56 research outputs found

    2010-2011, University of Memphis bulletin

    Get PDF
    University of Memphis bulletin containing the undergraduate catalog for 2010-2011.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1451/thumbnail.jp

    2011-2012, University of Memphis bulletin

    Get PDF
    University of Memphis bulletin containing the undergraduate catalog for 2011-2012.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1452/thumbnail.jp

    Logging Statements Analysis and Automation in Software Systems with Data Mining and Machine Learning Techniques

    Get PDF
    Log files are widely used to record runtime information of software systems, such as the timestamp of an event, the name or ID of the component that generated the log, and parts of the state of a task execution. The rich information of logs enables system developers (and operators) to monitor the runtime behavior of their systems and further track down system problems in development and production settings. With the ever-increasing scale and complexity of modern computing systems, the volume of logs is rapidly growing. For example, eBay reported that the rate of log generation on their servers is in the order of several petabytes per day in 2018 [17]. Therefore, the traditional way of log analysis that largely relies on manual inspection (e.g., searching for error/warning keywords or grep) has become an inefficient, a labor intensive, error-prone, and outdated task. The growth of the logs has initiated the emergence of automated tools and approaches for log mining and analysis. In parallel, the embedding of logging statements in the source code is a manual and error-prone task, and developers often might forget to add a logging statement in the software's source code. To address the logging challenge, many e orts have aimed to automate logging statements in the source code, and in addition, many tools have been proposed to perform large-scale log le analysis by use of machine learning and data mining techniques. However, the current logging process is yet mostly manual, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and content prediction, i.e., `where and what to log', are of high interest. In addition, approaches that can automatically mine and extract insight from large-scale logs are also well sought after. Thus, in this research, we focus on predicting the log statements, and for this purpose, we perform an experimental study on open-source Java projects. We introduce a log-aware code-clone detection method to predict the location and description of logging statements. Additionally, we incorporate natural language processing (NLP) and deep learning methods to further enhance the performance of the log statements' description prediction. We also introduce deep learning based approaches for automated analysis of software logs. In particular, we analyze execution logs and extract natural language characteristics of logs to enable the application of natural language models for automated log le analysis. Then, we propose automated tools for analyzing log files and measuring the information gain from logs for different log analysis tasks such as anomaly detection. We then continue our NLP-enabled approach by leveraging the state-of-the-art language models, i.e., Transformers, to perform automated log parsing

    Proceedings of the 19th Sound and Music Computing Conference

    Get PDF
    Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f

    Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach

    Get PDF
    الطاقة الشمسية هي واحدة من الطاقة المتجددة التي لا حصر لها في توليد الطاقة لبيئة خضراء ونظيفة وصحية. تمتص الألواح الشمسية المكونة من طبقة السيليكون طاقة الشمس وتتحول إلى كهرباء بواسطة عاكس خارج الشبكة. نقل الكهرباء يتم إما من هذا العاكس أو من المحول، التي تستهلكها وحدة (وحدات) الاستهلاك المتاحة للأغراض السكنية أو الاقتصادية. الشبكة العصبية الاصطناعية هي أساس الذكاء الاصطناعي وتحل العديد من المشاكل المعقدة التي يصعب من خلال الأساليب الإحصائية أو من قبل البشر. في ضوء ذلك، فإن الغرض من هذا العمل هو تقييم أداء نظام الطاقة الشمسية - المحولات - الاستهلاك (STC). قد يكون النظام في حالة انهيار كامل بسبب فشل كل من النظام الفرعي لأتمتة الطاقة الشمسية والمحول في وقت واحد أو وحدة الاستهلاك ؛ وإلا فإنه يعمل بكفاءة كاملة أو أقل. يتم النظر في حالات الفشل والإصلاحات المستقلة إحصائيًا. يتم استخدام ظاهرة الاحتمالات الأولية المدمجة مع المعادلات التفاضلية لفحص موثوقية النظام ، للنظام القابل للإصلاح وغير القابل للإصلاح، ولتحليل دالة التكلفة الخاصة به. يمكن تحسين دقة واتساق النظام من خلال نهج الشبكة العصبية للانتشار الأمامي والخلفي (FFBPNN). يمكن لآلية تعلم النسب المتدرجة أن تقوم بتحديث الأوزان العصبية وبالتالي النتائج تصل إلى الدقة المطلوبة في كل تكرار، وبغض النظر عن مشكلة تلاشي التدرج في الشبكات العصبية الأخرى، مما يزيد من كفاءة النظام في الوقت الفعلي. تم تصميم كود MATLAB لخوارزمية FFBP لتحسين قيم الموثوقية ووظيفة التكلفة من خلال تقليل الخطأ إلى الحد الأدنى حتى 0.0001. يتم النظر في الرسوم التوضيحية العددية مع جداول البيانات والرسوم البيانية الخاصة بهم، لتوضيح النتائج وتحليلها في شكل الموثوقية ووظيفة التكلفة، والتي قد تكون مفيدة لمحللي النظام.Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both solar power automation subsystem and transformer simultaneously or consumption unit; otherwise it works with fully or lesser efficiency. Statistically independent failures and repairs are considered. Using the elementary probabilities phenomenon incorporated with differential equations is employed to examine the system reliability, for repairable and non-repairable system, and to analyze its cost function. The accuracy and consistency of the system can be improved by feed forward- back propagation neural network (FFBPNN) approach. Its gradient descent learning mechanism can update the neural weights and hence the results up to the desired accuracy in each iteration, and aside the problem of vanishing gradient in other neural networks, that increasing the efficiency of the system in real time. MATLAB code for FFBP algorithm is built to improve the values of reliability and cost function by minimizing the error up to 0.0001 precision. Numerical illustrations are considered with their data tables and graphs, to demonstrate and analyze the results in the form of reliability and cost function, which may be helpful for system analyzers

    Analysis of scaling policies for NFV providing 5G/6G reliability levels with fallible servers

    Get PDF
    The softwarization of mobile networks enables an efficient use of resources, by dynamically scaling and re-assigning them following variations in demand. Given that the activation of additional servers is not immediate, scaling up resources should anticipate traffic demands to prevent service disruption. At the same time, the activation of more servers than strictly necessary results in a waste of resources, and thus should be avoided. Given the stringent reliability requirements of 5G applications (up to 6 nines) and the fallible nature of servers, finding the right trade-off between efficiency and service disruption is particularly critical. In this paper, we analyze a generic auto-scaling mechanism for communication services, used to de(activate) servers in a cluster, based on occupation thresholds. We model the impact of the activation delay and the finite lifetime of the servers on performance, in terms of power consumption and failure probability. Based on this model, we derive an algorithm to optimally configure the thresholds. Simulation results confirm the accuracy of the model both under synthetic and realistic traffic patterns as well as the effectiveness of the configuration algorithm. We also provide some insights on the best strategy to support an energy-efficient highly-reliable service: deploying a few powerful and reliable machines versus deploying many machines, but less powerful and reliable.The work of Jorge Ortin was funded in part by the Spanish Ministry of Science under Grant RTI2018-099063-B-I00, in part by the Gobierno de Aragon through Research Group under Grant T31_20R, in part by the European Social Fund (ESF), and in part by Centro Universitario de la Defensa under Grant CUD-2021_11. The work of Pablo Serrano was partly funded by the European Commission (EC) through the H2020 project Hexa-X (Grant Agreement no. 101015956), and in part by Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019-108713RB-C52/AEI/ 10.13039/501100011033). The work of Jaime Garcia-Reinoso was partially supported by the EC in the framework of H2020-EU.2.1.1. 5G EVE project (Grant agreement no. 815074). The work of Albert Banchs was partially supported by the EC in the framework of H2020-EU.2.1.1. 5G-TOURS project (Grant agreement no. 856950) also partially supported by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019- 108713RB-C52/AEI/10.13039/501100011033)

    Radio Communications

    Get PDF
    In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

    An Integrated Framework to Evaluate Off-Nominal Requirements and Reliability of Novel Aircraft Architectures in Early Design

    Get PDF
    One of the barriers to the development of novel aircraft architectures and technologies is the uncertainty related to their reliability and the safety risk they pose. In the conceptual and preliminary design stages, traditional system safety techniques rely on heuristics, experience, and historical data to assess these requirements. The limitations and off-nominal operational considerations generally postulated during traditional safety analysis may not be complete or correct for new concepts. Additionally, dearth of available reliability data results in poor treatments of epistemic and aleatory uncertainty for novel aircraft architectures. Two performance-based methods are demonstrated to solve the problem of improving the identification and characterization of safety related off-nominal requirements in early design. The problem of allocating requirements to the unit level is solved using a network-based bottom-up analysis algorithm combined with the Critical Flow Method. A Bayesian probability approach is utilized to better deal with epistemic and aleatory uncertainty while assessing unit level failure rates. When combined with a Bayesian decision theoretic approach, it provides a mathematically backed framework for compliance finding under uncertainty. To estimate multi-state reliability of complex systems, this dissertation contributes a modified Monte-Carlo algorithm that uses the Bayesian failure rate posteriors previously generated. Finally, multi-state importance measures are introduced to determine the sensitivity of different hazard severity to unit reliability. The developed tools, techniques, and methods of this dissertation are combined into an integrated framework with the capability to perform trade-studies informed by safety and reliability considerations for novel aircraft architectures in early preliminary design. A test distributed electric propulsion (T-DEP) aircraft inspired by the X-57 is utilized as a test problem to demonstrate this frameworkPh.D

    Reinforcement Learning

    Get PDF
    Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field
    corecore