Building a pathway to One Health surveillance and response in Asian countries

To detect and respond to emerging diseases more effectively,an integrated surveillance strategy needs to be applied to both human and animal health.Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat.It is not realistic to either invest only in efforts to detect emerging diseases,or to rely solely on event-based surveillance.A comprehensive strategy is needed,concurrently investigating and managing endemic zoonoses,studying evolving diseases which change their character and importance due to influences such as demographic and climatic change,and enhancing understanding of factors which are likely to influence the emergence of new pathogens.This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect.As yet there is no fully formed blueprint that can be applied in Asian countries.Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.

CNN-Based Intelligent Safety Surveillance in Green IoT Applications

Safety surveillance is considered one of the most important factors in many constructing industries for green internet of things(IoT)applications.However,traditional safety monitoring methods require a lot of labor source.In this paper,we propose intelligent safety surveillance(ISS)method using a convolutional neural network(CNN),which is an autosupervised method to detect workers whether or not wearing helmets.First,to train the CNN-based ISS model,the labeled datasets mainly come from two aspects:1)our labeled datasets with the full labeled on both helmet and pedestrian;2)public labeled datasets with the parts labeled either on the helmet or pedestrian.To fully take advantage of all datasets,we redesign CNN structure of network and loss functions based on YOLOv3.Then,we test our proposed ISS method based on the specific detection evaluation metrics.Finally,experimental results are given to show that our proposed ISS method enables the model to fully learn the labeled information from all datasets.When the threshold of intersection over union(IoU)between the predicted box and ground truth is set to 0.5,the average precision of pedestrians and helmets can reach 0.864 and 0.891,respectively.

Intelligent Video Surveillance System for Elderly People Living Alone Based on ODVS

Intelligent video surveillance for elderly people living alone using Omni-directional Vision Sensor (ODVS) is an important application in the field of intelligent video surveillance. In this paper, an ODVS is utilized to provide a 360° panoramic image for obtaining the real-time situation for the elderly at home. Some algorithms such as motion object detection, motion object tracking, posture detection, behavior analysis are used to implement elderly monitoring. For motion detection and object tracking, a method based on MHoEI(Motion History or Energy Images) is proposed to obtain the trajectory and the minimum bounding rectangle information for the elderly. The posture of the elderly is judged by the aspect ratio of the minimum bounding rectangle. And there are the different aspect ratios in accordance with the different distance between the object and ODVS. In order to obtain activity rhythm and detect variously behavioral abnormality for the elderly, a detection method is proposed using time, space, environment, posture and action to describe, analyze and judge the various behaviors of the elderly in the paper. In addition, the relationship between the panoramic image coordinates and the ground positions is acquired by using ODVS calibration. The experiment result shows that the above algorithm can meet elderly surveillance demand and has a higher recognizable rate.

Guest Editorial:Intelligent Video Surveillance and Related Technologies

Due to the increasing demand for developing a secure and smart living environment, the intelligent video surveillance technology has attracted considerable attention. Building an automatic, reliable, secure, and intelligent video surveillance system has spawned large research projects and triggered many popular research topics in several international conferences and workshops recently. This special issue of Journal of ElecWonic Science and Technology （JEST） aims to present recent advances in video surveillance systems which address the observation of people in an environment, leading to a real-time description of their actions and interactions.

Semi-automatic Video Annotation Tool to Generate Ground Truth for Intelligent Video Surveillance Systems

Generating ground truth data for developing object detection algorithms of intelligent surveillance systems is a considerably important yet time-consuming task; therefore, a user-friendly tool to annotate videos efficiently and accurately is required. In this paper, the development of a semi-automatic video annotation tool is described. For efficiency, the developed tool can automatically generate the initial annotation data for the input videos utilizing automatic object detection modules, which are developed independently and registered in the tool. To guarantee the accuracy of the ground truth data, the system also has several user-friendly functions to help users check and edit the initial annotation data generated by the automatic object detection modules. According to the experiment＇s results, employing the developed annotation tool is considerably beneficial for reducing annotation time; when compared to manual annotation schemes, using the tool resulted in an annotation time reduction of up to 2.3 times.

Artificial intelligence-assisted colonoscopy:A review of current state of practice and research

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs;however,colonoscopy practice can vary in terms of lesion detection,classification,and removal.Artificial intelligence(AI)-assisted decision support systems for endoscopy is an area of rapid research and development.The systems promise improved detection,classification,screening,and surveillance for colorectal polyps and cancer.Several recently developed applications for AIassisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas.However,their value for real-time application in clinical practice has yet to be determined owing to limitations in the design,validation,and testing of AI models under real-life clinical conditions.Despite these current limitations,ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination,including polypectomy procedures,are at the concept stage.However,further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice,to navigate the approval process from regulatory organizations and societies,and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety.This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

Artificial intelligence technologies for the detection of colorectal lesions: The future is now

Several studies have shown a significant adenoma miss rate up to 35%during screening colonoscopy,especially in patients with diminutive adenomas.The use of artificial intelligence(AI)in colonoscopy has been gaining popularity by helping endoscopists in polyp detection,with the aim to increase their adenoma detection rate(ADR)and polyp detection rate(PDR)in order to reduce the incidence of interval cancers.The efficacy of deep convolutional neural network(DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos.Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR.In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.

Advanced imaging and artificial intelligence for Barrett's esophagus:What we should and soon will do

Barrett’s esophagus(BE)is a well-established risk factor for esophageal adenocarcinoma.It is recommended that patients have regular endoscopic surveillance,with the ultimate goal of detecting early-stage neoplastic lesions before they can progress to invasive carcinoma.Detection of both dysplasia or early adenocarcinoma permits curative endoscopic treatments,and with this aim,thorough endoscopic assessment is crucial and improves outcomes.The burden of missed neoplasia in BE is still far from being negligible,likely due to inappropriate endoscopic surveillance.Over the last two decades,advanced imaging techniques,moving from traditional dye-spray chromoendoscopy to more practical virtual chromoendoscopy technologies,have been introduced with the aim to enhance neoplasia detection in BE.As witnessed in other fields,artificial intelligence(AI)has revolutionized the field of diagnostic endoscopy and is set to cover a pivotal role in BE as well.The aim of this commentary is to comprehensively summarize present evidence,recent research advances,and future perspectives regarding advanced imaging technology and AI in BE;the combination of computer-aided diagnosis to a widespread adoption of advanced imaging technologies is eagerly awaited.It will also provide a useful step-by-step approach for performing high-quality endoscopy in BE,in order to increase the diagnostic yield of endoscopy in clinical practice.

Innovative applications of artificial intelligence in zoonotic disease management

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.

A light-weight on-line action detection with hand trajectories for industrial surveillance

Most of the intelligent surveillances in the industry only care about the safety of the workers.It is meaningful if the camera can know what,where and how the worker has performed the action in real time.In this paper,we propose a light-weight and robust algorithm to meet these requirements.By only two hands'trajectories,our algorithm requires no Graphic Processing Unit(GPU)acceleration,which can be used in low-cost devices.In the training stage,in order to find potential topological structures of the training trajectories,spectral clustering with eigengap heuristic is applied to cluster trajectory points.A gradient descent based algorithm is proposed to find the topological structures,which reflects main representations for each cluster.In the fine-tuning stage,a topological optimization algorithm is proposed to fine-tune the parameters of topological structures in all training data.Finally,our method not only performs more robustly compared to some popular offline action detection methods,but also obtains better detection accuracy in an extended action sequence.

Smart Deep Learning Based Human Behaviour Classification for Video Surveillance

Real-time video surveillance system is commonly employed to aid security professionals in preventing crimes.The use of deep learning(DL)technologies has transformed real-time video surveillance into smart video surveillance systems that automate human behavior classification.The recognition of events in the surveillance videos is considered a hot research topic in the field of computer science and it is gaining significant attention.Human action recognition(HAR)is treated as a crucial issue in several applications areas and smart video surveillance to improve the security level.The advancements of the DL models help to accomplish improved recognition performance.In this view,this paper presents a smart deep-based human behavior classification(SDL-HBC)model for real-time video surveillance.The proposed SDL-HBC model majorly aims to employ an adaptive median filtering(AMF)based pre-processing to reduce the noise content.Also,the capsule network(CapsNet)model is utilized for the extraction of feature vectors and the hyperparameter tuning of the CapsNet model takes place utilizing the Adam optimizer.Finally,the differential evolution(DE)with stacked autoencoder(SAE)model is applied for the classification of human activities in the intelligent video surveillance system.The performance validation of the SDL-HBC technique takes place using two benchmark datasets such as the KTH dataset.The experimental outcomes reported the enhanced recognition performance of the SDL-HBC technique over the recent state of art approaches with maximum accuracy of 0.9922.

Artificial intelligence in dentistry:Harnessing big data to predict oral cancer survival

BACKGROUND Oral cancer is the sixth most prevalent cancer worldwide.Public knowledge in oral cancer risk factors and survival is limited.AIM To come up with machine learning(ML)algorithms to predict the length of survival for individuals diagnosed with oral cancer,and to explore the most important factors that were responsible for shortening or lengthening oral cancer survival.METHODS We used the Surveillance,Epidemiology,and End Results database from the years 1975 to 2016 that consisted of a total of 257880 cases and 94 variables.Four ML techniques in the area of artificial intelligence were applied for model training and validation.Model accuracy was evaluated using mean absolute error(MAE),mean squared error(MSE),root mean squared error(RMSE),R2 and adjusted R2.RESULTS The most important factors predictive of oral cancer survival time were age at diagnosis,primary cancer site,tumor size and year of diagnosis.Year of diagnosis referred to the year when the tumor was first diagnosed,implying that individuals with tumors that were diagnosed in the modern era tend to have longer survival than those diagnosed in the past.The extreme gradient boosting ML algorithms showed the best performance,with the MAE equaled to 13.55,MSE 486.55 and RMSE 22.06.CONCLUSION Using artificial intelligence,we developed a tool that can be used for oral cancer survival prediction and for medical-decision making.The finding relating to the year of diagnosis represented an important new discovery in the literature.The results of this study have implications for cancer prevention and education for the public.

一种多元数据融合的煤矿安全态势预测系统

煤矿井下智能化系统由通信、传感、调控等众多大大小小、同时运行的子系统组成。可有效地协同综采自动化系统,实现工作面自动化、智能化开采,尽可能降低安全事故发生的可能性,保障工作面安全高效开采。本研究结合传统采矿工艺,运用大数据、人工智能等先进信息技术,构建多元信息融合系统,对采煤工作面的安全监测监控、设备运行工况、开采过程以及人工测量等的多数据来源、多异构数据进行融合分析,对采煤工作面进行区域定性和定量的分析、评价及预测,反演出区域安全状态,同时将本系统应用于韩家湾煤矿。结果表明,本系统的应用改变了传统的信息融合为计量方式,有效降低了开采过程中因工作面环境恶劣造成设备或人员损伤的风险和停工停产损失,保障矿井安全高效生产。

System Structure and Calibration Models of Intelligent Photogrammetron

This paper describes the structure, geometric model and geometric calibrationof Photogrammetron Ⅰ - the first type of photogrammetron which is designed to be a coherent stereophotogrammetric system in which two cameras are mounted on a physical base but driven by anintelligent agent architecture. The system calibration is divided into two parts: the in-labcalibration determines the fixed parameters in advance of system operation, and the in-situcalibration keeps tracking the free parameters in real-time during the system operation. In a videosurveillance set-up, prepared control points are tracked in stereo image sequences, so that the freeparameters of the system can be continuously updated through iterative bundle adjustment and Kalmanfiltering.

Intelligent Biometric Information Management

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

面向智能视频监控的空中交通管制员图像分割

为解决复杂场景下空中交通管制员检测与分割精度低、鲁棒性差的问题,提出一种基于掩码区域卷积神经网络(Mask Region-based Convolutional Neural Networks, Mask R-CNN)的管制员图像分割模型ATC Mask R-CNN(ATC Mask Region-based Convolutional Neural Networks)。首先,构建管制员监控图像数据集(ATC Monitor Image Dataset, AMID)并用于模型训练、测试;其次,在主干网络中引入瓶颈注意力模块(Bottleneck Attention Module, BAM)以增强管制员特征提取,采取改进的柔性非极大值抑制算法(Soft Non-maximum Suppression, Soft-NMS)替代NMS算法进行候选框选取,提高对遮挡目标的检测分割;最后,基于AMID进行管制员图像分割试验。结果显示:ATC Mask R-CNN的精确率、召回率和平均精度分别为96.49%、95.62%和88.84%,表明了该方法的有效性。与Mask R-CNN相比,ATC Mask R-CNN有效降低了复杂场景的不利影响,更适用于管制员工作场景,可以为管制大厅安全管理自动化应用提供技术支撑。

不平衡数据下基于SVM增量学习的指挥信息系统状态监控方法

针对指挥信息系统历史状态样本有限的特点,基于支持向量机(support vector machines,SVM)设计了一种面向不平衡数据的SVM增量学习方法。针对系统正常/异常状态样本不平衡的情况,首先利用支持向量生成一部分新样本,然后通过分带的思想逐带产生分布更加均匀的新样本以调节原样本集的不平衡比。针对系统监控实时性要求高且在运行过程中会有新样本不断加入的特点,采用增量学习的方式对分类模型进行持续更新,在放松KKT(Karush-Kuhn-Tucker)更新触发条件的基础上,通过定义样本重要度并引入保留率和遗忘率的方式减少了增量学习过程中所需训练的样本数量。为了验证算法的有效性和优越性,实验部分在真实系统中获得的数据集以及UCI数据集中3类6组不平衡数据集中与现有的算法进行了对比。结果表明,所提算法能够有效实现对不平衡数据的增量学习,从而满足指挥信息系统状态监控的需求。

重大突发传染病智能化主动监测预警系统设计研究

建设智能化传染病主动监测预警系统是应对重大突发传染病疫情的必然要求、发展传染病监测预警技术的必然趋势、提高传染病监测预警能力的必然选择,提升重大突发传染病监测预警的科学性、及时性、准确性,需要智能化主动监测预警系统的支持。本文探讨了智能化传染病主动监测预警系统建设的必要性,系统梳理了国内外传染病监测预警系统建设现状和存在的问题;提出了重大突发传染病智能化主动监测预警系统的设计框架,涵盖多渠道主动监测、智能化早期预警、数据驱动的风险评估、智慧化处置与评估四大功能模块。在相关系统建设过程中,多源数据融合、智能早期预警、风险多维研判、智慧处置评估等关键技术攻关取得了一定进展,为系统构建和初步应用提供了坚实支撑。未来,需要多学科的协同合作来完善、维护和优化重大突发传染病智能化主动监测预警系统,为重大新发突发传染病防控提供更有效的手段,更好地预防和控制传染病的传播。

德国联邦情报局境外通讯战略性监控及其监督的新进展

[研究目的]继2016年之后,德国联邦议会于2021年3月再次对《联邦情报局法》进行修改。该法对情报机构收集境外外国人情报采取与美国不同的立场。对德国联邦情报局境外通讯战略性监控及其监督的新进展进行研究能够把握域外大规模情报监控及其监督制约的最新动向。[研究方法]通过对比分析、规范分析,对德国联邦情报局境外通讯战略性监控及其监督机制进行系统梳理;通过理性思辨方法,对该制度的新进展进行评析。[研究结论]德国2021年修改的《联邦情报局法》以《基本法》域外效力为基础,从监控目的、具体事由、事前审批、监控所收集数据的处理与留存、特殊数据保护、监督机制等方面全面重构了境外通讯战略性监控制度,且基于《基本法》域外效力的区别立法具有正当性和合理性,但适应性测试、截取通讯量限制等制度在贯彻比例原则上仍存争议。