Deep Learning-Based Maritime Environment Segmentation for Unmanned Surface Vehicles Using Superpixel Algorithms

Unmanned surface vehicles (USVs) are receiving increasing attention in recent years from both academia and industry. To make a high-level autonomy for USVs, the environment situational awareness is a key capability. However, due to the richness of the features in marine environments, as well as the complexity of the environment influenced by sun glare and sea fog, the development of a reliable situational awareness system remains a challenging problem that requires further studies. This paper, therefore, proposes a new deep semantic segmentation model together with a Simple Linear Iterative Clustering (SLIC) algorithm, for an accurate perception for various maritime environments. More specifically, powered by the SLIC algorithm, the new segmentation model can achieve refined results around obstacle edges and improved accuracy for water surface obstacle segmentation. The overall structure of the new model employs an encoder–decoder layout, and a superpixel refinement is embedded before final outputs. Three publicly available maritime image datasets are used in this paper to train and validate the segmentation model. The final output demonstrates that the proposed model can provide accurate results for obstacle segmentation.

New Results on Small and Dim Infrared Target Detection

Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriendly and have difficulty in achieving a robust performance in datasets with clouds occupying a large proportion of the image background. In this paper, we present new results by using an efficient method that extracts the candidate targets in the pre-processing stage and fuses the local scale, blob-based contrast map and gradient map in the detection stage. We also developed mid-wave infrared (MWIR) and long-wave infrared (LWIR) cameras for data collection experiments and algorithm evaluations. Experimental results using both publicly available datasets and image sequences acquired by our cameras clearly demonstrated that the proposed method achieves high detection accuracy with the mean AUC being at least 22.3% higher than comparable methods, and the computational cost beating the other methods by a large margin.

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the Model Conditional Processor and the Ensemble Model Output Statistics method. Using 2-years of reforecasts with daily timesteps this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations both in terms of the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead-times (up to 15 days) but is largest at short lead-times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time-series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end-users to make more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Research on Intelligent Perception and Cognitive Computing of Information Security System Based on Computer Big Data

Since the 21st century, the development of network technology has entered a new stage, and mankind has gradually entered the era of big data with information explosion. Computer big data technology has changed people’s lifestyles. People can obtain information and materials without going out to provide convenience for people’s lives. But it is affected in many ways, but we will also encounter some difficulties in the process of using it. Informatization has had a profound impact on many areas of human social life. Especially in the context of global informationization, the information security (IS) problems encountered by China at this stage are more prominent and obvious than ever before. IS issues have also become a hot topic for many scholars to pay attention to and study. Informatization has gradually penetrated into every aspect of daily production work. However, the flow of enterprise informatization has made the public suffer from IS problems while improving office efficiency. This paper analyzes the shortcomings of current enterprise IS situation awareness; studies the construction of enterprise IS situation awareness system through big data technology, artificial intelligence algorithm, and threat intelligence technology; and puts forward the enterprise level IS situation awareness system model, situation awareness system, architecture, and specific implementation method. After analyzing the system design and deployment, the threats to corporate IS can be discovered in a more timely manner, and based on risk judgment and threat tracking, the company’s detection capabilities against security threats and security attacks can be improved, and effective security incident handling can be provided. Supported by technical means, the security situational awareness system will bring different protection and prevention to our enterprise.

Avoidance of Cybersecurity Threats with the Deployment of a Web-Based Blockchain-Enabled Cybersecurity Awareness System

Modern information technology (IT) is well developed, and almost everyone uses the features of IT and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures to largely mitigate these threats. The ignorance of or lack of cybersecurity knowledge also causes a critical problem regarding confidentiality and privacy. It is not possible to fully avoid cybercrimes that often lead to sufficient business losses and spread forbidden themes (disgust, extremism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed WBCA trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses Blockchain technology to protect the program from potential threats. The proposed program is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users. A CentOS-based virtual private server is deployed for testing the proposed WBCA to determine its effectiveness. Finally, WBCA is also compared with other state-of-the-art web-based programs designed for cybersecurity awareness.

REPORTS AND AWARENESS OF RESTAURANT TAXPAYERS AND SOCIALIZATION OF THE SYSTEM TO INCREASE PAD IN BANJARBARU CITY

Reports and Awareness of Restaurant Taxpayers and Socialization of the System to Increase PAD in Banjarbaru City, under the guidance of Mr. H. Nurus Sjamsi and Mr. H. Ihil S Baron, as supervisors I and II.This research was conducted with the aim of analyzing the simultaneous effect of reporting, taxpayer awareness and system socialization on the PAD of Banjarbaru City; analyze the partial effect of reporting, awareness of taxpayers and the socialization of the system to PAD Kota Banjarbaru; and analyze the reporting variables, awareness of taxpayers and socialization that have a dominant effect on PAD of Banjarbaru City.Analysis of the data used in this study is quantitative descriptive with a sample of 80 taxpayer respondents, using multiple linear regression test data analysis.The results of the study concluded that the reporting, awareness of taxpayers and simultaneous socialization of the system significantly influence the original income of the Banjarbaru City. Partially reporting, awareness of taxpayers and socialization have a significant effect on the original income of the Banjarbaru City. The variable that has dominant influence on the original income of the Banjarbaru City is socialization, because the significant value is greater than the other variables.Keywords: Reporting, Taxpayer Awareness, System Socialization, PAD Banjarbaru CityAbstrak: Laporan Dan Kesadaran Wajib Pajak Restoran Serta Sosialisasi Sistem Untuk Meningkatkan PAD Kota Banjarbaru, dibawah bimbingan Bapak H. Nurus Sjamsi dan Bapak H. Ihil S Baron, selaku dosen pembimbing I dan II.Penelitian ini dilaksanakan dengan tujuan untuk menganalisis pengaruh simultan dari pelaporan, kesadaran wajib pajak dan sosialisasi sistem terhadap PAD Kota Banjarbaru; menganilisis pengaruh parsial dari pelaporan, kesadaran wajib pajak dan sosialisasi sistem terhadap PAD Kota Banjarbaru; dan menganalisis dari variabel pelaporan, kesadaran wajib pajak dan sosialisasi yang berpengaruh dominan terhadap PAD Kota Banjarbaru.Analisis data yang digunakan dalam penelitian ini adalah deskriptif kuantitatif dengan sampel sebanyak 80 responden wajib pajak, menggunakan analisis data uji regresi linier berganda.Hasil penelitian menyimpulkan bahwa pelaporan, kesadaran wajib pajak dan sosialisasi sistem secara simultan berpengaruh signifikan terhadap pendapatan asli daerah Kota Banjarbaru. Secara parsial pelaporan, kesadaran wajib pajak dan sosialisasi berpengaruh signifikan terhadap pendapatan asli daerah Kota Banjarbaru. Variabel yang berpengaruh dominan terhadap pendapatan asli daerah Kota Banjarbaru adalah sosialisasi, karena nilai signifikan lebih besar pengaruhnya dibanding dengan variabel yang lain.Kata kunci: Pelaporan, Kesadaran Wajib Pajak, Sosialisasi Sistem, PAD Kota Banjarbaru