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 …

Abstract

We propose thetextbf {AViNet} architecture for audiovisual saliency prediction. AViNet is a fully convolutional encoder-decoder architecture. The encoder combines visual features learned for action recognition, with audio embeddings learned via an aural network designed to classify objects and scenes. The decoder infers a saliency map via trilinear interpolation and 3D convolutions, combining hierarchical features. The overall architecture is conceptually simple, causal, and runs in real-time (60 fps). AViNet outperforms the state-of-the-art on ten (seven audiovisual and three visual-only) datasets while surpassing human performance on the CC, SIM, and AUC metrics for the AVE dataset. Visual features maximally account for saliency on existing datasets with audio-only contributing to minor gains, except in specific contexts like social events. Our work, therefore, motivates the need to curate saliency datasets reflective of real-life, where both the visual and aural modalities complimentarily drive saliency. Our code and pre-trained models are available at this https URL

Abstract

Semantics play an important role when it comes to automated systems using text or language and it is different for different domains. In this paper, we tackle the FinSim 2020 shared task at IJCAIPRICAI 2020. The task deals with designing a semantic model which can automatically classify short phrases/terms from financial domain into the most relevant hypernym (or top-level) concept in an external ontology. We perform several experiments using different kinds of word and phrase level embeddings to solve the problem in an unsupervised manner. We also explore the use of a supplementary financial domain data; either to learn better concept representation or generate more training samples. We discuss both the positive and negative results that we observed while applying these approaches.

Abstract

n this paper, we study current and upcoming frontiers across the landscape of skeleton-based human action recognition. To begin with, we benchmark state-of-the-art models on the NTU-120 dataset and provide multi-layered assessment of the results. To examine skeleton action recognition'in the wild', we introduce Skeletics-152, a curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. The results from benchmarking the top performers of NTU-120 on Skeletics-152 reveal the challenges and domain gap induced by actions' in the wild'. We extend our study to include out-of-context actions by introducing Skeleton-Mimetics, a dataset derived from the recently introduced Mimetics dataset. Finally, as a new frontier for action recognition, we introduce Metaphorics, a dataset with caption-style annotated YouTube videos of the popular social game Dumb Charades and interpretative dance performances. Overall, our work characterizes the strengths and limitations of existing approaches and datasets. It also provides an assessment of top-performing approaches across a spectrum of activity settings and via the introduced datasets, proposes new frontiers for human action recognition.