A5041652380- Sentiment Analysis and Opinion Mining
- Stock Market Forecasting Methods
- Blockchain Technology Applications and Security
- Advanced Text Analysis Techniques
- Image and Signal Denoising Methods
- Text and Document Classification Technologies
- Global Financial Crisis and Policies
- Time Series Analysis and Forecasting
- Topic Modeling
- Information Architecture and Usability
- Humor Studies and Applications
- Misinformation and Its Impacts
- Human Mobility and Location-Based Analysis
- Data Stream Mining Techniques
- Advanced Data Compression Techniques
- Cryptography and Data Security
- Real-time simulation and control systems
- Machine Learning and Algorithms
- Water Quality Monitoring Technologies
- Business, Education, Mathematics Research
- Adversarial Robustness in Machine Learning
- Traffic Prediction and Management Techniques
- Market Dynamics and Volatility
- Smart Grid Security and Resilience
- Language, Metaphor, and Cognition
Technische Universität Berlin
2025
Aalborg University
2022-2024
Universidad de Oriente
2016
Time series data from a variety of sensors and IoT devices need effective compression to reduce storage I/O bandwidth requirements. While most time databases systems rely on lossless compression, lossy techniques offer even greater space-saving with small loss in precision. However, the unknown impact downstream analytics applications requires semi-manual trial-and-error exploration. We initiate work that provides guarantees complex statistical features (which are strongly correlated...
We have successfully reproduced the evaluation results in paper. appreciate that authors prepared detailed documents and automation scripts for reproducibility. Our show similar trends relationships between alternatives compared support scientific contributions claims of original
The rapid increase of traffic data generated by different sensing systems opens many opportunities to improve transportation services. An important opportunity is enable stochastic routing that computes the arrival time probabilities for each suggested route instead only expected travel time. However, datasets typically have missing values, which prevents construction speeds. To address this limitation, we propose Stochastic Spatio-Temporal Graph Convolutional Network (SST-GCN) architecture...
With the lowering costs of sensors, high-volume and high-velocity data are increasingly being generated analyzed, especially in IoT domains like energy smart homes. Consequently, applications that require accurate short-term forecasts predictions also steadily increasing. In this paper, we provide an overview a novel end-to-end platform provides efficient ingestion, compression, transfer, query processing, machine learning-based analytics for high-frequency time series from IoT. The...
In this paper we present our contribution to SemEval-2014 Task 4: Aspect Based Sentiment Analysis (Pontiki et al., 2014), Subtask 2: Term Polarity for Laptop domain.The most outstanding feature in is the automatic building of a domain-depended sentiment resource using Latent Semantic Analysis.We induce, each term, two real scores that indicate its use positive and negative contexts domain interest.The aspect term polarity classification carried out phases: opinion words extraction...
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