Academic literature on the topic 'Names, Apache'

The San Carlos Apache Tribe is a leading defender of Oak Flat, a large public campground on the western flanks of the Pinal Mountains east of Phoenix. Oak Flat is sacred to many Apaches and other Native Americans and is listed in the U.S. National Register of Historic Places. The Tribe is among the parties gravely concerned about the Resolution Copper Mine, a joint venture of Rio Tinto Group and BHP Billiton Ltd. to privatize and industrialize Oak Flat’s public lands and minerals. Archaeological sites, place names, stories, and ceremonial uses affirm the pre-1875 Apache occupation and ongoing significance of Oak Flat. Historical records reveal how mining proponents combined industrial and annihilationist propaganda to portray Apaches in the Pinal Mountains as subhuman impediments to civilization and profit. This inflammatory rhetoric ignited vigilante and military campaigns between 1859 and 1874 that killed over 380 Pinal Apaches—including many women and children—then confined survivors onto the San Carlos Reservation. Mining across Pinal Apache territory followed promptly, claiming additional Apache lands inside and outside reservation borders. The stark historical injustice of the Pinal Apache Genocide requires recognition and redress via the responsible governments and industries, including their successors today. The obvious first step is simple avoidance of further harm to Apaches and Oak Flat. Truth must be a hallmark for comprehensive cost–benefit assessments of proposed alterations of Indigenous homelands. Reconciliation must be a planning goal for any mining or other consumptive uses of Indigenous sacred sites.

In this paper the primary types of Centris bees described by the British entomologist Frederick Smith deposited in the Natural History Museum, London and in the Oxford University Museum of Natural History, Oxford, United Kingdom were studied. To stabilize the application of some names, lectotypes were designated for C. agilis, C. apiformis (= C. aenea Lepeletier), C. ardens (= C. varia (Erichson)), C. aterrima, C. cineraria, C. concinna (= C. dentata), C. crassipes, C. dentata, C. difformis, C. discolor, C. elegans, C. ephippium, C. festiva, C. ignita (= C. agilis), C. insignis, C. insularis, C. maculifrons, C. melanochlaena, C. mexicana, C. modesta, C. nitida, C. perforator, C. personata (= C. longimana Fabricius), C. plumipes, C. rubella (= C. ferruginea Lepeletier), C. semicaerulea, C. simillima, C. tarsata, C. thoracica (= C. domingensis Dalla Torre) and Anthophora dimidiata (= C. nigerrima (Spinola)). Centris perforator nom. rev. and C. modesta nom. rev. are withdrawn from the synonymy of C. fuscata Lepeletier and C. obsoleta Lepeletier respectively, and consequently revalidated. Centris fulviventris Cresson and C. simillima are removed from the synonymy of C. lanipes (Fabricius), proposing the revalidation of the first species and the second one as its new junior synonym. Centris insignis and C. insignis scutellaris Friese are proposed as new junior synonyms of C. laticincta (Spinola). The critical study of the primary type of C. aterrima, for a long time a misidentified species, allowed for proposing C. anomala Snelling as its new junior synonym. As result of this synonymy, C. apache new species is here described based on specimens incorrectly considered as belonging to C. aterrima. In addition, a lectotype for Centris clypeata Friese (= C. nigrocaerulea Smith) is also designated.

Abstract Finding frequent itemsets in a continuous streaming data is an important data mining task which is widely used in network monitoring, Internet of Things data analysis and so on. In the era of big data, it is necessary to develop a distributed frequent itemset mining algorithm to meet the needs of massive streaming data processing. Apache Spark is a unified analytic engine for massive data processing which has been successfully used in many data mining fields. In this paper, we propose a distributed algorithm for mining frequent itemsets over massive streaming data named SWEclat. The algorithm uses sliding window to process streaming data and uses vertical data structure to store the dataset in the sliding window. This algorithm is implemented by Apache Spark and uses Spark RDD to store streaming data and dataset in vertical data format, so as to divide these RDDs into partitions for distributed processing. Experimental results show that SWEclat algorithm has good acceleration, parallel scalability and load balancing.

This article discusses the application of mechanical engineering concepts in a day-to-day TV serial world. Instrumenting weapons and armor showed that Viking chain mail could withstand the slashing attack of a Samurai katana in the TV series named Deadliest Warrior. The show pits fighters with different styles of fighting who never met—Spartan vs. Ninja or Apache vs. Gladiator —against one another. The show’s experts include a doctor, a computer programmer, and Desmoulin, the go-to guy for figuring out the impact of ancient arms. They feed their findings into a Monte Carlo simulation that runs hundreds of simulations and picks the most likely winner.

It is quite inadequate in providing formula retrieval function by traditional retrieval techniques used in full-text information retrieval system. The main reason is that there are many difficulties to extract the keywords of the mathematical formulas. In this paper, a detailed analysis of the structural characteristics of mathematical formulas and existing index mechanism of mathematical formula searching engine is fulfilled. Then a full-text index (named SLIndex) of mathematical formulas with B+ tree structure is designed and implemented which extracts the structured logic sub-tree feature as keywords of formulas and employs inverted index. Finally, a formula search engine model based on SLIndex is implemented in Apache 2.0 web server.

The latest and famous subject all over the cloud research area is Big Data; its main appearances are volume, velocity and variety. The characteristics are difficult to manage through traditional software and their various available methodologies. To manage the data which is occurring from various domains of big data are handled through Hadoop, which is open framework software which is mainly developed to provide solutions. Handling of big data analytics is done through Hadoop Map Reduce framework and it is the key engine of hadoop cluster and it is extensively used in these days. It uses batch processing system.Apache developed an engine named "Tez", which supports interactive query system and it won't writes any temporary data into the Hadoop Distributed File System(HDFS).The paper mainly focuses on performance juxtaposition of MapReduce and TeZ, performance of these two engines are examined through the compression of input files and map output files. To compare two engines we used Bzip compression algorithm for the input files and snappy for the map out files. Word Count and Terasort gauge are used on our experiments. For the Word Count gauge, the results shown that Tez engine has better execution time than Hadoop MapReduce engine for the both compressed and non-compressed data. It has reduced the execution time nearly 39% comparing to the execution time of the Hadoop MapReduce engine. Correspondingly for the terasort gauge, the Tez engine has higher execution time than Hadoop MapReduce engine.

The article is devoted to the analysis of individual and author’s features of the use of color names in Anatolii Moisiienko’s poetic idiolect. The color category is one of the basic components of ethnoculture, since it’s closely connected with the individuals’ ideas about the original cause of the universe, good and evil, joy and sorrow. Color is a psychic and physiological phenomenon, a means of influencing the personality and social consciousness. Each culture has its own color dominants, which are special universal elements of the world picture. Color nominations are effective markers that can be used to study national and individual characteristics, since fantasy and feelings produce the emergence of individual images that reflect the character of the nation. The color designations receive additional associative-connotative semantics in the poetic text. They take part in the creation of bright poetic images, in the transfer of the inner state of the lyrical hero by the author, in the depiction of a particular artistic space. These nominations reflect the conceptual picture of the world of the lyric hero through the linguistic picture of the world. The conducted research has shown that the conceptual picture of the world of the Anatolii Moisiienko’s lyric hero has a bipolar character, which is represented by words denoting bright and dark colors. These colors represent the universal notions of the world of good and evil, white and black. But the author’s feature is that the world of the lyrical hero is represented not only in black or white. In poetry, the author uses the names blue, blue, golden and black. The words that denote the first three colors have a positive or neutral connotation. Poetic contexts with the component “black” help the poet to convey the inner state of the lyric hero (fatigue, apathy) or negative developments. Author’s neologisms with the above component also take part in creating poetic image of time. Using the above words helps the author create a special artistic world, full of bright colors and expression.

Memory institutions must be able to grow a fully-functional repository incrementally as collections grow, without expensive enterprise storage, massive data migrations, and the performance limits that stem from the vertical storage strategies. The Digital Repository at Scale that Invites Computation (DRAS-TIC) Fedora research project, funded by a two-year National Digital Platform grant from the Institute for Museum and Library Services (IMLS), is producing open-source software, tested cluster configurations, documentation, and best-practice guides that enable institutions to manage linked data repositories with petabyte-scale collections reliably. DRAS-TIC is a research initiative at the University of Maryland (UMD). The first DRAS-TIC repository system, named Indigo, was developed in 2015 and 2016 through a collaboration between U.K.-based storage company, Archive Analytics Ltd., and the UMD iSchool Digital Curation Innovation Center (DCIC), through funding from an NSF DIBBs (Data Infrastructure Building Blocks) grant (NCSA “Brown Dog”). DRAS-TIC Indigo leverages industry standard distributed database technology, in the form of Apache Cassandra, to provide open-ended scaling of repository storage without performance degradation. With the DRAS-TIC Fedora initiative, we make use of the Trellis Linked Data Platform (LDP), developed by Aaron Coburn at Amherst College, to add the LDP API over similar Apache Cassandra storage. This paper will explain our partner use cases, explore the system components, and showcase our performance-oriented approach, with the most emphasis given to performance measures available through the analytical dashboard on our testbed website.

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

abstract: Continuous advancements in biomedical research have resulted in the production of vast amounts of scientific data and literature discussing them. The ultimate goal of computational biology is to translate these large amounts of data into actual knowledge of the complex biological processes and accurate life science models. The ability to rapidly and effectively survey the literature is necessary for the creation of large scale models of the relationships among biomedical entities as well as hypothesis generation to guide biomedical research. To reduce the effort and time spent in performing these activities, an intelligent search system is required. Even though many systems aid in navigating through this wide collection of documents, the vastness and depth of this information overload can be overwhelming. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also facilitate discovery of the unknown information implicitly conveyed in the texts. This thesis presents the different approaches used for large scale biomedical named entity recognition, and the challenges faced in each. It also proposes BioEve: an integrative framework to fuse a faceted search with information extraction to provide a search service that addresses the user's desire for "completeness" of the query results, not just the top-ranked ones. This information extraction system enables discovery of important semantic relationships between entities such as genes, diseases, drugs, and cell lines and events from biomedical text on MEDLINE, which is the largest publicly available database of the world's biomedical journal literature. It is an innovative search and discovery service that makes it easier to search/navigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
Dissertation/Thesis
M.S. Computer Science 2010

The trip didn’t make sense at the time. Most backpacking trips don’t. There are always more pressing things to do. We didn’t have the time or the money, but we went anyway. Sometimes you just gotta drive to the end of a long dirt road in the middle of the desert and keep walking. When Aravaipa Canyon lies at the end of that road, you know you won’t be disappointed. Mike and I had come to southeastern Arizona to hike the twelve-mile length of the Aravaipa Canyon Wilderness Area. “Laughing Waters” is the name the Apaches gave to the site. The Aravaipa band of the Western Apache lived here in the nineteenth century. They did well at first—hunting deer in the side canyons; gathering saguaro fruit, mesquite beans, and pinyon nuts; catching native fish that thrived in the creek. But by the 1870s, drought drove them out. When they sought relief at Camp Grant a few miles away a Tucson mob organized a massacre that left them decimated. The government relocated the remainder of the tribe in the White Mountain Reservation to the north. These canyon walls, reaching a thousand feet high in places, hold memories of children playing under reddish-brown hoodoos and dark stories etched in the desert varnish of the rock. Today the Bureau of Land Management regulates entry into the canyon, limiting permits to thirty hikers a day at the western entrance. For much of the way you slog through ankle- to knee-deep water, stopping at every bend to marvel at what rises before you. Towering red cliffs, stands of green willows and cottonwoods, jimson weed and desert marigolds, cactuses of every sort. This is a place where humans are outnumbered by bighorn sheep, where poisonous centipedes hide in thick grass, and serpentine side canyons darken ominously in the late afternoon sun. I’ve loved it since I first set eyes on it. At the start of this book I mentioned a night I’d spent alone in the desert near here a few years earlier. What I experienced that night would finally make sense on this subsequent trip into the canyon proper.

Whatever its name, Free/Libre or Open Source Software (FLOSS), diffusion represents one of the main evolutions of the Information Technology (IT) industry in recent years. Operating System Linux, or Web server Apache (more than 60% market share on its market), database MySQL or PHP languages are some examples of broadlyused FLOSS programs. One of the most original characteristics of this movement is its collective, cooperative software development organization in which a growing number of firms is involved (some figures in Lakhani & Wolf (2005)). Of course, programs, because they are codified information, are quite easy to exchange, and make the cooperation easier than in other industries. But, as pointed out by Stallman (1998), if sharing pieces of software within firms was a dominant practice in the 1950’s, it declined in the 1970’s, and almost disappeared in the 1980’s, before regaining and booming today.

Whatever its name, Free/Libre or Open Source Software (FLOSS), diffusion represents one of the main evolutions of the Information Technology (IT) industry in recent years. Operating System Linux, or Web server Apache (more than 60% market share on its market), database MySQL or PHP languages are some examples of broadly-used FLOSS programs. One of the most original characteristics of this movement is its collective, cooperative software development organization in which a growing number of firms is involved (some figures in Lakhani & Wolf (2005)). Of course, programs, because they are codified information, are quite easy to exchange, and make the cooperation easier than in other industries. But, as pointed out by Stallman (1998), if sharing pieces of software within firms was a dominant practice in the 1950’s, it declined in the 1970’s, and almost disappeared in the 1980’s, before regaining and booming today. This article aims at explaining the evolution (and the comeback) of a cooperative, non-market production. In the first part, we explain the decrease of cooperation as a consequence of the evolution of the computer users, of their demand, and of the industrial organization constructed to meet this demand. This theoretical and historical framework is used in the second part to understand the renewal of a cooperative organization, the FLOSS phenomenon, first among computer-literate users, and then within the industry.

It was one of those days, typical of England, when you have to work very hard to remember that above the thick, white cloud the sky is always blue. I was cycling up Cheltenham Road, feeling increasingly angry, when I saw a giant advertising hoarding had been erected around a disused car showroom that had, until recently, been a residential squat. It read: ‘New Development, a mix of 1, 2 and 3 bedroom flats. Prices start at just £199,000’. The advert included pictures of smart-looking kitchens, shiny surfaces, and anonymous faces grinning inanely at their fictional bathtubs. I started to cycle harder with each raging thought. I had woken up feeling dismal and my mood had become progressively worse as the day went on. At that time, I worked as a junior programme maker at BBC Radio 4. I had been told in a meeting that I needed to establish a ‘celebrity angle’ on a story that I was working on. It maddened me. What relevance do celebrities have to ordinary people’s lives? This was 2007. The Global Financial Crash was just months away. Back then I resembled a slightly scruffy, more politically engaged Bridget Jones. Single and painfully middle class, I smoked roll-up cigarettes and spent most of my time feeling frustrated that both national and international politics appeared to be moving to the Right while I, and millions of others, protested but got nowhere. Massive peaceful anti-war protests had been ignored by Britain’s ruling elite, and direct action carried increased risk of criminalization. Some saw violence as a resort—albeit the last one—but it was never my style, so instead I just felt increasingly frustrated. I was sick of joining ‘movements’ to quickly become nothing more than a ‘clicktavist’, and was not prepared to turn my back and sink into a state of total apathy. I felt extremely powerless and that made me angry. ‘Rachael!’ I heard someone call my name. It was Jim Dixon, an old friend and fellow graduate of the University of Bristol’s MA in Historical Archaeology.