Ordinal HyperPlane Loss

This research presents the development of a new framework for analyzing ordered class data, commonly called “ordinal class” data. The focus of the work is the development of classifiers (predictive models) that predict classes from available data. Ratings scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity and facial age estimation are examples of ordinal data for which data scientists may be asked to develop predictive classifiers. It is possible to treat ordinal classification like any other classification problem that has more than two classes. Specifying a model with this strategy does not fully utilize the ordering information of classes. Alternatively, the researcher may choose to treat the ordered classes as though they are continuous values. This strategy imposes a strong assumption that the real “distance” between two adjacent classes is equal to the distance between two other adjacent classes (e.g., a rating of ‘0’ versus ‘1,’ on an 11-point scale is the same distance as a ‘9’ versus a ‘10’). For Deep Neural Networks (DNNs), the problem of predicting k ordinal classes is typically addressed by performing k-1 binary classifications. These models may be estimated within a single DNN and require an evaluation strategy to determine the class prediction. Another common option is to treat ordinal classes as continuous values for regression and then adjust the cutoff points that represent class boundaries that differentiate one class from another. This research reviews a novel loss function called Ordinal Hyperplane Loss (OHPL) that is particularly designed for data with ordinal classes. OHPLnet has been demonstrated to be a significant advancement in predicting ordinal classes for industry standard structured datasets. The loss function also enables deep learning techniques to be applied to the ordinal classification problem of unstructured data. By minimizing OHPL, a deep neural network learns to map data to an optimal space in which the distance between points and their class centroids are minimized while a nontrivial ordering relationship among classes are maintained. The research reported in this document advances OHPL loss, from a minimally viable loss function, to a more complete deep learning methodology. New analysis strategies were developed and tested that improve model performance as well as algorithm consistency in developing classification models. In the applications chapters, a new algorithm variant is introduced that enables OHPLall to be used when large data records cause a severe limitation on batch size when developing a related Deep Neural Network

Materialisierte views in verteilten key-value stores

Distributed key-value stores have become the solution of choice for warehousing large volumes of data. However, their architecture is not suitable for real-time analytics. To achieve the required velocity, materialized views can be used to provide summarized data for fast access. The main challenge then, is the incremental, consistent maintenance of views at large scale. Thus, we introduce our View Maintenance System (VMS) to maintain SQL queries in a data-intensive real-time scenario.Verteilte key-value stores sind ein Typ moderner Datenbanken um große Mengen an Daten zu verarbeiten. Trotzdem erlaubt ihre Architektur keine analytischen Abfragen in Echtzeit. Materialisierte Views können diesen Nachteil ausgleichen, indem sie schnellen Zuriff auf Ergebnisse ermöglichen. Die Herausforderung ist dann, das inkrementelle und konsistente Aktualisieren der Views. Daher präsentieren wir unser View Maintenance System (VMS), das datenintensive SQL Abfragen in Echtzeit berechnet

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning

Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. However, recent advances in multi-agent learning mainly focus on value decomposition while leaving entity interactions still intertwined, which easily leads to over-fitting on noisy interactions between entities. In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle not only the joint value function into agent-wise value functions for decentralized execution, but also the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities. OPT facilitates filtering the noisy interactions between irrelevant entities and thus significantly improves generalizability as well as interpretability. Specifically, OPT introduces a sparse disagreement mechanism to encourage sparsity and diversity among discovered interaction prototypes. Then the model selectively restructures these prototypes into a compact interaction pattern by an aggregator with learnable weights. To alleviate the training instability issue caused by partial observability, we propose to maximize the mutual information between the aggregation weights and the history behaviors of each agent. Experiments on both single-task and multi-task benchmarks demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/OPT

Towards a taxonomy of reusable CRM requirements for the Not for Profit sector

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Traditional (or commercial) CRM is a well-defined domain but there is currently no generally accepted definition of what constitutes CRM in the not for profit (NfP) sector. Not for profit organisations are organisations which exist for a social purpose, are independent of the State, and which re-invest all of their financial surpluses in the services they offer or in the organisation itself. This research aims to answer the question “What exactly is CRM as applied to the NfP sector, what are its boundaries and what functions should an NfP CRM information system perform?” Grounded Theory Method (GTM) within a Design Science framework was used to collect, analyse, categorise, generalise and structure data from a number of NfP organisations and NfP information systems suppliers. An NfP CRM model was constructed from this data in the form of three multi-level taxonomies. The main taxonomy relates to generic and reusable information system requirements both functional and non-functional. Within this taxonomy the high-level categorisations of commercial CRM, namely “Marketing, “Sales” and “Service”, are greatly extended to reflect the special needs of the NfP sector and in particular a much broader definition of “customer”. The two minor taxonomies relate to issues of CRM strategy and CRM systems architecture which need to be considered alongside the system requirements. In addition to and resulting from the taxonomies, an over-arching definition of NfP CRM was developed. NfP organisations now have a framework that will enable them to know what to expect of CRM systems and from which they can select requirements to build their own specification of information system needs. Using the requirements taxonomy for this task will make the process of requirements analysis and specification easier, quicker, cheaper and more complete than using traditional methods. The framework will also allow NfP system suppliers to know what NfP organisations expect of their systems and will assist them with the specification of new system features. The minor taxonomies will provide NfP organisations with a series of strategic issues and systems architecture options that should be considered when implementing a CRM system. This research also demonstrates how GTM can be utilised: as the development phase of Design Research, as a general method of domain analysis, and as a tool to develop a taxonomy of reusable information system requirements

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

Easier Parallel Programming with Provably-Efficient Runtime Schedulers

Over the past decade processor manufacturers have pivoted from increasing uniprocessor performance to multicore architectures. However, utilizing this computational power has proved challenging for software developers. Many concurrency platforms and languages have emerged to address parallel programming challenges, yet writing correct and performant parallel code retains a reputation of being one of the hardest tasks a programmer can undertake. This dissertation will study how runtime scheduling systems can be used to make parallel programming easier. We address the difficulty in writing parallel data structures, automatically finding shared memory bugs, and reproducing non-deterministic synchronization bugs. Each of the systems presented depends on a novel runtime system which provides strong theoretical performance guarantees and performs well in practice