1,720,955 research outputs found
Explaining Cryptocurrency Market Trends: A Deep Learning and SHAP-Based Approach
Full text linkCryptocurrency markets are highly volatile, driven by rapidly shifting factors such as social media sentiment, trading volume, and macroeconomic signals. While deep learning (DL) models offer strong predictive performance, their black-box nature limits their adoption in decision-critical environments like crypto investing (Yang et al., 2023). Existing research primarily focuses on improving prediction accuracy but neglects the interpretability of model outputs (Basu et al., 2023). As a result, traders and institutional investors lack visibility into the rationale behind forecasts, reducing trust and limiting regulatory alignment. There is a need for frameworks that combine predictive accuracy with transparent explanations of what drives market behavior. This study draws on bounded rationality theory and behavioral finance, emphasizing that investor decisions depend on simplified cues and trust in model transparency. The integration of SHapley Additive exPlanations (SHAP) into DL predictions can enhance interpretability and guide more informed decision-making. We propose a conceptual framework that integrates DL models— such as LSTM and BERT—with SHAP to predict and explain cryptocurrency price trends(Bauer et al., 2023). The research explores: Which market indicators (e.g., volume, sentiment, volatility) most influence model predictions? How can SHAP enhance transparency in DL-based trading tools? The study is currently conceptual. We aim to build a DL pipeline using historical crypto price data and social sentiment, followed by SHAP-based feature importance analysis. Future validation will involve simulation-based back testing and stakeholder interviews. This research contributes to the growing literature on explainable AI in finance by demonstrating how SHAP can bridge the gap between accuracy and transparency in crypto prediction. Practically, it informs the development of interpretable AI tools for institutional investors, traders, and regulators navigating high-risk digital asset environments
The Cyber-Storm: NLP Adoption and the Escalating Risk of Cyberattacks
Full text linkAbstract Cyberattacks pose a significant risk, causing losses for organizations, and remain a major concern for stakeholders. The rapid advancement of artificial intelligence has driven organizations to adopt technologies such as Natural Language Processing (NLP) systems, often without fully understanding the associated security trade-offs. While NLP systems offer significant capabilities, they also introduce technological complexity, expand attack surfaces, and are prone to adversarial inputs. This study aims to conduct a pre- and post-NLP adoption analysis using firm-level data to examine whether NLP implementation leads to increased cyberattack risk. It further investigates how organizational and environmental factors moderate this relationship. By addressing these gaps, the study contributes to the cybersecurity and technology adoption literature and offers practical insights for balancing AI innovation with security resilience. Keywords NLP, AI, cyberattack, technological complexity, adversarial threats
Prompt-Tuned Cross-Level Transformer for Behavioral Risk Detection: A Deep Learning Multi-Granular Approach to Mass Shooting Threat Prediction
Full text linkMass shootings in educational environments represent a growing public safety crisis, as traditional security strategies have proven insufficient for early prevention. Although many perpetrators exhibit early warning signs through their online behavior, these signals often go undetected. This research introduces a novel deep learning framework for the proactive detection of mass shooting threats by analyzing social media content in real time. The model is built around a prompt-tuned cross-level transformer architecture, enhanced with structured behavioral lexicons related to violence, radicalization, and psychological distress. The proposed system integrates multi-granular threat analysis, simultaneously capturing signals at the post level (linguistic cues and sentiment patterns) and the user level (temporal posting behavior and aggregated risk). Prompt-tuning is used to adapt the transformer to subtle behavioral contexts with minimal supervision, while domain-specific lexical knowledge dynamically influences the model’s attention and interpretability. This multi-layered design allows the system to prioritize high-risk language, understand intent, and adapt to varying linguistic expressions of violence. The dataset for this research will be collected from publicly accessible sources on Reddit and YouTube comment threads on videos discussing mass shootings or violent ideologies. These platforms are known to contain real-world behavioral signals, including linguistic cues of violent intent, emotional distress, and radicalization. All data will be anonymized and processed in accordance with ethical research standards. The framework is grounded in General Strain Theory, which links emotional distress and perceived injustice to deviant behavior, and Routine Activity Theory, which contextualizes online behavior within the conditions that enable harmful actions. These theories support the model’s design, enabling it to move beyond surface-level text mining toward behaviorally-informed threat modeling. By training on annotated social media data and evaluating against traditional classifiers and deep learning baselines, the model is assessed for its precision, recall, F1-score, and false positive rate ,with special focus on real-world deployability. This study introduces an innovative and ethically designed AI framework that shifts school violence prevention from a reactive to a proactive strategy. It presents a prompt-tuned, cross-level transformer model capable of analyzing both individual posts and user behavior over time. The system uniquely integrates a domain-specific behavioral lexicon to enhance interpretability and threat detection accuracy. Moreover, it introduces new contextual variables—such as escalation patterns and term clustering—not commonly explored in prior research. Using real-world data from Reddit and YouTube, the model demonstrates how deep learning can power real-time, scalable early warning systems for public safety
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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