Abstract

The hearing challenged communities all over the world face difficulties to communicate with others. Machine translation has been one of the prominent technologies to facilitate communication with the deaf and hard of hearing community worldwide. We have explored and formulated the fundamental rules of Indian Sign Language(ISL) and implemented them as a translation mechanism of English Text to Indian Sign Language glosses. According to the formulated rules and sub-rules, the source text structure is identified and transferred to the target ISL gloss. This target language is such that it can be easily converted to videos using the Indian Sign Language dictionary. This research work also mentions the intermediate phases of the transfer process and innovations in the process such as Multi-Word Expression detection and synonym substitution to handle the limited vocabulary size of Indian Sign Language while producing semantically accurate translations.

Abstract

Automatic extraction of “significant” components of a legal contract, has the potential to simplify the end user’s comprehension. In essence, “significant” pieces of information have 1) information pertaining to material/practical details about a specific contract and 2) information that is novel or comes as a “surprise” for a specific type of contract. It indicates that the significance of a component may be defined at an individual contract level and at a contract-type level. A component, sentence, or paragraph, may be considered significant at a contract level if it contains contract-specific information (CSI), like names, dates, or currency terms. At a contract-type level, components that deviate significantly from the norm for the type may be considered significant (type-specific information (TSI)). In this paper, we present approaches to extract “significant” components from a contract at both these levels. We attempt to do this by identifying patterns in a pool of documents of the same kind. Consequently, in our approach, the solution is formulated in two parts: identifying CSI using a BERT-based contract-specific information extractor and identifying TSI by scoring sentences in a contract for their likelihood. In this paper, we even describe the annotated corpus of contract documents that we created as a first step toward the development of such a language-processing system. We also release a dataset of contract samples containing sentences belonging to CSI and TSI.

Abstract

3𝐷 fingerprint capture is less sensitive to skin moisture levels and avoids skin deformation, which is common in contact-based sensors, in addition to capturing depth information. Unfortunately, its adoption is limited due to high cost and system complexity. Photometric stereo provides an opportunity to build low-cost, simple sensors capable of high-quality 3𝐷 capture. However, it assumes that the surface being imaged is lambertian (unlike our fingers). We introduce the Split and Knit algorithm (SnK), a 3𝐷 reconstruction pipeline based on the photometric stereo for finger surfaces. It introduces an efficient way of estimating the direct illumination component, thus allowing us to do a higher-quality reconstruction of the entire finger surface. The algorithm also introduces a novel method to obtain the overall finger shape under NIR illumination, all using a single camera. Finally, we combine the overall finger shape and the ridge-valley point cloud to obtain a 3𝐷 finger phalange. The high-quality 3𝐷 reconstruction also results in better matching accuracy of the captured fingerprints. 1

Abstract

In this paper, four low-cost CO2 sensors are evaluated for IoT-based indoor air pollution monitoring. Specifically, CO2 sensors SCD30, Prana Air, MHZ14, and T6713 are evaluated against a standard reference Aeroqual S-500 device. The experiment was carried out in an indoor environment inside one of the labs in IIIT Hyderabad, India. It is shown that calibration is needed for some of these low-cost devices locally even though the sensors may be factory calibrated. For calibration, simple and widely-used machine learning algorithms are employed such as linear regression, least absolute deviation, random forest, support vector regression, and Gaussian regression. The parameters considered to assess the performance of these sensors are coefficient of determination (R 2 ), coefficient of variability (Cv), and root mean square error (RMSE). After calibration with a reference sensor, it is observed that these low-cost sensors operate well. Index Terms—IoT, Determination coefficient, Low-cost CO2 sensor, Coefficient of variability, Root mean square error.

Abstract

This paper proposes an IoT and machine learning (ML)-based novel method to estimate the air quality index (AQI) using traffic data in real-time. With the help of particulate matter (PM) monitoring nodes deployed in fifteen locations with diverse traffic scenarios of Indian roads, and using digital map service providers, a rich traffic dataset with approximately 210,000 samples has been collected. Three different ML models, namely random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), are trained on this dataset to predict the AQI category into five levels. The experimental results show an accuracy of 82.60% with the F1-score of 83.67% on the complete dataset. Apart from this, ML models were also trained on individual node datasets, and the behavior of AQI levels was observed. Index Terms—AQI Estimation, Traffic Data, Machine Learning, IoT

Abstract

The current experimental data allow for a sub-TeV colorless weak-singlet scalar or pseudoscalar. If such a singlet field is present together with TeV-range vectorlike top and bottom partners, there is a possibility that the heavy quarks decay dominantly to the singlet state and a third-generation quark, and the singlet state decays to quark and boson pairs. Such a possibility may arise in various models but it has not been explored experimentally, especially in the context of vectorlike-quark searches. We consider some minimal models, covering the possible weak representations of the top and bottom partners, that can be mapped to many well-motivated ultraviolet-complete theories. We chart out the possible interesting and unexplored signatures of the exotic decay of vectorlike quarks and identify benchmark points representing different signal topologies for the high luminosity LHC. We perform a general scan of the …

Abstract

This paper considers a low-earth-orbit (LEO) satellite-based topology for an internet-of-things (IoT) network, where multiple IoT devices broadcast the information to all the visible satellites over a shared channel using slotted ALOHA. The satellites selectively decode-and-forward (DF) the information from the IoT devices over orthogonal channels to the ground station (GS), which does maximal ratio combining (MRC). For decoding at the satellites, capture and successive interference cancellation (SIC) schemes are considered. For the considered topology, the closed-form expressions are derived for the end-to-end outage probability (OP) for an arbitrary number of IoT devices and satellites in the capture model and for the two-device, two satellite case in the case of the SIC model. The expressions are derived for both independent and non-identically distributed (inid) and independent and identically distributed (iid) uplink channels. The OP is analyzed as a function of the parameters like the number of satellites, the number of devices, and the desired data rate. The results demonstrate that the proposed approach leverages the benefits of mega-LEO satellites to make the topology feasible and attractive for low-powered and low-complexity IoT networks.

Abstract

This paper proposes a deep learning (DL)-based algorithm which is used for improving the performance of digit detection from internet-of-things (IoT)-based analog water meters. The DL algorithm is trained on a rich dataset of over 160,000 images collected from six water nodes deployed at locations with different environmental conditions. A detailed comparison between the proposed DL and machine learning (ML) algorithm is made based on detection accuracy, feature analysis, error analysis, and computational complexity analysis. It is observed that compared to the ML model, the proposed DL model maintained a higher detection accuracy and is more generalized in terms of feature extraction, which makes the algorithm robust.

Abstract

Although the city of Hyderabad in Telangana, India lies in seismic zone II, low to medium intensity tremors that pose a serious concern towards safety of the built environment are not uncommon. One such series of tremors occurred during 13–20 October 2020, in the financial district of Hyderabad and created a panic situation due to perceivable shaking and jolts with loud sounds associated with hydro-seismicity. To understand the safety of the city’s built environment, a study was conducted on low, medium and tall buildings using ground motions recorded at the International Institute of Information Technology (IIIT), Hyderabad, which is 2.3 km from the epicentre. The amplification of ground motion on the second floor of the Nilgiri Building in IIIT, Hyderabad was 1.2–2.3. The vibrations recorded on the ground floor of the Nilgiri Building were used to develop a site-specific response spectrum. This was further used to obtain the peak responses of the considered buildings through response spectrum analysis. The present study suggests that the low-rise buildings, mid-rise buildings and nonstructural elements in high-rise buildings are under threat in the case of high-intensity earthquakes.

Abstract

With Twitter growing as a preferred channel for outreach among major politicians, there have been focused efforts on online communication, even in election campaigns in primarily rural regions. In this paper, we examine the relationship between politicians’ use of social media and the level of urbanization and literacy by compiling a comprehensive list of Twitter handles of political party functionaries and election candidates in the run-up to the 2022 State Assembly elections in Uttar Pradesh, India. We find statistically significant relationships between political Twitter presence and levels of urbanization and with levels of literacy. We also find a strong correlation between vote share and Twitter presence in the winning party, a relationship that is even stronger in urban districts. This provides empirical evidence that social media is already a central part of electoral outreach processes in the Global South, but that this is