The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors. However, extracting green knowledge from location traces is not a trivial task. Conventional data analysis tools are usually not customized for handling the massive quantity, complex, dynamic, and distributed nature of location traces. To that end, in this paper, we provide a focused study of extracting energy-efficient transportation patterns from location traces. Specifically, we have the initial focus on a sequence of mobile recommendations. As a case study, we develop a mobile recommender system which has the ability in recommending a sequence of pick-up points for taxi drivers or a sequence of potential parking positions. The goal of this mobile recommendation system is to maximize the probability of business success. Along this line, we provide a Potential Travel Distance (PTD) function for evaluating each candidate sequence. This PTD function possesses a monotone property which can be used to effectively prune the search space. Based on this PTD function, we develop two algorithms, LCP and SkyRoute, for finding the recommended routes. Finally, experimental results show that the proposed system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and leading to the efficient use of energy.

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The rapid development of the technologies for online learning provides students with extensive resources for self-learning and brings new opportunities for data-driven research on educational management. An important issue of online learning is to diagnose the knowledge proficiency (i.e., the mastery level of a certain knowledge concept) of each student. Considering that it is a common case that students inevitably learn and forget knowledge from time to time, it is necessary to track the change of their knowledge proficiency during the learning process. Existing approaches either relied on static scenarios or ignored the interpretability of diagnosis results. To address these problems, in this article, we present a focused study on diagnosing the knowledge proficiency of students, where the goal is to track and explain their evolutions simultaneously. Specifically, we first devise an explanatory probabilistic matrix factorization model, Knowledge Proficiency Tracing (KPT), by leveraging educational priors. KPT model first associates each exercise with a knowledge vector in which each element represents a specific knowledge concept with the help of Q -matrix. Correspondingly, at each time, each student can be represented as a proficiency vector in the same knowledge space. Then, our KPT model jointly applies two classical educational theories (i.e., learning curve and forgetting curve ) to capture the change of students’ proficiency level on concepts over time. Furthermore, for improving the predictive performance, we develop an improved version of KPT, named Exercise-correlated Knowledge Proficiency Tracing (EKPT), by considering the connectivity among exercises with the same knowledge concepts. Finally, we apply our KPT and EKPT models to three important diagnostic tasks, including knowledge estimation, score prediction, and diagnosis result visualization. Extensive experiments on four real-world datasets demonstrate that both of our models could track the knowledge proficiency of students effectively and interpretatively.

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Nowadays, machine trading contributes significantly to activities in the equity market, and forecasting market movement under high-frequency scenario has become an important topic in finance. A key challenge in high-frequency market forecasting is modeling the dependency structure among stocks and business sectors, with their high dimensionality and the requirement of computational efficiency. As a group of powerful models, neural networks (NNs) have been used to capture the complex structure in many studies. However, most existing applications of NNs only focus on forecasting with daily or monthly data, not with minute-level data that usually contains more noises. In this article, we propose a novel double-layer neural (DNN) network for high-frequency forecasting, with links specially designed to capture dependence structures among stock returns within different business sectors. Various important technical indicators are also included at different layers of the DNN framework. Our model framework allows update over time to achieve the best goodness-of-fit with the most recent data. The model performance is tested based on 100 stocks with the largest capitals from the S8P 500. The results show that the proposed framework outperforms benchmark methods in terms of the prediction accuracy and returns. Our method will help in financial analysis and trading strategy designs.