Search Results (311)

The World Health Organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior studies have applied a single stand-alone algorithm, which is unable to deal with data complexities, prone to overfitting, and limited in generalization. To this end, our paper examined the performance of several ML algorithms for early-stage depression detection using two benchmark social media datasets (D1 and D2). More specifically, we incorporated sentiment indicators to improve our model performance. Our experimental results showed that sentence bidirectional encoder representations from transformers (SBERT) numerical vectors fitted into the stacking ensemble model achieved comparable F1 scores of 69% in the dataset (D1) and 76% in the dataset (D2). Our findings suggest that utilizing sentiment indicators as an additional feature for depression detection yields an improved model performance, and thus, we recommend the development of a depressive term corpus for future work. Full article

Autonomous driving systems are a rapidly evolving technology. Trajectory prediction is a critical component of autonomous driving systems that enables safe navigation by anticipating the movement of surrounding objects. Lidar point-cloud data provide a 3D view of solid objects surrounding the ego-vehicle. Hence, trajectory prediction using Lidar point-cloud data performs better than 2D RGB cameras due to providing the distance between the target object and the ego-vehicle. However, processing point-cloud data is a costly and complicated process, and state-of-the-art 3D trajectory predictions using point-cloud data suffer from slow and erroneous predictions. State-of-the-art trajectory prediction approaches suffer from handcrafted and inefficient architectures, which can lead to low accuracy and suboptimal inference times. Neural architecture search (NAS) is a method proposed to optimize neural network models by using search algorithms to redesign architectures based on their performance and runtime. This paper introduces TrajectoryNAS, a novel neural architecture search (NAS) method designed to develop an efficient and more accurate LiDAR-based trajectory prediction model for predicting the trajectories of objects surrounding the ego vehicle. TrajectoryNAS systematically optimizes the architecture of an end-to-end trajectory prediction algorithm, incorporating all stacked components that are prerequisites for trajectory prediction, including object detection and object tracking, using metaheuristic algorithms. This approach addresses the neural architecture designs in each component of trajectory prediction, considering accuracy loss and the associated overhead latency. Our method introduces a novel multi-objective energy function that integrates accuracy and efficiency metrics, enabling the creation of a model that significantly outperforms existing approaches. Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yields a minimum of 4.8 higger accuracy and 1.1* lower latency over competing methods on the NuScenes dataset. Full article

Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is an effective way to overcome this challenge. However, how to obtain high-quality samples for training the surrogate model and which method should be used to develop the surrogate model with higher accuracy remain important questions to explore. To this end, this paper innovatively adopted the quasi-Monte Carlo (QMC) method to sample from the prior space of unknown variables. Then, this paper established a variety of individual machine learning surrogate models, respectively, and screened three with higher training accuracy among them as the base-learning models (BLMs). The Stacking ensemble framework was utilized to integrate the three BLMs to establish the ensemble surrogate model for the groundwater LNAPL multiphase flow numerical simulation model. Finally, a hypothetical case of groundwater LNAPL contamination was designed. After evaluating the accuracy of the Stacking ensemble surrogate model, the differential evolution Markov chain (DE-MC) algorithm was applied to jointly identify information on groundwater LNAPL contamination source and key hydrogeological parameters. The results of this study demonstrated the following: (1) Employing the QMC method to sample from the prior space resulted in more uniformly distributed and representative samples, which improved the quality of the training data. (2) The developed Stacking ensemble surrogate model had a higher accuracy than any individual surrogate model, with an average R² of 0.995, and reduced the computational burden by 99.56% compared to the inversion process based on the simulation model. (3) The application of the DE-MC algorithm effectively solved the GLCSI problem, and the mean relative error of the identification results of unknown variables was less than 5%. Full article

Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion recognition lies in how to extract the affect-salient features fully. In this paper, we propose an end-to-end spatial-temporal feature extraction method named FFA-BiGRU for music emotion classification. Taking the log Mel-spectrogram of music audio as the input, this method employs an attention-based convolutional residual module named FFA, which serves as a spatial feature learning module to obtain multi-scale spatial features. In the FFA module, three group architecture blocks extract multi-level spatial features, each of which consists of a stack of multiple channel-spatial attention-based residual blocks. Then, the output features from FFA are fed into the bidirectional gated recurrent units (BiGRU) module to capture the temporal features of music further. In order to make full use of the extracted spatial and temporal features, the output feature maps of FFA and those of the BiGRU are concatenated in the channel dimension. Finally, the concatenated features are passed through fully connected layers to predict the emotion classification results. The experimental results of the EMOPIA dataset show that the proposed model achieves better classification accuracy than the existing baselines. Meanwhile, the ablation experiments also demonstrate the effectiveness of each part of the proposed method. Full article

Bufferbloat is one of the leading causes of high data transmission latency and jitter on the Internet, which severely impacts the performance of low-latency interactive applications such as online streaming, cloud-based gaming/applications, Internet of Things (IoT) applications, voice over IP (VoIP), real-time video conferencing, and so forth. There is currently a pressing need for developing Transmission Control Protocol (TCP) congestion control algorithms and bottleneck queue management schemes that can collaboratively control/reduce end-to-end latency, thus ensuring optimal quality of service (QoS) and quality of experience (QoE) for users. This paper introduces a novel solution by experimentally integrate the low latency, low loss, and scalable throughput (L4S) architecture (specified by the IETF in RFC 9330) in FreeBSD framework with the asynchronous advantage actor-critic (A3C) reinforcement learning algorithm. The first phase involves incorporating a modified dual-queue coupled active queue management (AQM) system for L4S into the FreeBSD networking stack, enhancing queue management and mitigating latency and packet loss. The second phase employs A3C to adjust and fine-tune the system performance dynamically. Finally, we evaluate the proposed solution’s effectiveness through comprehensive experiments, comparing it with traditional AQM-based systems. This paper contributes to the advancement of machine learning (ML) for transport protocol research in the field. The experimental implementation and results presented in this paper are made available through our GitHub repositories. Full article

Retroviral integration is mediated by intasome nucleoprotein complexes wherein a pair of viral DNA ends are bridged together by a multimer of integrase (IN). Atomic-resolution structures of HIV-1 intasomes provide detailed insights into the mechanism of integration and inhibition by clinical IN inhibitors. However, previously described HIV-1 intasomes are highly heterogeneous and have the tendency to form stacks, which is a limiting factor in determining high-resolution cryo-EM maps. We have assembled HIV-1 intasomes in the presence of excess IN C-terminal domain protein, which was readily incorporated into the intasomes. The purified intasomes were largely homogeneous and exhibited minimal stacking tendencies. The cryo-EM map resolution was further improved to 2.01 Å, which will greatly facilitate structural studies of IN inhibitor action and drug resistance mechanisms. The C-terminal 18 residues of HIV-1 IN, which are critical for virus replication and integration in vitro, have not been well resolved in previous intasome structures, and its function remains unclear. We show that the C-terminal tail participates in intasome assembly, resides within the intasome core, and forms a small alpha helix (residues 271–276). Mutations that disrupt alpha helix integrity impede IN activity in vitro and disrupt HIV-1 infection at the step of viral DNA integration. Full article

Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the unconstrained airspace maps, and the randomization of driving patterns. In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not require the manual selection of dictionaries and can automatically generate augmentation functions to achieve nonlinear trajectory space mapping. The model combines stacked neural networks to create a scalable depth approximator for approximating the finite-dimensional Koopman operator. In addition, the model uses finite-dimensional operator evolution to achieve end-to-end adaptive prediction. In particular, the model can gain some physical interpretability through operator visualization and generative dictionary functions, which can be used for downstream pattern recognition and anomaly detection tasks. Experiments show that the model performs well, particularly on flight training trajectory datasets. Full article

This study investigates the mechanical properties of tiled laminates, frequently used in FRP bridges, and a completely new class of composites for which currently no experimental literature is available. In this paper, first a microscopic examination of laminates extracted from bridge deck flanges is performed, revealing complex multi-ply structures and tiled laminates in the transverse direction of the bridge deck. The subsequent fabrication of tiled laminates in the transverse (i.e., weak) and longitudinal (i.e., strong) span direction explores stiffness and strength characteristics depending on the stacking angle. It is observed that the stiffness in both directions is only slightly reduced with increasing stacking angles, reaching a maximum decrease of 10%, while the failure strength is significantly reduced, particularly with longitudinal tiling, dropping by approximately 70% for a 2° stacking angle. Transverse tiling demonstrates a more moderate 45% strength reduction due to the presence of some 90° plies. Given the small reduction in the stiffness and the fact that in many applications the design is mainly governed by serviceability (i.e., stiffness) requirements than strength, this strength reduction may be acceptable, considering other advantages of the concept. Additionally, this research sheds light on failure mechanisms, emphasizing the role of ply assembly in stress distribution and highlighting the importance of gradual ply ends in reducing strain concentrations. These findings provide valuable insights for optimizing tiled laminates in structural applications, ensuring their effective and reliable use. Full article

Deformation effectively represents the structural integrity of concrete dams and acts as a clear indicator of their operational performance. Predicting deformation is critical for monitoring the safety of hydraulic structures. To this end, this paper proposes an artificial intelligence-based process for predicting concrete dam deformation. Initially, using the principles of feature engineering, the preprocessing of deformation safety monitoring data is conducted. Subsequently, employing a stacking model fusion method, a novel prediction process embedded with multiple artificial intelligence algorithms is developed. Moreover, three new performance indicators—a superiority evaluation indicator, an accuracy evaluation indicator, and a generalization evaluation indicator—are introduced to provide a comprehensive assessment of the model’s effectiveness. Finally, an engineering example demonstrates that the ensemble artificial intelligence method proposed herein outperforms traditional statistical models and single machine learning models in both fitting and predictive accuracy, thereby providing a scientific and effective foundation for concrete dam deformation prediction and safety monitoring. Full article

Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication. Full article

The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency. Full article

Dense object detection is widely used in automatic driving, video surveillance, and other fields. This paper focuses on the challenging task of dense object detection. Currently, detection methods based on greedy algorithms, such as non-maximum suppression (NMS), often produce many repetitive predictions or missed detections in dense scenarios, which is a common problem faced by NMS-based algorithms. Through the end-to-end DETR (DEtection TRansformer), as a type of detector that can incorporate the post-processing de-duplication capability of NMS, etc., into the network, we found that homogeneous queries in the query-based detector lead to a reduction in the de-duplication capability of the network and the learning efficiency of the encoder, resulting in duplicate prediction and missed detection problems. To solve this problem, we propose learnable differentiated encoding to de-homogenize the queries, and at the same time, queries can communicate with each other via differentiated encoding information, replacing the previous self-attention among the queries. In addition, we used joint loss on the output of the encoder that considered both location and confidence prediction to give a higher-quality initialization for queries. Without cumbersome decoder stacking and guaranteeing accuracy, our proposed end-to-end detection framework was more concise and reduced the number of parameters by about 8% compared to deformable DETR. Our method achieved excellent results on the challenging CrowdHuman dataset with 93.6% average precision (AP), 39.2% MR⁻², and 84.3% JI. The performance overperformed previous SOTA methods, such as Iter-E2EDet (Progressive End-to-End Object Detection) and MIP (One proposal, Multiple predictions). In addition, our method is more robust in various scenarios with different densities. Full article

Urban crowd flow prediction is an important task for transportation systems and public safety. While graph convolutional networks (GCNs) have been widely adopted for this task, existing GCN-based methods still face challenges. Firstly, they employ fixed receptive fields, failing to account for urban region heterogeneity where different functional zones interact distinctly with their surroundings. Secondly, they lack mechanisms to adaptively adjust spatial receptive fields based on temporal dynamics, which limits prediction performance. To address these limitations, we propose an Adaptive Receptive Field Graph Convolutional Network (ARFGCN) for urban crowd flow prediction. ARFGCN allows each region to independently determine its receptive field size, adaptively adjusted and learned in an end-to-end manner during training, enhancing model prediction performance. It comprises a time-aware adaptive receptive field (TARF) gating mechanism, a stacked 3DGCN, and a prediction layer. The TARF aims to leverage gating in neural networks to adapt receptive fields based on temporal dynamics, enabling the predictive network to adapt to urban regional heterogeneity. The TARF can be easily integrated into the stacked 3DGCN, enhancing the prediction. Experimental results demonstrate ARFGCN’s effectiveness compared to other methods. Full article

In the realm of intelligent transportation, pedestrian detection has witnessed significant advancements. However, it continues to grapple with challenging issues, notably the detection of pedestrians in complex lighting scenarios. Conventional visible light mode imaging is profoundly affected by varying lighting conditions. Under optimal daytime lighting, visibility is enhanced, leading to superior pedestrian detection outcomes. Conversely, under low-light conditions, visible light mode imaging falters due to the inadequate provision of pedestrian target information, resulting in a marked decline in detection efficacy. In this context, infrared light mode imaging emerges as a valuable supplement, bolstering pedestrian information provision. This paper delves into pedestrian detection and tracking algorithms within a multi-modal image framework grounded in deep learning methodologies. Leveraging the YOLOv4 algorithm as a foundation, augmented by a channel stack fusion module, a novel multi-modal pedestrian detection algorithm tailored for intelligent transportation is proposed. This algorithm capitalizes on the fusion of visible and infrared light mode image features to enhance pedestrian detection performance amidst complex road environments. Experimental findings demonstrate that compared to the Visible-YOLOv4 algorithm, renowned for its high performance, the proposed Double-YOLOv4-CSE algorithm exhibits a notable improvement, boasting a 5.0% accuracy rate enhancement and a 6.9% reduction in logarithmic average missing rate. This research’s goal is to ensure that the algorithm can run smoothly even on a low configuration 1080 Ti GPU and to improve the algorithm’s coverage at the application layer, making it affordable and practical for both urban and rural areas. This addresses the broader research problem within the scope of smart cities and remote ends with limited computational power. Full article

Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score. Full article