In mobile edge computing (MEC) environments, with the increase in the number of web services possessing the same or similar functions, the prediction of nonfunctional quality of service (QoS) indicators turns increasingly vital in satisfying the diverse needs of different users. Despite the significant achievements of QoS prediction approaches such as collaborative filtering (CF), they often fail to obtain informative representations due to complex user-service-time QoS data. Moreover, changing network conditions and overlooked temporal factors lead to reduced accuracy. In response to these challenges, this paper proposes a novel deep learning to hash method for time-aware QoS prediction built on a VQ-VAE (namedPred$_{QoS}$QoS). In the proposedPred$_{QoS}$QoS, inspired by advanced CF approaches, we make QoS predictions at a specific moment for the target user through its similar timeslots. Specifically, we train a codebook to discretize the continuous latent representation obtained by the encoder based on vector quantization (VQ), which is motivated by the generative vector-quantized variational autoencoder (VQ-VAE) model, allowing us to derive compact binary codes representing the QoS data. Then, the similarities between QoS data are determined, helping to make effective and efficient predictions. Furthermore, the time-awarePred$_{QoS}$QoSapproach incorporates a temporal factor by training on multiple QoS data (including the user, service, and time dimensions). As a result, the discrete hash codes (i.e., QoS data representations) derived from the encoder can fully uncover the time factor’s dynamic impact on the QoS data, thereby yielding significantly improved prediction performance. In summary, thePred$_{QoS}$QoSapproach learns compact hash codes for original QoS data while taking time into account, enabling accurate predictions to be produced through similar timeslots for the target users. Finally, comprehensive experiments carried out on the real-world WS-DREAM dataset affirm the exceptional performance of thePred$_{QoS}$QoS.

Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuading users, which is still challenging even for state-of-the-art large language models (LLMs).Previous works focus on retrievalbased models or generative models in a specific domain due to a lack of data across multiple domains.In this paper, we leverage GPT-4 to create the first multi-domain persuasive dialogue dataset DailyPersuasion.Then we propose a general method named PersuGPT to learn a persuasion model based on LLMs through intent-to-strategy reasoning, which summarizes the intent of user's utterance and reasons next strategy to respond.Moreover, we design a simulation-based preference optimization, which utilizes a learned user model and our model to simulate next turns and estimate their rewards more accurately.Experimental results on two datasets indicate that our proposed method outperforms all baselines in terms of automatic evaluation metric Win-Rate and human evaluation.The code and data are available at https://persugpt.github.io.

In the rapidly evolving landscape of the Internet of Things (IoT), concerns about privacy and security have become significant as interconnected devices communicate and collaborate. Fingerprints, serving as unique biometric identifiers, play a crucial role in the authentication and identification processes within this interconnected and exchanged network. However, attention is often directed towards the disclosure of visible fingerprints, overlooking latent fingerprints. This is primarily due to the challenges involved in extracting latent fingerprints, especially those remaining on the adhesive side of tape. Traditional methods physically/chemically peel tape to extract these fingerprints, but cause irreversible damage to the tape, hindering accurate fingerprint extraction. In this context, our investigation reveals that Optical Coherence Tomography (OCT) technology allows for the extraction of high-quality OCT fingerprint images from the adhesive side of tape, yielding precise fingerprint recognition and gender classification results. Concretely, we build a novel type of robotic-arm spectral-domain OCT (SD-OCT), which is software-controlled for the movement of the sample arm, making sample scanning more flexible and efficient. Furthermore, we utilize a deep learning network to perform representation learning on OCT fingerprints for the purpose of gender classification. In the first branch, we input OCT fingerprints into an EfficientNet-B3 network to learn their spatial domain features. Simultaneously, in the second branch, we design a network that utilizes Discrete Cosine Transform (DCT) to extract frequency domain features from OCT fingerprints. Ultimately, we integrate the spatial and frequency domain features extracted from OCT fingerprint images to generate comprehensive features. Therefore, in this paper, we introduce a novel Gender Classification approach based on Spatio-Frequency Feature Fusion of OCT Fingerprint Images (named GenClassOCT-SF). The GenClassOCT-SF involves a robotic-arm SD-OCT system for superior-quality fingerprints acquisition and a deep learning network for spatial and frequency domain feature extraction. The fusion of these features enables highly accurate gender classification. Finally, we conduct gender classification experiments on the collected OCT fingerprint dataset to demonstrate the effectiveness of our proposed method.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (namedMHDP$_{SVD\_{A}RIMA}$) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show thatMHDP$_{SVD\_{A}RIMA}$approach is effective and efficient in predicting missing healthcare data.