With the development of mobile applications into a part of modern life, the user usage behavior data of mobile applications can well reflect the attribute characteristics of users. For many downstream applications, in...
With the development of mobile applications into a part of modern life, the user usage behavior data of mobile applications can well reflect the attribute characteristics of users. For many downstream applications, including advertising, recommendations provide effective support. To provide users with customized services and optimize user experience, industry and scholars have been exploring feasible solutions. However, automatic user modeling based on mobile app usage faces unique challenges, including (1) poor generalization performance of modeling with a single downstream task, (2) uneven distribution of user behavior over time, and severe sparsity in many long-tail apps. In this paper, we propose a custom model (GPTCN) by which we overcome these challenges and optimize on top of it, achieving the goal of reducing manual effort and improving performance. Experimental results show that GPTCN outperforms the state-of-the-art general vector generation models in ACC, providing a more comprehensive, robust and general user representation model.
Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking. They typically capture context by incorporating multiple video frames. However, thes...
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With the development of society, network security becomes more and more important. It is inevitable to establish the risk index system to effectively monitor the security of network. In the previous study, due to abse...
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With the development of cloud computing, more and more data is stored in cloud servers, which leads to an increasing degree of privacy of data stored in cloud servers. For example, in the critical domain of medical va...
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Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications ...
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Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and ***,deficiencies in these systems can lead to significant operational *** paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter *** paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and *** BP neural network is first trained to capture the nonlinear dynamic characteristics of the *** network is then combined with the PID controller to forma hybrid control *** output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight *** experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 ***,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.
Background Tuberculosis(TB)is a major infectious disease with significant public health *** widespread transmission,prolonged treatment duration,notable side effects,and high mortality rate pose severe *** study exami...
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Background Tuberculosis(TB)is a major infectious disease with significant public health *** widespread transmission,prolonged treatment duration,notable side effects,and high mortality rate pose severe *** study examines the epidemiological characteristics of TB globally and across major regions,providing a scientific basis for enhancing TB prevention and control measures *** The ecological study used data from the Global Burden of Disease(GBD)Study *** assessed new incidence cases,deaths,disability-adjusted life years(DALYs),and trends in age-standardized incidence rates(ASIRs),mortality rates(ASMRs),and DALY rates for drug-susceptible tuberculosis(DS-TB),multidrug-resistant tuberculosis(MDR-TB),and extensively drug-resistant tuberculosis(XDR-TB)from 1990 to 2021.A Bayesian age-period-cohort model was applied to project ASIR and *** In 2021,the global ASIR for all HIV-negative TB was 103.00 per 100,000 population 95%uncertainty interval(UI):92.21,114.91 per 100,000 populationl,declining by 0.40%(95%UI:-0.43,-0.38%)compared to *** global ASMR was 13.96 per 100,000 population(95%UI:12.61,15.72 per 100,000 population),with a decline of 0.44%(95%UI:-0.61,-0.23%)since *** global age-standardized DALY rate for HIV-negative TB was 580.26 per 100,000 population(95%UI:522.37,649.82 per 100,000 population),showing a decrease of 0.65%(95%UI:-0.69,-0.57 per 100,000 population)from *** global ASIR of MDR-TB has not decreased since 2015,instead,it has shown a slow upward trend in recent *** ASIR of XDR-TB has exhibited significant increase in the past 30 *** projections indicate MDR-TB and XDR-TB are expected to see signifcant increases in both ASIR and ASMR from 2022 to 2035,highlighting the growing challenge of drug-resistant *** This study found that the ASIR of MDR-TB and XDR-TB has shown an upward trend in recent *** reduce the TB burden,it is essential to enhance health infrastru
Background Bronchopulmonary dysplasia(BPD)is a common chronic lung disease in extremely preterm *** outcome and clinical burden vary dramatically according to *** some prediction tools for BPD exist,they seldom pay at...
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Background Bronchopulmonary dysplasia(BPD)is a common chronic lung disease in extremely preterm *** outcome and clinical burden vary dramatically according to *** some prediction tools for BPD exist,they seldom pay attention to disease severity and are based on populations in developed *** study aimed to develop machine learning prediction models for BPD severity based on selected clinical factors in a Chinese *** In this retrospective,single-center study,we included patients with a gestational age<32 weeks who were diagnosed with BPD in our neonatal intensive care unit from 2016 to *** collected their clinical information during the maternal,birth and early postnatal *** factors were selected through univariable and ordinal logistic regression *** models based on logistic regression(LR),gradient boosting decision tree,XGBoost(XGB)and random forest(RF)models were implemented and assessed by the area under the receiver operating characteristic curve(AUC).Results We ultimately included 471 patients(279 mild,147 moderate,and 45 severe cases).On ordinal logistic regression,gestational diabetes mellitus,initial fraction of inspiration O_(2) value,invasive ventilation,acidosis,hypochloremia,C-reactive protein level,patent ductus arteriosus and Gram-negative respiratory culture were independent risk factors for BPD *** the XGB,LR and RF models(AUC=0.85,0.86 and 0.84,respectively)all had good *** We found risk factors for BPD severity in our population and developed machine learning models based on *** models have good performance and can be used to aid in predicting BPD severity in the Chinese population.
Self-supervised pre-training followed by fine-tuning is a potent paradigm for few-shot learning, leveraging extensive unlabeled data with remarkable efficacy. Current self-supervised methods often lean towards Vision ...
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Network packets record communication behaviors and details, which is important for security audits, attack detection, and forensic analysis. For the effectiveness and timeliness of security analysis, it is necessary t...
Network packets record communication behaviors and details, which is important for security audits, attack detection, and forensic analysis. For the effectiveness and timeliness of security analysis, it is necessary to fully store network packets and build an efficient packet index. However, the existing packet indexing algorithms based on the radix tree ignore the distribution characteristics of network traffic and use internal nodes with the same capacity for index construction, resulting in wasted disk space and poor retrieval performance. As a solution, $w$ e propose ANTI, an adaptive network traffic indexing algorithm similar to Adaptive Radix Tree, which can adaptively switch internal nodes with different capacity according to the density of network traffic and compress the common prefix and distinct suffix of traffic attributes to balance the index construction performance and space utilization. We also implement a packet-aware network traffic archiving and indexing system to achieve full packet archival, efficient indexing, and fast retrieval. Finally, we empirically evaluate ANTI in IPv4 (IPv6) traffic scenarios, and the results confirm the effectiveness of ANTI as well as the benefit of adopting ANTI for enhancing indexing and retrieval performance compared with other state-of-art algorithms.
Transition videos play a crucial role in media production, enhancing the flow and coherence of visual narratives. Traditional methods like morphing often lack artistic appeal and require specialized skills, limiting t...
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