arXiv subjects

Shivakant Mishra

Publications and source records attributed to Shivakant Mishra.

At least 19 recordsLinked to original sources

arXiv2024

The Toxicity Phenomenon Across Social Media

Social media platforms have evolved rapidly in modernity without strong regulation. One clear obstacle faced by current users is that of toxicity. Toxicity on social media manifests through a number of forms, including harassment, negativity, misinformation or other means of divisiveness. In this paper, we characterize literature surrounding toxicity, formalize a definition of toxicity, propose a novel cycle of internet extremism, list current approaches to toxicity detection, outline future directions to minimize toxicity in future social media endeavors, and identify current gaps in research space. We present a novel perspective of the negative impacts of social media platforms and fill a gap in literature to help improve the future of social media platforms.

cs.SI↗

arXiv2024

Container Data Item: An Abstract Datatype for Efficient Container-based Edge Computing

We present Container Data Item (CDI), an abstract datatype that allows multiple containers to efficiently operate on a common data item while preserving their strong security and isolation semantics. Application developers can use CDIs to enable multiple containers to operate on the same data, synchronize execution among themselves, and control the ownership of the shared data item during runtime. These containers may reside on the same server or different servers. CDI is designed to support microservice based applications comprised of a set of interconnected microservices, each implemented by a separate dedicated container. CDI preserves the important isolation semantics of containers by ensuring that exactly one container owns a CDI object at any instant and the ownership of a CDI object may be transferred from one container to another only by the current CDI object owner. We present three different implementations of CDI that allow different containers residing on the same server as well containers residing on different servers to use CDI for efficiently operating on a common data item. The paper provides an extensive performance evaluation of CDI along with two representative applications, an augmented reality application and a decentralized workflow orchestrator.

cs.DC↗

arXiv2024

PureConnect: A Localized Social Media System to Increase Awareness and Connectedness in Environmental Justice Communities

Frequent disruptions like highway constructions are common now-a-days, often impacting environmental justice communities (communities with low socio-economic status with disproportionately high and adverse human health and environmental effects) that live nearby. Based on our interactions via focus groups with the members of four environmental justice communities impacted by a major highway construction, a common concern is a sense of uncertainty about project activities and loss of social connectedness, leading to increased stress, depression, anxiety and diminished well-being. This paper addresses this concern by developing a localized social media system called PureConnect with a goal to raise the level of awareness about the project and increase social connectedness among the community members. PureConnect has been designed using active engagement with four environmental justice communities affected by a major highway construction. It has been deployed in the real world among the members of the four environmental justice communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureConnect is potentially useful in improving community members' well-being and the members appreciate the functionalities it provides.

cs.SI↗

arXiv2024

PureNav: A Personalized Navigation Service for Environmental Justice Communities Impacted by Planned Disruptions

Planned disruptions such as highway constructions are commonplace nowadays and the communities living near these disruptions generally tend to be environmental justice communities -- low socioeconomic status with disproportionately high and adverse human health and environmental effects. A major concern is that such activities negatively impact people's well-being by disrupting their daily commutes via frequent road closures and increased dust and air pollution. This paper addresses this concern by developing a personalized navigation service called PureNav to mitigate the negative impacts of disruptions in daily commutes on people's well-being. PureNav has been designed using active engagement with four environmental justice communities affected by major highway construction. It has been deployed in the real world among the members of the four communities, and a detailed analysis of the data collected from this deployment as well as surveys show that PureNav is potentially useful in improving people's well-being. The paper describes the design, implementation, and evaluation of PureNav, and offers suggestions for further improving its efficacy.

cs.SI↗

arXiv2023

An IoT Architecture Leveraging Digital Twins: Compromised Node Detection Scenario

Modern IoT (Internet of Things) environments with thousands of low-end and diverse IoT nodes with complex interactions among them and often deployed in remote and/or wild locations present some unique challenges that make traditional node compromise detection services less effective. This paper presents the design, implementation and evaluation of a fog-based architecture that utilizes the concept of a digital-twin to detect compromised IoT nodes exhibiting malicious behaviors by either producing erroneous data and/or being used to launch network intrusion attacks to hijack other nodes eventually causing service disruption. By defining a digital twin of an IoT infrastructure at a fog server, the architecture is focused on monitoring relevant information to save energy and storage space. The paper presents a prototype implementation for the architecture utilizing malicious behavior datasets to perform misbehaving node classification. An extensive accuracy and system performance evaluation was conducted based on this prototype. Results show good accuracy and negligible overhead especially when employing deep learning techniques such as MLP (multilayer perceptron).

cs.CR↗

arXiv2023

A Dynamic Distributed Scheduler for Computing on the Edge

Edge computing has become a promising computing paradigm for building IoT (Internet of Things) applications, particularly for applications with specific constraints such as latency or privacy requirements. Due to resource constraints at the edge, it is important to efficiently utilize all available computing resources to satisfy these constraints. A key challenge in utilizing these computing resources is the scheduling of different computing tasks in a dynamically varying, highly hybrid computing environment. This paper described the design, implementation, and evaluation of a distributed scheduler for the edge that constantly monitors the current state of the computing infrastructure and dynamically schedules various computing tasks to ensure that all application constraints are met. This scheduler has been extensively evaluated with real-world AI applications under different scenarios and demonstrates that it outperforms current scheduling approaches in satisfying various application constraints.

cs.DC↗

arXiv2023

A Fog-based Smart Agriculture System to Detect Animal Intrusion

Smart agriculture is one of the most promising areas where IoT-enabled technologies have the potential to substantially improve the quality and quantity of the crops and reduce the associated operational cost. However, building a smart agriculture system presents several challenges, including high latency and bandwidth consumption associated with cloud computing, frequent Internet disconnections in rural areas, and the need to keep costs low for farmers. This paper presents an end-to-end, fog-based smart agriculture infrastructure that incorporates edge computing and LoRa-based communication to address these challenges. Our system is deployed to transform traditional agriculture land of rural areas into smart agriculture. We address the top concern of farmers - animals intruding - by proposing a solution that detects animal intrusion using low-cost PIR sensors, cameras, and computer vision. In particular, we propose three different sensor layouts and a novel algorithm for predicting animals' future locations. Our system can detect animals before they intrude into the field, identify them, predict their future locations, and alert farmers in a timely manner. Our experiments show that the system can effectively and quickly detect animal intrusions while maintaining a much lower cost than current state-of-the-art systems.

eess.SY↗

arXiv2023

Understanding the Impact of Culture in Assessing Helpfulness of Online Reviews

Online reviews have become essential for users to make informed decisions in everyday tasks ranging from planning summer vacations to purchasing groceries and making financial investments. A key problem in using online reviews is the overabundance of online that overwhelms the users. As a result, recommendation systems for providing helpfulness of reviews are being developed. This paper argues that cultural background is an important feature that impacts the nature of a review written by the user, and must be considered as a feature in assessing the helpfulness of online reviews. The paper provides an in-depth study of differences in online reviews written by users from different cultural backgrounds and how incorporating culture as a feature can lead to better review helpfulness recommendations. In particular, we analyze online reviews originating from two distinct cultural spheres, namely Arabic and Western cultures, for two different products, hotels and books. Our analysis demonstrates that the nature of reviews written by users differs based on their cultural backgrounds and that this difference varies based on the specific product being reviewed. Finally, we have developed six different review helpfulness recommendation models that demonstrate that taking culture into account leads to better recommendations.

cs.IR↗

arXiv2022

Deradicalizing YouTube: Characterization, Detection, and Personalization of Religiously Intolerant Arabic Videos

Growing evidence suggests that YouTube's recommendation algorithm plays a role in online radicalization via surfacing extreme content. Radical Islamist groups, in particular, have been profiting from the global appeal of YouTube to disseminate hate and jihadist propaganda. In this quantitative, data-driven study, we investigate the prevalence of religiously intolerant Arabic YouTube videos, the tendency of the platform to recommend such videos, and how these recommendations are affected by demographics and watch history. Based on our deep learning classifier developed to detect hateful videos and a large-scale dataset of over 350K videos, we find that Arabic videos targeting religious minorities are particularly prevalent in search results (30%) and first-level recommendations (21%), and that 15% of overall captured recommendations point to hateful videos. Our personalized audit experiments suggest that gender and religious identity can substantially affect the extent of exposure to hateful content. Our results contribute vital insights into the phenomenon of online radicalization and facilitate curbing online harmful content.

cs.SI↗

arXiv2021

Analyzing Behavioral Changes of Twitter Users After Exposure to Misinformation

Social media platforms have been exploited to disseminate misinformation in recent years. The widespread online misinformation has been shown to affect users' beliefs and is connected to social impact such as polarization. In this work, we focus on misinformation's impact on specific user behavior and aim to understand whether general Twitter users changed their behavior after being exposed to misinformation. We compare the before and after behavior of exposed users to determine whether the frequency of the tweets they posted, or the sentiment of their tweets underwent any significant change. Our results indicate that users overall exhibited statistically significant changes in behavior across some of these metrics. Through language distance analysis, we show that exposed users were already different from baseline users before the exposure. We also study the characteristics of two specific user groups, multi-exposure and extreme change groups, which were potentially highly impacted. Finally, we study if the changes in the behavior of the users after exposure to misinformation tweets vary based on the number of their followers or the number of followers of the tweet authors, and find that their behavioral changes are all similar.

cs.CY↗

arXiv2021

Incorporating Individual and Group Privacy Preferences in the Internet of Things

This paper presents a new privacy negotiation mechanism for an IoT environment that is both efficient and practical to cope with the IoT special need of seamlessness. This mechanism allows IoT users to express and enforce their personal privacy preferences in a seamless manner while interacting with IoT deployments. A key contribution of the paper is that it addresses the privacy concerns of individual users as well as a group of users where privacy preferences of all individual users are combined into a group privacy profile to be negotiated with the IoT owner. In addition, the proposed mechanism satisfies the privacy requirements of the IoT deployment owner. Finally, the proposed privacy mechanism is agnostic to the actual IoT architecture and can be used over a user-managed, edge-managed or a cloud-managed IoT architecture. Prototypes of the proposed mechanism have been implemented for each of these three architectures, and the results show the capability of the protocol to negotiate privacy while adding insignificant time overhead.

cs.CR↗

arXiv2021

Analyzing Twitter Users' Behavior Before and After Contact by the Internet Research Agency

Social media platforms have been exploited to conduct election interference in recent years. In particular, the Russian-backed Internet Research Agency (IRA) has been identified as a key source of misinformation spread on Twitter prior to the 2016 U.S. presidential election. The goal of this research is to understand whether general Twitter users changed their behavior in the year following first contact from an IRA account. We compare the before and after behavior of contacted users to determine whether there were differences in their mean tweet count, the sentiment of their tweets, and the frequency and sentiment of tweets mentioning @realDonaldTrump or @HillaryClinton. Our results indicate that users overall exhibited statistically significant changes in behavior across most of these metrics, and that those users that engaged with the IRA generally showed greater changes in behavior.

cs.CY↗

arXiv2021

Panorama: A Framework to Support Collaborative Context Monitoring on Co-Located Mobile Devices

A key challenge in wide adoption of sophisticated context-aware applications is the requirement of continuous sensing and context computing. This paper presents Panorama, a middleware that identifies collaboration opportunities to offload context computing tasks to nearby mobile devices as well as cloudlets/cloud. At the heart of Panorama is a multi-objective optimizer that takes into account different constraints such as access cost, computation capability, access latency, energy consumption and data privacy, and efficiently computes a collaboration plan optimized simultaneously for different objectives such as minimizing cost, energy and/or execution time. Panorama provides support for discovering nearby devices and cloudlets/cloud, computing an optimal collaboration plan, distributing computation to participating devices, and getting the results back. The paper provides an extensive evaluation of Panorama via two representative context monitoring applications over a set of Android devices and a cloudlet/cloud under different constraints.

cs.DC↗

arXiv2020

An edge-based architecture to support the execution of ambience intelligence tasks using the IoP paradigm

In an IoP environment, edge computing has been proposed to address the problems of resource limitations of edge devices such as smartphones as well as the high-latency, user privacy exposure and network bottleneck that the cloud computing platform solutions incur. This paper presents a context management framework comprised of sensors, mobile devices such as smartphones and an edge server to enable high performance, context-aware computing at the edge. Key features of this architecture include energy-efficient discovery of available sensors and edge services for the client, an automated mechanism for task planning and execution on the edge server, and a dynamic environment where new sensors and services may be added to the framework. A prototype of this architecture has been implemented, and an experimental evaluation using two computer vision tasks as example services is presented. Performance measurement shows that the execution of the example tasks performs quite well and the proposed framework is well suited for an edge-computing environment.

cs.DC↗

arXiv2019

Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity

In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime.

cs.HC↗

arXiv2019

Hateful People or Hateful Bots? Detection and Characterization of Bots Spreading Religious Hatred in Arabic Social Media

Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.

cs.SI↗

arXiv2019

GEVR: An Event Venue Recommendation System for Groups of Mobile Users

In this paper, we present GEVR, the first Group Event Venue Recommendation system that incorporates mobility via individual location traces and context information into a "social-based" group decision model to provide venue recommendations for groups of mobile users. Our study leverages a real-world dataset collected using the OutWithFriendz mobile app for group event planning, which contains 625 users and over 500 group events. We first develop a novel "social-based" group location prediction model, which adaptively applies different group decision strategies to groups with different social relationship strength to aggregate each group member's location preference, to predict where groups will meet. Evaluation results show that our prediction model not only outperforms commonly used and state-of-the-art group decision strategies with over 80% accuracy for predicting groups' final meeting location clusters, but also provides promising qualities in cold-start scenarios. We then integrate our prediction model with the Foursquare Venue Recommendation API to construct an event venue recommendation framework for groups of mobile users. Evaluation results show that GEVR outperforms the comparative models by a significant margin.

cs.HC↗

arXiv2016

Investigating Factors Influencing the Latency of Cyberbullying Detection

Cyberbullying in online social networks has become a critical problem, especially among teenagers who are social networks' prolific users. As a result, researchers have focused on identifying distinguishing features of cyberbullying and developing techniques to automatically detect cyberbullying incidents. While this research has resulted in developing highly accurate classifiers, two key practical issues related to identifying cyberbullying have largely been ignored, namely scalability of cyberbullying detection services and timeliness of raising alerts whenever a cyberbullying incident is suspected. These two issues are the subject of this paper. We propose a multi-stage cyberbullying detection solution that drastically reduces the classification time and the time to raise cyberbullying alerts. The proposed solution is highly scalable, does not sacrifice accuracy for scalability, and is highly responsive in raising alerts. The solution is comprised of three novel components, an initial predictor, a multilevel priority scheduler, and an incremental classification mechanism. We have implemented this solution and utilized data obtained from the Vine online social network to demonstrate the utility of each of these components via a detailed performance evaluation. We show that our complete solution is significantly more scalable and responsive than the current state-of-the-art.

cs.SI↗