Paper assesses authorship verification methods for real-world applicability.
problem Lack of clear criteria for evaluating authorship verification methods.
method Proposes criteria and evaluates 12 existing methods on three corpora.
result Some methods perform well on specific cases but struggle with cross-topic verification.
Paper develops a classification method using matrix-variate t-distributions.
problem Classifying matrix-valued observations with dependence structure.
method Develops an Expectation-Maximization algorithm for discriminant analysis.
result Method shows promise on various datasets.
Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesti…
Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have develo…
New model helps identify suspect footwear from crime scene prints.
problem Identifying a suspect's footwear from crime scene prints among thousands of similar shoes.
method Developed a hierarchical Bayesian model with spatially varying coefficients.
result Improved accuracy and reliability in forensic shoe print analysis.
New algorithm predicts geolocation of fungi samples with high accuracy.
problem Identifying the origin of biological material at crime scenes.
method Ensemble of deep neural network classifiers trained on Voronoi partitions.
result More than half of geolocation errors under 100 kilometers for continental analysis and nearly 90% accuracy for global analysis.
Bayesian neural networks improve reliability in multimedia forensics.
problem Challenges with out-of-distribution data in multimedia authentication.
method Proposes Bayesian neural networks (BNN) for forensic tasks.
result BNNs provide distributions for better reliability and out-of-distribution detection.
Paper proposes a method to locate power grid recordings using ENF sequences.
problem Locating power grid recordings without concurrent power signals.
method Extract ENF sequences from power and audio recordings, develop multi-class SVM model.
result Validation of location authenticity of recordings using ENF sequences.
Recently, image forensics community has paied attention to the research on the design of effective algorithms based on deep learning technology and facts proved that combining the domain knowledge of image forensics and deep learning would achieve more robust and better performance than the traditional schemes. Instead…
Owing to the rapid growth of touchscreen mobile terminals and pen-based interfaces, handwriting-based writer identification systems are attracting increasing attention for personal authentication, digital forensics, and other applications. However, most studies on writer identification have not been satisfying because …
We introduce a Bayesian solution for the problem in forensic speaker recognition, where there may be very little background material for estimating score calibration parameters. We work within the Bayesian paradigm of evidence reporting and develop a principled probabilistic treatment of the problem, which results in a…
Deep learning improves forensic matching of casings.
problem Identifying if two casings came from the same firearm.
method Contrastive neural networks trained on casings dataset.
result Contrastive learning achieved better ROC AUC than CMC.
Graph Convolutional Networks show promise for AML in Bitcoin transactions.
problem Combining safety and financial inclusion in AML for cryptocurrencies.
method Using Graph Convolutional Networks (GCN) for binary classification of illicit transactions.
result GCN outperforms other methods in binary classification of illicit transactions.
Network analysis detects insider trading by flagging coordinated trades.
problem Detecting insider trading due to limited labelled data.
method Data-driven network approach using SEC trade data.
result Algorithm identifies insider trading clusters with high accuracy.
Detects AI-synthesized speech using cepstral and bispectral analysis.
problem Validating the authenticity of speech from AI-generated content.
method Integrates cepstral and bispectral analysis for distinguishing human from AI-synthesized speech.
result Higher-order statistics show less correlation for human speech compared to AI-synthesis, and cepstral analysis reveals unique power components.
System identifies power grid location from media recordings.
problem Identifying the origin of power distribution grid from media recordings.
method Cascaded SVM and pole-matching classifiers for grid identification.
result Cascaded system improves accuracy by 15.57%.
Retrieving indexed documents, not by their topical content but their writing style opens the door for a number of applications in information retrieval (IR). One application is to retrieve textual content of a certain author X, where the queried IR system is provided beforehand with a set of reference texts of X. Autho…
New framework classifies generated images into finer categories.
problem Difficulty in detecting new types of generated images.
method Scalable deep metric learning for multi-class classification.
result Improved detection performance on new types of generated data.
Paper proposes ICCN to learn correlations between text, audio, and video for multimodal sentiment analysis.
problem Improving multimodal sentiment analysis by learning hidden correlations between text and audio/video features.
method Interaction Canonical Correlation Network (ICCN) using deep canonical correlation analysis (DCCA).
result Empirical results confirm the effectiveness of ICCN in capturing useful information from all three views.
Deep learning detects face swapping with high accuracy and uncertainty.
problem Photo-realistic face swapping without consent.
method Deep transfer learning for face swapping detection, human subject rankings for comparison.
result True positive rates >96% with minimal false alarms, uncertainty provided for each prediction.
This paper analyzes text in financial disclosures to improve financial analysis.
problem Insufficient analysis of unstructured text in financial disclosures.
method Reviews and explores methods in computational linguistics and NLP.
result Highlights limitations of sentiment metrics and suggests future research areas.
An evolutionary algorithm separates mixed DNA profiles in forensic genetics.
problem Deconvolving mixed DNA profiles from crime samples.
method Multiple population evolutionary algorithm (MEA) with guided mutation.
result The MEA successfully deconvoluted DNA profiles from crime samples.
Deep learning models outperform classical methods in text classification.
problem Improving text classification accuracy using deep learning.
method Comprehensive review of deep learning models and datasets for text classification.
result Deep learning models outperform classical methods on various text classification tasks.
A novel method extracts topological features from word embeddings for text classification.
problem High dimensional and noisy text representations in natural language processing.
method Persistent homology for topological data analysis on word embeddings.
result Topological features outperform conventional text mining features on long textual documents.
Detects malware-infected clients and malicious domains using transfer learning.
problem Detecting malware-infected computers and malicious web domains from encrypted HTTPS traffic.
method Transfer learning with sluice networks to bootstrap each other's detection models.
result Outperforms known reference models and detects previously unknown malware and domains.
Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk …
New distress dictionary improves bankruptcy prediction from disclosure text.
problem Bankruptcy prediction from financial disclosures.
method Proposes a distress dictionary based on managers' sentences, quantifies linguistic features, and builds predictive models.
result Predictive models based on the distress dictionary outperform existing methods.
Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take …
New formula classifies product reviews into higher and lower ratings based on sentiment analysis.
problem Lack of research on using sentiment analysis for classifying text into ratings.
method Redefined sentiment proportions as a triangle structure to derive variables for classifying text into higher and lower ratings.
result Proved a dependence exists between sentiments and ratings.
Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and…
This study reviews text-based stock market analysis methods.
problem Insufficient analysis of unstructured textual data in stock market predictions.
method Reviews existing literature, covers data types, representation techniques, and analysis methods.
result Identifies open problems and suggests future research directions.
Social media is increasingly used by humans to express their feelings and opinions in the form of short text messages. Detecting sentiments in the text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or mood of a community. Sentiments can be expressed…
This research adapts superpixels for Shapley value computation in DNA profile classification.
problem Efficiently computing Shapley values for large, multidimensional time-series data.
method Adapting the concept of superpixels to streamline Shapley value computation for time-series-like data.
result Realistic, accurate, and fast computation of Shapley values for DNA profile classification.
R package sentometrics analyzes text sentiment for predictions.
problem Unlocking value from textual data using sentiment analysis.
method Optimized textual sentiment indexation with R package sentometrics.
result Forecasted CBOE Volatility Index using text sentiment data.
Deep-learning method estimates bone 3D structure from X-ray images.
problem Estimating bone 3D structure from X-ray images.
method Triplet loss-trained neural network selecting closest 3D bone shape from predefined set.
result Average RMS distance of 1.08 mm between predicted and true shapes.
Paper generates diverse, readable adversarial texts from scratch.
problem Text classification models are easily fooled by adversarial examples.
method Trained a conditional variational autoencoder (VAE) with adversarial loss and utilized GANs to generate consistent adversarial texts.
result Successfully generates adversarial texts with higher success rate and acceptable quality.
New method considers subjectivity in text analysis using 'Room Theory'.
problem Detecting emotions in text considering subjective context.
method Framework Theory, Word2Vec, similarity measure between words.
result Measures relative relevance of emotions for a document.
Developed text classification system for Azerbaijani language.
problem Text clustering problem in Azerbaijani language.
method Machine learning and embedding techniques.
result System successfully categorizes news, product reviews, and more.
Can textual data be compressed intelligently without losing accuracy in evaluating sentiment? In this study, we propose a novel evolutionary compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression), which makes use of Parts-of-Speech tags to compress text in a way that sacrifices minimal classification…
Paper explores applying TDA to text classification, improving model performance.
problem Applying TDA to text classification is challenging due to the complexity of text geometry.
method Used word embeddings and TF-IDF vectors to extract topological features from text.
result Topological features improve classification results, especially in ensemble models.
In this work we investigate tick-by-tick data provided by the TRTH database for several stocks on three different exchanges (Paris - Euronext, London and Frankfurt - Deutsche Börse) and on a 5-year span. We use a simple algorithm that helps the synchronization of the trades and quotes data sources, providing enhancemen…
Study enhances cryptocurrency sentiment analysis using TikTok and Twitter data.
problem Lack of comprehensive sentiment analysis in cryptocurrency markets.
method Multimodal analysis of TikTok and Twitter data using large language models.
result TikTok's video sentiment influences speculative assets and short-term trends.
The paper analyzes LIME for text data and provides theoretical guarantees.
problem LIME's lack of theoretical guarantees in explaining complex models.
method Theoretical analysis of LIME for text data, focusing on decision trees and linear models.
result LIME provides meaningful explanations for simple models like decision trees and linear models.
Over the past decades, statisticians and machine-learning researchers have developed literally thousands of new tools for the reduction of high-dimensional data in order to identify the variables most responsible for a particular trait. These tools have applications in a plethora of settings, including data analysis in…
This paper improves sentiment classification by combining text, audio, and video data using DCCA.
problem Improving sentiment classification accuracy using multi-modal data.
method Deep Canonical Correlation Analysis (DCCA) for combining text, audio, and video embeddings.
result One-Step DCCA outperforms current state-of-the-art in multi-modal embedding learning.
An unsupervised method clusters patient incident reports for content analysis.
problem Lack of methods to extract interpretable content from electronic healthcare records.
method Combines text-embedding with paragraph vectors and graph-theoretical multiscale community detection.
result Extracts high-intrinsic-consistency groups of patient incident reports.
Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several researc…
Cluster analysis is a field of data analysis that extracts underlying patterns in data. One application of cluster analysis is in text-mining, the analysis of large collections of text to find similarities between documents. We used a collection of about 30,000 tweets extracted from Twitter just before the World Cup st…