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

Abstract

Fake news in India: scale, diversity, solution, and opportunities Page 1 india region hot topics 80 COMMUNICATIONS OF THE ACM | NOVEMBER 2022 | VOL. 65 | NO. 11 India is a nation that realizes unity in diversity. Indians follow different religions, practice different customs and traditions, and speak diverse languages.e These factors and others make it difficult to detect fake news in India. More specifically, we observe the following: ˲ Multilinguality. Mother tongue of Indians is diverse. There are 22 official languages and only 10.67% of the population converse in English.f The current fake news detection solutions are most effective for English and might fail to identify and process information in other languages. ˲ Instant messaging plat- form. We cannot undermine WhatsApp’s role in forming and mobilizing the online public. Since WhatsApp is end-to-end encrypted, identifying and quashing false stories is possible …

Abstract

Google Play’s policy forbids the use of incentivized installs, ratings, and reviews to manipulate the placement of apps. However, there still exist apps that incentivize installs for other apps on the platform. To understand how install-incentivizing apps affect users, we examine their ecosystem through a socio-technical lens and perform a mixed-methods analysis of their reviews and permissions. Our dataset contains 319K reviews collected daily over five months from 60 such apps that cumulatively account for over 160.5M installs. We perform qualitative analysis of reviews to reveal various types of dark patterns that developers incorporate in install-incentivizing apps, highlighting their normative concerns at both user and platform levels. Permissions requested by these apps validate our discovery of dark patterns, with over 92% apps accessing sensitive user information. We find evidence of fraudulent reviews on install-incentivizing apps, following which we model them as an edge stream in a dynamic bipartite graph of apps and reviewers. Our proposed reconfiguration of a state-of-the-art microcluster anomaly detection algorithm yields promising preliminary results in detecting this fraud. We discover highly significant lockstep behaviors exhibited by reviews that aim to boost the overall rating of an install-incentivizing app. Upon evaluating the 50 most suspicious clusters of boosting reviews detected by the algorithm, we find (i) near-identical pairs of reviews across 94% (47 clusters), and (ii) over 35% (1,687 of 4,717 reviews) present in the same form near-identical pairs within their cluster. Finally, we conclude with a discussion on how fraud is intertwined with labor and poses a threat to the trust and transparency of Google Play

Abstract

Targeted image retrieval has long been a challenging problem since each person has a different perception of different features leading to inconsistency among users in describing the details of a particular image. Due to this, each user needs a system personalized according to the way they have structured the image in their mind. One important application of this task is suspect identifcation in forensic investigations where a witness needs to identify the suspect from an existing criminal database. Existing methods require the attributes for each image or suffer from poor latency during training and inference. We propose a new approach to tackle this problem through explicit relevance feedback by introducing a novel loss function and a corresponding scoring function. For this, we leverage contrastive learning on the user feedback to generate the next set of suggested images while improving the level of personalization with each user feedback iteration.

Abstract

Socio-political protests often lead to grave consequences when they occur. The early detection of such protests is very important for taking early precautionary measures. However, the main shortcoming of protest event detection is the scarcity of sufficient training data for specific language categories, which makes it difficult to train data-hungry deep learning models effectively. Therefore, cross-lingual and zero-shot learning models are needed to detect events in various low-resource languages. This paper proposes a multi-lingual crossdocument level event detection approach using pre-trained transformer models developed for Shared Task 1 at CASE 2022. The shared task constituted four subtasks for event detection at different granularity levels, i.e., document level to token level, spread over multiple languages (English, Spanish, Portuguese, Turkish, Urdu, and Mandarin). Our system achieves an average F1 score of 0.73 for document-level event detection tasks. Our approach secured 2 nd position for the Hindi language in subtask 1 with an F1 score of 0.80. While for Spanish, we secure 4 th position with an F1 score of 0.69. Our code is available at https://github. com/nehapspathak/campros/.