Human activity recognition has played a crucial role in healthcare information systems due to the fast adoption of artificial intelligence (AI) and the internet of thing (IoT). Most of the existing methods are still limited by computational energy, transmission latency, and computing speed. To address these challenges, we develop an efficient human activity recognition in-memory computing architecture for healthcare monitoring. Specifically, a mechanism-oriented model of Ag/a-Carbon/Ag memristor is designed, serving as the core circuit component of the proposed in-memory computing system. Then, one-transistor-two-memristor (1T2M) crossbar array is proposed to perform high-efficiency multiply-accumulate (MAC) operation and high-density memory in the proposed scheme. To facilitate understanding of the proposed efficient human activity recognition in-memory computing design, self-attention ConvLSTM module, multi-head convolutional attention module, and recognition module are proposed. Furthermore, the proposed system is applied to perform human activity recognition, which contains eleven different human activities, including five different postural falls, and six basic daily activities. The experimental results show that the proposed system has advantages in recognition performance (≥ 0.20% accuracy, ≥ 1.10% F1-score) and time consumption (approximately 8∼10 times speed up) compared to existing methods, indicating an advancement in smart healthcare applications.

Negative emotion (e.g., anger, fear) may influence normal driver behavior, resulting in serious traffic accidents. Thus, developing an automatic driver emotion classification method is necessary and urgent. Most of the existing methods are performed in realistic indoor environment and always lack effective utilization of heterogeneous information, resulting in low accuracy and reliability. In this paper, a novel dual-pathway driver emotion classification network using multi-task learning strategy is proposed. To illustrate the design of the proposed driver emotion classification network, three modules are constructed: 1) visual-facial data processing module; 2) driving behavioral data processing module; 3) fusion output module. Meanwhile, considering the influence of emotional states on driving behavior, a comprehensive analysis is conducted to distinguish the positive, neutral, and negative influence on driving behavior. Furthermore, a joint verification in both realistic indoor environment (i.e., laboratory simulation on the PPB-Emo dataset) and real-world outdoor scenario is performed. The experimental results illustrate that the proposed network exhibits superior performance in terms of classification accuracy and response time, achieving good balance between classification accuracy and running speed in internet of things scenarios.

Conversational recommender systems (CRS) enable traditional recommender systems to interact with users by asking questions about attributes and recommending items. The attribute-level and item-level feedback of users can be utilized to estimate users' preferences. However, existing works do not fully exploit the advantage of explicit item feedback --- they only use the item feedback in rather implicit ways such as updating the latent user and item representation. Since CRS has multiple chances to interact with users, leveraging the context in the conversation may help infer users' implicit feedback (e.g., some specific attributes) when recommendations get rejected. To address the limitations of existing methods, we propose a new CRS framework called Conversational Recommender with Implicit Feedback (CRIF). CRIF formulates the conversational recommendation scheme as a four-phase process consisting of offline representation learning, tracking, decision, and inference. In the inference module, by fully utilizing the relation between users' attribute-level and item-level feedback, our method can explicitly deduce users' implicit preferences. Therefore, CRIF is able to achieve more accurate user preference estimation. Besides, in the decision module, to better utilize the attribute-level and item-level feedback, we adopt inverse reinforcement learning to learn a flexible decision strategy that selects the suitable action at each conversation turn. Through extensive experiments on four benchmark CRS datasets, we validate the effectiveness of our approach, which significantly outperforms the state-of-the-art CRS methods.

Spatial audio object coding (SAOC) is an effective method to transmit multiple audio objects. It divides the full frequency band into 28 subbands and extracts spatial parameters for de-coding. In this way, objects can be encoded into a downmix signal with a few parameters. However, using the same parameters in one subband will cause frequency aliasing distortion, which severely impacts the listening experience. Existing studies to enhance SAOC cannot eliminate the aliasing distortion of all objects effectively. This paper describes a new structure to balance the bit-rate and decode quality based on time-frequency (TF) shifting and scheduling. In this structure, a TF shifting strategy (contains global shifting and local shifting) is proposed to reduce frequency aliasing distortion. Furthermore, a scheduling strategy is used to decide which part should be shifted according to the degree of aliasing. From the experiment results, the performance of the proposed method is better than SAOC and other enhanced methods.

Spatial audio object coding (SAOC) is an effective method to transmit multiple audio objects. Audio systems can provide personalized services under this framework. However, this method causes frequency aliasing distortion, which severely impacts the listening experience. The multi-step SAOC (MS-SAOC) scheme was proposed to enhance the sound quality of each audio object by using residual information. Compared with SAOC, the bit-rate increases three times due to the residual data of multiple objects. In this paper, an efficient multi-step residual coding method is proposed to reduce the residual bit-rate of MS-SAOC. A two-level filter is designed to remove redundant residual information, and the limited residual information can efficiently compensate for frequency aliasing distortion. From experiment results, the residual bit-rate is half of MS-SAOC, and the sound quality is maintained at the Good-Excellent level.

The efficient transmission of the audio objects can be achieved by spatial audio object coding (SAOC) method that conveys a mono downmix signal together with side information parameters that enable object reconstruction in the decoder. To allow the transmission of audio objects at low bitrates, we present a new audio coding method with convolutional auto-encoder (CAE) and dense convolutional network (DenseNet) mixture model, optimizing the compression of side information parameters of audio objects. It has two main advantages: 1) Different from the linear transform methods, CAE can dig the nonlinear relationship of side information parameters and can effectively reduce the dimension of side information parameters; 2) DenseNet is adding in the decoder to make full use of low dimensional features of side information parameters, which improves the audio quality at low bitrate. Experiments show that our method outperforms base-line methods permitting bitrates as low as 1 kbps per object.