5G NR wireless positioning demonstrates exceptional potential in indoor and GNSS-denied environments due to its low latency, high signal quality, and precise measurement capabilities. However, non-line-of-sight (NLOS) propagation significantly degrades performance, necessitating robust LOS/NLOS signal identification. This paper presents an innovative framework combining Gramian Angular Fields (GAFs), Recurrence Plots (RPs), Kolmogorov-Arnold Networks (KAN), and a Mean Teacher semi-supervised approach to address these challenges.Our method converts raw channel impulse response (CIR) sequences into two-dimensional Field Images through GAF-GADF encoding, enabling rich spatial-temporal feature extraction. A hybrid feature learning strategy is employed: (1) a lightweight CNN extracts visual features from Field Images, and (2) a KAN-based module processes manually extracted CIR features through spline-activated layers to capture nonlinear relationships. A consistency factor is introduced in the fusion module to enhance model updates, while the Mean Teacher framework leverages unlabeled data through exponential moving average weight smoothing and consistency loss regularization.Extensive experiments on a real-world dataset validate the proposed approach: supervised classification achieves 99.1% accuracy, while semi-supervised learning attains 89.8% accuracy, both surpassing state-of-the-art methods. These results demonstrate the framework’s effectiveness in enhancing 5G NR positioning reliability in obstructive environments.

Climate change and digital transformation have significant effects on all aspects of society. With the increasing importance of state-owned enterprises (SOEs) that represent governments' stand to respond to environmental challenges, this study investigates how state ownership affects green innovation and how digital transformation factors play a role at both the firm level and the provincial level. Based on the data analysis of Chinese listed firms between 2008 and 2021, we find that state ownership hinders green innovation. However, with a high degree of digital transformation at the firm level and digital innovation capability at the province level, the state ownership and green innovation relationship can be weakened. Overall, this study advances both green innovation and SOE innovation literature by bridging state ownership, green innovation, and digital-related factors. We advocate government and SOE managers to invest more in digital transformation and improve their digital capabilities for better green innovation output.

Edge-cloud collaborative learning emerges as a promising paradigm for adapting pre-trained deep neural network (DNN) models to the ever-changing edge data environments and specific downstream tasks. However, the heterogeneity of edge devices and unlabeled local data hinder the effectiveness of existing collaborative learning approaches. To address the above issues, we proposeACL, a novel adaptive edge-cloud collaborative learning paradigm for heterogeneous devices with unlabeled local data. InACL, we first useFedNAS, a neural architecture search algorithm designed for collaborative learning to generate a customized model on each participating device, and then a lightweight semi-supervised collaborative learning frameworkHSSCLis used to fine-tune the pre-trained DNN model. Compared with the SOTA collaborative learning approaches,ACLachieves significant accuracy improvement, averaging 31.5% for image classification and 15.5% for object detection. Furthermore, it reduces time overhead by 3.1-5.1× and memory overhead by 6.3-12.5×. We will release our models and tools.

Deploying deep learning models to edge devices for low-latency and privacy-preserving applications has become a trend. To adapt to heterogeneous devices and data, it is significant to generate customized models. However, existing model adaptation approaches require edge devices to make interactions (collecting hardware information or local data) with the cloud, which raises privacy concerns, increases communication costs, and burdens the cloud. By contrast, we proposeCamoNet, a universal on-device model adaptation framework with zero interaction between devices and the cloud. InCamoNet, a lightweight on-device neural architecture search module is utilized to quickly generate a customized model for subsequent on-device training, followed by an on-device contrastive transfer learning module to effectively leverage unlabeled data for fine-tuning the customized model. Extensive experimental results show thatCamoNetcan effectively run on various edge devices. Compared with the SOTA model adaptation approaches,CamoNetachieves significant accuracy improvement by 25.2% on average for image classification, 10.1% on average for object detection, and reduces the training memory by 4.8-11.4×. We will open-source our models and tools for edge AI developers.

The prediction of long-term activities of groups of users and clusters of activities around a subject in social networks is a very challenging task. In this paper, we propose a novel temporal neural network framework that tracks user engagement and activity associated with particular subjects (e.g. CVE IDs) across online platforms. The framework is able to simulate which user will do what activity and at what time. Furthermore, this framework captures groups of users reacting to an event. It also captures responses to an event on a platform and the influence of the event on activity on other platforms over time. The proposed framework aims to predict future user activity related to specific subjects across platforms. The framework also illustrates the importance influence of activities that occur on other platforms when predicting user activity for particular events on a different platform. The learned model can do simulations in a timely manner. We evaluated our user group activity prediction method on the CVE (Common Vulnerabilities and Exposures) related user groups (software vulnerability) using 3 public online social network datasets: Github, Reddit, and Twitter. Groups of users who work on a particular CVE ID are identified. Each user group has information on all users' activities related to a CVE ID. The 3 datasets from Github, Reddit, and Twitter contain more than 490,000 cross platform activities related to over 20,000 user groups (CVE IDs) from more than 50,000 users. Compared to the proposed baseline, our simulation method is better in both predictions of total activity volume over time and activity associated with an individual CVE ID.

Here we investigate the application of convolutional neural networks (CNNs) to predict the survival time of patients with Glioblastoma Multiforme (GBM) brain tumor. Our dataset consists of T1-weighted high-resolution MRI images of just 68 GBM patients. We compare two analytic methods for predicting survival time. The first consists of training a small convolutional neural network (CNN) and the second uses extracted deep features from a pre-trained CNN. Our method is completely automated, except for tumor region segmentation. In addition, we utilize a snapshot ensemble approach to boost test accuracy when dealing with limited availability of medical images for CNN training purposes. Our approach achieves an accuracy of 72.06% using a trained small network and 66.18% using a pre-trained deep CNN. Our results compare favorably with the accuracy of 54.41% using histogram of oriented gradients (HOG) features and a non-neural network classifier.

This paper introduces a decomposition approach to address the problem of predicting different user activities at hour granularity over a long period of time. Our approach involves two steps. First, we used a temporal neural network ensemble to predict the number of each type of activity that occurred in a day. Second, we used a set of neural networks to assign the events to a user-repository pair in a particular hour. We focused this work on a subset of the public GitHub dataset that records the activities of over 2 million users on over 400,000 software repositories. Our experiments show we were able to predict hourly user-repo activity with reasonably low error. Our simulations are accurate for 1-3 weeks (168-504 hours) after inception, with accuracy gradually falling off. It was shown that activity on Twitter and Reddit increases the accuracy of activity prediction on GitHub for most events.

Activity on social media is seen as a relevant sensor for different aspects of the society. In a heavily digitized society, security vulnerabilities pose a significant threat that is publicly discussed on social media. This study presents a comparison of user-generated content related to security vulnerabilities on three digital platforms: two social media conversation channels (Reddit and Twitter) and a collaborative software development platform (GitHub). Our data analysis shows that while more security vulnerabilities are discussed on Twitter, relevant conversations go viral earlier on Reddit. We show that the two social media platforms can be used to accurately predict activity on GitHub.

Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt with at the feature vector level (create synthetic feature vectors or discard some examples from the larger class) or by assigning differential costs to errors. Here, we introduce a novel method for over-sampling minority class examples at the image level, rather than the feature vector level. Our method was applied to the problem of Glioblastoma patient survival group prediction. Synthetic minority class examples were created by adding Gaussian noise to original medical images from the minority class. Uniform local binary patterns (LBP) histogram features were then extracted from the original and synthetic image examples with a random forests classifier. Experimental results show the new method (Image SMOTE) increased minority class predictive accuracy and also the AUC (area under the receiver operating characteristic curve), compared to using the imbalanced dataset directly or to creating synthetic feature vectors.

Finding appropriate feature representations from radiological images is a vital task for prediction and diagnosis. Deep convolutional neural networks have recently achieved state-of-the-art performance in classification problems from several different domains. Research has also shown the feasibility of using a pre-trained deep neural network as a feature extractor when only a small dataset is available. This paper proposes a novel image feature extraction method for predicting survival time from brain tumor magnetic resonance images using pretrained deep neural networks. Since all tumors are different sizes, we also explore different image resizing methods in the paper. We demonstrate that deep features can result in better survival time prediction with the highest accuracy of 95.45% versus conventional feature extraction methods from magnetic resonance images of the brain.