Determining the Limits of Automated Program Recognition

This working paper was submitted as a Ph.D. thesis proposal.Program recognition is a program understanding technique in which stereotypic computational structures are identified in a program. From this identification and the known relationships between the structures, a hierarchical description of the program's design is recovered. The feasibility of this technique for small programs has been shown by several researchers. However, it seems unlikely that the existing program recognition systems will scale up to realistic, full-sized programs without some guidance (e.g., from a person using the recognition system as an assistant). One reason is that there are limits to what can be recovered by a purely code-driven approach. Some of the information about the program that is useful to know for common software engineering tasks, particularly maintenance, is missing from the code. Another reason guidance must be provided is to reduce the cost of recognition. To determine what guidance is appropriate, therefore, we must know what information is recoverable from the code and where the complexity of program recognition lies. I propose to study the limits of program recognition, both empirically and analytically. First, I will build an experimental system that performs recognition on realistic programs on the order of thousands of lines. This will allow me to characterize the information that can be recovered by this code-driven technique. Second, I will formally analyze the complexity of the recognition process. This will help determine how guidance can be applied most profitably to improve the efficiency of program recognition.MIT Artificial Intelligence Laborator

Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Evaluating the graphics processing unit for digital audio synthesis and the development of HyperModels

The extraordinary growth in computation in single processors for almost half a century is becoming increasingly difficult to maintain. Future computational growth is expected from parallel processors, as seen in the increasing number of tightly coupled processors inside the conventional modern heterogeneous system. The graphics processing unit (GPU) is a massively parallel processing unit that can be used to accelerate particular digital audio processes; However, digital audio developers are cautious of adopting the GPU into their designs to avoid any complications the GPU architecture may have. For example, linear systems simulated using finite-difference-based physical model synthesis is highly suited for the GPU, but developers will be reluctant to use it without a complete evaluation of the GPU for digital audio. Previously limited by computation, the audio landscape could see future advancement by providing a comprehensive evaluation of the GPU in digital audio and developing a framework for accelerating particular audio processes. This thesis is separated into two parts; Part one evaluates the utility of the GPU as a hardware accelerator for digital audio processing using bespoke performance benchmarking suites. The results suggest that the GPU is appropriate under particular conditions; For example, the sample buffer size dispatched to the GPU must be within 32 to 512 to meet real-time digital audio requirements. However, despite some constraints, the GPU could support linear finite-difference-based physical models with 4X higher resolution than the equivalent CPU version. These results suggest that the GPU is superior to the CPU for high-resolution physical models. Therefore, the second part of this thesis presents the design of the novel HyperModels framework to facilitate the development of real-time linear physical models for interaction and performance. HyperModels uses vector graphics to describe a model's geometry and a domain-specific language (DSL) to define the physics equations that operate in the physical model. An implementation of the HyperModels framework is then objectively evaluated by comparing the performance with manually written CPU and GPU equivalent versions. The automatically generated GPU programs from HyperModels were shown to outperform the CPU versions for resolutions 64x64 and above whilst maintaining similar performance to the manually written GPU versions. To conclude part 2, the expressibility and usability of HyperModels is demonstrated by presenting two instruments built using the framewor

UAVino

UAVino is a drone solution that uses aerial imagery to determine the overall plant health and water content of vineyards. In general, the system focuses on automating crop inspection by taking aerial imagery of a vineyard, conducting post-processing, and outputting an easily interpreted map of the vineyard\u27s overall health. The project\u27s key innovation is an auto-docking system that allows the drone to automatically return to its launch point and recharge in order to extend mission duration. Long term, UAVino is envisioned as a multi-year, interdisciplinary project involving both the Santa Clara University Robotics Systems Laboratory and local wineries in order to develop a fully functional drone agricultural inspection service

Power systems generation scheduling and optimisation using evolutionary computation techniques

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.Overseas Development Agenc

Panther - January 1964 - Vol. XXXVIII No. 8

https://digitalcommons.pvamu.edu/pv-panther-newspapers/1573/thumbnail.jp

Structure preserving estimators to update socio-economic indicators in small areas

Official statistics are intended to support decision makers by providing reliable information on different population groups, identifying what their needs are and where they are located. This allows, for example, to better guide public policies and focus resources on the population most in need. Statistical information must have some characteristics to be useful for this purpose. This data must be reliable, up-to-date and also disaggregated at different domain levels, e.g., geographically or by sociodemographic groups (Eurostat, 2017). Statistical data producers (e.g., national statistical offices) face great challenges in delivering statistics with these three characteristics, mainly due to lack of resources. Population censuses collect data on demographic, economic and social aspects of all persons in a country which makes information at all domains of interest available. They quickly become outdated since they are carried out only every 10 years, especially in developing countries. Furthermore, administrative data sources in many countries have not enough quality to produce statistics that are reliable and comparable with other relevant sources. On the contrary, national surveys are conducted more frequently than censuses and offer the possibility of studying more complex topics. Due to their sample sizes, direct estimates are only published based on domains where the estimates reach a specific level of precision. These domains are called planned domains or large areas in this thesis, and the domains in which direct estimates cannot be produced due to lack of sample size or low precision will be called small areas or domains. Small area estimation (SAE) methods have been proposed as a solution to produce reliable estimates in small domains. These methods allow improving the precision of direct estimates, as well as providing reliable information in domains where the sample size is zero or where direct estimates cannot be obtained by combining data from censuses and surveys (Rao and Molina, 2015). Thereby, the variables obtained from both data sources are assumed to be highly correlated but the census actually may be outdated. In these cases, structure preservation estimation (SPREE) methods offer a solution when the target indicator is a categorical variable, with at least two categories (for example, the labor market status of an individual can be categorised as: ‘employed’, ‘unemployed’, and ‘out of labor force’). The population counts are arranged in contingency tables: by rows (domains of interest) and columns (the categories of the variable of interest) (Purcell and Kish, 1980). These types of estimators are studied in Part I of this work. In Chapter 1, SPREE methods are applied to produce postcensal population counts for the indicators that make up the ‘health’ dimension of the multidimensional poverty index (MPI) defined by Costa Rica. This case study is also used to illustrate the functionalities of the R spree package. It is a user-friendly tool designed to produce updated point and uncertainty estimates based on three different approaches: SPREE (Purcell and Kish, 1980), generalised SPREE (GSPREE) (Zhang and Chambers, 2004), and multivariate SPREE (MSPREE) (Luna-Hernández, 2016). SPREE-type estimators help to update population counts by preserving the census structure and relying on new and updated totals that are usually provided by recent survey data. However, two scenarios can jeopardise the use of standard SPREE methods: a) the indicator of interest is not available in the census data e.g., income or expenditure information to estimate monetary based poverty indicators, and b) the total margins are not reliable, for instance, when changes in the population distribution between areas are not captured correctly by the surveys or when some domains are not selected in the sample. Chapters 2 and 3 offer a solution for these cases, respectively. Chapter 2 presents a two-step procedure that allows obtaining reliable and updated estimates for small areas when the variable of interest is not available in the census. The first step is to obtain the population counts for the census year using a well-known small-area estimation approach: the empirical best prediction (EBP) (Molina and Rao, 2010) method. Then, the result of this procedure is used as input to proceed with the update for postcensal years by implementing the MSPREE (Luna-Hernández, 2016) method. This methodology is applied to the case of local areas in Costa Rica, where incidence of poverty (based on income) is estimated and updated for postcensal years (2012-2017). Chapter 3 deals with the second scenario where the population totals in local areas provided by the survey data are strengthened by including satellite imagery as an auxiliary source. These new margins are used as input in the SPREE procedure. In the case study in this paper, annual updates of the MPI for female-headed households in Senegal are produced. While the use of satellite imagery and other big data sources can improve the reliability of small-area estimates, access to survey data that can be matched with these novel sources is restricted for confidentiality reasons. Therefore, a data dissemination strategy for micro-level survey data is proposed in the paper presented in Part II. This strategy aims to help statistical data producers to improve the trade-off between privacy risk and utility of the data that they release for research purposes