11 research outputs found
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SafeVchat: Detecting Obscene Content and Misbehaving Users in Online Video Chat Services ; CU-CS-1077-11
Melatonin interaction with abscisic acid in the regulation of abiotic stress in Solanaceae family plants
Solanaceous vegetable crops are cultivated and consumed worldwide. However, they often confront diverse abiotic stresses that significantly impair their growth, yield, and overall quality. This review delves into melatonin and abscisic acid (ABA) biosynthesis and their roles in abiotic stress responses. It closely examines the intricate interplay between melatonin and ABA in managing stress within plants, revealing both collaborative and antagonistic effects and elucidating the underlying molecular mechanisms. Melatonin and ABA mutually influence each otherās synthesis, metabolism and that of other plant hormones, a key focus of this study. The study highlights melatoninās role in aiding stress management through ABA-dependent pathways and key genes in the melatonin-ABA interaction. Specifically, melatonin downregulates ABA synthesis genes and upregulates catabolism genes, leading to reduced ABA levels. It also directly scavenges H2O2, enhancing antioxidant enzyme activities, thereby underscoring their collaborative role in mediating stress responses. Moreover, the interplay between melatonin and ABA plays an essential role in multiple physiological processes of plants, including stomatal behaviors, wax accumulation, delay leaf senescence, seed germination, and seedlings growth, among others. Recognizing these relationships in Solanaceae vegetable crops holds great importance for improving agricultural practices and crop quality. In summary, this review offers a comprehensive overview of recent studies on the melatoninĀ and ABA interplay, serving as a valuable resource for researchers and breeders dedicated to fortifying crop resilience and productivity within challenging environments
Fighting against misbehaving users on online social networks
Along with the huge popularity of online social networks (OSNs), an increasing number of misbehaving users abuse OSNs by disseminating malicious spam content as well as sexually explicit content. The prevalence of misbehaving users is a critical threat to the members of OSNs, particularly for underage children with little knowledge about online safety. The overarching theme of this thesis is to analyze the fundamental weaknesses of misbehaving users and design practical methods to fight against misbehaving users on OSNs. This thesis presents effective methods from three different dimensions to protect OSN users from malicious spam as well as X-rated content disseminated by misbehaving users.The contribution of the first part of this thesis is the design of an effective method for detecting for-profit spam messages on one of the most popular OSNs around the world, Twitter, in order to undermine the economic incentives of misbehaving users who are responsible for the massive amounts of spam messages on Twitter (i.e., spammers). I proposed to detect for-profit spam messages by exploiting the differences in profit strategy between spammers and other users. Typically, spammers strive to maximize the profits of spam messages by (1) reducing the expenses on associated key support resources (i.e., accounts used for posting spam messages and host infrastructures for supporting spam URLs in for-profit spam messages), and (2) increasing revenues via engaging with as many users as possible. In contrast, other users are either not driven by profits or avoid using the support resources that are generally only used by spammers. Moreover, I designed novel features for quantifying the prices of key support resources and the potential revenues associated with for-profit messages. Based on the prices of key support resources and potential revenues associated with for-profit messages, I further designed an effective profit based spam detector that can accurately detect for-profit spam messages on Twitter.The contribution of the second part of this thesis is the design of an effective method for identifying X-rated Twitter accounts that are created for promoting sexually explicit content on Twitter. I proposed to exploit the implicit collective wisdom behind social connections and entities (i.e., URLs, hashtags and mentions) in Twitter messages for X-rated Twitter account detection, and constructed a link-entity graph to represent the implicit collective wisdom. As X-rated accounts are mostly connected with normal accounts and post many normal entities, the link-entity graph is full of noisy links connecting nodes with different labels. This makes existing graph based classification techniques built based on the pairwise similarity assumption (i.e., linked nodes should share similar labels) ineffective. I proposed an iterative social based classifier (ISC) that is resistant to the noisy links. Evaluations using large-scale real-world Twitter data demonstrate the efficacy of ISC.The contribution of the third part of this thesis is the design of a novel method that can accurately and quickly identify misbehaving users who broadcast inappropriate videos in online video chat services. I proposed to exploit the contextual information, including the orientation of web cameras and the environment contexts. I designed effective features to capture the contextual information to significantly improve the detection accuracy. Furthermore, I proposed a novel Fine-Grained Cascaded (FGC) classification approach that systematically evaluates the classification capacities of different features and automatically generates the optimal feature extraction order to allow for earlier classification with partial features. Evaluation results using real- world data from Chatroulette, one of the largest online video chat service providers, which randomly pair strangers for video based chatting, demonstrate the high classification accuracy and speed of FGC.Avec lāimmense populariteĢ des reĢseaux sociaux en ligne (RS), un nombre croissant dāutilisateurs ayant une mauvaise conduite abuse des RS en diffusant des polluriels malveillants ainsi que du contenu sexuellement explicite. La preĢvalence de la mauvaise conduite des utilisateurs est une grave menace pour les utilisateurs des RS, en particulier pour les enfants ayant peu de connaissances sur la seĢcuriteĢ en ligne. Le theĢme principal de cette theĢse est dāanalyser les faiblesses fondamentales des mauvais comportements des utilisateurs et la creĢation de meĢthodes pratiques pour lutter contre les utilisateurs probleĢmatiques sur les reĢseaux sociaux. La contribution de la premieĢre partie de cette theĢse est la conception dāune meĢthode efficace pour deĢtecter les messages non solliciteĢs aĢ but lucratif sur lāun des RS les plus populaires aĢ travers le monde, Twitter, afin de saper les incitations eĢconomiques des utilisateurs suspects qui sont responsables de la quantiteĢ massive de polluriels sur Twitter (c.-aĢ-d. les polluposteurs). Jāai proposeĢ de deĢtecter les messages aĢ but lucratif en exploitant les diffeĢrences dans la strateĢgie de profit entre les polluposteurs et les autres utilisateurs. En outre, jāai concu de nouvelles fonctions afin de quantifier les prix des ressources de soutien cleĢs et les revenus potentiels associeĢs aux messages aĢ but lucratif. En māappuyant sur les prix des ressources de soutien cleĢs et les revenus potentiels associeĢs aux messages aĢ but lucratif, jāai ensuite concu un deĢtecteur efficace de polluriels baseĢ sur les profits pouvant deĢtecter avec preĢcision les messages aĢ but lucratif mails sur Twitter. La contribution de la deuxieĢme partie de cette theĢse est la conception dāune meĢthode efficace pour identifier les comptes Twitter dāadultes qui sont creĢeĢs pour la promotion du contenu sexuellement explicite sur Twitter. Jāai proposeĢ dāexploiter la sagesse collective implicite derrieĢre les liens sociaux et les entiteĢs dans les messages Twitter pour la deĢtection des comptes Twitter des adultes, et construit un graphique lien-entiteĢ chargeĢ de repreĢsenter la sagesse collective implicite. Comme les comptes adultes sont pour la plupart lieĢs aĢ des comptes normaux et publient de nombreuses entiteĢs normales, le graphique lienentiteĢ est plein de liens bruyants (noisy links) reliant les noeuds avec des eĢtiquettes diffeĢrentes. Cela rend les techniques de classification baseĢes sur le graphique existant construites sur la base de lāhypotheĢse de similariteĢ par paires inefficace. Jāai proposeĢ un classificateur iteĢratif social baseĢ (ISC) qui reĢsiste aux liens bruyants. Les eĢvaluations utilisant des donneĢes aĢ grande eĢchelle dans le monde reĢel Twitter deĢmontrent lāefficaciteĢ du ISC. La contribution de la troisieĢme partie de cette theĢse est la conception dāune meĢthode effi- cace pour identifier les utilisateurs suspects qui diffusent des videĢos inapproprieĢes dans les services de clavardage videĢo en ligne. Jāai proposeĢ dāexploiter les informations contextuelles, y compris lāorientation des cameĢras Web et les contextes de lāenvironnement. Jāai concu des fonctionnaliteĢs efficaces pour capturer les informations contextuelles afin dāameĢliorer de manieĢre significative la preĢcision de deĢtection. En outre, Jāai proposeĢ une approche nouvelle de classification par grain fin en cascade (FGC) qui eĢvalue sys- teĢmatiquement les capaciteĢs de classification des fonctionnaliteĢs diffeĢrentes et geĢneĢre automatiquement la fonctionnaliteĢ dāextraction optimale pour permettre la classification anteĢrieure avec des fonctionnaliteĢs partielles. Les reĢsultats de lāeĢvaluation aĢ lāaide de donneĢes du monde reĢel de Chatroulette, un des plus grands fournisseurs de services de clavardage videĢo en ligne qui apparient au hasard des eĢtrangers pour la videĢo baseĢe sur le clavardage, deĢmontrent lāefficaciteĢ et lāefficaciteĢ du FGC