Mining Unit Tests for Discovery and Migration of Math APIs

Today's programming languages are supported by powerful third-party APIs. For a given application domain, it is common to have many competing APIs that provide similar functionality. Programmer productivity therefore depends heavily on the programmer's ability to discover suitable APIs both during an initial coding phase, as well as during software maintenance. The aim of this work is to support the discovery and migration of math APIs. Math APIs are at the heart of many application domains ranging from machine learning to scientific computations. Our approach, called MATHFINDER, combines executable specifications of mathematical computations with unit tests (operational specifications) of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code comprised of API methods to compute the expression by mining unit tests of the API methods. We present a sequential version of our unit test mining algorithm and also design a more scalable data-parallel version. We perform extensive evaluation of MATHFINDER (1) for API discovery, where math algorithms are to be implemented from scratch and (2) for API migration, where client programs utilizing a math API are to be migrated to another API. We evaluated the precision and recall of MATHFINDER on a diverse collection of math expressions, culled from algorithms used in a wide range of application areas such as control systems and structural dynamics. In a user study to evaluate the productivity gains obtained by using MATHFINDER for API discovery, the programmers who used MATHFINDER finished their programming tasks twice as fast as their counterparts who used the usual techniques like web and code search, IDE code completion, and manual inspection of library documentation. For the problem of API migration, as a case study, we used MATHFINDER to migrate Weka, a popular machine learning library. Overall, our evaluation shows that MATHFINDER is easy to use, provides highly precise results across several math APIs and application domains even with a small number of unit tests per method, and scales to large collections of unit tests.

Tin coating requirements in fish cans

Prawn, crab and clam meat were processed in experimental ca s having reduced internal tin coating of 5.6 GSM. Conventional cans having 11.2 GSM tin coating were used as control. Results showed that experimental cans behaved normally when used for canning prawns, provided the lacquer film was perfect with no exposure of metal. When there was a discontinuity in lacquer film exposing the metal blackening took place in such areas. Areas subjected to severe strains like the lock seam side and expansion rings on can ends were found to be more prone to blackening. Experimental cans were found unsuitable for canning crab meat or clam meat because in both cases the can wall as well as the contents underwent discoloration, in all cases.

人口统计信息空间可视化系统研究

Population research is a front area concerned by domestic and overseas, especially its researches on its spatial visualization and its geo-visualization system design, which provides a sound base for understanding and analysis of the regional difference in population distribution and its spatial rules. With the development of GIS, the theory of geo-visualization more and more plays an important role in many research fields, especially in population information visualization, and has been made the big achievements recently. Nevertheless, the current research is less attention paid to the system design for statistical-geo visualization for population information. This paper tries to explore the design theories and methodologies for statistical-geo-visualization system for population information. The researches are mainly focused on the framework, the methodologies and techniques for the system design and construction. The purpose of the research is developed a platform for population atlas by the integration of the former owned copy software of the research group in statistical mapping system. As a modern tool, the system will provide a spatial visual environment for user to analyze the characteristics of population distribution and differentiate the interrelations of the population components. Firstly, the paper discusses the essentiality of geo-visualization for population information and brings forward the key issue in statistical-geo visualization system design based on the analysis of inland and international trends. Secondly, the geo-visualization system for population design, including its structure, functionality, module, user interface design, is studied based on the concepts of theory and technology of geo-visualization. The system design is proposed and further divided into three parts: support layer, technical layer, user layer. The support layer is a basic operation module and main part of the system. The technical layer is a core part of the system, supported by database and function modules. The database module mainly include the integrated population database (comprises spatial data, attribute data and geographical features information), the cartographic symbol library, the color library, the statistical analysis model. The function module of the system consists of thematic map maker component, statistical graph maker component, database management component and statistical analysis component. The user layer is an integrated platform, which provides the functions to design and implement a visual interface for user to query, analysis and management the statistic data and the electronic map. Based on the above, China's E-atlas for population was designed and developed by the integration of the national fifth census data with 1:400 million scaled spatial data. The atlas illustrates the actual development level of the population nowadays in China by about 200 thematic maps relating with 10 map categories(environment, population distribution, sex and age, immigration, nation, family and marriage, birth, education, employment, house). As a scientific reference tool, China's E-atlas for population has already received the high evaluation after published in early 2005. Finally, the paper makes the deep analysis of the sex ratio in China, to show how to use the functions of the system to analyze the specific population problem and how to make the data mining. The analysis results showed that: 1. The sex ratio has been increased in many regions after fourth census in 1990 except the cities in the east region, and the high sex ratio is highly located in hilly and low mountain areas where with the high illiteracy rate and the high poor rate; 2. The statistical-geo visualization system is a powerful tool to handle population information, which can be used to reflect the regional differences and the regional variations of population in China and indicate the interrelations of the population with other environment factors. Although the author tries to bring up a integrate design frame of the statistical-geo visualization system, there are still many problems needed to be resolved with the development of geo-visualization studies.

Isotopic imprints of mountaintop mining contaminants.

Mountaintop mining (MTM) is the primary procedure for surface coal exploration within the central Appalachian region of the eastern United States, and it is known to contaminate streams in local watersheds. In this study, we measured the chemical and isotopic compositions of water samples from MTM-impacted tributaries and streams in the Mud River watershed in West Virginia. We systematically document the isotopic compositions of three major constituents: sulfur isotopes in sulfate (δ(34)SSO4), carbon isotopes in dissolved inorganic carbon (δ(13)CDIC), and strontium isotopes ((87)Sr/(86)Sr). The data show that δ(34)SSO4, δ(13)CDIC, Sr/Ca, and (87)Sr/(86)Sr measured in saline- and selenium-rich MTM impacted tributaries are distinguishable from those of the surface water upstream of mining impacts. These tracers can therefore be used to delineate and quantify the impact of MTM in watersheds. High Sr/Ca and low (87)Sr/(86)Sr characterize tributaries that originated from active MTM areas, while tributaries from reclaimed MTM areas had low Sr/Ca and high (87)Sr/(86)Sr. Leaching experiments of rocks from the watershed show that pyrite oxidation and carbonate dissolution control the solute chemistry with distinct (87)Sr/(86)Sr ratios characterizing different rock sources. We propose that MTM operations that access the deeper Kanawha Formation generate residual mined rocks in valley fills from which effluents with distinctive (87)Sr/(86)Sr and Sr/Ca imprints affect the quality of the Appalachian watersheds.

Gold mining in the Peruvian Amazon: global prices, deforestation, and mercury imports.

Many factors such as poverty, ineffective institutions and environmental regulations may prevent developing countries from managing how natural resources are extracted to meet a strong market demand. Extraction for some resources has reached such proportions that evidence is measurable from space. We present recent evidence of the global demand for a single commodity and the ecosystem destruction resulting from commodity extraction, recorded by satellites for one of the most biodiverse areas of the world. We find that since 2003, recent mining deforestation in Madre de Dios, Peru is increasing nonlinearly alongside a constant annual rate of increase in international gold price (∼18%/yr). We detect that the new pattern of mining deforestation (1915 ha/year, 2006-2009) is outpacing that of nearby settlement deforestation. We show that gold price is linked with exponential increases in Peruvian national mercury imports over time (R(2) = 0.93, p = 0.04, 2003-2009). Given the past rates of increase we predict that mercury imports may more than double for 2011 (∼500 t/year). Virtually all of Peru's mercury imports are used in artisanal gold mining. Much of the mining increase is unregulated/artisanal in nature, lacking environmental impact analysis or miner education. As a result, large quantities of mercury are being released into the atmosphere, sediments and waterways. Other developing countries endowed with gold deposits are likely experiencing similar environmental destruction in response to recent record high gold prices. The increasing availability of satellite imagery ought to evoke further studies linking economic variables with land use and cover changes on the ground.

Recent national trends in Salvia divinorum use and substance-use disorders among recent and former Salvia divinorum users compared with nonusers.

CONTEXT: Media and scientific reports have indicated an increase in recreational use of Salvia divinorum. Epidemiological data are lacking on the trends, prevalence, and correlates of S. divinorum use in large representative samples, as well as the extent of substance use and mental health problems among S. divinorum users. OBJECTIVE: To examine the national trend in prevalence of S. divinorum use and to identify sociodemographic, behavioral, mental health, and substance-use profiles of recent (past-year) and former users of S. divinorum. DESIGN: Analyses of public-use data files from the 2006-2008 United States National Surveys on Drug Use and Health (N = 166,453). SETTING: Noninstitutionalized individuals aged 12 years or older were interviewed in their places of residence. MAIN MEASURES: Substance use, S. divinorum, self-reported substance use disorders, criminality, depression, and mental health treatment were assessed by standardized survey questions administered by the audio computer-assisted self-interviewing method. RESULTS: Among survey respondents, lifetime prevalence of S. divinorum use had increased from 0.7% in 2006 to 1.3% in 2008 (an 83% increase). S. divinorum use was associated with ages 18-25 years, male gender, white or multiple race, residence of large metropolitan areas, arrests for criminal activities, and depression. S. divinorum use was particularly common among recent drug users, including users of lysergic acid diethylamide (53.7%), ecstasy (30.1%), heroin (24.2%), phencyclidine (22.4%), and cocaine (17.5%). Adjusted multinomial logistic analyses indicated polydrug use as the strongest determinant for recent and former S. divinorum use. An estimated 43.0% of past-year S. divinorum users and 28.9% of former S. divinorum users had an illicit or nonmedical drug-use disorder compared with 2.5% of nonusers. Adjusted logistic regression analyses showed that recent and former S. divinorum users had greater odds of having past-year depression and a substance-use disorder (alcohol or drugs) than past-year alcohol or drug users who did not use S. divinorum. CONCLUSION: S. divinorum use is prevalent among recent or active drug users who have used other hallucinogens or stimulants. The high prevalence of substance use disorders among recent S. divinorum users emphasizes the need to study health risks of drug interactions.

Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.

Punishment, blame and stigma after conflict: the experience of politically motivated former prisoners in Northern Ireland

This paper examines the relationship between the politics of blame in post-conflict Northern Ireland and the treatment of politically motivated former prisoners. Using the examples of direct and indirect discrimination in the areas of employment and access to mental health services, the paper considers how the discursive operation of blaming produces evasions and attributions of guilt. It argues that such blaming practices have very real material consequences for the allocation or withholding of goods and burdens in the community. The paper notes also that the ‘cause of victims’ is often appropriated by the press and other political actors for their own purposes, frequently to block the provision of public goods to one particular group of ex-combatants: ex-politically motivated prisoners. It concludes by posing a series of questions about blaming, justice and the moral authority of the victim in a transitional justice context. The claim of the paper is simply to offer some starting points for understanding the relationship between processes of blame, stigma and social exclusion.

High Percentage Inorganic Arsenic Content of Mining Impacted and Nonimpacted Chinese Rice

Two approaches were undertaken to characterize the arsenic (As) content of Chinese rice. First, a national market basket survey (n = 240) was conducted in provincial capitals, sourcing grain from China's premier rice production areas. Second, to reflect rural diets, paddy rice (n = 195) directly from farmers fields were collected from three regions in Hunan, a key rice producing province located in southern China. Two of the sites were within mining and smeltery districts, and the third was devoid of large-scale metal processing industries. Arsenic levels were determined in all the samples while a subset (n = 33) were characterized for As species, using a new simple and rapid extraction method suitable for use with Hamilton PRP-X100 anion exchange columns and HPLC-ICP-MS. The vast majority (85%) of the market rice grains possessed total As levels <150 ng g(-1). The rice collected from mine-impacted regions, however, were found to be highly enriched in As, reaching concentrations of up to 624 ng g(-1). Inorganic As (As(i)) was the predominant species detected in all of the speciated grain, with As(i) levels in some samples exceeding 300 ng g(-1). The As(i) concentration in polished and unpolished Chinese rice was successfully predicted from total As levels. The mean baseline concentrations for As(i) in Chinese market rice based on this survey were estimated to be 96 ng g(-1) while levels in mine-impacted areas were higher with ca. 50% of the rice in one region predicted to fail the national standard.

The legacy of past manuring practices on soil contamination in remote rural areas

This paper demonstrates that there can be a legacy of contamination on former arable land in remote rural areas as a result of past manuring practices. In the first part of the study four farms abandoned in the late 19th to mid-20th century were investigated with samples collected from residual material in domestic hearths, the midden heaps, kailyards (walled garden for vegetables), infields (intensively managed arable land) and outfields (less intensively managed land for cropping or grazing). Consistent sequences in concentration values were found for such elements as Pb, Zn, Cu and P in the order hearth>midden>kailyard>infield>outfield. Such patterns can in part be explained in terms of atmospheric deposition on peat and turf which were subsequently burnt in hearths to result in enhanced elemental concentrations. The ash then was deposited in midden heaps and subsequently on kailyards or infields. In the second part, microanalytical results from St. Kilda are discussed. Enhanced loadings of Pb and Zn were found in the old arable land. The highest levels of Zn were found in small fragments of carbonised and humified material and bone fragments; in contrast Pb tended to be more uniformly distributed. Seabird waste was extensively applied to the arable land and some of the Zn may have accumulated in the soil by this pathway. The retention of Zn in bone is likely to have been very minor given the rarity of bone fragments as evident in thin sections (0.3%); this compares with 6.8% for black carbonised particles which are likely to provide the main storage sites for Zn.

Arsenic Speciation and Localization in Horticultural Produce Grown in a Historically Impacted Mining Region

A field and market basket study (similar to 1300 samples) of locally grown fruits and vegetables from historically mined regions of southwest (SW) England (Cornwall and Devon), and as reference, a market basket study of similarly locally grown produce from the northeast (NE) of Scotland (Aberdeenshire) was conducted to determine the concentration of total and inorganic arsenic present in produce from these two geogenically different areas of the U.K. On average 98.5% of the total arsenic found was present in the inorganic form. For both the market basket and the field survey, the highest total arsenic was present in open leaf structure produce (i.e., kale, chard, lettuce, greens, and spinach) being most likely to soil/dust contamination of the open leaf structure. The concentration of total arsenic in potatoes, swedes, and carrots was lower in peeled produce compared to unpeeled produce. For baked potatoes, the concentration of total arsenic in the skin was higher compared to the total arsenic concentration of the potato flesh, this difference in localization being confirmed by laser ablation inductively coupled plasma mass spectroscopy (LA-ICP-MS). For all above ground produce (e.g., apples), peeling did not have a significant effect on the concentration of total arsenic present.

Characterisation of physiological conditions and evaluation of well-being in two species of bivalve molluscs (Ruditapes philippinarum and Chamelea gallina) intensively exploited in coastal and lagoon areas of the Northern Adriatic Sea (Italy)

Tese de Doutoramento, Ecologia, Especialidade de Ecofisiologia, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2007

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Design and Development of data mining models for the predictions of manpower placement in the technical Domain

Data mining is one of the hottest research areas nowadays as it has got wide variety of applications in common man’s life to make the world a better place to live. It is all about finding interesting hidden patterns in a huge history data base. As an example, from a sales data base, one can find an interesting pattern like “people who buy magazines tend to buy news papers also” using data mining. Now in the sales point of view the advantage is that one can place these things together in the shop to increase sales. In this research work, data mining is effectively applied to a domain called placement chance prediction, since taking wise career decision is so crucial for anybody for sure. In India technical manpower analysis is carried out by an organization named National Technical Manpower Information System (NTMIS), established in 1983-84 by India's Ministry of Education & Culture. The NTMIS comprises of a lead centre in the IAMR, New Delhi, and 21 nodal centres located at different parts of the country. The Kerala State Nodal Centre is located at Cochin University of Science and Technology. In Nodal Centre, they collect placement information by sending postal questionnaire to passed out students on a regular basis. From this raw data available in the nodal centre, a history data base was prepared. Each record in this data base includes entrance rank ranges, reservation, Sector, Sex, and a particular engineering. From each such combination of attributes from the history data base of student records, corresponding placement chances is computed and stored in the history data base. From this data, various popular data mining models are built and tested. These models can be used to predict the most suitable branch for a particular new student with one of the above combination of criteria. Also a detailed performance comparison of the various data mining models is done.This research work proposes to use a combination of data mining models namely a hybrid stacking ensemble for better predictions. A strategy to predict the overall absorption rate for various branches as well as the time it takes for all the students of a particular branch to get placed etc are also proposed. Finally, this research work puts forward a new data mining algorithm namely C 4.5 * stat for numeric data sets which has been proved to have competent accuracy over standard benchmarking data sets called UCI data sets. It also proposes an optimization strategy called parameter tuning to improve the standard C 4.5 algorithm. As a summary this research work passes through all four dimensions for a typical data mining research work, namely application to a domain, development of classifier models, optimization and ensemble methods.

Evolving Classifiers - Evolutionary Algorithms in Data Mining

Data mining means to summarize information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the internet. In this report we introduce an approach for utilizing evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, Göb and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.