Collaborating Authors

Multiple Identifications in Multi-Armed Bandits Machine Learning

We study the problem of identifying the top $m$ arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that were previously out of reach. In particular we show that this idea of successive accepts and rejects applies to the multi-bandit best arm identification problem.

Fine-tuning Pre-trained Contextual Embeddings for Citation Content Analysis in Scholarly Publication Artificial Intelligence

Citation function and citation sentiment are two essential aspects of citation content analysis (CCA), which are useful for influence analysis, the recommendation of scientific publications. However, existing studies are mostly traditional machine learning methods, although deep learning techniques have also been explored, the improvement of the performance seems not significant due to insufficient training data, which brings difficulties to applications. In this paper, we propose to fine-tune pre-trained contextual embeddings ULMFiT, BERT, and XLNet for the task. Experiments on three public datasets show that our strategy outperforms all the baselines in terms of the F1 score. For citation function identification, the XLNet model achieves 87.2%, 86.90%, and 81.6% on DFKI, UMICH, and TKDE2019 datasets respectively, while it achieves 91.72% and 91.56% on DFKI and UMICH in term of citation sentiment identification. Our method can be used to enhance the influence analysis of scholars and scholarly publications.

Least square based ensemble deep learning for inertia tensor identification of combined spacecraft


The high accurate identification of inertia tensor of combined spacecraft, which is composed of a servicing spacecraft and a target, is necessary to perform attitude control. Due to the uncertainty of the operating environments of combined spacecraft, the measurement noise of the angular rate may be very complex and will seriously influence the identification accuracy. This paper proposes a least square based weighted ensemble deep learning method to realize a highly accurate identification for the inertia tensor of combined spacecraft in complex operating environments. In this method, a single deep neural network regression model is firstly constructed as an individual model for the ensemble deep learning, and then is trained by enough training data and a designed training strategy. After obtaining a certain number of accurate and diverse single models, all the outputs of single models are combined by several linear functions.

WiPIN: Operation-free Person Identification using WiFi Signals Machine Learning

Person identification is critical for sensitive applications such as system login/unlock, access control and payment. In this paper, we present an operation-free person identification system, namely WiPIN, that identifies biometric features of users using Wi-Fi signals. Our approach is based on an entirely new insight that different persons have distinct effects, including the absorption and reflection, on the Wi-Fi signals. We show that through effective signal processing and feature extraction/matching designs, the Channel State Information (CSI) used in recent Wi-Fi protocols can be utilized for person identification without requiring any collaborative operations, such as wiping, walking, or speaking. We theoretically analyzed the interaction between the human body and Wi-Fi Signals via an interactive model. We proposed a mapping rule between variation patterns of Wi-Fi signals and human biologic features, and demonstrated the feasibility of establishing CSI based person identifiers. We conducted extensive experiments over commodity off-the-shelf Wi-Fi devices. The results show WiPIN achieves 92% accuracy in person identification over a group of 30 users, with sufficient robustness to environment noises.

Opportunities and challenges of machine learning approaches for biomarker signature identification in psychiatry


The identification of reproducible biomarkers is an important step toward personalized medicine of psychiatric disorders. A large repertoire of machine learning tools is available that can aid in identifying such biomarker patterns from high-dimensional biological data. However, in psychiatry, the identification of clinically useful patterns has been challenging, due to the biological complexity and heterogeneity of the disorders, and the low effect sizes of individual biological markers. The incorporation of additional biological knowledge, such as information on biological network structure, or data from diverse modalities, is a promising route to make high-dimensional data more accessible for machine learning, and to identify more meaningful biological illness signatures. Here, we describe opportunities of such integrative analytics approaches and discuss unresolved challenges.