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This paper examines the dynamic relationship between electoral polls and indicators of economic and financial uncertainty during the last two U.S. presidential elections (2020 and 2024). Using daily polling data on Donald Trump and measures such as the Aruoba-Diebold-Scotti Business Conditions Index, the 5-year Breakeven Inflation Rate, the Trade Policy Uncertainty index, and the VIX, we estimate conditional correlation models to capture time-varying interactions. The analysis reveals that in 2020, correlations between polls and uncertainty measures were highly dynamic and event-driven, reflecting the influence of exogenous shocks (COVID-19, oil price collapse) and political milestones (primaries, debates). In contrast, during the 2024 campaign, correlations remained close to zero, stable, and largely unresponsive to shocks, suggesting that entrenched polarization and non-economic events (e.g., assassination attempt, candidate changes) muted the economic channel. The study highlights how the interplay between voter sentiment, financial markets, and uncertainty varies across electoral contexts, offering a methodological contribution through the application of Dynamic Conditional Cor

Women's safety and security are paramount for a modern society. Crimes against women occur in daylight as well as in low-light conditions. Often, such events are captured through real-world surveillance cameras that operate at lower resolutions. Despite substantial progress in CV-related research, video anomaly detection (VAD) focused on women's safety has not yet been adequately addressed. Existing video anomaly datasets contain well-lit, high-resolution, close-shot videos, and fail to represent women-centric anomalies such as chain snatching, stalking, inappropriate touch, and other subtle forms of crime against women. To address these problems, we propose the ExtrAnom dataset, a new multi-modal benchmark containing 1001 videos with textual descriptions, 500 normal and 501 anomalous, classified into 5 different types of women-centric crimes. The dataset comprises low-light (8%), low-resolution videos (13%), long-shot (15%), along with daylight (64%) anomalous videos. And it covers anomalous events like stalking (3.9%), chain snatching (17.6%), kidnapping (7.3%), assassinations (2.3%), harassment (18.9%), and normal (50%). Each video is supplemented with 4 textual annotations, inc

Using transaction-level matched trades from Polymarket's 2024 U.S. presidential election market, we study how traders and prices respond to three precisely timed political shocks: the first Biden-Trump debate, the assassination attempt on Trump, and Biden's withdrawal. We find that trading rises after each event, with new entry at the assassination and withdrawal and, among incumbents, a response concentrated on already-active traders and those whose pre-event portfolios receive material event-time gains. We also show that the three shocks produce different Trump-price paths, depending on whether the news moves Biden and Harris together against Trump or reallocates probability between them. Biden's withdrawal generates the most trading yet the smallest Trump-price move because it shifts probability from Biden to Harris after weeks of market anticipation, and the linked candidate prices show that the main repricing runs from Biden to Harris. Finally, the debate's initial price move reverses while the assassination's persists, a difference we trace to transitory and permanent price impact, respectively.

On July 13, 2024, at the Trump rally in Pennsylvania, someone attempted to assassinate Republican Presidential Candidate Donald Trump. This attempt sparked a large-scale discussion on social media. We collected posts from X (formerly known as Twitter) one week before and after the assassination attempt and aimed to model the short-term effects of such a ``shock'' on public opinions and discussion topics. Specifically, our study addresses three key questions: first, we investigate how public sentiment toward Donald Trump shifts over time and across regions (RQ1) and examine whether the assassination attempt itself significantly affects public attitudes, independent of the existing political alignments (RQ2). Finally, we explore the major themes in online conversations before and after the crisis, illustrating how discussion topics evolved in response to this politically charged event (RQ3). By integrating large language model-based sentiment analysis, difference-in-differences modeling, and topic modeling techniques, we find that following the attempt the public response was broadly sympathetic to Trump rather than polarizing, despite baseline ideological and regional disparities.

The deployment of traditional deep learning models in high-risk security tasks in an unlabeled, data-non-exploitable video intelligence environment faces significant challenges. In this paper, we propose a lightweight anomaly detection framework based on color features for surveillance video clips in a high sensitivity tactical mission, aiming to quickly identify and interpret potential threat events under resource-constrained and data-sensitive conditions. The method fuses unsupervised KMeans clustering with RGB channel histogram modeling to achieve composite detection of structural anomalies and color mutation signals in key frames. The experiment takes an operation surveillance video occurring in an African country as a research sample, and successfully identifies multiple highly anomalous frames related to high-energy light sources, target presence, and reflective interference under the condition of no access to the original data. The results show that this method can be effectively used for tactical assassination warning, suspicious object screening and environmental drastic change monitoring with strong deployability and tactical interpretation value. The study emphasizes the

Business Email Compromise (BEC) is a high-impact social engineering threat with extreme operational asymmetry: false negatives can trigger large financial losses, while false positives primarily incur investigation and delay costs. This paper compares two BEC detection paradigms under a cost-sensitive decision framework: (i) a semantic transformer approach (DistilBERT) for contextual language understanding, and (ii) a forensic psycholinguistic approach (CatBoost) using engineered linguistic and structural cues. We evaluate both on a hybrid dataset (N = 7,990) combining legitimate corporate email and AI-synthesised adversarial fraud generated across 30 BEC taxonomies, including character-level Unicode obfuscations. We add classical baselines (TF-IDF+LogReg and character n-gram+Linear SVM), an ablation study for the Smiling Assassin Score, and a homoglyph-map sensitivity analysis. DistilBERT achieves AUC = 1.0000 and F1 = 0.9981 at 7.403 ms per email on GPU; CatBoost achieves AUC = 0.9860 and F1 = 0.9382 at 0.855 ms on CPU. A three-way cost-sensitive decision policy (auto-allow, auto-block, manual review) optimises expected financial loss under a 1:5,167 false-negative-to-false-posit

Sentiment analysis of textual content has become a well-established solution for analyzing social media data. However, with the rise of images and videos as primary modes of expression, more information on social media is conveyed visually. Among these, facial expressions serve as one of the most direct indicators of emotional content in images. This study analyzes a dataset of Instagram posts related to the 2024 U.S. presidential election, spanning April 5, 2024, to August 9, 2024, to compare the relationship between textual and facial sentiment. Our findings reveal that facial expressions align with text sentiment, where positive sentiment aligns with happiness, although neutral and negative facial expressions provide critical information beyond negative valence. Furthermore, during politically significant events such as Donald Trump's conviction and assassination attempt, posts depicting Trump showed a 12% increase in negative sentiment. Crucially, Democrats use their opponent's fear to depict weakness, whereas Republicans use their candidate's anger to depict resilience. Our research highlights the potential of integrating facial expression analysis with textual sentiment analy

Mexico has experienced a notable surge in assassinations of political candidates and mayors. This article argues that these killings are largely driven by organized crime, aiming to influence candidate selection, control local governments for rent-seeking, and retaliate against government crackdowns. Using a new dataset of political assassinations in Mexico from 2000 to 2021 and instrumental variables, we address endogeneity concerns in the location and timing of government crackdowns. Our instruments include historical Chinese immigration patterns linked to opium cultivation in Mexico, local corn prices, and U.S. illicit drug prices. The findings reveal that candidates in municipalities near oil pipelines face an increased risk of assassination due to drug trafficking organizations expanding into oil theft, particularly during elections and fuel price hikes. Government arrests or killings of organized crime members trigger retaliatory violence, further endangering incumbent mayors. This political violence has a negligible impact on voter turnout, as it targets politicians rather than voters. However, voter turnout increases in areas where authorities disrupt drug smuggling, raisin

In developed nations assassinations are rare and thus the impact of such acts on the electoral and political landscape is understudied. In this paper, we focus on Twitter data to examine the effects of Japan's former Primer Minister Abe's assassination on the Japanese House of Councillors elections in 2022. We utilize sentiment analysis and emotion detection together with topic modeling on over 2 million tweets and compare them against tweets during previous election cycles. Our findings indicate that Twitter sentiments were negatively impacted by the event in the short term and that social media attention span has shortened. We also discuss how "necropolitics" affected the outcome of the elections in favor of the deceased's party meaning that there seems to have been an effect of Abe's death on the election outcome though the findings warrant further investigation for conclusive results.

Personal item tracking devices are popular for locating lost items such as keys, wallets, and suitcases. Originally created to help users find personal items quickly, these devices are now being abused by stalkers and domestic abusers to track their victims' location over time. Some device manufacturers created `anti-stalking features' in response, and later improved on them after criticism that they were insufficient. We analyse the effectiveness of the anti-stalking features with five brands of tracking devices through a gamified naturalistic quasi-experiment in collaboration with the Assassins' Guild student society. Despite participants knowing they might be tracked, and being incentivised to detect and remove the tracker, the anti-stalking features were not useful and were rarely used. We also identify additional issues with feature availability, usability, and effectiveness. These failures combined imply a need to greatly improve the presence of anti-stalking features to prevent trackers being abused.

The Resistance: Avalon is a partially observable social deduction game. This area of AI game playing is fairly undeveloped. Implementing an AI for this game involves multiple components specific to each phase as well as role in the game. In this paper, we plan to iteratively develop the required components for each role/phase by first addressing the Assassination phase which can be modeled as a machine learning problem. Using a publicly available dataset from an online version of the game, we train classifiers that emulate an Assassin. After trying various classification techniques, we are able to achieve above average human performance using a simple linear support vector classifier. The eventual goal of this project is to pursue developing an intelligent and complete Avalon player that can play through each phase of the game as any role.

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