Search Results (236)

The performance of radar mode recognition has been significantly enhanced by the various architectures of deep learning networks. However, these approaches often rely on supervised learning and are susceptible to overfitting on the same dataset. As a transitional phase towards Cognitive Multi-Functional Radar (CMFR), Adaptive Multi-Function Radar (AMFR) possesses the capability to emit identical waveform signals across different working modes and states for task completion, with dynamically adjustable waveform parameters that adapt based on scene information. From a reconnaissance perspective, the valid signals received exhibit sparsity and localization in the time series. To address this challenge, we have redefined the reconnaissance-focused research priorities for radar systems to emphasize behavior analysis instead of pattern recognition. Based on our initial comprehensive digital system simulation model of a radar, we conducted reconnaissance and analysis from the perspective of the reconnaissance side, integrating both radar and reconnaissance aspects into environmental simulations to analyze radar behavior under realistic scenarios. Within the system, waveform parameters on the radar side vary according to unified rules, while resource management and task scheduling switch based on operational mechanisms. The target in the reconnaissance side maneuvers following authentic behavioral patterns while adjusting the electromagnetic space complexity in the environmental aspect as required. The simulation results indicate that temporal annotations in signal flow data play a crucial role in behavioral analysis from a reconnaissance perspective. This provides valuable insights for future radar behavior analysis incorporating temporal correlations and sequential dependencies. Full article

Iris is one of the most widely used biometric modalities because of its uniqueness, high matching performance, and inherently secure nature. Iris segmentation is an essential preliminary step for iris-based biometric authentication. The authentication accuracy is directly connected with the iris segmentation accuracy. In the last few years, deep-learning-based iris segmentation methodologies have increasingly been adopted because of their ability to handle challenging segmentation tasks and their advantages over traditional segmentation techniques. However, the biggest challenge to the biometric community is the scarcity of open-source resources for adoption for application and reproducibility. This review provides a comprehensive examination of available open-source iris segmentation resources, including datasets, algorithms, and tools. In the process, we designed three U-Net and U-Net++ architecture-influenced segmentation algorithms as standard benchmarks, trained them on a large composite dataset (>45K samples), and created 1K manually segmented ground truth masks. Overall, eleven state-of-the-art algorithms were benchmarked against five datasets encompassing multiple sensors, environmental conditions, demography, and illumination. This assessment highlights the strengths, limitations, and practical implications of each method and identifies gaps that future studies should address to improve segmentation accuracy and robustness. To foster future research, all resources developed during this work would be made publicly available. Full article

Career and College Readiness (CCR) generates higher levels of persistence, grit, motivation, and competencies in performing work- or school-related tasks among learners during post-secondary life. One primary limitation defining the present scholarship on CCR is the authentic analysis of the on the ground or field practices high schools perform to increase learners’ CCR competencies. The inadequate research on actual CCR practices in high schools motivated this study. To learn more about how high schools prepare students for post-secondary life, 16 principals from high schools provided detailed narratives on CCR practices in their institutions. Principals completed interviews of one to two hours using Microsoft Teams, supplying the researcher with specific information and examples of how their institutions equip students for future careers and higher education. Manual qualitative thematic analysis of the entire transcript guided the organization and interpretation of the findings, allowing the presentation of meaningful themes supported by a plethora of illustrations. Six themes representing 18 distinct CCR practices emerged, and the themes were rigorous curriculum, content knowledge, key academic behaviors, key cognitive strategies, multiculturalism, and citizenship development. Results partially supported the pervasive CCR model based on Conley’s readiness index. Increasingly, diversity and citizenship learning have defined high schools’ CCR work in preparing learners to become effective local and global citizens. The current investigation paves the way for future observational and field research uncovering whether schools truly prepare students or not, and such preparation varies across community, country, and institutional characteristics. Full article

Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data. Full article

Spam reviews pose a significant challenge to the integrity of online platforms, misleading consumers and undermining the credibility of genuine feedback. This paper introduces an innovative AI-generated spam review detection framework that leverages Deep Learning algorithms and Natural Language Processing (NLP) techniques to identify and mitigate spam reviews effectively. Our framework utilizes multiple Deep Learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), to capture intricate patterns in textual data. The system processes and analyzes large volumes of review content to detect deceptive patterns by utilizing advanced NLP and text embedding techniques such as One-Hot Encoding, Word2Vec, and Term Frequency-Inverse Document Frequency (TF-IDF). By combining three embedding techniques with four Deep Learning algorithms, a total of twelve exhaustive experiments were conducted to detect AI-generated spam reviews. The experimental results demonstrate that our approach outperforms the traditional machine learning models, offering a robust solution for ensuring the authenticity of online reviews. Among the models evaluated, those employing Word2Vec embeddings, particularly the BiLSTM_Word2Vec model, exhibited the strongest performance. The BiLSTM model with Word2Vec achieved the highest performance, with an exceptional accuracy of 98.46%, a precision of 0.98, a recall of 0.97, and an F1-score of 0.98, reflecting a near-perfect balance between precision and recall. Its high F2-score (0.9810) and F0.5-score (0.9857) further highlight its effectiveness in accurately detecting AI-generated spam while minimizing false positives, making it the most reliable option for this task. Similarly, the Word2Vec-based LSTM model also performed exceptionally well, with an accuracy of 97.58%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. The CNN model with Word2Vec similarly delivered strong results, achieving an accuracy of 97.61%, a precision of 0.97, a recall of 0.96, and an F1-score of 0.97. This study is unique in its focus on detecting spam reviews specifically generated by AI-based tools rather than solely detecting spam reviews or AI-generated text. This research contributes to the field of spam detection by offering a scalable, efficient, and accurate framework that can be integrated into various online platforms, enhancing user trust and the decision-making processes. Full article

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time–frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources. Full article

Face recognition is a widely used computer vision, which plays an increasingly important role in user authentication systems, security systems, and consumer electronics. The models for most current applications are based on high-definition digital cameras. In this paper, we focus on digital images derived from historical motion picture films. Historical motion picture films often have poorer resolution than modern digital imagery, making face detection a more challenging task. To approach this problem, we first propose a trunk–branch concatenated multi-task cascaded convolutional neural network (TB-MTCNN), which efficiently extracts facial features from blurry historical films by combining the trunk with branch networks and employing various sizes of kernels to enrich the multi-scale receptive field. Next, we build a deep neural network-integrated object-tracking algorithm to compensate for failed recognition over one or more video frames. The framework combines simple online and real-time tracking with deep data association (Deep SORT), and TB-MTCNN with the residual neural network (ResNet) model. Finally, a state-of-the-art image restoration method is employed to reduce the effect of noise and blurriness. The experimental results show that our proposed joint face recognition and tracking network can significantly reduce missed recognition in historical motion picture film frames. Full article

In this paper, a method for augmenting samples of side-scan sonar seafloor sediment images based on CBAM-BCEL1-INGAN is proposed, aiming to address the difficulties in acquiring and labeling datasets, as well as the insufficient diversity and quantity of data samples. Firstly, a Convolutional Block Attention Module (CBAM) is integrated into the residual blocks of the INGAN generator to enhance the learning of specific attributes and improve the quality of the generated images. Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. Finally, augmented samples are input into an AlexNet classifier to verify their authenticity. Experimental results demonstrate the excellent performance of the method in generating images of coarse sand, gravel, and bedrock, as evidenced by significant improvements in the Frechet Inception Distance (FID) and Inception Score (IS). The introduction of the CBAM and BCEL1 loss function notably enhances the quality and details of the generated images. Moreover, classification experiments using the AlexNet classifier show an increase in the recognition rate from 90.5% using only INGAN-generated images of bedrock to 97.3% using images augmented using our method, marking a 6.8% improvement. Additionally, the classification accuracy of bedrock-type matrices is improved by 5.2% when images enhanced using the method presented in this paper are added to the training set, which is 2.7% higher than that of the simple method amplification. This validates the effectiveness of our method in the task of generating seafloor sediment images, partially alleviating the scarcity of side-scan sonar seafloor sediment image data. Full article

In the rapidly evolving field of biometric authentication, deep learning has become a cornerstone technology for face detection and recognition tasks. However, traditional optimizers often struggle with challenges such as overfitting, slow convergence, and limited generalization across diverse datasets. To address these issues, this paper introduces NestYogi, a novel hybrid optimization algorithm that integrates the adaptive learning capabilities of the Yogi optimizer, anticipatory updates of Nesterov momentum, and the generalization power of stochastic weight averaging (SWA). This combination significantly improves both the convergence rate and overall accuracy of deep neural networks, even when trained from scratch. Extensive data augmentation techniques, including noise and blur, were employed to ensure the robustness of the model across diverse conditions. NestYogi was rigorously evaluated on two benchmark datasets Labeled Faces in the Wild (LFW) and YouTube Faces (YTF), demonstrating superior performance with a detection accuracy reaching 98% and a recognition accuracy up to 98.6%, outperforming existing optimization strategies. These results emphasize NestYogi’s potential to overcome critical challenges in face detection and recognition, offering a robust solution for achieving state-of-the-art performance in real-world applications. Full article

As teachers, we are living and working in times of abundant challenge and change. These challenges transpire across different education levels and sectors, including K–12, vocational, tertiary, and adult learning. Within this vast education ecosystem, a major challenge for all teachers is to allocate time, effort, and resources to ensure that their students receive a quality education with real-world implications, influencing soft-skill attainment, such as teamwork, communication, and critical thinking skills. In this article, the authors discuss, through a theoretical lens, the value of considering a national and universal approach to self- and peer-evaluations of authentic assessment tasks to improve teacher practice in Australia. Currently, there is modest opportunity amongst K–12 and tertiary teachers to comprehensively learn together, limiting cross-fertilisation of practice and interconnectedness, and as a national community of practice. The authors argue in this paper that offering an avenue to share knowledge and practice in authentic assessment design could potentially assist in addressing this challenge. Therefore, the article is dedicated to exploring the barriers and opportunities to advance a national and universal approach to transferable professional development in authentic assessment practice within the Australian education ecosystem. Full article

With the use of Generative AI (GenAI), Online Social Networks (OSNs) now generate a huge volume of content data. Yet, user-generated content on OSNs, aided by GenAI, presents challenges in analyzing and understanding its characteristics. In particular, tweets generated by GenAI at the request of authentic human users present difficulties in determining the gendered variation of the content. The vast amount of data generated from tweets’ content necessitates a thorough investigation into the gender-specific language used in these tweets. This study explores the task of predicting the gender of text content in tweets generated by GenAI. Through our analysis and experimentation, we have achieved a remarkable 90% accuracy in attributing gender-specific language to these tweets. Our research not only highlights the potential of GenAI in gender prediction but also underscores the sophisticated techniques employed to decipher the refined linguistic cues that differentiate male and female language in GenAI-generated content. This advancement in understanding and predicting gender-specific language in GenAI-generated tweets covers the way for more refined and accurate content analysis in the evolving landscape of OSNs. Full article

A Wireless Body Area Network (WBAN), introduced into the healthcare sector to improve patient care and enhance the efficiency of medical services, also brings the risk of the leakage of patients’ privacy. Therefore, maintaining the communication security of patients’ data has never been more important. However, WBAN faces issues such as open medium channels, resource constraints, and lack of infrastructure, which makes the task of designing a secure and economical communication scheme suitable for WBAN particularly challenging. Signcryption has garnered attention as a solution suitable for resource-constrained devices, offering a combination of authentication and confidentiality with low computational demands. Although the advantages offered by existing certificateless signcryption schemes are notable, most of them only have proven security within the random oracle model (ROM), lack public ciphertext authenticity, and have high computational overheads. To overcome these issues, we propose a certificateless anonymous signcryption (CL-ASC) scheme suitable for WBAN, featuring anonymity of the signcrypter, public verifiability, and public ciphertext authenticity. We prove its security in the standard model, including indistinguishability, unforgeability, anonymity of the signcrypter, and identity identifiability, and demonstrate its superiority over relevant schemes in terms of security, computational overheads, and storage costs. Full article

With the implementation of conceptual labeling on online learning resources, knowledge-concept recommendations have been introduced to pinpoint concepts that learners may wish to delve into more deeply. As the core subject of learning, learners’ preferences in knowledge concepts should be given greater attention. Research indicates that learners’ preferences for knowledge concepts are influenced by the characteristics of their group structure. There is a high degree of homogeneity within a group, and notable distinctions exist between the internal and external configurations of a group. To strengthen the group-structure characteristics of learners’ behaviors, a multi-task strategy for knowledge-concept recommendations is proposed; this strategy is called Knowledge-Concept Recommendations with Heterogeneous Graph-Contrastive Learning. Specifically, due to the difficulty of accessing authentic social networks, learners and their structural neighbors are considered positive contrastive pairs to construct self-supervision signals on the predefined meta-path from heterogeneous information networks as auxiliary tasks, which capture the higher-order neighbors of learners by presenting different perspectives. Then, the Information Noise-Contrastive Estimation loss is regarded as the main training objective to increase the differentiation of learners from different professional backgrounds. Extensive experiments are constructed on MOOCCube, and we find that our proposed method outperforms the other state-of-the-art concept-recommendation methods, achieving $6.66\%$ with $HR@5$, $8.85\%$ with $NDCG@5$, and $8.68\%$ with $MRR$. Full article

In the domain of space rendezvous and docking, visual navigation plays a crucial role. However, practical applications frequently encounter issues with poor image quality. Factors such as lighting uncertainties, spacecraft motion, uneven illumination, and excessively dark environments collectively pose significant challenges, rendering recognition and measurement tasks during visual navigation nearly infeasible. The existing image enhancement methods, while visually appealing, compromise the authenticity of the original images. In the specific context of visual navigation, space image enhancement’s primary aim is the faithful restoration of the spacecraft’s mechanical structure with high-quality outcomes. To address these issues, our study introduces, for the first time, a dedicated unsupervised framework named SpaceLight for enhancing on-orbit navigation images. The framework integrates a spacecraft semantic parsing network, utilizing it to generate attention maps that pinpoint structural elements of spacecraft in poorly illuminated regions for subsequent enhancement. To more effectively recover fine structural details within these dark areas, we propose the definition of a global structure loss and the incorporation of a pre-enhancement module. The proposed SpaceLight framework adeptly restores structural details in extremely dark areas while distinguishing spacecraft structures from the deep-space background, demonstrating practical viability when applied to visual navigation. This paper is grounded in space on-orbit servicing engineering projects, aiming to address visual navigation practical issues. It pioneers the utilization of authentic on-orbit navigation images in the research, resulting in highly promising and unprecedented outcomes. Comprehensive experiments demonstrate SpaceLight’s superiority over state-of-the-art low-light enhancement algorithms, facilitating enhanced on-orbit navigation image quality. This advancement offers robust support for subsequent visual navigation. Full article

Smart devices have become the core ingredient in maintaining human society, where their applications span basic telecommunication, entertainment, education, and even critical security tasks. However, smartphone security measures have not kept pace with their ubiquitousness and convenience, exposing users to potential security breaches. Shading light on shortcomings of traditional security measures such as PINs gives rise to biometrics-based security measures. Open-set authentication with pretrained Transformers especially shows competitive performance in this context. Bringing this closer to practice, we propose CoreTemp, a greedy coreset sampled template, which offers substantially faster authentication speeds. In parallel with CoreTemp, we design a fast match algorithm where the combination shows robust performance in open-set mobile biometrics authentication. Designed to resemble the effects of ensembles with marginal increment in computation, we propose PIEformer+, where its application with CoreTemp has state-of-the-art performance. Benefiting from much more efficient authentication speeds to the best of our knowledge, we are the first to attempt identification in this context. Our proposed methodology achieves state-of-the-art results on HMOG and BBMAS datasets, particularly with much lower computational costs. In summary, this research introduces a novel integration of greedy coreset sampling with an advanced form of pretrained, implicitly ensembled Transformers (PIEformer+), greatly enhancing the speed and efficiency of mobile biometrics authentication, and also enabling identification, which sets a new benchmark in the relevant field. Full article