Abstract

The Koyna-Warna region of Maharashtra, India, is one of the most signifcant worldwide examples for reservoir-induced seismicity. The area is highly vulnerable to earthquakes, and it has experienced over 1 lakh number of shocks since 1963. The largest known earthquake of magnitude 6.5 (Richter scale) occurred on 10th December 1967. Many low and moderate earthquake events have occurred over the past 50 years. A structured survey using rapid visual screening was carried out for existing RC buildings. The seismic risk index depends on three parameters, viz. hazard, exposure, and vulnerability. Many existing RC buildings in the Koyna-Warna region are designed to resist the gravity loads only without any seismic resistant provisions. Hence, there is a need to study the risk index of these RC buildings to assess future serious risks. In this study, the rapid visual survey of 120 existing RC buildings has been done through a modifed EDRI method (Earthquake Disaster Risk Index) to evaluate the seismic risk index of the Koyna-Warna region (Zone-IV as per IS: 1893–2016). The results depict that the risk index of RC buildings in the Koyna-Warna region is in severe damage condition and hence there is a need to take an initiatives for earthquake preparedness plan, with emphasis on retroftting measures, to reduce the loss of human life and damage to physical infrastructure in future seismic events.

Abstract

Search strategies for the third-generation leptoquarks (LQs) are distinct from other LQ searches, especially when they decay to a top quark and a τ lepton. We investigate the cases of all TeV-scale scalar and vector LQs that decay to either a top-tau pair (charge-1/3 and 5/3 LQs) or a top-neutrino pair (charge2/3 LQs). One can then use the boosted top (which can be tagged efficiently using jet-substructure techniques) and high-pT τ leptons to search for these LQs. We consider two search channels with either one or two taus along with at least one hadronically decaying boosted top quark. We estimate the high luminosity LHC (HL-LHC) search prospects of these LQs by considering both symmetric and asymmetric pair and single production processes. Our selection criteria are optimised to retain events from both pair and single production processes. The combined signal has better prospects than the traditional searches. We include new three-body single production processes to enhance the single production contributions to the combined signal. We identify the interference effect that appears in the dominant single production channel of charge-1/3 scalar LQ (S 1/3 ). This interference is constructive if S 1/3 is weak-triplet and destructive, for a singlet one. As a result, their LHC prospects differ appreciably

Abstract

The U1 leptoquark (LQ) is known to be a suitable candidate for explaining the semileptonic B-decay anomalies. We derive precise limits on its parameter space relevant for the anomalies from the current LHC high-pT dilepton data. We consider an exhaustive list of possible B-anomalies-motivated simple scenarios with one or two new couplings that can also be used as templates for obtaining bounds on more complicated scenarios. To obtain precise limits, we systematically consider all possible U1 production processes that can contribute to the dilepton searches including the resonant pair and single productions, nonresonant t-channel U1 exchange, as well as its large interference with the Standard Model background. We demonstrate how the inclusion of generally ignored resonant productions and signal-background interference contributions in the dilepton signal can lead to stringent exclusion limits. We point out new search channels of U1 that can act as unique tests of the flavour-motivated models. The template scenarios can also be used for future U1 searches at the LHC. We compare the LHC limits with other relevant flavor bounds and find that a TeV-scale U1 can accommodate both RD(∗) and RK(∗) anomalies while satisfying all the bounds.

Abstract

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner

Abstract

Social media platforms have democratized the publication process resulting into easy and viral propagation of information. However, spread of rumors via such media often results into undesired and extremely impactful political, economic, social, psychological and criminal consequences. Several manual as well as automated efforts have been undertaken in the past to solve this critical problem. Existing automated methods are text based, user credibility based or use signals from the tweet propagation tree. We aim at using the text, user, propagation tree and temporal information jointly for rumor detection on Twitter. This involves several challenges like how to handle text variations on Twitter, what signals from user profile could be useful, how to best encode the propagation tree information, and how to incorporate the temporal signal. Our novel architecture, T 3N (Text and Temporal Tree Network), leverages deep learning based architectures to encode text, user and tree information in a temporal-aware manner. Our extensive comparisons show that our proposed methods outperform the state-of-the-art techniques by ∼7 and ∼6 percent points respectively on two popular benchmark datasets, and also lead to better early detection results.

Abstract

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5% detection and 0.5% false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks.

Abstract

A secure authentication protocol plays a crucial role in securing communications over wireless and mobile networks. Due to resource-limitations and the nature of the wireless channel, the global mobile networks are highly susceptible to various attacks. Recently, an efficient authentication system for global roaming has been proposed in the literature. In this article, we first show that the analyzed authentication system is vulnerable man-in-the-middle attack, replay attack and Denial-of-Service (DoS) attack, and it does not ensure untraceability and local password-verification process to identify wrong passwords. To fix these security flaws, we propose a more efficient and robust authentication system for roaming in mobility networks. We use the formal verification tools like ProVerif, Automated Validation of Internet Security Protocols and Applications (AVISPA) and Burrows-Abadi-Needham (BAN) logic to check the regularity of the authentication protocol. Moreover, we prove the secrecy of a session key through the formal security using the random oracle model, known as Real-Or-Random (ROR) model. Finally, a detailed performance evaluation proves that the security protocol not only provides a security strength, but also preserves the low computational overhead. Thus, the proposed authentication protocol is secure and computationally efficient as compared to other relevant schemes.

Abstract

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (eg, crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyse Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users' context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme's security and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.

Abstract

This paper presents a methodology submitted to the FinSBD-2 shared task to extract well formed sentences, lists and items from noisy unstructured financial PDF documents in English language. The proposed architecture for document structure identification, is a combination of deep learning and heuristic based approaches. We use two unidirectional Long Short-Term Memory (LSTM) encoders to get the sentence split tokens from the set of all the possible split points. Further, the outputs are passed on to an attention based LSTM network to select only the well formed sentences from all the possible sentences. These outputs are merged to ultimately produce all possible well formed sentences. Apart from the sentences, lists and items are identified using a combination of heuristics which identify patterns in the data. The final F1 score, 0.217 on this task, is obtained by comparing the start and end indices of sentences, lists and items. We have presented another parameter, which is used to evaluate the class coverage by checking the overlap between the predicted and ground truth sentences and obtained an average 40% class coverage score. This metric is more useful for industry researchers who require coverage of the content rather than character level precision. The proposed approach will empower both academia and industry researchers in their effort to handle noisy documents for various NLP tasks by providing a simple, fast and robust approach to identify structure in their documents.

Abstract

In one aspect, a computerized method for automatically generating digital video content from scripts includes a media engine. The media engine receives a script and a plurality of flags; sending the script to a natural language processing (NLP) engine. The NLP engine parses the script. The script is broken into a set of keywords and phrases in a JSON format. The NLP engine, based on the keywords and phrases and the plurality of flags, obtains a relevant background scene for the video and a relevant set of assets for the video, and a set of associated attributes of each of the set of assets. In an asset is a character or object for the video. The NLP engine provides the parsed script in the JSON format and the relevant background scene of the video and the relevant set of assets of the video, and the set of associated attributes of each of the set of assets to a layout engine. The layout engine, based on the parsed script …