MSTREAM detects anomalies in multi-aspect data streams.
problem Detecting unusual group anomalies in dynamic multi-aspect data.
method Online streaming multi-aspect anomaly detection framework.
result MSTREAM outperforms state-of-the-art baselines.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,786 papers · 148 categories
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48 results for “aspect detection”
Improved aspect detection from few seed keywords.
problem Fine-grained aspect detection from user reviews is labor-intensive.
method Weakly supervised co-training with student-teacher approach.
result Significant improvement in F1 scores over previous methods.
Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decompositioncs.SI
HiJoD detects misinformation using multiple aspects and outperforms state-of-the-art methods.
problem Detecting misinformation with limited labeled data.
method Hierarchical Joint Decomposition framework combining content, context, and domain features.
result HiJoD achieves F1-scores of 74% and 81% on Twitter and Politifact datasets respectively, and is faster than similar approaches.
This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes. To demonstrate its potential, this model is employed to evaluate the performance of a large set of acoustic events detection systems. This model allows us to expli…
Graph-based semi-supervised learning for aspect term extraction.
problem Aspect term detection and sentiment analysis in reviews.
method Label spreading algorithm on graph sparsified by kNN for efficient classification.
result The approach achieves good results with limited labeled data.
We present a reinforcement learning approach for detecting objects within an image. Our approach performs a step-wise deformation of a bounding box with the goal of tightly framing the object. It uses a hierarchical tree-like representation of predefined region candidates, which the agent can zoom in on. This reduces t…
This review examines techniques for detecting anomalies in time series data.
problem Detecting anomalies in time series data.
method Taxonomy based on main aspects of outlier detection techniques.
result Structured and comprehensive state-of-the-art on outlier detection techniques in time series.
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013,…
The goal of machine learning is to provide solutions which are trained by data or by experience coming from the environment. Many training algorithms exist and some brilliant successes were achieved. But even in structured environments for machine learning (e.g. data mining or board games), most applications beyond the…
The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the dat…
Deep learning improves skin cancer detection.
problem Early detection of skin cancer is challenging due to lack of professionals and instruments.
method Overview of recent deep learning models applied to skin cancer detection.
result Deep learning models enhance skin cancer detection accuracy.
Paper proposes an online anomaly detection method for real-time systems.
problem Rare events endanger profitability, safety, and environmental aspects.
method Online inverse cumulative distribution-based approach with dynamic process limits.
result Eliminates common problems of offline anomaly detectors and provides low-latency detection.
The increasing illegal parking has become more and more serious. Nowadays the methods of detecting illegally parked vehicles are based on background segmentation. However, this method is weakly robust and sensitive to environment. Benefitting from deep learning, this paper proposes a novel illegal vehicle parking detec…
New method tackles anomaly detection in video surveillance using continual learning.
problem Challenges in continual learning for high-dimensional applications like video surveillance.
method Transfer learning and continual learning for online anomaly detection.
result Significantly reduces training complexity and continual learning from recent data.
New algorithm detects changes in high-dimensional data with mean and variance.
problem Challenges in detecting changes in high-dimensional data with mean and variance.
method Complete graph-based approach to detect changes of mean and variance from low to high-dimensional online data.
result The proposed method outperforms existing methods in terms of detection power.
Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is to detect an occurring fall. This information can be used to trigger the necessary assistance in case of injury. This can be done by using either ambient-based sensor…
Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains. One of the most important aspect of network analysis is community detection or network clustering. Until recently, the major focus have been on discovering community structure in single (i.e., monople…
This work bridges outlier and drift detection by comparing inputs to a part of the reference distribution.
problem Monitoring machine learning models to ensure they operate within their validated distribution.
method Comparing a set of inputs to a chosen part of the reference distribution.
result A new approach that bridges outlier detection and drift detection.
Detecting depression early from social media texts.
problem Early diagnosis and prevention of depression.
method Topic analysis and learned confidence scores.
result Achieved good results compared to state of the art.
A new framework detects anomalous inputs to DNNs.
problem Detecting adversarial and out-of-distribution inputs in DNNs.
method Unsupervised meta-algorithm with configurable components based on statistical testing.
result The proposed method effectively detects adversarial and OOD inputs.
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debatescs.LG
Paper proposes using word embeddings to detect trolls in social media debates.
problem Preventing online harassment through rapid detection of offensive posts.
method Word embedding models for identifying fast-changing topics and negative content.
result GloVe model helps in discovering new keywords for trolling detection.
A dataset for detecting online hate speech from YouTube and Reddit comments.
problem Detecting and preventing hate speech on social media platforms.
method Created a dataset with two variants: binary and multi-label, based on YouTube and Reddit comments, using Figure-Eight crowdsourcing platform.
result Demonstrated that even a small amount of labelled data can help detect hate speech occurrences.
A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In …
Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relatively small subgraph in an enormous, potentially uninteresting background. This aspect characterizes network detection as …
Amazon SageMaker Model Monitor detects drift in deployed ML models.
problem Ensuring high performance of ML models in production environments.
method Automatically detects data, concept, bias, and feature attribution drift in real-time.
result Maintains high quality models by providing alerts and corrective actions.
This paper detects function-level obfuscation in binary code using graph-based methods.
problem Detecting and characterizing function-level obfuscation in binary code.
method Graph-based approaches, including GNNs, are compared on various datasets.
result GNNs outperform baselines in function-level obfuscation detection, especially in a 11-class classification task.
We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…
Specific language impairment (SLI) detection pipeline from transcriptions of spontaneous narrativescs.CL
Study detects SLI in children from spontaneous narrative transcripts.
problem Detecting Specific Language Impairment (SLI) in children.
method Three-stage pipeline: feature extraction, dimensionality reduction, and classification.
result 97.13% accuracy in identifying SLI from transcripts.
Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments' recog…
Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fas…
A new score SiNNE improves OAM efficiency and accuracy.
problem Comparing outlier scores across subspaces of different dimensions.
method Introducing SiNNE, a new score independent of subspace dimensionality.
result SiNNE produces better or at least as good results as existing scores and significantly improves runtime.
EnsemFDet detects fraud by solving subproblems on small graphs, scaling up e-commerce fraud detection.
problem Detecting and preventing fraud in e-commerce, especially in large-scale bipartite graphs.
method EnsemFDet uses an ensemble approach to decompose the problem into smaller subproblems, solving them in parallel.
result EnsemFDet is up to 100x faster than state-of-the-art methods while maintaining high accuracy.
Deep learning uses alphabet frequencies to accurately classify fake news.
problem Classifying fake news from trustworthy news.
method Used deep learning algorithms on alphabet frequencies of text without sequence information.
result Achieved high accuracy (85%) in classifying fake news.
Study uses interviews to automatically detect BD and BPD with good accuracy.
problem Challenges in distinguishing BD and BPD from clinical interviews.
method Developed a multi-modal dataset and used a linear classifier with selected features from interviews.
result Different sets of features characterize BD and BPD, providing insights into their differences.
Detection of emerging topics are now receiving renewed interest motivated by the rapid growth of social networks. Conventional term-frequency-based approaches may not be appropriate in this context, because the information exchanged are not only texts but also images, URLs, and videos. We focus on the social aspects of…
Detecting the completeness of a Finsler manifold via potential theory for its infinity Laplacianmath.AP
Study potential theory to detect completeness of Finsler manifolds.
problem Detecting completeness of Finsler manifolds via potential theory.
method Potential theoretic aspects of eikonal and infinity Laplace operator, Liouville properties, maximum principles at infinity, viscosity solutions.
result Forward completeness of Finsler manifolds can be detected using Liouville properties and maximum principles at infinity.
Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.
problem Outlier detection in interval-valued data.
method Proposed a novel approach based on Shapley value for interval-valued data.
result Fine-grained interpretation of outliers with variable contributions.
One of the key challenges of performing label prediction over a data stream concerns with the emergence of instances belonging to unobserved class labels over time. Previously, this problem has been addressed by detecting such instances and using them for appropriate classifier adaptation. The fundamental aspect of a n…
The problem of detecting communities in a graph is maybe one the most studied inference problems, given its simplicity and widespread diffusion among several disciplines. A very common benchmark for this problem is the stochastic block model or planted partition problem, where a phase transition takes place in the dete…
Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and…
SUOD accelerates OD for large, diverse models.
problem Training and scoring new samples with many unsupervised, heterogeneous OD models.
method Data reduction, model approximation, and taskload optimization.
result SUOD accelerates OD for over 20 benchmark datasets and a real-world case.
New model improves classifier security against evasion attacks by selecting features.
problem Security of machine learning classifiers against evasion attacks.
method Adversary-aware feature selection model incorporating specific adversary assumptions.
result Classifier security worsens with feature selection, but new model improves it.
This paper studies two important signal processing aspects of equilibrium behavior in non-cooperative games arising in social networks, namely, reinforcement learning and detection of equilibrium play. The first part of the paper presents a reinforcement learning (adaptive filtering) algorithm that facilitates learning…
Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote the…
This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem…
Improved object detection for scientific document images.
problem Current object detectors fail to accurately localize regions in scientific document images.
method Revised R-CNN model with region embedding for fine-grained proposals.
result 17% mAP improvement over standard object detection models.
CNNs can develop blind spots due to uneven padding in feature maps.
problem Spatial bias in convolutional networks leads to blind spots in certain tasks.
method Identified and analyzed the role of padding in convolutional networks, proposing solutions to mitigate bias.
result Mitigating spatial bias improves model accuracy, especially in tasks like small object detection.
UN-AVOIDS visualizes and detects anomalies without needing labeled data.
problem The need for a unified framework to visualize and detect anomalies.
method UN-AVOIDS is an unsupervised, nonparametric approach that transforms data into a new space (NCDF) for both visualization and detection.
result UN-AVOIDS assigns invariant anomalous scores and achieves high AUC in detecting anomalies.