Database of audio records

Diplomka a prakticky castDiplome with partical part

Recommender System Using Collaborative Filtering Algorithm

With the vast amount of data that the world has nowadays, institutions are looking for more and more accurate ways of using this data. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. However, to reach this goal, systems need to parse a lot of data and collect information, sometimes from different resources, and predict how the user will like the product or item. The computation power needed is considerable. Also, companies try to avoid flooding customer mailboxes with hundreds of products each morning, thus they are looking for one email or text that will make the customer look and act. The motivation to do the project comes from my eagerness to learn website design and get a deep understanding of recommender systems. Applying machine learning dynamically is one of the goals that I set for myself and I wanted to go beyond that and verify my result. Thus, I had to use a large dataset to test the algorithm and compare each technique in terms of error rate. My experience with applying collaborative filtering helps me to understand that finding a solution is not enough, but to strive for a fast and ultimate one. In my case, testing my algorithm in a large data set required me to refine the coding strategy of the algorithm many times to speed the process. In this project, I have designed a website that uses different techniques for recommendations. User-based, Item-based, and Model-based approaches of collaborative filtering are what I have used. Every technique has its way of predicting the user rating for a new item based on existing users’ data. To evaluate each method, I used Movie Lens, an external data set of users, items, and ratings, and calculated the error rate using Mean Absolute Error Rate (MAE) and Root Mean Squared Error (RMSE). Finally, each method has its strengths and weaknesses that relate to the domain in which I am applying these methods

Leveraging Public Knowledge Project\u27s Open Conference Systems for Digital Scholarship

The Media History Exchange (MHX) is an archive, social network, conference management tool, and collaborative workspace for the international, interdisciplinary community of researchers studying the history of journalism and communication. It opens a new scholarly space between the academic conference and the peer-reviewed journal by archiving “born digital” conference papers and abstracts that frequently have not been saved previously. In the spring of 2017, MHX migrated to the Public Knowledge Project’s Open Conference Systems. If your library is interested in expanding its digital scholarship offerings to include conference support, or offers its own library-focused conference, this technology might be exactly what you need. Co-author: Elliot King, Ph.D. (Loyola University Maryland

Using SMS text messaging for teaching and data collection in the behavioral sciences

Recent interest in university teaching has focused on interactivity in lectures and practical classes, and teachers in several fields have set up systems in which students can interact with the lecturer using mobile-phone-based SMS text messaging. This approach has particular potential in psychology, where students could use SMS messaging as a way of responding in simple psychology experiments or demonstrations. We describe a simple architecture for an SMS-based responding, using an SMS-to-HTTP message relay service, and a PHP/MySQL input-output handler. We describe briefly two experiments we have run using the system. The first experiment examined anchoring effects in an SMS-based auction. The second experiment examined delay discounting, with participants indicating their intertemporal preferences using SMS. Finally, we evaluate the feedback we obtained from students about the practical and conceptual issues surrounding text-message-based responding

BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment

Synergy: An Energy Monitoring and Visualization System

The key to becoming a more sustainable society is first learning to take responsibility for the role we play in energy consumption. Real-time energy usage gives energy consumers a sense of responsibility over what they can do to accomplish a much larger goal for the planet, and practically speaking, what they can do to lower the cost to their wallets. Synergy is an energy monitoring and visualization system that enables users to gather information about the energy consumption in a building – small or large – and display that data for the user in real-time. The gathered energy usage data is processed on the edge before being stored in the cloud. The two main benefits of edge processing are issuing electricity hazard warnings immediately and preserving user privacy. In addition to being a scalable solution that intended for use in individual households, commercial offices and city power grids, Synergy is open-source so that it can be implemented more widely. This paper contains a system overview as well as initial finding based on the data collected by Synergy before assessing the impact the system can have on society

Integrating Case-Based Reasoning with Adaptive Process Management

The need for more flexiblity of process-aware information systems (PAIS) has been discussed for several years and different approaches for adaptive process management have emerged. Only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. The knowledge about changes has not yet been exploited by any of these systems. To overcome this practical limitation, PAIS must capture the whole process life cycle and all kinds of changes in an integrated way. They must allow users to deviate from the predefined process in exceptional situations, and assist them in retrieving and reusing knowledge about previously performed changes. In this report we present a proof-of concept implementation of a learning adaptive PAIS. The prototype combines the ADEPT2 framework for dynamic process changes with concepts and methods provided by case-based reasoning(CBR) technology

A FRAMEWORK FOR INTELLIGENT VOICE-ENABLED E-EDUCATION SYSTEMS

Although the Internet has received significant attention in recent years, voice is still the most convenient and natural way of communicating between human to human or human to computer. In voice applications, users may have different needs which will require the ability of the system to reason, make decisions, be flexible and adapt to requests during interaction. These needs have placed new requirements in voice application development such as use of advanced models, techniques and methodologies which take into account the needs of different users and environments. The ability of a system to behave close to human reasoning is often mentioned as one of the major requirements for the development of voice applications. In this paper, we present a framework for an intelligent voice-enabled e-Education application and an adaptation of the framework for the development of a prototype Course Registration and Examination (CourseRegExamOnline) module. This study is a preliminary report of an ongoing e-Education project containing the following modules: enrollment, course registration and examination, enquiries/information, messaging/collaboration, e-Learning and library. The CourseRegExamOnline module was developed using VoiceXML for the voice user interface(VUI), PHP for the web user interface (WUI), Apache as the middle-ware and MySQL database as back-end. The system would offer dual access modes using the VUI and WUI. The framework would serve as a reference model for developing voice-based e-Education applications. The e-Education system when fully developed would meet the needs of students who are normal users and those with certain forms of disabilities such as visual impairment, repetitive strain injury (RSI), etc, that make reading and writing difficult