Cache Equalizer: A Cache Pressure Aware Block Placement Scheme for Large-Scale Chip Multiprocessors

This paper describes Cache Equalizer (CE), a novel distributed cache management scheme for large scale chip multiprocessors (CMPs). Our work is motivated by large asymmetry in cache sets usages. CE decouples the physical locations of cache blocks from their addresses for the sake of reducing misses caused by destructive interferences. Temporal pressure at the on-chip last-level cache, is continuously collected at a group (comprised of cache sets) granularity, and periodically recorded at the memory controller to guide the placement process. An incoming block is consequently placed at a cache group that exhibits the minimum pressure. CE provides Quality of Service (QoS) by robustly offering better performance than the baseline shared NUCA cache. Simulation results using a full-system simulator demonstrate that CE outperforms shared NUCA caches by an average of 15.5% and by as much as 28.5% for the benchmark programs we examined. Furthermore, evaluations manifested the outperformance of CE versus related CMP cache designs

Characterization of polybenzimidazole (PBI) film at high temperatures

Polybenzimidazole, a linear thermoplastic polymer with excellent thermal stability and strength retention over a wide range of temperatures, was evaluated for its potential use as the main dielectric in high temperature capacitors. The film was characterized in terms of its dielectric properties in a frequency range of 50 Hz to 100 kilo-Hz. These properties, which include the dielectric constant and dielectric loss, were also obtained in a temperature range from 20 C to 300 C with an electrical stress of 60 Hz, 50 V/mil present. The alternating and direct current breakdown voltages of silicone oil impregnated films as a function of temperature were also determined. The results obtained indicate that while the film remained relatively stable up to 200 C, it exhibited an increase in its dielectric properties as the temperature was raised to 300 C. It was also found that conditioning of the film by heat treatment at 60 C for six hours tended to improve its dielectric and breakdown properties. The results are discussed and conclusions made concerning the suitability of the film as a high temperature capacitor dielectric

83 Orange Peels

83 ORANGE PEELS is a feature-length film written and directed by Klara Hammoud and produced by Biddayat as part of the requirements for earning a Master of Fine Arts in Entrepreneurial Digital Cinema from the University of Central Florida. The project aims to challenge existing conventions of the documentary filmmaking on multiple levels – aesthetic, narrative, and technical– while also examining growing importance of workflow throughout all aspects of production. These challenges were both facilitated and necessitated by the limited resources available to the production team and the academic context of the production. This thesis is a record of the film, from concept to completion and preparation for delivery to an audience

Suggestions for a preschool curriculum in Lebanon

Québec Université Laval, Bibliothèque 201

An Experience of the Conservation of Historic Buildings’ Facades in Old Saida City

This article delves into the nuanced experience and challenges involved in conserving historic building facades within an old Saida city neighborhood marked by neglect and limited restoration efforts due to class dynamics and discrimination by heritage curators. Nevertheless, its buildings have been subjected to lack of maintenance and repair which led to processes of degradation with time and loss of some cultural heritage [6]. Focusing on a deprived area, the paper examines the challenges and opportunities encountered in conserving architectural heritage amidst socioeconomic constraints. Damage related to the collapse of building elements necessitates an investigation into the underlying causes to prevent such occurrences. This involves identifying a set of parameters to assess the hazards of façades and public exposure. Through a blend of community involvement in close coordination between the author who drives innovative conservation techniques, UNDP and the Municipality of Saida, the project sheds light on the potential for safeguarding the historical character to the damaged historic Musalkhiyyeh street arcades and façades, Kaniset el-amercani, Musallabiyyeh old market streets amongst with a specific square called “Furn el Saha” in old Saida historic city. The project falls under the UNDP project “Improving Living Conditions in Gatherings Host Communities”. The conservation project aimed to conserve those buildings, and promoted histories of places and people’s memories connected to the selected heritage sites. The project rehabilitated internally and externally for the three selected areas. It addressed both the physical deterioration and build the knowledge about the importance of the sites. By documenting this journey, valuable lessons emerge for policymakers, urban planners, and conservationists seeking to address heritage preservation in marginalized communities. Historic structures design and construction tell us much about the cultures and the history of a community that created them and about the traditions and events from which our society grew

Factors affecting students’ attitude and performance when using a web-enhanced learning environment

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The purpose of this thesis is to investigate the use of a course management system in a University learning environment and the factors that affect students' attitude and performance in such environments and to study the relationship between these factors. The course management system that was used in the research studies in this thesis was WebCT. Three in-field studies were carried out to achieve the aim of this research thesis. A mixture of qualitative and quantitative approaches was used in the studies. Data from participants were collected via questionnaires, interviews, and numerical data from the WebCT tracking system. First the relationship between the students' attitude towards using WebCT and their module leaders' attitude towards using it was studied. Then, the relationship between students' cognitive styles and their satisfaction, their achievement, and their way of using WebCT was investigated. Finally, a model of the critical factors affecting students‟ attitudes to WebCT, use of WebCT and achievement was developed and tested. The model is divided into three main dimensions. The three dimensions are 1) The learner dimension: students' interaction with their classmates, students' capability of using the internet, students' capability of using WebCT. 2) The instructor dimension: Instructor's technical competence, instructor's way of presenting materials on WebCT, interaction between students and their instructor. 3) The technology dimension: usefulness, ease of use, flexibility, quality. The results suggested that students have a positive attitude towards using a course management system (WebCT) on their courses. Also, the results indicated that students' use of WebCT is a positive indicator of their academic achievement (in terms of performance on specific modules). It was also found that instructor attitude and way of using WebCT affects students' attitude and performance when using WebCT. The Technology dimension was found to be a positive indicator of students' attitude and use of WebCT. The Instructor dimension was also found to be a positive indicator of students' attitude and achievement in WebCT. Moreover, the Learner dimension was found to be a positive indicator of students' attitude, use of WebCT and achievement.Sponsored by AlBaath Universit

MapReduce network enabled algorithms for classification based on association rules

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization

MATHEMATICS BEHIND MACHINE LEARNING

Artificial intelligence (AI) is a broad field of study that involves developing intelligentmachines that can perform tasks that typically require human intelligence. Machinelearning (ML) is often used as a tool to help create AI systems. The goal of ML isto create models that can learn and improve to make predictions or decisions based on given data. The goal of this thesis is to build a clear and rigorous exposition of the mathematical underpinnings of support vector machines (SVM), a popular platform used in ML. As we will explore later on in the thesis, SVM can be implemented in practice: image classification, text classification, or face recognition. We start by introducing the process of classification in a mathematical framework, then we interpret the algorithm of SVM using linear algebra, analysis, statistics, and topology. We will prove that SVM is a reliable technique using Lagrange multipliers, inner product spaces, metrics spaces, Slater’s theorem, and the kernel trick