Shah Khalid Khan
Bio: Shah Khalid Khan is an academic researcher from RMIT University. The author has contributed to research in topics: Base station & Backhaul (telecommunications). The author has an hindex of 8, co-authored 21 publications receiving 152 citations.
Papers
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TL;DR: A variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs are described, which can transform large volumes of text into effective vector representations capturing the same semantic information.
Abstract: Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS). We describe a variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs. These models can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP related tasks. In the end, this survey briefly discusses the commonly used ML and DL based classifiers, evaluation metrics and the applications of these word embeddings in different NLP tasks.
52 citations
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TL;DR: This study aims to analyse, synthesise, and interpret critical areas for the roll-out and progression of CAVs in combating cyber-attacks, and presents the CAVs communication framework in an integrated form, i.e., from In-Vehicle (IV) communication to Vehicle-to-Vehicles (V2X) communication with a visual flowchart to provide a transparent picture of all the interfaces for potential cyber- attacks.
48 citations
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30 Jun 2021TL;DR: For a survey of word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS), see as mentioned in this paper.
Abstract: Word representation has always been an important research area in the history of natural language processing (NLP). Understanding such complex text data is imperative, given that it is rich in information and can be used widely across various applications. In this survey, we explore different word representation models and its power of expression, from the classical to modern-day state-of-the-art word representation language models (LMS). We describe a variety of text representation methods, and model designs have blossomed in the context of NLP, including SOTA LMs. These models can transform large volumes of text into effective vector representations capturing the same semantic information. Further, such representations can be utilized by various machine learning (ML) algorithms for a variety of NLP-related tasks. In the end, this survey briefly discusses the commonly used ML- and DL-based classifiers, evaluation metrics, and the applications of these word embeddings in different NLP tasks.
46 citations
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10 Mar 2021TL;DR: A consolidated synthesis on the role of UAVs and mmWave in 5G, emphasis on recent developments and challenges, and the scope of artificial intelligence and machine learning techniques as an efficient solution for combating the dynamic and complex nature of U AV‐based cellular communication networks are discussed.
Abstract: Next‐generation wireless communication networks, in particular, the densified 5G will bring many developments to the existing telecommunications industry. The key benefits will be the high...
37 citations
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TL;DR: This study investigates, analyzes and describes the distinctive rich characteristics of mmWave propagation in Access and backhaul network simultaneously using UAV, and highlights the impact of UAV location to maximize the performance of an Amplify-and-Forward UAV based relay for providing enhanced coverage to the users.
Abstract: Future wireless communication, especially the densified 5G network using millimeter-Wave (mmWave) will bring numerous innovations to the current telecommunication industry. In such scenario, the use of Unmanned Aerial Vehicle (UAV) as Base Station (BS) becomes one of the viable options for providing 5G services. The focus of this study is to investigate, analyze and describe the distinctive rich characteristics of mmWave propagation in Access and backhaul network simultaneously using UAV. The mathematical framework is formulated for calculating UE (User Equipment) received power for the relay path (BS–UAV–UE) based on Friis Transmission Equation. We conduct simulations using the ray-tracing simulator in different scenarios while comparing and verifying the simulation results vs mathematical equations. Using ray racing simulator, the effectiveness of diffracted, reflected, and scattered paths versus direct paths is described. Furthermore, using extensive simulations, we highlight the impact of UAV location to maximize the performance of an Amplify-and-Forward UAV based relay for providing enhanced coverage to the users.
36 citations
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TL;DR: A comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented in this article, where UAVs are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions.
Abstract: Providing ubiquitous connectivity to diverse device types is the key challenge for 5G and beyond 5G (B5G). Unmanned aerial vehicles (UAVs) are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions. Compared to the communications with fixed infrastructure, UAV has salient attributes, such as flexible deployment, strong line-of-sight (LoS) connection links, and additional design degrees of freedom with the controlled mobility. In this paper, a comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented. We first briefly introduce essential background and the space-air-ground integrated networks, as well as discuss related research challenges faced by the emerging integrated network architecture. We then provide an exhaustive review of various 5G techniques based on UAV platforms, which we categorize by different domains including physical layer, network layer, and joint communication, computing and caching. In addition, a great number of open research problems are outlined and identified as possible future research directions.
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297 citations
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TL;DR: This paper presents D I C E T, a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account.
158 citations
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TL;DR: This study presents a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020 and supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic.
Abstract: Social media (and the world at large) have been awash with news of the COVID-19 pandemic With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020 The tweets have been labeled into positive, negative, and neutral sentiment classes We analyzed the collected tweets for sentiment classification using different sets of features and classifiers Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic
157 citations