Subtle Manipulation Research Articles

Deep learning-based high-frequency jump test for detecting stock market manipulation: evidence from China’s securities market

PurposeThis study aims to tackle the critical issue of detecting stock market manipulation, which undermines the integrity and stability of financial markets globally. Even enhanced with machine learning, traditional statistical methods often struggle to analyze high-frequency trading data effectively due to inherent noise and the limited availability of publicly known manipulation cases. This leads to poor model generalization and a tendency toward over-fitting. Focusing on China's securities market, our study introduces an innovative approach that employs deep learning-based high-frequency jump tests to overcome these challenges and to develop a more effective method for identifying manipulative activities.Design/methodology/approachWe employed the “Jump Variation – Time-of-Day” (JV-TOD) non-parametric technique for jump tests on high-frequency data, coupled with the synthetic minority over-sampling technique (SMOTE) algorithm for re-balancing sample data. Our approach trains a deep neural network (DNN) on refined data to enhance its ability to identify manipulation patterns accurately.FindingsOur results show that the deep neural network model, calibrated with high-frequency price jump data, identifies manipulation behavior more specifically and accurately than traditional models. The model achieved an accuracy rate of 94.64%, an F1-score of 95.26% and a recall rate of 95.88%, significantly outperforming traditional models. These results demonstrate the effectiveness of our approach in mitigating over-fitting and improving the robustness of market manipulation detection.Practical implicationsThe proposed model provides regulatory entities and financial institutions with a more efficient tool to monitor and counteract market manipulation, thereby improving market fairness and investor protection.Originality/valueBy integrating the JV-TOD jump test with deep learning, this study proposed a new approach to market manipulation detection. The innovation is in its capacity to detect subtle manipulation signals that traditional methods typically overlook. Our model, which is trained on jump test data enhanced by the SMOTE algorithm, excels at learning complex manipulation patterns. This enhances both detection accuracy and robustness. In contrast to existing methods that are challenged by the noisy and intricate nature of high-frequency data, our approach shows enhanced performance in identifying nuanced market manipulations, offering a more effective and reliable method for detecting market manipulation.

Metaphor as an Ideological-Conveying Process in Panegyrics: The Ifẹ̀ and Ò̩yó̩ Ethnic Groups of Yorùbá in Perspective

Panegyrics, as a genre of oral literature, play a significant role in the social, historical and political systems of the Yorùbá people. Through the recitation of praise songs, panegyric reciters often seek to create awareness about the hegemonic status of the individuals or entities being praised. However, the ideologies expressed through the use of metaphor in these panegyrics are usually subtle and covert, thereby enabling the subtle manipulation of the praised individual(s). To uncover the subtle ways metaphors are constructed in panegyrics to serve various purposes, including the maintenance of unequal power relations, this paper examines the use of metaphor as a strategic linguistic device for conveying ideological messages in the panegyrics of the Ifẹ̀ and Ò̩yó̩ ethnic groups within the broader Yorùbá cultural context. It therefore evaluates how metaphor is employed in Yorùbá panegyrics to communicate ideological content in Ifẹ̀ and Ò̩yó̩ panegyrics. By shedding light on the covert ways metaphors are constructed in the panegyrics, the paper offers insights into how language can be strategically employed to maintain and perpetuate unequal power structures within a society. It also highlights the versatility of metaphor as a rhetorical device, capable of conveying complex ideological messages in a subtle and aesthetically pleasing manner, as evident in the Yorùbá panegyric genre. Keywords: Metaphor, Discourse, Critical Discourse Analysis, Ideology and Panegyrics

Endless Rondo or “Variations on a Theme of Phil Connors”? Musical Form, Function, and Leitmotivic Design in Groundhog Day

Abstract This article addresses music's influence on the dramatic and formal discourse in the film Groundhog Day. Suggesting that the film's ludic and narrative traits are further exhibited through its combination of both musical rondo and variation form, I address Groundhog Day through the lens of musical form, discussing its effect on Phil's transformational experience of time as Chronos to time as Kairos, which ultimately helps him break the time loop. I then demonstrate music's multiple diegetic and nondiegetic functions within the film: (1) as musical commentary; (2) as a conduit between God and Phil; and (3) as leitmotif through composer George Fenton's subtle manipulation of the Eighteenth Variation of Rachmaninov's Rhapsody on a Theme of Paganini. I conclude with a semiotic reading of the film's banquet scene, demonstrating how music's diegetic and nondiegetic interaction with the film and its form elevates the discourse surrounding Phil and the film's musico-cinematic form to a higher degree of dignity.

Optimizing feature extraction for tampering image detection using deep learning approaches

Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.

The impact of intermediate option availability on consumers' choice: The moderating role of price roundness

Given the surge in consumer demand and rapid inflation, businesses have turned their attention to premiumization as a means of market segmentation. Building upon previous studies that highlight the significance of the compromise effect in gaining market share for intermediate options, this research examines the feasibility of revenue management strategies using 'psychological pricing' in hospitality and tourism products. By investigating choice behaviors related to intermediate options, we explore how subtle pricing manipulations can enhance the compromise effect. Through five experiments, we validate our hypotheses. Our findings not only extend the literature on pricing behavior and hospitality concerning rounded pricing but also provide insights on how to strategically adjust psychological pricing to align with real-time market conditions.

Real-Time Deepfake Video Detection Using Eye Movement Analysis with a Hybrid Deep Learning Approach

Deepfake technology uses artificial intelligence to create realistic but false audio, images, and videos. Deepfake technology poses a significant threat to the authenticity of visual content, particularly in live-stream scenarios where the immediacy of detection is crucial. Existing Deepfake detection approaches have limitations and challenges, prompting the need for more robust and accurate solutions. This research proposes an innovative approach: combining eye movement analysis with a hybrid deep learning model to address the need for real-time Deepfake detection. The proposed hybrid deep learning model integrates two deep neural network architectures, MesoNet4 and ResNet101, to leverage their respective architectures’ strengths for effective Deepfake classification. MesoNet4 is a lightweight CNN model designed explicitly to detect subtle manipulations in facial images. At the same time, ResNet101 handles complex visual data and robust feature extraction. Combining the localized feature learning of MesoNet4 with the deeper, more comprehensive feature representations of ResNet101, our robust hybrid model achieves enhanced performance in distinguishing between manipulated and authentic videos, which cannot be conducted with the naked eye or traditional methods. The model is evaluated on diverse datasets, including FaceForensics++, CelebV1, and CelebV2, demonstrating compelling accuracy results, with the hybrid model attaining an accuracy of 0.9873 on FaceForensics++, 0.9689 on CelebV1, and 0.9790 on CelebV2, showcasing its robustness and potential for real-world deployment in content integrity verification and video forensics applications.

Feature fusion Vision Transformers using MLP-Mixer for enhanced deepfake detection

Deepfake technology, utilizing deep learning and computer vision, presents significant security threats by generating highly realistic synthetic media, such as images and videos. In this paper, we propose a feature fusion-based method for deepfake detection, harnessing the capabilities of three Vision Transformer (ViT) architectures: Dual Attention Vision Transformers (DaViT), Inception Transformer (iFormer), and Group Propagation Vision Transformer (GPViT). The proposed feature fusion method integrates the local–global contextual understanding of DaViT, the frequency spectrum broadening of iFormer, and the high-resolution feature retention of GPViT to provide a comprehensive analysis of visual data, crucial for detecting subtle manipulations typical in deepfakes. An MLP-Mixer (Multi-Layer Perceptron-Mixer) model is proposed for the feature fusion process for effective feature integration, enhancing the detection capabilities while preserving the individual strengths of each ViT model. The performance of the proposed method is evaluated using standard datasets, namely FaceForensics++ and Celeb-DF, demonstrating its robustness and generalizability across various scenarios. For the four manipulation techniques in the FaceForensics++ dataset, namely DeepFakes (DF), Face2Face (F2F), FaceSwap (FS), and NeuralTextures (NT), the proposed method achieves area under the curve (AUC) of 99.75%, 97.04%, 98.78%, and 87.49% respectively, Moreover, it demonstrates an AUC of 99.84% on the Celeb-DF dataset. The performance of the proposed model has been evaluated against many state-of-the-art methods to ensure its robustness and generalization capabilities.

Deceived by Immersion: A Systematic Analysis of Deceptive Design in Extended Reality

The well-established deceptive design literature has focused on conventional user interfaces. With the rise of extended reality (XR), understanding deceptive design’s unique manifestations in this immersive domain is crucial. However, existing research lacks a full, cross-disciplinary analysis that analyzes how XR technologies enable new forms of deceptive design. Our study reviews the literature on deceptive design in XR environments. We use thematic synthesis to identify key themes. We found that XR’s immersive capabilities and extensive data collection enable subtle and powerful manipulation strategies. We identified eight themes outlining these strategies and discussed existing countermeasures. Our findings show the unique risks of deceptive design in XR, highlighting implications for researchers, designers, and policymakers. We propose future research directions that explore unintentional deceptive design, data-driven manipulation solutions, user education, and the link between ethical design and policy regulations.

The Grammar of Persuasion: A Meta-Analytic Review Disconfirming the Role of Nouns as Linguistic Cues of Subsequent Behavior

Recent discoveries suggest that nouns, as a word class, exhibit a greater capacity to model real-life behavior. In some previous studies participants prompted with a noun label rather than a semantically corresponding verb ( be a voter vs. vote) were more likely to vote. However, many attempts to replicate the original results have shown unsuccessful. To shed light on the effectiveness of subtle linguistic manipulations, we conducted a meta-analysis of 28 effect sizes ( N = 13,376), being an exact or conceptual replications of original studies. Although the average effect was significant ( g = 0.12), the concurrent diagnostics indicated a low trust in the replicability or stability of the effect. Based on that, we cannot conclude that the greater effectiveness of nouns as cues has been reliably demonstrated. We discuss theoretical prospects involving the verb-action link as well as the self-verification motives.

A Study on Corporate Greenwashing on Manufacturing of Lithium Batteries of Electric Vehicle (EV)

The emergence of electric vehicles (EVs) as a promising solution to environmental concerns has coincided with a troubling phenomenon—greenwashing. This paper explores the deceptive practices employed by some entities in the EV industry to exaggerate their environmental credentials. The primary objective is to shed light on the subtle manipulations and misleading marketing strategies that obscure the true ecological impact of EVs.
 Examining case studies and industry trends, the paper delves into instances where companies emphasize the zero-emission aspect of EVs while overlooking the entire lifecycle's environmental toll, from raw material extraction to production and disposal. The allure of a greener alternative has led to a competitive landscape where distinguishing between authentic sustainability and superficial marketing becomes challenging for consumers.
 Furthermore, the paper delves into the motivations behind greenwashing in the EV sector. It investigates how these practices serve as a market appeal strategy, attracting environmentally conscious consumers, and how they contribute to gaining a competitive edge in an increasingly saturated market. By identifying greenwashing, the paper advocates for consumer protection and encourages corporate accountability, emphasizing the importance of transparent and genuine commitment to sustainability in the rapidly evolving landscape of electric vehicles.

Spin-Selective Memtransistors with Magnetized Graphene.

Spin-polarized bands in pristine and proximity-induced magnetic materials are promising building blocks for future devices. Conceptually new memory, logic, and neuromorphic devices are conceived based on atomically thin magnetic materials and the manipulation of their spin-polarized bands via electrical and optical methods. A critical remaining issue is the direct probe and the optimized use of the magnetic coupling effect in van der Waals heterostructures, which requires further delicate design of atomically thin magnetic materials and devices. Here, a spin-selective memtransistor with magnetized single-layered graphene on a reactive antiferromagnetic material, CrI3, is reported. The spin-dependent hybridization between graphene and CrI3 atomic layers enables the spin-selective bandgap opening in the single-layered graphene and the electric field control of magnetization in a specific CrI3 layer. The microscopic working principle is clarified by the first-principles calculations and theoretical analysis of the transport data. Reliable memtransistor operations (i.e., memory and logic device-combined operations), as well as a spin-selective probe of Landau levels in the magnetized graphene, are achieved by using the subtle manipulation of the magnetic proximity effect via electrical means.

Synthesis of UV-resistant and colorless polyimide films for optoelectrical applications

Due to their excellent mechanical properties and intrinsic flexibility, polyimides (PIs) are promising candidates for optoelectrical applications under harsh conditions such as flexible organic solar cells as well as flexible smart windows, etc. Much progress has been made on their optical transmittance; however, there remain significant concerns about their environmental stability, particularly their UV resistance. Herein, 4 types of colorless polyimides (CPIs) with different molecular structures containing trifluoromethyl, ethers, or fluorenes are carefully designed, and the dependence of their UV resistance on structures is explored systematically. It is found that the introduction of isopropylidene, ethers and fluorenes effectively enhances the UV resistance of CPI and its initial performance (optical transparency, thermal stability, and toughness) simultaneously as a result of the subtle manipulation of the conjugation structures. Specifically, the optimized polyimide film shows decent optical properties (T550 nm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{550{\\rm{nm}}}$$\\end{document} ~ 88%, yellowness index ~3.26), and thermal stability (T5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{5 \\% }$$\\end{document} ~ 503 °C in N2 atmosphere, Tg\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{{\\rm{g}}}$$\\end{document} ~ 312 °C). Moreover, after high-intensity UV irradiation, CPI not only maintains over 90% of mechanical properties but also retains excellent optical properties (T550 nm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{550{\\rm{nm}}}$$\\end{document} ~ 88%) and thermal stability (T5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{5 \\% }$$\\end{document} ~ 506 °C). The design strategy paves the way for enhancing the durability of PIs for energy conversion and electronic applications resisting harsh conditions.

“Qayṣar-i Hindūstān Vīktūriyā”: Negotiating Loyalty in Late Nineteenth-Century Parsi Laudatory Verse

To mark Queen Victoria's jubilee celebrations, many Indian authors composed laudatory literature and music in their vernacular languages. Although these works were often dismissed as “enthusiastic effusions” from poets of dubious ability, they offer intricate examples of the varied meanings the queen's presence had for Indian writers. They illustrate the subtle manipulation of laudatory verse for purposes other than praise; and they frequently offer instances of sophisticated, multilingual intertextuality and reuses of literary traditions of praise (and subversion) in India's precolonial past. This article examines examples of Persianate laudatory writing produced by three Parsi writers in colonial India and demonstrates how these works performed “loyalty” in contested, ambivalent ways.

Unmasking the Techniques of Modern Brainwashing: Understanding Influence in Today's Society

In our contemporary fast-paced society, concerns regarding subtle manipulation tactics employed by various entities, including governments and businesses, have become increasingly prevalent. These concerns are exacerbated by the rise in fraudulent schemes that afflict individuals in areas such as education, health, and finances, contributing to widespread anxiety. The proliferation of smartphones, the internet, and data collection has further heightened the specter of fraud. This research endeavors to address these concerns by examining and categorizing prevalent brainwashing techniques. It aims to empower individuals to recognize and resist such tactics, enhancing their vigilance against potential traps. It is essential to note that the term "brainwashing" is not inherently negative, as various forms of influence, including media, can also be labeled as brainwashing. Therefore, there is a need to mitigate excessive anxiety related to brainwashing and acknowledge that everything in life carries some degree of influence. The study categorizes common brainwashing methods into five key areas: conformity, psychological support, physical support, yielding profits, and physical assault. Each method is analyzed in detail, shedding light on the mechanisms that render individuals susceptible to manipulation. Notably, the method of physical assault, despite being one of the most disturbing and pervasive, remains widely employed in various institutions, including schools and families.

A Subtle Change in the Flexible Achiral Spacer Does Matter in Supramolecular Chirality: Two‐Fold Odd‐Even Effect in Polymer Assemblies

AbstractPrecise control over the chirality and morphologies of polymer assemblies, a remaining challenge for both chemists and materials scientists, is receiving ever‐increasing attention in the recent years. Herein, we report the subtle manipulation of the achiral spacers from the chiral stereocenter to the azobenzene (Azo) unit, of which the chiroptical consistency or chiroptical inversion of self‐assemblies could be successfully controlled and present “two‐fold” odd‐even effect. Furthermore, morphological transitions from 0D spherical micelles, 1D worms, and nanowires to 3D vesicles, spindle‐ and dumbbell‐shaped vesicles were also unexpectedly found to exhibit odd‐even correlations. These observations were collectively elucidated by mesomorphic properties, stacking modes, chiroptical dynamics, and stimuli‐responsive behaviors. Negligible modifications to the spacer structures can enable remarkable modulation of supramolecular chirality and anisotropic topologies in polymer assemblies, which is of great significance for the design of complex chiral functional polymers.

A Subtle Change in the Flexible Achiral Spacer Does Matter in Supramolecular Chirality: Two-Fold Odd-Even Effect in Polymer Assemblies.

Precise control over the chirality and morphologies of polymer assemblies, a remaining challenge for both chemists and materials scientists, is receiving ever-increasing attention in the recent years. Herein, we report the subtle manipulation of the achiral spacers from the chiral stereocenter to the azobenzene (Azo) unit, of which the chiroptical consistency or chiroptical inversion of self-assemblies could be successfully controlled and present "two-fold" odd-even effect. Furthermore, morphological transitions from 0D spherical micelles, 1D worms, and nanowires to 3D vesicles, spindle- and dumbbell-shaped vesicles were also unexpectedly found to exhibit odd-even correlations. These observations were collectively elucidated by mesomorphic properties, stacking modes, chiroptical dynamics, and stimuli-responsive behaviors. Negligible modifications to the spacer structures can enable remarkable modulation of supramolecular chirality and anisotropic topologies in polymer assemblies, which is of great significance for the design of complex chiral functional polymers.

The power of synergy in discourse: Exploring persuasive language in English mass media

Contemporary English-language media discourse exerts a powerful influence over public opinion and attitudes, necessitating an exploration into the nuanced mechanisms of persuasive communication. This study examines the concept of synergistic pragmatic effects, focusing on how the combination of diverse language structures influences the efficacy of persuasive verbal interaction. Using a corpus of British and American newspaper articles and opinion essays, this research adopts a robust functional-linguistic approach. It applies both qualitative and quantitative analyses to dissect language techniques such as metaphor, irony, humor, emotionally charged lexemes, hyperbole, interdiscursive references, and rhetorical questions. The findings highlight the transformative power of expertly utilized synergies in achieving communicative goals and refining message interpretation. The interaction of varied language structures provokes particular cognitive and emotional reactions in audiences, shaping their perception of the presented information. This insight is crucial for discourse producers aiming to resonate with their target audience. Additionally, a heightened awareness of these synergies can make recipients less vulnerable to subtle speech manipulation. The practical implications of this research are most salient in the domain of teaching English as a foreign language for cross-cultural professional communication. By melding elements of functional-linguistic analysis, critical thinking, media literacy, and language proficiency, learners can better identify and counteract speech manipulation in media texts. This study augments the current literature on language's role in media communication, underscoring the need for continued research into the interplay between language, discourse, and persuasion in media.

The connected workplace: Characteristics and social consequences of work surveillance in the age of datification, sensorization, and artificial intelligence

Because of COVID-19 lockdowns, managers and administrators have begun to look for new ways to monitor and control their stranded-at-home workforce. Yet long before the pandemic already, advancements in datification, sensorization, and artificial intelligence have given rise to what we call connected workplace surveillance. At the heart of this new mode of employee monitoring and control is the extension of the scope of data collection beyond what is necessary and reasonable for performance appraisals or managerial oversight. This includes treating an employee’s body as a data source, disrespecting the boundaries between business and private life, or using gathered surveillance information for subtle persuasion, manipulation, and coercion. This article provides a new perspective on control theory, examining the characteristics of connected surveillance and comparing it to visual or computerized surveillance. Taking an employee-centric position, it also proposes a research agenda for critical, behavioral, and design-oriented scholars who wish to explore the identified issues.

Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph

Ensuring the integrity of scientific literature is essential for advancing knowledge and research. However, the credibility and trustworthiness of scholarly publications are compromised by manipulated citations. Traditional methods, such as manual inspection and basic statistical analyses, have limitations in detecting intricate patterns and subtle manipulations of citations. In recent years, network-based approaches have emerged as promising techniques for identifying and understanding citation manipulation. This study introduces a novel method to identify potential citation manipulation in academic papers using perturbations of a deep embedding model. The key idea is to reconstruct meaningful connections represented by citations within a network by exploring, to some extent, longer alternative paths. These indirect pathways enable the recovery of reliable citations while estimating their trustworthiness. The investigation takes a comprehensive approach to link prediction, leveraging the consistent behavior of prominent connections when exposed to network perturbations. Through numerical experiments, the method demonstrates a high capability to identify reliable citations as the core of the analyzed data and to raise suspicions about unreliable references that may have been manipulated. This research presents a refined method for tackling the urgent problem of citation manipulation in academic papers. It harnesses statistical sampling and graph-embedding techniques to evaluate the credibility of scholarly publications with a substantial assessment of the whole citation graph.

Transformer-Auxiliary Neural Networks for Image Manipulation Localization by Operator Inductions

Image manipulation localization (IML), which seeks to accurately segment tampered regions that are artfully fastened into a normal image, is a fundamental yet challenging computer vision task. Despite that impressive results have been achieved by some progressive deep learning methods, they usually fail in capturing the subtle manipulation artifacts at different object scales, which are not competent to generate a perfect segmentation mask with complete and fine object structures. Besides, the problem of coarse boundaries also occurs frequently. To this end, in this paper, we propose a Transformer-Auxiliary by operator-induced neural Network (TANet) to localize forged regions for IML. Specifically, a stacked multi-scale transformer (SMT) branch is first introduced as a compensation for feature representations of the mainstream convolutional neural network branch. SMT can detect structured abnormalities of the input image at multi-levels by operating on patches of different sizes. Then TANet explicitly exploits an operator induction module (OIM) to excavate valuable and manipulated region-related boundary semantics to guide the representative learning of the mainstream branch. The OIM encourages the network to generate features that highlight object structure, thereby promoting precise boundary localization of forged regions. We conduct extensive experiments on various datasets and settings to validate the effectiveness of TANet. Results show that TANet outperforms the state-of-the-art methods by a large margin under widely-used evaluation metrics.