Consumer wearable devices like smartwatches enable real-time tracking of vital signs with various sensors. Accordingly, experts may leverage smart home techniques for treating sleep disorders by targeting specific sleep stages. To facilitate this scenario, this paper focuses on sleep stage classification based on body movement and heart rate signals detected by wearables in real time. Due to their limited battery capacity, it is crucial to balance the trade-off between power efficiency and classification accuracy. To address the problem, inspired by multi-tasking, we propose COPS, an innovative framework that includes a power-efficient shallow classifier for simple cases and a deep classifier for complex instances. COPS introduces an intelligent switch, CESwitch, to determine a confidence score that directs input to either the shallow or deep classifier. By selectively activating the shallow classifier, the overall expected power consumption could be lower. Two strategies of CESwitch, namely Confidence Delegation and Agreement Verification, are proposed and examined. Notably, both COPS and CESwitch can be seamlessly integrated with existing deep sleep stage classifiers. Comprehensive experimental results on two real datasets manifest that COPS outperforms state-of-the-art lightweight and deep sleep stage classifiers by reducing 77.8% computational cost in terms of FLOPs with only 1.9% accuracy drop. Moreover, adapting to an existing deep model saves up to 32.7% in FLOPs compared to its original architecture.

With the rise of Internet-of-Things devices, the analysis of sensor-generated energy time series data has become increasingly important. This is especially crucial for detecting rare events like unusual electricity usage or water leakages in residential and commercial buildings, which is essential for optimizing energy efficiency and reducing costs. However, existing detection methods on large-scale data may fail to correctly detect rare events when they do not behave significantly differently from standard events or when their attributes are non-stationary. Additionally, the capacity of computational resources to analyze all time series data generated by an increasing number of sensors becomes a challenge. This situation creates an emergent demand for a workload-bounded strategy. To ensure both effectiveness and efficiency in detecting rare events in massive energy time series, we propose a heuristic-based framework called HALE . This framework utilizes an explore–exploit selection process that is specifically designed to recognize potential features of rare events in energy time series. HALE involves constructing an attribute-aware graph to preserve the attribute information of rare events. A heuristic-based random walk is then derived based on partial labels received at each time period to discover the non-stationarity of rare events. Potential rare event data are selected from the attribute-aware graph, and existing detection models are applied for final confirmation. Our study, which was conducted on three actual energy datasets, demonstrates that the HALE framework is both effective and efficient in its detection capabilities. This underscores its practicality in delivering cost-effective energy monitoring services.

The growing adoption of electric vehicles (EVs) has resulted in an increased demand for public EV charging infrastructure. Currently, the collaboration between these stations has become vital for efficient charging scheduling and cost reduction. However, most existing scheduling methods primarily focus on recommending charging stations without considering users’ charging preferences. Adopting these strategies may require considerable modifications to how people charge their EVs, which could lead to a reluctance to follow the scheduling plan from charging services in real-world situations. To address these challenges, we propose the POSKID framework in this article. It focuses on spatial-temporal charging scheduling, aiming to recommend a feasible charging arrangement, including a charging station and a charging time slot, to each EV user while minimizing overall operating costs and ensuring users’ charging satisfaction. The framework adopts an online charging mechanism that provides recommendations without prior knowledge of future electricity information or charging requests. To enhance users’ willingness to accept the recommendations, POSKID incorporates an incentive strategy and a novel embedding method combined with Bayesian personalized analysis. These techniques reveal users’ implicit charging preferences, enhancing the success probability of the charging scheduling task. Furthermore, POSKID integrates an online candidate arrangement selection and an explore-exploit strategy to improve the charging arrangement recommendations based on users’ feedback. Experimental results using real-world datasets validate the effectiveness of POSKID in optimizing charging management, surpassing other strategies. The results demonstrate that POSKID benefits each charging station while ensuring user charging satisfaction.

Personality inference has received widespread attention for its potential to infer psychological well being, job satisfaction, romantic relationship success, and professional performance. In this research, we focus on Conscientiousness, one of the well studied Big Five personality traits, which determines if a person is self-disciplined, organized, and hard-working. Research has shown that Conscientiousness is related to a person's academic and workplace success. For an expert to evaluate a person's Conscientiousness, long-term observation of the person's behavior at work place or at home is usually required. To reduce this evaluation effort as well as to cope with the increasing trend of human behavior turning digital, there is a need to conduct the evaluation using digital traces of human behavior. In this paper, we propose a novel framework, called HAPE, to automatically infer an individual's Conscientiousness scores using his/her behavioral data in an E-learning system. We first determine how users learn in the E-learning system, and design a novel Pattern Relational Graph Embedding method to learn the representations of users, their learning actions, and learning situations. The interaction between users, learning actions and situations characterizes the learning style of a user. Through experimental studies on real data, we demonstrate that HAPE framework outperforms the baseline methods in the Conscientiousness inference task.

The arise of E-learning systems has led to an anytime-anywhere-learning environment for everyone by providing various online courses and tests. However, due to the lack of teacher-student interaction, such ubiquitous learning is generally not as effective as offline classes. In traditional offline courses, teachers facilitate real-time interaction to teach students in accordance with personal aptitude from students' feedback in classes. Without the interruption of instructors, it is difficult for users to be aware of personal unknowns. In this paper, we address an important issue on the exploration of 'user unknowns' from an interactive question-answering process in E-learning systems. A novel interactive learning system, called CagMab, is devised to interactively recommend questions with a round-by-round strategy, which contributes to applications such as a conversational bot for self-evaluation. The flow enables users to discover their weakness and further helps them to progress. In fact, despite its importance, discovering personal unknowns remains a challenging problem in E-learning systems. Even though formulating the problem with the multi-armed bandit framework provides a solution, it often leads to suboptimal results for interactive unknowns recommendation as it simply relies on the contextual features of answered questions. Note that each question is associated with concepts and similar concepts are likely to be linked manually or systematically, which naturally forms the concept graphs. Mining the rich relationships among users, questions and concepts could be potentially helpful in providing better unknowns recommendation. To this end, in this paper, we develop a novel interactive learning framework by borrowing strengths from concept-aware graph embedding for learning user unknowns. Our experimental studies on real data show that the proposed framework can effectively discover user unknowns in an interactive fashion for the recommendation in E-learning systems.