Abstract. Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and timeconsuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects' intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).

Clickbait is a type of web content advertisements designed to entice readers into clicking accompanying links. Usually, such links will lead to articles that are either misleading or non-informative, making the detection of clickbait essential for our daily lives. Automated clickbait detection is a relatively new research topic. Most recent work handles the clickbait detection problem with deep learning approaches to extract features from the metadata of content. However, little attention has been paid to the relationship between the misleading titles and the target content, which we found to be an important clue for enhancing clickbait detection. In this work, we propose a deep similarity-aware attentive model to capture and represent such similarities with better expressiveness. In particular, we present the ways of either using similarity only or integrating it with other available quality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods.

Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most real-world CQA systems is the lack of effective matching between questions and the potential good answerers, which adversely affects the efficient knowledge acquisition and circulation. On the one hand, a requester might experience many low-quality answers without receiving a quality response in a brief time; on the other hand, an answerer might face numerous new questions without being able to identify their questions of interest quickly. Under this situation, expert recommendation emerges as a promising technique to address the above issues. Instead of passively waiting for users to browse and find their questions of interest, an expert recommendation method raises the attention of users to the appropriate questions actively and promptly. The past few years have witnessed considerable efforts that address the expert recommendation problem from different perspectives. These methods all have their issues that need to be resolved before the advantages of expert recommendation can be fully embraced. In this survey, we first present an overview of the research efforts and state-of-the-art techniques for the expert recommendation in CQA. We next summarize and compare the existing methods concerning their advantages and shortcomings, followed by discussing the open issues and future research directions.

Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. However, most existing work in this field employs domain-specific approaches and is thus ineffective in complex situations where multi-modality sensor data are collected. Moreover, the wearable sensor data are less informative than the conventional data such as texts or images. In this paper, to improve the adaptability of such classification methods across different application domains, we turn this classification task into a game and apply a deep reinforcement learning scheme to deal with complex situations dynamically. Additionally, we introduce a selective attention mechanism into the reinforcement learning scheme to focus on the crucial dimensions of the data. This mechanism helps to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier. We carry out several experiments on three wearable sensor datasets and demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines.

Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore, it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.

Mashups are a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs, which are generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for their composition tasks. In particular, we propose a probabilistic matrix factorization approach with implicit correlation regularization to solve the recommendation problem and enhance the recommendation diversity. We conjecture that the co-invocation of APIs in real-world mashups is driven by both the explicit textual similarity and implicit correlations of APIs such as the similarity or the complementary relationship of APIs. We develop a latent variable model to uncover the latent correlations between APIs by analyzing their co-invocation patterns. We further explore the relationships of topics/categories to the proposed approach. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb.

With the rapid increase in urbanisation, the concept of smart cities has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, smart cities have the potential to improve various indicators of residents quality of life.However, threats to data integrity may affect the delivery of such benefits, especially in the IoT environment where most devices are inherently dynamic and have limited resources. Prior work has focused on ensuring integrity of data in a piecemeal manner and covering only some parts of the smart city ecosystem. In this paper, we address integrity of data from an end-to-end perspective, i.e., from the data source to the data consumer. We propose a holistic framework for ensuring integrity of data in smart cities that covers the entire data lifecycle. Our framework is founded on three fundamental concepts, namely, secret sharing, fog computing and blockchain. We provide a detailed description of various components of the framework and also utilize smart healthcare as use case.

Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowledge-aware recommendation, we proposed Knowledge-Guided deep Reinforcement learning (KGRL) to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation. This model is implemented upon the actor-critic network framework. It maintains a local knowledge network to guide decision-making and employs the attention mechanism to capture long-term semantics between items. We have conducted comprehensive experiments in a simulated online environment with six public real-world datasets and demonstrated the superiority of our model over several state-of-the-art methods.