Text-to-MIDI generation offers editable and hierarchical control over symbolic music generation. Previous approaches either convert text into a limited set of musical attributes and generate music based on these attributes, which limits semantic controllability, or use end-to-end models that map text directly to music without deeply aligning the features of both modalities, often resulting in a lack of structural coherence and mismatches in key, meter, and tempo. We propose MIDILM, which addresses these limitations by employing text conditioning with a dual-path decoder that processes textual and musical information through separate feedforward paths following a shared masked self-attention mechanism. On the MidiCaps benchmark, MIDILM outperformed the strongest baseline, with relative improvements ranging from 6.07% on CLAP to 144.77% on TB across semantic alignment and structural metrics. These gains confirm its ability to enhance both semantic controllability and structural coherence. Collectively, we expect that MIDILM will serve as a useful reference framework for future investigations into controllable and structurally faithful cross-modal music generation.

Environmental perception provides the necessary information for unmanned ground vehicles to recognize and interact with surrounding objects. Velodyne light detection and ranging (LiDAR) is widely used for this purpose due to its significant advantages such as high precision and being uninfluenced by varying illuminations. However, the unstructured distribution of LiDAR point clouds always affects the performance of feature extraction and object recognition. Moreover, the numbers of parameters in most deep learning models of object recognition are very large and the training process costs lots of computation consumption. This paper proposes a broad learning system (BLS) variant with a unified space autoencoder (USAE) as a lightweight model to recognize 3D objects. When the proposed method was evaluated on the LiDAR point cloud dataset and ModelNet10 dataset, the experimental results indicated that the recognition accuracy of our USAE-BLS model was similar to that of state-of-the-art 3D object recognition models. Moreover, the USAE-BLS has a much smaller model size and shorter training time than that of the deep learning models.

Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.

There exists an electromagnetic shielding effect on radio signals in a tunnel, which results in no satellite positioning signal in the tunnel scenario. Moreover, because vehicles always drive at a high speed on the highway, the real-time localization system (RTLS) has a bottleneck in a highway scenario. Thus, the navigation and positioning service in tunnel and highway is an important technology difficulty in the construction of a smart transportation system. In this paper, a new technology combined downlink time difference of arrival (DL-TDoA) is proposed to realize precise and automated RTLS in tunnel and highway scenarios. The DL-TDoA inherits ultra-wideband (UWB) technology to measure the time difference of radio signal propagation between the location tag and four different location base stations, to obtain the distance differences between the location tag and four groups of location base stations. The proposed solution achieves a higher positioning efficiency and positioning capacity to achieve dynamic RTLS. The DL-TDoA technology based on UWB has several advantages in precise positioning and navigation, such as positioning accuracy, security, anti-interference, and power consumption. In the final experiments on both static and dynamic tests, DL-TDoA represents high accuracy and the mean errors of 11.96 cm, 37.11 cm, 50.06 cm, and 87.03 cm in the scenarios of static tests and 30 km/h, 60 km/h, and 80 km/h in dynamic tests, respectively, which satisfy the requirements of RTLS.

Existing telehealth systems do not perform as effectively as would be expected due to their asymmetric focus on sensing and monitoring with little support or assurance to affect or alter behaviors. We developed Action-based Behavior Model (ABM) that supports persuasive telehealth. However, ABM requires an ongoing assessment of user behavior response and compliance to cyber influence. Method: To measure compliance, we developed Situation-based Assess Tree (SAT) as a methodology and an algorithm for domain-specific behavior assessment under ABM. We followed a proof-of-concept validation approach based on a trace-driven simulation. Results: Preliminary results demonstrate that SAT is sentient to the full spectrum of compliance clearly discerning between compliant and noncompliant user responses. Results also demonstrate SAT's ability to learn different user personas through the assessment process.

As demands for human activity recognition technology increase, simulation of human activities for providing datasets and testing purposes is becoming increasingly important. Traditional simulation, however, is based on an event-driven approach, which focuses on single sensor events and models within a single human activity. It requires detailed description and processing of every low-level event that enters into an activity scenario. For many realistic and complex human scenarios, the event-driven approach burdens the simulator users with complicated low-level specifications required to configure and run the simulation. It also increases computational complexity and impedes scalable simulation. Thus, we propose a novel, context-driven approach to simulating human activities in smart spaces. In the proposed approach, vectors of sensors rather than single sensor events drive the simulation quicker from one context to another. Abstracting the space state into contexts highly simplifies the tasks and efforts of the simulation user in setting up and configuring the simulation components for smart space and human activities. We present the context-driven simulation approach and show how it works. Then we present fundamental concepts and algorithms and provide a comparative performance study between the event- and context-driven simulation approaches.

Emerging smart space applications are increasingly relying on capabilities for recognizing human activities. Activity recognition research is however challenged and slowed by the lack of data necessary for testing and validation. Collecting data through live-in trials in real world deployments is often very expensive and complicated. Legitimate limitations on the use of human subjects also renders a much smaller dataset than desired to be collected. To address this challenge, we propose a scenario generation approach in which a small set of scenarios is used to generate new relevant and realistic scenarios, and hence increase the base of testing data needed for activity recognition validation. Unlike existing methods for generating scenarios, which usually focus on scenario structure and complexity, we propose a Bayesian-based approach that learns the stochastic characteristics of a small number of collected datasets to generate additional scenarios of similar characteristics. Our approach is prolific and can generate enormous datasets with high degree of realism at affordable cost. The proposed approach is validated using a Viterbi-based algorithm and a real dataset case study. The validation experiment confirms that the generated dataset has highly similar stochastic characteristics as that of the real dataset.