Abstract

Existing methods to manage privileges in smart home systems have not considered allocating privileges to users based on (i) the relationship of the user with the device, (ii) the location and risk of the device and (iii) the current environment. In this work, we take a multi perspective view on the problem of sharing fine-grained privileges of IoT devices among multiple users in a smart home. We propose the concepts of user role (subset of privileges specific to each device), tasks and security levels (labels for each privilege) to allot right privileges to users. Thereby, limiting the exploitation of privileges assigned to legitimate insiders of the house. Thus, our work matches the aspirations of previous surveys [8] on building a comprehensive access control system to manage privileges in a shared smart home. CCS CONCEPTS

Abstract

Determination of seismic safety of existing buildings is a time consuming and challenging process. Instead, rapid survey methods were developed which identify deficient structures from a large building stock in a city or town. This paper presents a comparison and critical review of existing rapid visual survey methods used for seismic assessment of existing reinforced concrete buildings. The study focuses on rapid visual survey methods developed for the safety assessment of reinforced concrete buildings in the Indian subcontinent and a widely used method in the United States. Comparison is carried out in various ways. Initially, a direct comparison is made based on vulnerable parameters and damage grades proposed by each method. Later, a scoring system is developed to highlight the differences and rank the selected methods. This system considers the general description, physical parameters, and damage description. Finally, as a case study, a rapid visual survey was conducted on 100 reinforced concrete buildings in each of the three cities (i.e., Pithoragarh, Gangtok, and Agartala) in India. These cities have different seismic, geological, and topographical conditions. The results show that all five methods give different outputs for the same sample surveyed buildings in each city. It was observed that there are many uncommon vulnerability parameters amongst each selected method. The results show a considerable variation in the weights assigned to each vulnerable parameter in all five methods.

Abstract

Increasing demands of wearable health monitoring seek dedicated spectrum for short range wireless communication of biosignals, for which frequency band near 400 MHz has been utilized recently. While using the dedicated spectrum, biosignal communication must not cause any out of band radiations and interference, for which there is a strong need to develop spurfree, low phase noise and energy efficient wireless transmitters (TX). In this research, for the first time we present design and implementation of a reference-spur-free, low phase noise, sub-mW dual modulation mode (frequency shift keying (FSK) and on-off keying (OOK)) 400 MHz TX for wearable health monitoring, where RF synthesis depends solely on an extremely low phase noise oscillator, which uses a 400 MHz surface acoustic wave resonator and does not include any power hungry, spurgenerating frequency multiplier or phase/frequency lock loop. The proposed TX has been designed and fabricated in 180 nm CMOS technology. Measurement results show that the energy efficiencies of the proposed TX in FSK and OOK mode are 1.8 nJ/bit at 250 kb/s and 0.23 nJ/bit at 2 Mb/s, respectively. To prove the utility of the proposed TX, biosignal communication is also demonstrated with the help of a commercial receiver in actual operating scenarios, where the measured bit error rate in FSK mode was < 10−5 for range of 2 m. Index Terms—Health-monitoring, wearable, spur-free, transmitter, FSK, OOK, 400 MHz, MedRadio

Abstract

Estimation of basic material properties of masonry and understanding its compressive behaviour is the initial crucial steps in analyzing the performance of masonry buildings. Masonry being a complex and inelastic material has a large variation in mechanical properties such as compressive strength and modulus of elasticity. The variation for Indian masonry is even higher, as most of the manufacturing and construction process is performed manually. Mechanical characterization of solid clay bricks, mortar cubes (1:4 cement-sand proportion) and masonry prisms are performed using laboratory tests to determine the compressive properties. The non-linear stress strain behaviour of masonry and its constituents is also presented. Based on the results of the present study and past literature, variation in mechanical properties of Indian masonry & its constituents is studied based on two different mortar grades (1:4 and 1:6). It is observed that these properties have a large portion of aleatory variability, due to variations in constituent materials and workmanship. The relationship between the compressive strength and elastic modulus of brick, mortar and masonry is also presented. Further, the relative performance of different empirical models in predicting the properties of masonry is compared and their efficacy is examined using the statistical and error-assessment parameters. Results showed that the compressive strength of the Indian masonry can be determined using most of the empirical relations available worldwide, with reasonable accuracy; however, the elastic modulus of masonry cannot be estimated with the same level of confidence.

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Abstract

The localization of acoustic sources is a parameter estimation problem where the parameters of interest are the direction of arrivals (DOAs). The DOA estimation problem can be formulated as a sparse parameter estimation problem and solved using compressive sensing (CS) methods. In this paper, the CS method of sparse Bayesian learning (SBL) is used to find the DOAs. We specifically use multi-frequency SBL leading to a non-convex optimization problem, which is solved using fixed-point iterations. We evaluate SBL along with traditional DOA estimation methods of conventional beamforming (CBF) and multiple signal classification (MUSIC) on various source localization tasks from the open access LOCATA dataset. The comparative study shows that SBL significantly outperforms CBF and MUSIC on all the considered tasks.

Abstract

The evolution of social media platforms have empowered everyone to access information easily. Social media users can easily share information with the rest of the world. This may sometimes encourage spread of fake news, which can result in undesirable consequences. In this work, we train models which can identify health news related to COVID-19 pandemic as real or fake. Our models achieve a high F1-score of 98.64%. Our models achieve second place on the leaderboard, tailing the first position with a very narrow margin 0.05% points.

Abstract

We propose a knowledge-based approach for extraction of Cause-Effect (CE) relations from biomedical text. Our approach is a combination of an unsupervised machine learning technique to discover causal triggers and a set of high-precision linguistic rules to identify cause/effect arguments of these causal triggers. We evaluate our approach using a corpus of 58,761 Leukaemia-related PubMed abstracts consisting of 568,528 sentences. We could extract 152,655 CE triplets from this corpus where each triplet consists of a cause phrase, an effect phrase and a causal trigger. As compared to the existing knowledge base - SemMedDB (Kilicoglu et al., 2012), the number of extractions are almost twice. Moreover, the proposed approach outperformed the existing technique SemRep (Rindflesch and Fiszman, 2003) on a dataset of 500 sentences.

Abstract

Every year publicly listed companies file financial reports to give insights about their activities. These reports are meant for shareholders or general public to evaluate the company’s health and decide whether to buy or sell stakes in the company. However, these annual financial reports tend to be long, and it is time-consuming to go through the reports for each company. We propose a Goal Guided Summarization technique through which the summary is extracted. The goal, in our case, is the decision to buy or sell company’s shares. We use hierarchical neural models for achieving this goal while extracting summaries. By the means of intrinsic and extrinsic evaluation we observe that the summaries extracted by our approach can model the decision of buying and selling shares better compared to summaries extracted by other summarization techniques as well as the complete document itself. We also observe that the summary extractor model can be used to construct stock portfolios which give better returns compared to major stock index.