Standardizing catch per unit effort by machine learning techniques in longline fisheries: a case study of bigeye tuna in the Atlantic Ocean

Support vector machine (SVM) is shown to have better performance in catch per unit of effort (CPUE) standardization than other methods. The SVM performance highly relates to its parameters selection and has not been discussed in CPUE standardization. Analyzing the influence of parameter selection on SVM performance for CPUE standardization could improve model construction and performance, and thus provide useful information to stock assessment and management. We applied SVM to standardize longline catch per unit fishing effort of fishery data for bigeye tuna (Thunnus obesus) in the tropical fishing area of Atlantic Ocean and evaluated three parameters optimization methods: a Grid Search method, and two improved hybrid algorithms, namely SVMs in combination with the particle swarm optimization (PSO-SVM), and genetic algorithms (GA-SVM), in order to increase the strength of SVM. The mean absolute error (MAE), mean square error (MSE), three types of correlation coefficients and the normalized mean square error (NMSE) were computed to compare the algorithm performances. The PSO-SVM and GA-SVM algorithms had particularly high performances of indicative values in the training data and dataset, and the performances of PSO-SVM were marginally better than GA-SVM. The Grid search algorithm had best performances of indicative values in testing data. In general, PSO was appropriate to optimize the SVM parameters in CPUE standardization. The standardized CPUE was unstable and low from 2007 to 2011, increased during 2011- 2013, then decreased from 2015 to 2017. The abundance index was lower compared with before 2000 and showed a decreasing trend in recent years

Sparse Density Estimation with Measurement Errors

This paper aims to build an estimate of an unknown density of the data with measurement error as a linear combination of functions from a dictionary. Inspired by the penalization approach, we propose the weighted Elastic-net penalized minimal $\ell_2$-distance method for sparse coefficients estimation, where the adaptive weights come from sharp concentration inequalities. The optimal weighted tuning parameters are obtained by the first-order conditions holding with a high probability. Under local coherence or minimal eigenvalue assumptions, non-asymptotical oracle inequalities are derived. These theoretical results are transposed to obtain the support recovery with a high probability. Then, some numerical experiments for discrete and continuous distributions confirm the significant improvement obtained by our procedure when compared with other conventional approaches. Finally, the application is performed in a meteorology data set. It shows that our method has potency and superiority of detecting the shape of multi-mode density compared with other conventional approaches.Comment: 34 pages, 4 figure

On the Expected Discounted Penalty Function for the Classical Risk Model with Potentially Delayed Claims and Random Incomes

We focus on the expected discounted penalty function of a compound Poisson risk model with random incomes and potentially delayed claims. It is assumed that each main claim will produce a byclaim with a certain probability and the occurrence of the byclaim may be delayed depending on associated main claim amount. In addition, the premium number process is assumed as a Poisson process. We derive the integral equation satisfied by the expected discounted penalty function. Given that the premium size is exponentially distributed, the explicit expression for the Laplace transform of the expected discounted penalty function is derived. Finally, for the exponential claim sizes, we present the explicit formula for the expected discounted penalty function

Automating Intersection Marking Data Collection and Condition Assessment at Scale With An Artificial Intelligence-Powered System

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with existing roadway Geographic information systems (GIS) data and aerial images. The system harnesses emerging artificial intelligence (AI) techniques such as deep learning and multi-task learning to enhance its robustness, accuracy, and computational efficiency. AI models were developed to detect lane-use arrows (85% mean average precision) and crosswalks (89% mean average precision) and to assess the degradation conditions of markings (91% overall accuracy for lane-use arrows and 83% for crosswalks). Data acquisition and computer vision modules developed were integrated and a graphical user interface (GUI) was built for the system. The proposed system can fully automate the processes of marking data collection and condition assessment on a large scale with almost zero cost and short processing time. The developed system has great potential to propel urban science forward by providing fundamental urban infrastructure data for analysis and decision-making across various critical areas such as data-driven safety management and prioritization of infrastructure maintenance

Secure Software Engineering Education: Knowledge Area, Curriculum and Resources

This paper reviews current efforts and resources in secure software engineering education, with the goal of providing guidance for educators to make use of these resources in developing secure software engineering curriculum. These resources include Common Body of Knowledge, reference curriculum, sample curriculum materials, hands-on exercises, and resources developed by industry and open source community. The relationship among the Common Body of Knowledge proposed by the Department of Homeland Security, the Software Engineering Institute at Carnegie Mellon University, and ACM/IEEE are discussed. The recent practices on secure software engineering education, including secure software engineering related programs, courses, and course modules are reviewed. The course modules are categorized into four categories to facilitate the adoption of these course modules. Available hands-on exercises developed for teaching software security are described and mapped to the taxonomy of coding errors. The rich resources including various secure software development processes, methods and tools developed by industry and open source community are surveyed. A road map is provided to organize these resources and guide educators in adopting these resources and integrating them into their courses

Relationship between Microstructure and Properties of Cu-Cr-Ag-(Ce) Alloy Using Microscopic Investigation

Microstructure, precipitation hardening response, and mechanical and physical properties of Cu-Cr-Ag alloy and Cu-Cr-Ag-Ce alloy have been investigated using transmission electron microscopy, scanning electron microscope, optical microscope, electrical conductivity analysis, and tensile test. The influence of element Ce on the matrix refinement, impurity removal, and precipitation in the Cu-Cr-Ag alloys has been analyzed. The experimental results show that the strength and electrical conductivity of Ce containing alloys are greater than those of Ce-free alloys after each processing step. Improvement of strength and electrical conductivity of the Cu-Cr-Ag alloy by adding Ce element is attributed to removing oxygen and sulfur from as-cast alloy