Abstract: Navigation apps use transportation recommendation. Previous transportation recommendation solutions only consider routes in one transportation mode (uni-modal, e.g., taxi, bus, cycle) and ignore situational context, resulting in poor user experience. We propose $\mathsf {Hydra}$,…
Abstract: Due to the low signal-to-noise ratio of scalp EEG, brain–computer interface (BCI) development requires accurate electroencephalogram (EEG) pattern decoding for specific mental tasks. Machine learning can optimize EEG patterns for better decoding. Existing algorithms…
Abstract: Distributing large sparse matrix operations in scientific computing is like hypergraph partitioning. Hypergraphs have “hyperedges” that connect any number of nodes. Thus, solving or approximating hypergraph partitioning is NP-hard. Current algorithms solve this problem…
Abstract: Daily communication is influenced by many factors. These factors allow conversational systems to generate positive responses and build friendly relationships with users. This study examines emotion and intention as response generators. We create a…
Abstract: Given a user’s purchase/rating trajectories, sequential recommendation recommends the next few items they’re most likely to buy/review. It helps users choose their favorites. This manuscript uses hybrid associations models (HAM) to generate sequential recommendations…
Abstract: Entity resolution (ER), a classification problem, is difficult on real data due to dirty values. The best ER solutions use learning models like deep neural networks, which need lots of labeled training data. High-quality…
Abstract: Machine learning libraries need static and dynamic clustering algorithms. The sliding window model or simpler models have dominated dynamic machine learning and data mining algorithm development. Many real-world applications require arbitrary deletions and insertions….
Abstract: Factorization Machines (FMs) help recommender systems overcome cold-start and data sparsity by incorporating side information. Traditional FMs use the inner product to model second-order interactions between feature vectors. Inner products violate feature vector triangle…
Abstract: A new deep visual odometry (VO) method that selects memory and refines poses considers global information. Existing learning-based methods treat VO as a pure tracking problem by recovering camera poses from image snippets, resulting…
Abstract: Social network analyses and applications face the user alignment problem, which matches users across networks. State-of-the-art methods embed users into the low-dimensional representation space, where their features are preserved, and establish user correspondence based…
Abstract: IoT is transforming human life. However, the rapid adoption of IoT makes cyberspace more vulnerable, especially to IoT-based attacks on cyber-physical systems. With billions of IoT devices, detecting and preventing these attacks is crucial….
Abstract: The distance metric that measures object dissimilarity is crucial to categorical data clustering. Most clustering methods ignore the relative order of ordinal values when calculating dissimilarity between nominal and ordinal attributes. Interdependence between nominal…
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