An electroencephalography (EEG) based brain activity recognition is a fundamental eld of study for a number of signi cant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. erefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly relies on the expert experience. In this paper, we a empt to solve the above challenges by proposing an approach which has be er EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Speci cally, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. en, we adopt an Autoencoder layer to automatically re ne the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the e ect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. e experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.

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The composition of Web services has gained a considerable momentum as a paradigm for enabling Business-to-Business (B2B) Collaborations. Numerous technologies supporting this new paradigm are rapidly emerging, thereby creating a need for methodologies that bring these technologies together. The identification and documentation of relevant patterns, both at the analysis and design levels, is an important step in this direction.

Session-based recommender systems (SBRS) are an emerging topic in the recommendation domain and have attracted much attention from both academia and industry in recent years. Most of existing works only work on modelling the general item-level dependency for recommendation tasks. However, there are many more other challenges at different levels, e.g., item feature level and session level, and from various perspectives, e.g., item heterogeneity and intra-and inter-item feature coupling relations, associated with SBRS. In this paper, we provide a systematic and comprehensive review on SBRS and create a hierarchical and in-depth understanding of a variety of challenges in SBRS. To be specific, we first illustrate the value and significance of SBRS, followed by a hierarchical framework to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities. Further, a summary together with a detailed introduction of the research progress is provided. Lastly, we share some prospects in this research area.

Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on conversational question answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy a user’s information needs. While the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers over the recent years. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations. Differently from other RS approaches, including content-based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs are a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area. Finally, we share some new research directions in this vibrant area.

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