Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic methods like colonoscopy suffering from suboptimal patient compliance rates. This study develops an intelligent diagnostic model based on tongue images to assist in CRC diagnosis, leveraging the integrative potential of traditional tongue diagnosis and modern machine learning. Between June 2023 and July 2024, we collected and processed 1,389 tongue images from CRC patients and 1,543 from non-colorectal cancer (NCRC) participants. Our methodology combines innovative image segmentation using the Segment Anything Model (SAM) with Grounding DINO, extracts both hand-crafted features (color, texture, shape) and deep learning features via Swin-Transformer, and employs feature fusion and selection techniques. The diagnostic model achieves an accuracy of 87.93% (F1-score: 0.9072) in internal validation. In an independent external cohort of 119 CRC patients and 221 NCRC participants, it demonstrates 85.18% precision (recall: 85%, F1-score: 0.8507). This non-invasive, cost-effective approach demonstrates significant potential as a complementary screening tool for CRC, particularly in regions with limited access to conventional diagnostic resources.

Accurate polyp segmentation in colonoscopy images is essential for early colorectal cancer detection but remains a challenging problem due to the limitations in existing methods for optimizing boundary features and aligning cross-level representations. Specifically, the indistinct polyp boundaries and scale variations across different feature levels pose significant challenges for segmentation accuracy. To address these issues, we propose a dual domain collaborative network (DDCNet) that introduces two novel modules: a frequency context enhancement module (FCEM), which operates in the frequency domain to refine high- and low-frequency features, and a cross-level shift-recalibrated fusion module (CSFM), which improves multi-scale feature alignment in the spatial domain. The FCEM improves boundary precision by adaptively refining high-frequency boundary features and enhancing low-frequency contextual information, while the CSFM mitigates cross-level feature misalignment by dynamically recalibrating multi-scale features throughout the encoder-decoder architecture. Additionally, we design a hybrid loss function that integrates boundary, cross-entropy, and frequency consistency losses to further boost segmentation performance. Experimental results on three benchmark datasets (Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB) demonstrate that DDCNet achieves state-of-the-art performance, with Dice coefficients of 0.9343, 0.9447, and 0.8155, respectively. These results represent improvements of 1.0%-1.5% over the best existing methods. Ablation studies further validate the individual contributions of FCEM, CSFM, and the hybrid loss function. Additionally, we compared the proposed loss function with three commonly used functions.

Scoliosis significantly impacts quality of life, highlighting the need for effective early scoliosis screening (SS) and intervention. However, current SS methods often involve physical contact, undressing, or radiation exposure. This study introduces an innovative, non-invasive SS approach utilizing a monocular RGB camera that eliminates the need for undressing, sensor attachment, and radiation exposure. We introduce a novel approach that employs Parameterized Human 3D Reconstruction (PH3DR) to reconstruct 3D human models, thereby effectively eliminating clothing obstructions, seamlessly integrated with an ISANet segmentation network, which has been enhanced by Multi-Scale Fusion Attention (MSFA) module we proposed for facilitating the segmentation of distinct human trunk and limb features (HTLF), capturing body surface asymmetries related to scoliosis. Additionally, we propose a Swin Transformer-enhanced CMU-Pose to extract human skeleton features (HSF), identifying skeletal asymmetries crucial for SS. Finally, we develop a fusion model that integrates the HTLF and HSF, combining surface morphology and skeletal features to improve the precision of SS. The experiments demonstrated that PH3DR and MSFA significantly improved the segmentation and extraction of HTLF, whereas ST-based CMU-Pose substantially enhanced the extraction of HSF. Our final model achieved a comparable F1 (0.895$\pm$0.014) to the best-performing baseline model, with only 0.79% of the parameters and 1.64% of the FLOPs, achieving 36 FPS-significantly higher than the best-performing baseline model (10 FPS). Moreover, our model outperformed two spine surgeons, one less experienced and the other moderately experienced. With its patient-friendly, privacy-preserving, and easily deployable solution, this approach is particularly well-suited for early SS and routine monitoring.

High Frequency (HF) RFID technology enhances operations in logistics, retail, and access control, necessitating efficient tag identification. This paper addresses the challenge of tag collisions in HF RFID systems, where existing protocols such as Time Division Multiple Access (TDMA) are not fully optimized for HF requirements. We introduce a novel hybrid-mode protocol utilizing the ISO 15693 standard, which supports either 2 or 16 branches per node in its tree-based anti-collision mechanism. By dynamically switching configurations based on real-time tag density, our protocol significantly improves identification efficiency. We validate our approach through theoretical analysis and extensive simulations, along with real-world experiments, demonstrating marked improvements in operational speed and efficiency. This study is the first to tailor a time-efficient tag identification protocol for commercial HF RFID systems, offering substantial benefits in operational efficiency and user experience across various industries.

Objective: To objectively measure Traditional Chinese Medicine (TCM) constitution by moving beyond local features and Body Mass Index (BMI), which only reflects general obesity, through the analysis of overall body shape features. Methods: A deep learning-based parametric 3D human reconstruction approach (PARE) was employed to extract ten body shape parameters (ß0-ß9) from clinical data of 129 subjects. The associations between these parameters and TCM constitution were analyzed using Shapley decomposition to assess their individual contributions. Results: Significant correlations were identified between six TCM constitutions (Phlegm-dampness, Special, Damp-heat, Qi-deficiency, Yin-deficiency, and Yang-deficiency) and body shape parameters. Shapley decomposition quantified the contribution levels of each morphological parameter to the formation of these constitutions. Conclusions: This study provides preliminary insights into the connections between body shape features and TCM constitution using 3D human reconstruction technology, introducing a novel method and perspective for understanding their interplay and advancing the objectification and automation of TCM constitution identification.

With 52.8 billion RFID tags used worldwide in 2024, a common basic functionality needed by RFID-enabled applications is cardinality estimation — to quickly estimate the number of distinct tags in an RFID system. Although many advanced solutions have been proposed over the past decade, they suffer from one major limitation in practical use: they need to either modify the existing RFID standard or obtain MAC-layer information, both of which however cannot be supported by commercial off-the-shelf (COTS) devices. In this paper, we revisit the counting problem and propose a novel counting scheme called average time duration based counter (ATD) that quickly estimates the number of distinct tags in a standards-compliant manner. Compared with existing work, the competitive advantage of ATD is that it can be directly deployed on a COTS RFID system, with no need for any hardware modifications. In ATD, we found a new and measurable indicator — the time duration between two adjacent singleton slots, which depends on the number of tags. Following this observation, we derive the theoretical relationship between the time indicator and the number of tags and then give the proof of the estimation as well as its parameter settings. Additionally, we propose a flag-flipping solution to address the overlapping problem in the multi-reader case. We implement ATD in a COTS RFID system with 1000 tags. Experimental results show that ATD is$4.2\times $faster than the baseline of tag inventory; the performance gain will be further increased in a larger RFID system.

With the rapid development of Internet technology, online learning and online education are becoming more and more popular. Intelligent learning diagnosis has become an effective means to guarantee the quality of online learning, and has become a research hotspot in the direction of education informatization. Concept map is an intuitive visual tool that can discover the concepts poorly mastered by students, and provide useful clues for identifying learning disabilities of students. This paper proposed a learning diagnosis method constructed based on the concept map. First, it groups learners, then uses direct hashing and pruning (DHP) to generate association rules between the different concepts, and finally uses the DHP algorithm to produce and construct the concept map automatically to discover the concepts poorly mastered by students, and realize the automatic diagnosis of learning problems. Case studies that have been done in college mathematics classes in some universities of Lianyungang have fully verified the effectiveness of our method.

With the rapid development of cloud computing, large scale of cloud services are provided to users. Recommender systems have been proven to be valuable tools to deal with information overload and be able to provide appropriate recommendations to users. The cloud environment is dynamic and uncertain, which makes the quality of cloud services time-sensitive. However, most existing recommender systems did not take temporal influence into consideration, therefore could not accommodate the dynamic cloud environment. In view of this challenge, we propose a temporal-aware hybrid collaborative recommendation method for cloud service. It aims at providing users with appropriate recommendations from time-sensitive cloud services. In our method, by distinguishing temporal QoS metrics from stable QoS metrics, temporal influence is integrated into classical neighborhood-based collaborative recommender algorithm. Besides, to get an optimal recommendation, a temporal-aware latent factor model based on tensor decomposition is proposed and combined to improve the recommendation performance. Finally, experiments are designed and conducted to demonstrate the efficiency of our method.

Web service recommendation plays an important role in building reliable service-oriented systems for both the service providers and the active users. However, with the proliferation of web services on the World Wide Web, traditional service recommendation is hard to accurately provide customized services to active users. In this paper, we propose a novel web service recommender model using collaborative filtering to improve the prediction of Quality-of-Services. Benefiting from the accuracy of hybrid recommenders, we extend the idea of optimized predicting order and design the Graph to describe the neighborhood. Furthermore, a new algorithm using adjusted topological sorting for Graph is proposed to generate the optimized order while predicting. Finally, we conduct extensive experiments to evaluate our proposed model, in which a real data set with 1.5 million invocation information is taken as input. The experiment results show that our model achieves higher prediction accuracy than other models.

Identifying outlier objects against main community evolution trends is not only meaningful itself for the purpose of finding novel evolution behaviors, but also helpful for better understanding the mainstream of community evolution. With the definition of community belongingness matrix of data objects, we constructed the transition matrix to least square optimize the pattern of evolutionary quantity between two consecutive belongingness snapshots. A set of properties about the transition matrix is discussed, which reveals its close relation to the step by step community membership change. The transition matrix is further optimized using robust regression methods by minimizing the disturbance incurred by the outliers, and the outlier factor of the anomalous object was defined. Being aware that large proportion of trivial but nomadic objects may exist in large datasets. This paper focus only on the influential community evolutionary outliers which both show remarkable difference from the main body of their community and sharp changes of their membership role within the communities. An algorithm on detection such kind of outliers are purposed in the paper. Experimental results on both synthetic and real world datasets show that the proposed approach is highly effective and efficient in discovering reasonable influential evolutionary community outliers.