NETWORK ACTIVITY AUDITING USING LINUX RUNNING OFF A LOOPBACK ROOT FILESYSTEM

Areas where network traffic auditing helps network administrators is to identify sources of network activities with regards to the quality of data transmission (such as packet losses and latency) and quantity of data transmitted (such as absolute number of bytes and packets, as well as their rate of transmission per second), which are used to take the next step to remedy problems raised by them. In this project, we utilized a Debian GNU/Linux operating system running off a Loopback Root Filesystem as a network traffic auditing system. The project covers the design of the Linux system, use of Argus (Audit Record Generation and Utilization System-a network traffic auditing suite of tools), and the interpretation of the data gathered. The focus is to evaluate the designed system, analyze the data gathered and propose the next steps to improve the network traffic auditing system and the network it has audited

Parallel classification and optimization of telco trouble ticket dataset

In the big data age, extracting applicable information using traditional machine learning methodology is very challenging. This problem emerges from the restricted design of existing traditional machine learning algorithms, which do not entirely support large datasets and distributed processing. The large volume of data nowadays demands an efficient method of building machine-learning classifiers to classify big data. New research is proposed to solve problems by converting traditional machine learning classification into a parallel capable. Apache Spark is recommended as the primary data processing framework for the research activities. The dataset used in this research is related to the telco trouble ticket, identified as one of the large volume datasets. The study aims to solve the data classification problem in a single machine using traditional classifiers such as W-J48. The proposed solution is to enable a conventional classifier to execute the classification method using big data platforms such as Hadoop. This study’s significant contribution is the output matrix evaluation, such as accuracy and computational time taken from both ways resulting from hyper-parameter tuning and improvement of W-J48 classification accuracy for the telco trouble ticket dataset. Additional optimization and estimation techniques have been incorporated into the study, such as grid search and cross-validation method, which significantly improves classification accuracy by 22.62% and reduces the classification time by 21.1% in parallel execution inside the big data environment

NETWORK ACTIVITY AUDITING USING LINUX RUNNING OFF A LOOPBACK ROOT FILESYSTEM

Areas where network traffic auditing helps network administrators is to identify sources of network activities with regards to the quality of data transmission (such as packet losses and latency) and quantity of data transmitted (such as absolute number of bytes and packets, as well as their rate of transmission per second), which are used to take the next step to remedy problems raised by them. In this project, we utilized a Debian GNU/Linux operating system running off a Loopback Root Filesystem as a network traffic auditing system. The project covers the design of the Linux system, use of Argus (Audit Record Generation and Utilization System-a network traffic auditing suite of tools), and the interpretation of the data gathered. The focus is to evaluate the designed system, analyze the data gathered and propose the next steps to improve the network traffic auditing system and the network it has audited

Measuring learner's performance in e-learning recommender systems

A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test

Rank Aggregation for Course Sequence Discovery

In this work, we adapt the rank aggregation framework for the discovery of optimal course sequences at the university level. Each student provides a partial ranking of the courses taken throughout his or her undergraduate career. We compute pairwise rank comparisons between courses based on the order students typically take them, aggregate the results over the entire student population, and then obtain a proxy for the rank offset between pairs of courses. We extract a global ranking of the courses via several state-of-the art algorithms for ranking with pairwise noisy information, including SerialRank, Rank Centrality, and the recent SyncRank based on the group synchronization problem. We test this application of rank aggregation on 15 years of student data from the Department of Mathematics at the University of California, Los Angeles (UCLA). Furthermore, we experiment with the above approach on different subsets of the student population conditioned on final GPA, and highlight several differences in the obtained rankings that uncover hidden pre-requisites in the Mathematics curriculum

Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning

Satellite image analysis has numerous useful applications in various domains. Extracting their visual information has been made easier using remote sensing and deep learning technologies that intelligently interpret clear visual cues. However, satellite information has the potential for more complex tasks, such as recommending business locations and categories based on the implicit patterns and structures of the regions of interest. Nonetheless, this task is significantly more challenging due to the absence of obvious visual cues and the highly similar appearance of each location. This study aims to analyze satellite image similarity between business class categories and investigate the capabilities of state-of-the-art deep learning models for learning non-obvious visual cues. Specifically, a satellite image dataset is constructed using business locations and annotated with the business categories for image structural similarity analysis, followed by business category classification via fine-tuning of deep learning classifiers. The models are then analyzed by visualizing the features learned to determine if they could capture hidden information for such a task. Experiments show that business locations have significantly high SSIM regardless of categories, and deep learning models only recorded a top accuracy of 60%. However, feature visualization using Grad-CAM shows that the models learn biased features and disregard highly informative details such as roads. It is concluded that typical learning models and strategies are insufficient to effectively solve this complex visual problem; thus, further research should be done to formulate solutions for such non-obvious classifications with the potential to support business recommendation applications

Education and Learning Science and Technology; Researchers at Multimedia University release new data on education and learning science and technology

In this paper, we aim to address this need by proposing a novel e-learning recommender system framework that is based on two conceptual foundations-peer learning and social learning theories that encourage students to cooperate and learn among themselves, researchers in Malaysia report

Prostate Cancer Presenting as Left Supraclavicular Lymphadenopathy and a Review of the Literature

Aims:Patients presenting with a neck mass are commonly seen by the ENT surgeon. They are also usually related to head and neck tumours. However, cervical node involvement from the prostate is rare, especiallyas an initial presentation of the disease. We report a case of prostate carcinoma presenting with a left supraclavicular lymph node.Presentation of Case:A 61-year-old gentleman presented to our clinic with a rapidly growing left sided neck mass. Fine needle aspiration cytology (FNAC) of the neck mass was interpreted as metastatic carcinoma. It was later revealed by the patient that he had been experiencing lower urinary tract symptoms (LUTS). PSA was 1331ng/ml. He was referred to our urology service andwas treated as metastatic prostate cancer.Discussion: Prostate cancer commonly spreads to the regional lymph nodes, pelvic organs, or the axial skeleton. Distant metastases to the cervical nodes are rare and accounts for 0.3-1% of cases.Conclusion: In male patients presenting with left supraclavicular mass, it is important for the clinician to keep in mind of the possibility of metastases from prostatic malignancy

Wireless Underground Sensor Network: Improving EBMR Protocol for the Purpose of Energy Conservation and Data Transmission Reliability

Wireless Underground Sensor Networks have been widely used to transmit information such as transmitting environmental information. The main issue in Wireless Underground Sensor Networks is the power consumption since the reliability of data depends on the power availability and longevity. In this paper we proposed a new data transmission method in EBMR protocol by using a Dynamic Window Acknowledgement that is able to reduce the number of acknowledgement send between sender and receiver. As a result, it helps to reduce the energy consumption of network devices

Prediction of voltage collapse in electric power systems

With the increased loading and exploitation of the power transmission system, the problem of voltage instability and voltage collapse has become a growing concern. Voltage instability of large power systems has been considered as a complex problem due to the large number of power system components participating in the voltage collapse process. As a result, two distinct methods have been adopted for voltage stability analysis, that is, the power flow based static method and the time simulation based dynamic method. This paper analyses the basic mechanism of voltage collapse by using the static and dynamic load models. A newly developed indicator using the line stability factors is proposed with the aim of predicting voltage collapse in transmission networks. The mathematical concept of the line stability factors is explained and the factor which acts as indicator of proximity to voltage collapse is defined such that it varies in the range between 0 (system stable) and 1 (voltage collapse). The line stability factors are easily calculated and uses information of a normal load flow. Tests carried out by using the line stability factors as indicators of proximity to voltage collapse illustrate the advantages and simplicity of using the factors. The prediction of voltage collapse caused by a uniform increase of the load as well as due to other contingencies such as line outage and step increase in mechanical load, is accurately obtained by using the line stability factor