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This work introduces the Query/Hit (Q/H) learning model. The setup consists of two agents. One agent, Alice, has access to a streaming source, while the other, Bob, does not have direct access to the source. Communication occurs through sequential Q/H pairs: Bob sends a sequence of source symbols (queries), and Alice responds with the waiting time until each query appears in the source stream (hits). This model is motivated by scenarios with communication, computation, and privacy constraints that limit real-time access to the source. The error exponent for sequential hypothesis testing under the Q/H model is characterized, and a querying strategy, the Dynamic Scout-Sentinel Algorithm (DSSA), is proposed. The strategy employs a mutual information neural estimator to compute the error exponent associated with each query and to select the query with the highest efficiency. Extensive empirical evaluations on both synthetic and real-world datasets -- including mouse movement trajectories, typesetting patterns, and touch-based user interactions -- are provided to evaluate the performance of the proposed strategy in comparison with baselines, in terms of probability of error, query choice, and time-to-detection.

Dataset obfuscation refers to techniques in which random noise is added to the entries of a given dataset, prior to its public release, to protect against leakage of private information. In this work, dataset obfuscation under two objectives is considered: i) rank-preservation: to preserve the row ordering in the obfuscated dataset induced by a given rank function, and ii) anonymity: to protect user anonymity under fingerprinting attacks. The first objective, rank-preservation, is of interest in applications such as the design of search engines and recommendation systems, feature matching, and social network analysis. Fingerprinting attacks, considered in evaluating the anonymity objective, are privacy attacks where an attacker constructs a fingerprint of a victim based on its observed activities, such as online web activities, and compares this fingerprint with information extracted from a publicly released obfuscated dataset to identify the victim. By evaluating the performance limits of a class of obfuscation mechanisms over asymptotically large datasets, a fundamental trade-off is quantified between rank-preservation and user anonymity. Single-letter obfuscation mechanisms are considered, where each entry in the dataset is perturbed by independent noise, and their fundamental performance limits are characterized by leveraging large deviation techniques. The optimal obfuscating test-channel, optimizing the privacy-utility tradeoff, is characterized in the form of a convex optimization problem which can be solved efficiently. Numerical simulations of various scenarios are provided to verify the theoretical derivations.

The rapid development of IoT networks has led to a research trend in designing effective security features for them. Due to the power-constrained nature of IoT devices, the security features should remain as lightweight as possible. Currently, most of the IoT network traffic is unencrypted. The leakage of smart devices' unencrypted data can come with the significant cost of a privacy breach. To have a secure channel with encrypted traffic, two endpoints in a network have to authenticate each other and calculate a short-term key. They can then communicate through an authenticated and secure channel. This process is referred to as authenticated key exchange (AKE). Although Datagram Transport Layer Security (DTLS) offers an AKE protocol for IoT networks, research has proposed more efficient and case-specific alternatives. This paper presents LAKEE, a straightforward, lightweight AKE protocol for IoT networks. Our protocol employs elliptic curve cryptography for generating a short-term session key. It reduces the communication and computational overhead of its alternatives while maintaining or improving their security strength. The simplicity and low overhead of our protocol make it a fit for a network of constrained devices.

Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean square estimation error at each node (accuracy objective), and ii) the mean square distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between the former and latter objectives. Assuming a general stochastic model, the sensor fusion algorithm optimizing this tradeoff is characterized through a computable optimization problem, and a Cramer-Rao type lower bound for the achievable accuracy-consensus loss is obtained. Finding the optimal sensor fusion algorithm is computationally complex. To address this, a general class of low-complexity Brooks-Iyengar Algorithms are introduced, and their performance, in terms of accuracy and consensus objectives, is compared to that of optimal linear estimators through case study simulations of various scenarios.

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to the tweets of candidates are good indicators for predicting actual election outcome. However, we observe that the number of tweets mentioning a party and the sentiment of voters towards the party shown in tweets are not aligned with the election result. We also use machine learning models on various features such as linguistic, word embeddings and moral dimensions for predicting the election result (win or lose). The random forest model using tweet features provides promising results for predicting if the tweet belongs to a winning or losing candidate., Comment: 15 pages, 11 figures and 2 tables

Social media platforms have emerged as a hub for political and social interactions, and analyzing the polarization of opinions has been gaining attention. In this study, we have proposed a measure to quantify polarization on social networks. The proposed metric, unlike state-of-the-art methods, does not assume a two-opinion case and applies to multiple opinions. We tested our metric on different networks with a multi-opinion scenario and varying degrees of polarization. The scores obtained from the proposed metric were comparable to state-of-the-art methods on binary opinion-based benchmark networks. The technique also differentiated among networks with different levels of polarization in a multi-opinion scenario. We also quantified polarization in a retweet network obtained from Twitter regarding the usage of drugs like hydroxychloroquine or chloroquine in treating COVID-19. Our metric indicated a high level of polarized opinions among the users. These findings suggest uncertainty among users in the benefits of using hydroxychloroquine and chloroquine drugs to treat COVID-19 patients., Comment: 14 pages, 4 figures and 1 table

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users., Comment: 21 pages, 7 figures, 1 Table, 6 supplementary figures, Accepted in Frontiers in Psychology

Many different technologies are used to detect pests in the crops, such as manual sampling, sensors, and radar. However, these methods have scalability issues as they fail to cover large areas, are uneconomical and complex. This paper proposes a crowdsourced based method utilising the real-time farmer queries gathered over telephones for pest surveillance. We developed data-driven strategies by aggregating and analyzing historical data to find patterns and get future insights into pest occurrence. We showed that it can be an accurate and economical method for pest surveillance capable of enveloping a large area with high spatio-temporal granularity. Forecasting the pest population will help farmers in making informed decisions at the right time. This will also help the government and policymakers to make the necessary preparations as and when required and may also ensure food security.

Behavioral biometrics are key components in the landscape of research in continuous and active user authentication. However, there is a lack of large datasets with multiple activities, such as typing, gait and swipe performed by the same person. Furthermore, large datasets with multiple activities performed on multiple devices by the same person are non-existent. The difficulties of procuring devices, participants, designing protocol, secure storage and on-field hindrances may have contributed to this scarcity. The availability of such a dataset is crucial to forward the research in behavioral biometrics as usage of multiple devices by a person is common nowadays. Through this paper, we share our dataset, the details of its collection, features for each modality and our findings of how keystroke features vary across devices. We have collected data from 117 subjects for typing (both fixed and free text), gait (walking, upstairs and downstairs) and touch on Desktop, Tablet and Phone. The dataset consists a total of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; 1.7 million data-points for swipes; and enables future research to explore previously unexplored directions in inter-device and inter-modality biometrics. Our analysis on keystrokes reveals that in most cases, keyhold times are smaller but inter-key latencies are larger, on hand-held devices when compared to desktop. We also present; detailed comparison with related datasets; possible research directions with the dataset; and lessons learnt from the data collection.

Ortega Hypothesis considers masses, i.e., a large number of average people who are not specially qualified as being instrumental in any system's progress. This hypothesis has been reasonably examined in the scientific domain where it has been supported by a few works while refuted by many others, resulting in no clear consensus. While the hypothesis has only been explored in the scientific domain so far, it has hardly been examined in other fields. Given the large-scale collaboration facilitated by the modern Q&A portals where a crowd with a diverse skill-set contributes, an investigation of this hypothesis becomes necessary for informed policy-making. In this work, we investigate the research question inspired by Ortega Hypothesis in StackOverflow where we examine the contribution made by masses and check whether the system may continue to function well even in their absence. The results point towards the importance of masses in Q&A portals for the little but useful contribution that they provide. The insights obtained from the study may help in devising informed incentivization policies enabling better utilization of the potential of the users., Comment: 11 pages, 4 figures, 3 tables

In collaborative knowledge building settings, the existing knowledge in the system is perceived to set stage for the manifestation of more knowledge, termed as the phenomenon of triggering. Although the literature points to a few theories supporting the existence of this phenomenon, these have never been validated in real collaborative environments, thus questioning their general prevalence. In this work, we provide a mechanized way to observe the presence of triggering in knowledge building environments. We implement the method on the most-edited articles of Wikipedia and show how the existing factoids lead to the inclusion of more factoids in these articles. The proposed technique may further be used in other collaborative knowledge building settings as well. The insights obtained from the study will help the portal designers to build portals enabling optimal triggering., Comment: 10 pages, 5 figures, 4 tables (A preliminary version of this paper is available in the proceedings of OpenSym 2018), "Characterizing the Triggering Phenomenon in Wikipedia." Proceedings of the 14th International Symposium on Open Collaboration. ACM, 2018

For network scientists, it has always been an interesting problem to identify the influential nodes in a given network. The k-shell decomposition method is a widely used method which assigns a shell-index value to each node based on its influential power. The k-shell method requires the global information of the network to compute the shell-index of a node that is infeasible for large-scale real-world dynamic networks. In this work, we propose a method to estimate the shell-index of a node using its local information. We also propose hill-climbing based approach to hit the top-ranked nodes in a small number of steps. We further discuss a method to estimate the rank of a node based on the proposed estimator., Comment: The preliminary version of this paper is submitted in ASONAM 2018(Ph.D. Forum track) and it is under review