A unifying co-operative web caching architecture

Network caching of objects has become a standard way of reducing network traffic and latency in the web. However, web caches exhibit poor performance with a hit rate of about 30%. A solution to improve this hit rate is to have a group of proxies form co-operation where objects can be cached for later retrieval. A co-operative cache system includes protocols for hierarchical and transversal caching. The drawback of such a system lies in the resulting network load due to the number of messages that need to be exchanged to locate an object. This paper proposes a new co-operative web caching architecture, which unifies previous methods of web caching. Performance results shows that the architecture achieve up to 70% co-operative hit rate and accesses the cached object in at most two hops. Moreover, the architecture is scalable with low traffic and database overhead

A study on implementing probabilistic packet marking in IPv6

Lack of source authentication in the IP protocol helps to encourage denial-of-service attacks. The open and trusting nature of the protocol makes the task of identifying an attacker difficult if the attacker chooses to spoof the source address. Probabilistic Packet Marking (PPM) is an IP traceback approach that seeks to identify attackers by marking individual packets with portion of the attack path, and relies on the volume of attack traffic generated to reconstruct the whole path. In this work, we consider the fragmentation problem associated with the overloading of the identification field in IPv4 packet header in PPM implementation, and demonstrate how this can be resolved in IPv6. We show that the flow label field in the IPv6 datagram header can be safely and effectively overloaded to implement PPM schemes, and present simulation results verifying the applicability and efficiency of our approach

Modeling the performance of an outcome based educational framework

In this paper, we introduce an Outcomes Based Educational model that has been adopted by Zayed University, a newly established institution in the United Arab Emirates. We provide an overview of the learning outcomes assessment courses used to support and assist students in their development of the university learning outcomes. We introduce the assessment process and the e-portfolio. The academic program model is a new concept that uses the outcome-based approach and the grade point average technique. This hybrid model is complex and includes many unsolved issues. In order to understand and clarify some of these issues, we propose to use neural networks that provide a mathematical model. To simplify the complexities of the academic model, we use a reduced map of the relationships between students\u27 activities and the learning outcomes to be used in the assessment process. The model is tested using students\u27 works. The neural networks based model is used to help decision makers improve the educational model

Performance analysis of probabilistic packet marking in IPv6

Probabilistic packet marking (PPM) has received considerable attention as an IP traceback approach against distributed Denial-of-Service attack, which is one of the most challenging security threat in the Internet. PPM is a technique that seeks to identify the source of such attacks by marking individual packets with portion of the attack path, and then relies on the volume of attack traffic generated to ensure that the whole path can be reconstructed. However, modifying the identification field in the IPv4 packet header to mark packet incurs backward incompatibility for IP fragmented packets. In this paper, we address this issue and analyze the viability of PPM under the next-generation Internet Protocol, IPv6. In doing so, we consider the flaws inherent to IPv4 implementations that limit their backward compatibility, and demonstrate how these shortcomings can be avoided in IPv6. We show that the Flow Label field in the IPv6 datagram header can be safely and effectively overloaded to implement PPM schemes, and present simulation results verifying the applicability and efficiency of this approach. © 2007 Elsevier B.V. All rights reserved

Assessing a New Academic Model Using Artificial Neural Networks

Zayed University-ZU is a young University that adopted a new Academic Program Model-APM. The new academic program is an Outcome Based Education-OBE model that was developed to address challenges that face a rapidly changing society such as the UAE. The Learning Outcome-LO academic model was designed around six learning outcomes; Information Literacy and Communication, Information Technology, Critical Thinking and Reasoning, Global Awareness, Teamwork, Leadership, to help students develop critical intellectual capacities and skills that will help them. Furthermore, the new academic model focuses on the process of student learning. To simulate the APM performance, a mathematical model that uses neural networks and fuzzy logic was developed. The neural network was trained on mapping 40 activities into a set of 24 indicators. Then, we used a number of well chosen fuzzy If-Then rules obtained from expert knowledge to classify the 24 indicators into the six ZU Learning Outcomes. The simulations were all successfully performed using the MATLAB simulation package. We believe that the combined Neural Networks-Fuzzy logic modeling technique is a very effective and efficient tool to study how the APM performs under various conditions

A Novel Outcome-Based Educational Model and its Effect on Student Learning, Curriculum Development, and Assessment

Introduction We live in a rapidly changing world driven by technology and economy necessitating the production of qualified and well-prepared professionals. Employers are demanding that university graduates not only have the knowledge, but the appropriate skills to be effective and productive in the workplace. In order to adapt to these challenges, universities worldwide are thinking about how to redesign their academic models. A recent US national panel report calls for a dramatic reorganization of undergraduate education to ensure that all college students receive not just access to college, but an education of lasting value. The report also recommends colleges help students become intentional life-long learners, and to create new assessments that require students to apply their learning to the real world (Greater Expectation, 2002). Zayed University (ZU), a laptop university (each student and faculty owns a laptop) based in the United Arab Emirates, has adopted a new educational concept in the region, which is an Outcome-Based learning approach. This new Academic Program Model (APM) is designed to continuously improve the curriculum and provide students with the knowledge and skills to succeed in a rapidly changing world. The life-long learning outcomes, being the kernel of the courses, provide focus to the curriculum in the APM. Furthermore, all courses are designed to clearly show the experiences that students draw upon achieving a Learning Outcome. The ZU OBE learning approach is framed by three sets of learning outcomes. Two are course embedded (general education and major learning outcomes), and the third (the ZU learning outcomes (ZULO)) is a set of higher intellectual outcomes. To fulfill their ZULO requirements, students compile evidence of their achievement in electronic portfolios, which are assessed by a faculty panels. The APM is driven by five critical components: the outcome based curriculum, the e-portfolios, the learning communities, the use of information technology, and the support of the center for teaching and learning assessment. Universities in the USA and worldwide are taking a critical look at their educational systems. A recent US national panel report calls for a dramatic reorganization of undergraduate education to ensure that all college aspirants receive not just access to college, but an education of lasting value. The report also recommends colleges help students become intentional life-long learners, and to create new assessments that require students to apply their learning to the real world (Greater Expectation, 2002). Furthermore, universities in the US and worldwide are complaining about the problem of grade inflation (Rosovsky & Hartley, 2002). A number of academic institutions in the US have moved to an outcome-based education framework to move away from the grade point average driven academic framework. In North America, accreditations institutions (such as North Central Association) are asking academic institutions to present a method to assess students learning outcomes in the general education courses. In Columbia College, Columbia, Missouri, assessment of the student learning outcomes in the Information Literacy course is done by giving them a pre-test and a post-test. During the first day of the course, students are given a multiple-choice test about computer literacy. The same test is given to the students during the last week of the course as part of their final examination. The difference between the two grades is used as a measure of their progress. A new academic institution in the gulf region has tackled the above issues by adopting an academic framework that is based on the outcome-based education while still using the grade point average. This academic model is a hybrid approach that accommodates learning outcomes to measure the learning process and uses grades to accommodate the classic academic system. We anticipate that this model will insure that grade inflation is under control and that students are achieving the learning outcomes to become life-long learners (Bouslama, Lansari, Al-Rawi, & Abonamah, 2002).

A Novel Outcome-Based Educational Model and its Effect on Student Learning, Curriculum Development, and Assessment

Introduction We live in a rapidly changing world driven by technology and economy necessitating the production of qualified and well-prepared professionals. Employers are demanding that university graduates not only have the knowledge, but the appropriate skills to be effective and productive in the workplace. In order to adapt to these challenges, universities worldwide are thinking about how to redesign their academic models. A recent US national panel report calls for a dramatic reorganization of undergraduate education to ensure that all college students receive not just access to college, but an education of lasting value. The report also recommends colleges help students become intentional life-long learners, and to create new assessments that require students to apply their learning to the real world (Greater Expectation, 2002). Zayed University (ZU), a laptop university (each student and faculty owns a laptop) based in the United Arab Emirates, has adopted a new educational concept in the region, which is an Outcome-Based learning approach. This new Academic Program Model (APM) is designed to continuously improve the curriculum and provide students with the knowledge and skills to succeed in a rapidly changing world. The life-long learning outcomes, being the kernel of the courses, provide focus to the curriculum in the APM. Furthermore, all courses are designed to clearly show the experiences that students draw upon achieving a Learning Outcome. The ZU OBE learning approach is framed by three sets of learning outcomes. Two are course embedded (general education and major learning outcomes), and the third (the ZU learning outcomes (ZULO)) is a set of higher intellectual outcomes. To fulfill their ZULO requirements, students compile evidence of their achievement in electronic portfolios, which are assessed by a faculty panels. The APM is driven by five critical components: the outcome based curriculum, the e-portfolios, the learning communities, the use of information technology, and the support of the center for teaching and learning assessment. Universities in the USA and worldwide are taking a critical look at their educational systems. A recent US national panel report calls for a dramatic reorganization of undergraduate education to ensure that all college aspirants receive not just access to college, but an education of lasting value. The report also recommends colleges help students become intentional life-long learners, and to create new assessments that require students to apply their learning to the real world (Greater Expectation, 2002). Furthermore, universities in the US and worldwide are complaining about the problem of grade inflation (Rosovsky & Hartley, 2002). A number of academic institutions in the US have moved to an outcome-based education framework to move away from the grade point average driven academic framework. In North America, accreditations institutions (such as North Central Association) are asking academic institutions to present a method to assess students learning outcomes in the general education courses. In Columbia College, Columbia, Missouri, assessment of the student learning outcomes in the Information Literacy course is done by giving them a pre-test and a post-test. During the first day of the course, students are given a multiple-choice test about computer literacy. The same test is given to the students during the last week of the course as part of their final examination. The difference between the two grades is used as a measure of their progress. A new academic institution in the gulf region has tackled the above issues by adopting an academic framework that is based on the outcome-based education while still using the grade point average. This academic model is a hybrid approach that accommodates learning outcomes to measure the learning process and uses grades to accommodate the classic academic system. We anticipate that this model will insure that grade inflation is under control and that students are achieving the learning outcomes to become life-long learners (Bouslama, Lansari, Al-Rawi, & Abonamah, 2002).

Managerial insights for AI/ML implementation: a playbook for successful organizational integration

Abstract In the contemporary business environment, the assimilation of artificial intelligence (AI) and machine learning (ML) is pivotal for fostering innovation and ensuring long-term growth. This paper examines the strategic aspects of AI/ML adoption, emphasizing that its success rests not just on technology but also on strategic alignment, collaboration, and robust leadership. Highlighting the indispensable role of senior leaders, the paper offers a managerial framework for AI/ML integration, ensuring its alignment with organizational goals. Using real-world examples, the paper presents how AI/ML can be strategically embedded to enhance customer interactions, streamline operations, and unveil new revenue streams. The objective is to provide senior leaders with an understanding, enabling them to harness AI/ML effectively, ensuring their organizations remain at the innovation forefront in a digital age dominated by disruptive AI/ML technologies

Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction

Prediction models of coronavirus disease utilizing machine learning algorithms range from forecasting future suspect cases, predicting mortality rates, to building a pattern for country-specific pandemic end date. To predict the future suspect infection and death cases, we categorized the approaches found in the literature into: first, a purely data-driven approach, whose goal is to build a mathematical model that relates the data variables including outputs with inputs to detect general patterns. The discovered patterns can then be used to predict the future infected cases without any expert input. The second approach is partially data-driven; it uses historical data, but allows expert input such as the SIR epidemic algorithm. This approach assumes that the epidemic will end according to medical reasoning. In this paper, we compare the purely data-driven and partially-data driven approaches by applying them to data from three countries having different past pattern behavior. The countries are the US, Jordan, and Italy. It is found that those two prediction approaches yield significantly different results. Purely data-driven approach depends totally on the past behavior and does not show any decline in the number of the infected cases if the country did not experience any decline in the number of cases. On the other hand, a partially data-driven approach guarantees a timely decline of the infected curve to reach zero. Using the two approaches highlights the importance of human intervention in pandemic prediction to guide the learning process as opposed to the purely data-driven approach that predicts future cases based on the pattern detected in the data