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First Sea Lord

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First Sea Lord (bokstavelig «Første herre av havet»; øverste kommandant av den britiske flåten), eller fulle tittel The First Sea Lord and Chief of Naval Staff (1SL/CNS) er den profesjonelle overhodet av Storbritannias kongelige marine (Royal Navy) og hele den maritime tjeneste (Naval Service). Tittelen er særegen for Storbritannia, den tilsvarende norske posisjonen er admiral. Den opprinnelig tittelen var First Naval Lord («Første maritime herre»). Konseptet med en profesjonell (ikke politisk utnevnt) First Naval Lord ble innført i 1805, og denne tittelen ble endret til First Sea Lord ved utnevnelsen av John Arbuthnot Fisher i 1904. Fra 1923 og framover var First Sea Lord også et medlem av Sjefene av stabkomiteen (Chiefs of Staff Committee, CSC) kids football shirts. Han sitter nå også i det britiske forsvarsrådet (Defence Council), som er en del av Forsvarsministeriet

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, og i admiralitet (Admiralty Board).

Den nåværende First Sea Lord er admiral Philip Jones som ble utnevnt i april 2016.

Rob Hrytsak

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Rob Hrytsak (born August 11, 1965) is a retired Canadian professional ice hockey player.

After two years of junior hockey in Canada, Hrytsak joined the Johnstown Chiefs of the All-American Hockey League in January 1988. Hrytsak scored the first goal in Chiefs‘ history on January 13, 1988. At the 8:51 mark of the first period, Hrytsak stole a cross-ice pass at center ice and was alone on a breakaway baby socks wholesale. Hrytsak then lifted the puck over Carolina Thunderbirds‘ goaltender Bruce Billes shoulder. Hrytsak and the Chiefs won the first game in Chiefs‘ history 5-3. The Chiefs would finish the regular season 13-13-0 and Hrytsak would share the team lead in points with teammate Scott Rettew with 61.

Hrytsak returned to Johnstown for the 1988-89 ECHL season and led the team in goals (40). Hrytsak’s total was also second-highest in the ECHL, with only Virginia’s Mike Chighisola outscoring Hrytsak with 45 goals. Hrystak would lead the Chiefs to the inaugural Riley Cup Finals against the Carolina Thunderbirds. Hrytsak would have one of the best Finals performances in ECHL history, scoring 7 goals and 12 points against the Thunderbirds, but the Chiefs would lose the Finals to the Thunderbirds four games to three. Hrytsak’s 7 goals in a Finals series remains an ECHL record and his 12 points in a Finals series was a record that has only been topped by two ECHL players since 1989 (Darren Schwartz and Devin Edgerton, both of the 1993 Wheeling Thunderbirds).

After a brief stint in Knoxville where he sat out most nights, Hrytsak returned to Johnstown in December 1990. He remained with the team until the completion of the 1991-92 season

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. In parts of 5 seasons with the Chiefs, Hrytsak scored 98 goals, 156 assists, and 254 points. Hrytsak’s 254 remains an all-time Chiefs record, as the franchise has been inactive since the completion of the 2009-10 ECHL, and his 156 assists and 98 goals are respectively second and fourth-highest in franchise history 1l bpa free water bottle.

Hrytsak played two seasons in Germany’s Oberliga. Hrystak led Iserlohner EC with 65 goals in only 46 games, which was the fourth highest total during the 1994-95 Oberliga season.

Hrytsak returned to the minors at the start of the 1996–97 season, and split time between four clubs, with three of them based in the WPHL. Hrytsak also bounced between the Fort Worth Fire of the Central Hockey League and the Shreveport Mudbugs of the WPHL, scoring 30 points in 47 games between the two teams.

Hrytsak briefly returned to Germany to join the Dinslakener Cobras, a fourth-tier German club-level team. In 8 post-season games with the team, Hrytsak scored twenty-four points and accumulated ninety-nine penalty minutes. He retired at the completion of the 1999-2000 season.

EM i friidrett 1958 – 100 meter herrer

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Dette er de offisielle resultatene for øvelsen 100 meter herrer ved EM i friidrett 1958 i Stockholm, Sverige.

Bjørn Nilsen representerte Norge og kom til semifinalen.

Rekorder før dette mesterskapet:

Følgende ny mesterskapsrekord på øvelsen ble satt under konkurransen:

1934  • 1938  • 1946  • 1950  • 1954  • 1958  • 1962  • 1966  • 1969  • 1971  • 1974  • 1978  • 1982  • 1986  • 1990  • 1994  • 1998  • 2002  • 2006  • 2010  • 2012  • 2014  • 2016

1934  • 1938  • 1946  • 1950  • 1954  • 1958  • 1962  • 1966  • 1969  • 1971  • 1974  • 1978  • 1982  • 1986 &nbsp

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;• 1990  • 1994  • 1998  • 2002  • 2006  • 2010  • 2012  • 2014  • 2016

1934: Christiaan Berger | 1938: Martinus Osendarp | 1946: John Archer | 1950: Etienne Bally | 1954: Heinz Fütterer | 1958: Armin Hary | 1962: Claude Piquemal | 1966: Wieslaw Maniak | 1969: Valerij Borzov | 1971: Valerij Borzov | 1974: Valerij Borzov | 1978: Pietro Mennea | 1982: Frank Emmelmann | 1986: Linford Christie | 1990: Linford Christie | 1994: Linford Christie | 1998: Darren Campbell | 2002: Francis Obikwelu | 2006: Francis Obikwelu | 2010: Christophe Lemaitre | 2012: Christophe Lemaitre | 2014: James Dasaolu | 2016: Churandy Martina

1896: Thomas Burke  • 1900: Frank Jarvis  • 1904: Archie Hahn  • 1906: Archie Hahn  • 1908: Reggie Walker  • 1912: Ralph Craig  • 1920: Charley Paddock  • 1924: Harold Abrahams &nbsp

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;• 1928: Percy Williams  • 1932: Eddie Tolan  • 1936: Jesse Owens  • 1948: Harrison Dillard  • 1952: Lindy Remigino  • 1956: Bobby Morrow  • 1960: Armin Hary  • 1964: Bob Hayes  • 1968: Jim Hines  • 1972: Valerij Borzov  • 1976: Hasely Crawford  • 1980: Allan Wells  • 1984: Carl Lewis  • 1988: Carl Lewis  • 1992: Linford Christie  • 1996: Donovan Bailey  • 2000: Maurice Greene  • 2004: Justin Gatlin  • 2008: Usain Bolt  • 2012: Usain Bolt  • 2016: Usain Bolt

1983: Carl Lewis  • 1987: Carl Lewis  • 1991: Carl Lewis  • 1993: Linford Christie  • 1995: Donovan Bailey  • 1997: Maurice Greene  • 1999: Maurice Greene  • 2001: Maurice Greene  • 2003: Kim Collins  • 2005: Justin Gatlin  • 2007: Tyson Gay  • 2009: Usain Bolt  • 2011: Yohan Blake  • 2013: Usain Bolt  • 2015: Usain Bolt

Torino 1934 · Paris/Wien 1938 · Oslo 1946 · Brüssel 1950 · Bern 1954 · Stockholm 1958 · Beograd 1962 · Budapest 1966 · Athen 1969 · Helsingfors 1971 · Roma 1974 · Praha 1978 · Athen 1982 · Stuttgart 1986 · Split 1990 · Helsingfors 1994 · Budapest 1998 · München 2002 · Göteborg 2006 · Barcelona 2010 · Helsingfors 2012 · Zürich 2014 · Amsterdam 2016

Speech analytics

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Speech analytics is the process of analyzing recorded calls to gather information, brings structure to customer interactions and exposes information buried in customer contact center interactions with an enterprise. Although it often includes elements of automatic speech recognition, where the identities of spoken words or phrases are determined, it may also include analysis of one or more of the following:

One use of speech analytics applications is to spot spoken keywords or phrases, either as real-time alerts on live audio or as a post-processing step on recorded speech. This technique is also known as audio mining. Other uses include categorization of speech, for example in the contact center environment, to identify calls from unsatisfied customers.

Speech analytics in contact centers can be used to extract critical business intelligence that would otherwise be lost. By analyzing and categorizing recorded phone conversations between companies and their customers, useful information can be discovered relating to strategy, product, process, operational issues and contact center agent performance. This information gives decision-makers insight into what customers really think about their company so that they can quickly react. In addition, speech analytics can automatically identify areas in which contact center agents may need additional training or coaching, and can automatically monitor the customer service provided on calls.

There are three main approaches „under the hood“: the phonetic approach; large-vocabulary continuous speech recognition (LVCSR, more commonly known as speech-to-text, full transcription or ASR – automatic speech recognition), and direct phrase recognition.

Some speech analytics vendors use the „engine“ of a 3rd party and there are some speech analytics vendors that have developed their own proprietary engine.

This is the fastest approach for processing, mostly because the size of the grammar is very small. The basic recognition unit is a phoneme. There are only few tens of unique phonemes in most languages, and the output of this recognition is a stream (text) of phonemes, which can then be searched.

Much slower processing, since the basic unit is a set of words (bi-grams, tri-grams etc.), it needs to have hundreds of thousands of words to match the audio against. The output however is a stream of words, making it richer to work with. It can surface new business issues, the queries are much faster, and the accuracy is higher than the phonetic approach[citation needed]. Most importantly because the complete semantic context is in the index it is possible to find and focus on business issues very rapidly.

Rather than first converting speech into phonemes or text, this approach directly analyzes speech, looking for specific phrases that have been pre-defined as being important to the business. Because no data is lost in conversion using this approach, the results of this method generally provide the highest data reliability[citation needed].

The proposed set of classifiers is based on three main classifiers: kNN, C4.5 and SVM RBF Kernel. This set achieves better performance than each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM with Hybrid kernels and the set of classifiers which consists of the following two basic classifiers: C5.0 and Neural Network. The proposed variant achieves better performance than the other two sets of classifiers.

Making a meaningful comparison of the accuracy of different speech analytics systems can be difficult. The output of LVCSR systems can be scored against reference word-level transcriptions to produce a value for the word error rate (WER), but because phonetic systems use phones as the basic recognition unit, rather than words, comparisons using this measure cannot be made.

When speech analytics systems are used to search for spoken words or phrases

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, what matters to the user is the accuracy of the search results that are returned. Because the impact of individual recognition errors on these search results can vary greatly, measures such as word error rate are not always helpful in determining overall search accuracy from the user perspective.

Measures such as precision and recall, commonly used in the field of information retrieval, are typical ways of quantifying the response of a speech analytics search system. Precision measures the proportion of search results that are relevant to the query. Recall measures the proportion of the total number of relevant items that were returned by the search results. Where a standardised test set has been used, measures such as precision and recall can be used to directly compare the search performance of different speech analytics systems.

These measures of accuracy can be illustrated by the following example Yellow Women Dresses. Imagine a user searches a set of audio files for a specific phrase, and the search returns 10 files. If 9 of the 10 search results do in fact contain the search phrase, the precision is 90% (9 out of 10). If the total number of files that actually contain the phrase is 18 then the recall is 50% (9 out of 18).

According to the US Government Accountability Office, “data reliability refers to the accuracy and completeness of computer-processed data, given the uses they are intended for.” In the realm of Speech Recognition and Analytics, “completeness” is measured by the “detection rate”, and usually as accuracy goes up, the detection rate goes down[citation needed].

Speech analytics provides advanced functionality that gleans valuable intelligence from thousands—even millions—of customer calls, so managers can take quick action. Contact centers record customer conversations but, the sheer number of recordings can exceed the ability to review and analyze. Speech analytics solutions can mine recorded customer interactions to surface the intelligence essential for building effective cost containment and customer service strategies. Used in combination with other workforce optimization suite components like quality monitoring and agent scorecards, Speech analytics can pinpoint cost drivers

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, identify strengths and weaknesses with processes and products, and help understand how the marketplace perceives offerings.

Speech analytics is designed with the business user in mind. It can provide automated trend analysis to show what’s happening in contact centers. The solution can isolate the words and phrases used most frequently within a given time period, as well as indicate whether usage is trending up or down. This information makes it easy for supervisors, analysts, and others in the organization to spot changes in consumer behavior and take action to reduce call volumes—and increase customer satisfaction.

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