Design of an Information Security Service for Medical Artificial Intelligence

The medical convergence industry has gradually adopted ICT devices,which has led to legacy security problems related to ICT devices.However,it has been difficult to solve these problems due to data resource issues.Such problems can cause a lack of reliability in medical artificial intelligence services that utilize medical information.Therefore,to provide reliable services focused on security internalization,it is necessary to establish a medical convergence environment-oriented security management system.This study proposes the use of system identification and countermeasures to secure systemreliabilitywhen using medical convergence environment information in medical artificial intelligence.We checked the life cycle of medical information and the flow and location of information,analyzed the security threats that may arise during the life cycle,and proposed technical countermeasures to overcome such threats.We verified the proposed countermeasures through a survey of experts.Security requirements were defined based on the information life cycle in the medical convergence environment.We also designed technical countermeasures for use in the security management systems of hospitals of diverse sizes.

Bioinformatics Analysis and Identification of Key Candidate Genes Associated with Nephrotoxicity Induced by Cobalt

Cobalt(Co)is a silver-gray,high-intensity,widely distributed metal element that exists in cobalt compounds,and its common valences are bivalence(Co2+)and trivalence(Co3+)[1].The main routes of Co-exposure are occupational and environmental exposures.The human body can be exposed to high concentrations of Co2+through inhalation of contaminated air,consumption of contaminated food and water,or ingestion of Co-containing supplements[2].

Detection of Coal Mine Spontaneous Combustion by Fuzzy Inference System

The spontaneous combustion is a smoldering process and characterized by a slow burning speed and a long duration. Therefore, it is a hazard to coal mines. Early detection of coal mine spontaneous combustion is quite difficult because of the complexity of different coal mines. And the traditional threshold discriminance is not suitable for spontaneous combustion detection due to the uncertainty of coalmine combustion. Restrictions of the single detection method will also affect the detection precision in the early time of spontaneous combustion. Although multiple detection methods can be adopted as a complementarity to improve the accuracy of detection, the synthesized method will in- crease the complicacy of criterion, making it difficult to estimate the combustion. To solve this problem, a fuzzy inference system based on CRI （Compositional Rule of Inference） and fuzzy reasoning method FITA （First Infer Then Aggregate） are presented. And the neural network is also developed to realize the fuzzy inference system. Finally, the effectiveness of the inference system is demonstrated bv means of an experiment.

XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission.Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry.However,real-time training and classifying network traffic pose challenges,as they can lead to the degradation of the overall dataset and difficulties preventing attacks.Additionally,existing semi-supervised learning research might need to analyze the experimental results comprehensively.This paper proposes XA-GANomaly,a novel technique for explainable adaptive semi-supervised learning using GANomaly,an image anomalous detection model that dynamically trains small subsets to these issues.First,this research introduces a deep neural network(DNN)-based GANomaly for semi-supervised learning.Second,this paper presents the proposed adaptive algorithm for the DNN-based GANomaly,which is validated with four subsets of the adaptive dataset.Finally,this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations,reconstruction error visualization,and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage,semi-supervised learning,and adaptive learning.Compared to other single-class classification techniques,the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13%and 8%of F1 scores and 4.17%and 11.51%for accuracy,respectively.Furthermore,experiments of the proposed adaptive learning reveal mostly improved results over the initial values.An analysis and monitoring system based on the combination of the three explainable methodologies is also described.Thus,the proposed method has the potential advantages to be applied in practical industry,and future research will explore handling unbalanced real-time datasets in various scenarios.

Precise conversion between virtual world and real world in stereoscopic imaging

A method for precise conversion between virtual world and real world is put forward in this paper. The method aims to precisely establish the connection between the virtual coordinates and the real coordinates with OpenGL. In the virtual world, two virtual cameras are set to capture the left and right perspective planar images, and coordinates of the planar images can be calculated by the perspective projection model. With coordinates of planar images, coordinates of the stereo- scopic image synthesized in the real world can be calculated by the binocular observation model. Therefore, the corresponding connection between the two systems is established. Experimental re- suits match data from this method well. Therefore, this method can precisely realize the conversion and the interactivity, laying a solid foundation for further study.

Blockchain Technology Based Information Classification Management Service

Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information,but also the expansion of areas and assets to be protected.In terms of information security,it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets.Also,it has caused difficulties in managing those issues due to reasons such as mutual interference,countless security events and logs’data,etc.Within this security environment,an organization should identify and classify assets based on the value of data and their security perspective,and then apply appropriate protection measures according to the assets’security classification for effective security management.But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets.In this paper,we propose an information classification management service based on blockchain,which presents and uses a model of the value of data and the security perspective.It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain.The proposed service reduces assets to be protected and security solutions to be applied,and provides security measures at the platform level rather than individual security solutions,by using blockchain.In the rapidly changing security environment of Industry 4.0,this proposed service enables economic security,provides a new integrated security platform,and demonstrates service value.

Applying Artificial Intelligence(AI)to improve fire response activities

This research discusses how to use a real-time Artificial Intelligence(AI)object detection model to improve on-site incident command and personal accountability in fire response.We utilized images of firegrounds obtained from an online resource and a local fire department to train the AI object detector,YOLOv4.Consequently,the real-time AI object detector can reach more than ninety percent accuracy when counting the number of fire trucks and firefighters on the ground utilizing images from local fire departments.Our initial results indicate AI provides an innovative method to maintain fireground personnel accountability at the scenes of fires.By connecting cameras to additional emergency management equipment(e.g.,cameras in fire trucks and ambulances or drones),this research highlights how this technology can be broadly applied to various scenarios of disaster response,thus improving on-site incident fire command and enhancing personnel accountability on the fireground.

Robotics for Disaster Warning and Response in the UAE

Over the past decade,robot systems have become more commonplace and increasingly autonomous.In recent years,first responders have started to use novel technologies at the scene of disasters in order to save more lives.Technologies are also used for early warning,surveillance and to enhance disaster response capabilities.Increasingly,technologies like robots are used for warning people,monitoring compliance,SAR(Search and Rescue),damage assessment,to search disaster sites.In the case of emergency situations,emergency guidance robots are sent inside of buildings or deployed to search for victims,guide evacuees to safety and other unsafe response tasks.This paper explores the application of robotics for disaster warning and response,benefits and factors influencing deployment of robots,in order to justify the effective usage of robotics for disaster management in the UAE(United Arab Emirates).A pilot study is conducted to achieve this aim,with 24 participants selected through random sampling from three emergency organizations in the country.To increase knowledge and usage of robotics for future disaster warning and response in UAE,it is needful to continue to highlight the role of robotics deployment in helping to minimize risks and disaster impacts on first responders and the public.

Navigating Interoperability in Disaster Management:Insights of Current Trends and Challenges in Saudi Arabia

In this rapid review,we critically scrutinize the disaster management infrastructure in Saudi Arabia,illuminating pivotal issues of interoperability,global cooperation,established procedures,community readiness,and the integration of cuttingedge technologies.Our exploration uncovers a significant convergence with international benchmarks,while pinpointing areas primed for enhancement.We recognize that continual commitments to infrastructural progression and technology adoption are indispensable.Moreover,we underscore the value of robust community involvement and cross-border collaborations as key factors in bolstering disaster response capabilities.Importantly,we spotlight the transformative influence of emerging technologies,such as artificial intelligence and the Internet of Things,in elevating the effectiveness of disaster management strategies.Our review champions in all-encompassing approach to disaster management,which entails harnessing innovative technologies,nurturing resilient communities,and promoting comprehensive disaster management strategies,encapsulating planning,preparedness,response,and recovery.As a result of our analysis,we provide actionable recommendations to advance Saudi Arabia's disaster management framework.Our insights are timely and crucial,considering the escalating global focus on disaster response in the face of increasing disaster and humanitarian events.

Awareness and attitudes regarding Helicobacter pylori infection among university students:a national cross-sectional survey

Helicobacter pylori(H.pylori)infection is one of the most common chronic bacterial infections all over the world.University students are a group with strong comprehensive ability.Their cognition and behavior can exert great impact on society.However,up to now,reports on the awareness and attitudes regarding H.pylori infection among university students are scarce.This study aimed to survey dietary,habits,knowledge,and attitudes towards H.pylori infection.A total of 5794 participants,including undergraduates,postgraduates,and doctoral students,were recruited from the top 100 universities in China.A selfconstructed questionnaire was used to assess the knowledge and attitudes of students toward H.pylori infection and its impact.In our study,most of the population preferred dining in the canteen(69.6%),whereas 20.6% chose restaurants or takeaway.Up to 24.1% of the respondents had at least one lifestyle habit associated with H.pylori colonization.Almost half had at least one digestive symptom related to H.pylori infection.Most students were aware of its association with gastritis(84.4%)and peptic ulcer(86.6%).However,only half of them were aware of its association with gastric cancer(57.9%).Furthermore,only 14.1% of the respondents had been tested for H.pylori,and 25.1% of them tested positive.The H.pylori-detection rate was higher in Hunan province compared with Guangdong and Jilin provinces.Regarding knowledge of H.pylori,65.4% of the respondents had known about it,and 24.3% correctly answered all questions.When comparing the acquisition of H.pylori knowledge between tested and untested students,32.5% of the tested participants answered all questions correctly,which was significantly higher than the untested group(13.1%).There was no significant difference between genders in H.pylori knowledge and detection.University students are highly educated population.If they were fully aware of the harm of H.pylori infection,their parents,friends,and even future families would benefit,thus reducing the incidence of H.pylori infection,as well as gastric cancer and healthcare finances.This survey not only investigated but also spread the awareness of H.pylori among university students,which is of great medical,economic and sociological importance.

Advancing emergency preparedness in a postpandemic world:global collaboration and innovative approaches for hospitals

As the world transitions into a postpandemic era,hospitals and healthcare systems must adapt their emergency management plans to address the unique challenges that remain.Building upon the previous Hospital Emergency Management Plan^([1])during the coronavirus disease 2019 pandemic,this commentary offers updated and novel suggestions for emergency preparedness,emphasizing the need for international coordination and the implementation of innovative strategies.

A geocomputational analysis of Twitter activity around different world cities

The penetration and use of social media services differs from city to city.This paper is aimed to provide a comparison of the use of Twitter between different cities of the world.We present a temporal analysis of activity on Twitter in 15 cities.Our study consists of two parts:First,we created temporal graphs of the activity in the 15 cities,through which hours of high and low activity could be identified.Second,we created heat map visualizations of the Twitter activities during the period of 19 September 2012–25 September 2013.The heat map visualizations make the periods of intense and sparse activity apparent and provide a snapshot of the activity during the whole year.

Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series

In the context of rapid digitization in industrial environments,how effective are advanced unsupervised learning models,particularly hybrid autoencoder models,at detecting anomalies in industrial control system(ICS)datasets?This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things(IoT)devices,which can significantly improve the reliability and safety of these systems.In this paper,we propose a hybrid autoencoder model,called ConvBiLSTMAE,which combines convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)to more effectively train complex temporal data patterns in anomaly detection.On the hardware-in-the-loopbased extended industrial control system dataset,the ConvBiLSTM-AE model demonstrated remarkable anomaly detection performance,achieving F1 scores of 0.78 and 0.41 for the first and second datasets,respectively.The results suggest that hybrid autoencoder models are not only viable,but potentially superior alternatives for unsupervised anomaly detection in complex industrial systems,offering a promising approach to improving their reliability and safety.