In this paper, we described an ongoing project called CS++ that aims to expanding a CS curriculum in cybersecurity with minimum requirement of additional resources by plugging open cybersecurity courseware in the existing curriculum. Each courseware unit covers a special topic of cybersecurity technology and can be plugged in one or multiple related CS courses; altogether, the open cybersecurity courseware delivers a comprehensive view of cybersecurity knowledge. After being plugged with the open courseware units, a typical CS curriculum is turned to a CS++ curriculum with comprehensive coverage of cybersecurity knowledge.
The k Nearest Neighbors (KNN) algorithm has been widely applied in various supervised learning tasks due to its simplicity and effectiveness. However, the quality of KNN decision making is directly affected by the quality of the neighborhoods in the modeling space. Efforts have been made to map data to a better feature space either implicitly with kernel functions, or explicitly through learning linear or nonlinear transformations. However, all these methods use pre-determined distance or similarity functions, which may limit their learning capacity. In this paper, we propose a novel deep learning architecture, which is called the Deep Similarity-Enhanced K Nearest Neighbors (DSE-KNN), to learn an optimized similarity function of the data directly towards the goal of optimizing the KNN decision making. In other words, the type of similarity function that is used in our method is not pre-determined but rather learned to map data to a high-dimensional feature space where the accuracy of the KNN decision making is maximized. Experimental results show that DSE-KNN outperforms other common machine learning methods on classifying different types of disease datasets and predicting daily price direction of different stock ETFs.
This article focuses on the order planning and scheduling of rod and wire production. The method improves the current order planning and scheduling based on slabs and coils by using Gantt char and referring to the characteristic of rod and wire production with pass system and solves the problem of the applicability of the order planning and scheduling to the large quantities rod and wire carbon steel production.
“Modern Chinese” by Huang Bo-rong(2nd edition)has been adopted as teaching materials in the higher educational institutions for many years.We found out some deficiency existed in the book Ⅱ while using it.The author analyses and gives suggestions on the “the confirmation of sentence structure”,“the judgement and correction of the problem sentences”,“the definitio n and application of the rhetorical case”in this thesis.
The utilization of cloud computing resources contributes to a fast growth of e-business and e-government where businesses and government agencies often require processing a large volume of service requests in a constrained period of time. In order to achieve on-time completion of a large volume of time-constrained parallel processes, this paper proposes a novel dynamic checkpoint selection strategy (CSS DM ) for monitoring cloud business workflows. CSS DM monitors the execution time points rather than activities along the business workflows. The paper also provides some parameters that can track the execution process of each virtual machine. This mechanism significantly increases the efficiency in monitoring large volume processes so as to improve the quality of service in both e-business and e-government sectors. Simulation results show that CSS DM has excellent performance in different scenarios even when certain amounts of noises are added.
Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency.
In the latest generation of video coding standard – H.265/HEVC, the partition of Coding Tree Unit (CTU) into CU (Coding Unit) is a very critical yet time-consuming component. Traditional methods find the optimum partition mode for each CTU through iterative and exhaustive search, which is a very tim
Network threat detection has been challenging due to the complexities of attack activities and the limitation of historical threat data to learn from. To help enhance the existing practices of using analytics, machine learning, and artificial intelligence methods to detect the network threats, we propose an integrated modelling framework, where Knowledge Graph is used to analyze the users' activity patterns, Imbalanced Learning techniques are used to prune and weigh Knowledge Graph, and LLM is used to retrieve and interpret the users' activities from Knowledge Graph. The proposed framework is applied to Agile Threat Detection through Online Sequential Learning. The preliminary results show the improved threat capture rate by 3%-4% and the increased interpretabilities of risk predictions based on the users' activities.
The evolution of machine learning and computer vision in technology has driven a lot of improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras and cognitive pieces of equipment placed in the wilderness have been growing exponentially. However, the resources(human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 years, 17000 hours at a rate of 40 hours/ week to label 3.2 million images from the Serengeti wild park for our research, we are going to focus on wild data, and keep proving that deep learning can do better and faster than the human equivalent labour for the same task. Moreover, this is also an opportunity to present some custom Capsule Networks architectures to the deep learning community while solving the above-mentioned critical problem. Incidentally, we are going to take advantage of these data to make a comparative study on multiple Deep learning models. Specifically, VGG-net, RES-net and a custom made Convolutional-Capsule Network. We benchmark our work with the Serengeti project where Mohammed Mohammed Sadegh et al. recently published a 92% top-1 accuracy [15] and Gomez et al. had a 58% top-1 accuracy [8]. We successfully reached 96.4% top-1 accuracy on the same identification task. Concurrently, we reach up to 79.48% top-1 testing accuracy on a big complex dataset using capsule network, which out-perform the best results of Capsule networks on a complex dataset from Edgar Xi et al. with 71% testing accuracy [5,23,18].
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