Английский язык. Учебно-методический комплекс по направлению подготовки 230700 «Прикладная информатика»

6.I was surprised that she … say such rude words. must

would will should

7.My grandfather … recite poems in six languages many years ago. should

shall could need

7.Прочитайте, переведите письменно текст:

Robots Learn to Handle Objects

Infants spend their first few months learning to find their way around and manipulating objects, and they are very flexible about it: Cups can come in different shapes and sizes, but they all have handles. So do pitchers, so we pick them up the same way. Similarly, your personal robot in the future will need the ability to generalize, for example, to handle your particular set of dishes and put them in your particular dishwasher. In Cornell's Personal Robotics Laboratory, a team led by AshutoshSaxena, assistant professor of computer science, is teaching robots to manipulate objects and find their way around in new environments. They reported two examples of their work at the 2011 Robotics: Science and Systems Conference June 27 at the University of Southern California.

A common thread running through the research is «machine learning» – programming a computer to observe events and find commonalities. With the right programming, for example, a computer can look at a wide array of cups, find their common characteristics and then be able to identify cups in the future. A similar process can teach a robot to find a cup's handle and grasp it correctly. Other researchers have gone this far, but Saxena's team has found that placing objects is harder than picking them up, because there are many options. A cup is placed upright on a table, but upside down in a dishwasher, so the robot must be trained to make those decisions.

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In early tests they placed a plate, mug, martini glass, bowl, candy cane, disc, spoon and tuning fork on a flat surface, on a hook, in a stemware holder, in a pen holder and on several different dish racks. Surveying its environment with a 3-D camera, the robot randomly tests small volumes of space as suitable locations for placement. For some objects it will test for «caging» – the presence of vertical supports that would hold an object upright. It also gives priority to «preferred» locations: A plate goes flat on a table, but upright in a dishwasher.

After training, their robot placed most objects correctly 98 percent of the time when it had seen the objects and environments previously, and 95 percent of the time when working with new objects in a new environment. Performance could be improved, the researchers suggested, by longer training. But first, the robot has to find the dish rack. Just as we unconsciously catalog the objects in a room when we walk in, Saxena and colleague Thorsten Joachims, associate professor of computer science, have developed a system that enables a robot to scan a room and identify its objects. Pictures from the robot's 3-D camera are stitched together to form a 3-D image of an entire room that is then divided into segments, based on discontinuities and distances between objects. The goal is to label each segment.

The researchers trained a robot by giving it 24 office scenes and 28 home scenes in which they had labeled most objects. The computer examines such features as color, texture and what is nearby and decides what characteristics all objects with the same label have in common. In a new environment, it compares each segment of its scan with the objects in its memory and chooses the ones with the best fit.

In tests, the robot correctly identified objects about 83 percent of the time in home scenes and 88 percent in offices. In a final test, it successfully located a keyboard in an unfamiliar room. Again, Saxena said, context gives this robot an advantage. The keyboard only shows up as a few pixels in the image, but the monitor is easily found, and the robot uses that information to locate the keyboard. Robots still have a long way to go to learn like humans, the researchers admit. «I would be really happy if we could build a robot that would even act like a six-month-old baby», Saxena said.

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8. Прочитайте, переведите текст. Письменно составьте рассказ о своем родном городе:

One of the largest cities in the world, New York is situated at the mouth of the Hudson River1. New York was founded by the Dutch2. It is interesting to know that Manhattan Island3, the central part of the city, was bought from the local Indians for $24 by the Dutch.

In the 18th century New York grew into the largest city of the United States of America. Now New York is a great sea port, the leading textile and the financial centre of the country.

Manhattan Island with the Wall Street3 district is the heart of America’s business and culture. There, at Broadway and 116 Street is the campus of Columbia University, the biggest educational establishment of New York.

New York’s theatre district is often referred to as «Broadway», but most of the theatres are located actually on the side streets near Times Square. They are active year-round with top-stars performing in various plays.

It is easy to find one’s way in New York. Avenues, except Broadway, run north and south; streets run east and west and are numbered. The building numbers get higher as you move away from the Fifth Avenue and towards the rivers.

Subway (the metro is called subway in the USA) provides the cheapest and the fastest way to travel. There are no buses on the most of the avenues and on the principal streets.

1Река Гудзон.

2Манхэттен.

3Голландцы.

4Уолл-стрит.

Контрольная работа № 3

1.Перепишите предложения, подчеркните причастия. Письменно переведите предложения:

1.Introducing the new guests, the hostess looked strangely nervous.

2.Not knowing grammar, one cannot speak foreign language correctly.

3.Nothing can save the sinking ship now, we must take care of the passengers.

4.We have had a number of worrying telephone calls today, but now everything is all right.

5.Arriving at the city they used to stay at our flat.

6.Being given the answer, the producer opened the door.

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2.Перепишите предложения. Найдите и подчеркните герундий. Переведите предложения:

1.There’s no use talking to him, you cannot change his mind.

2.Nowadays children usually prefer watching TV to reading books.

3.She is thinking of quitting her job and going to another city.

4.On hearing the end of the story we could not help laughing.

5.This art exhibition is worth visiting.

6.Would you mind ringing me up tomorrow morning?

3.Перепишите и переведите предложения, обращая внимание на причастия и герундий:

1.Being introduced, the guests kept smiling.

2.The student was ashamed of having made so many mistakes in the test.

3.Having been translated, the article was published in the scientific magazine.

4.I hate being asked personal questions.

5.Being pleased with the student's answer the examiner did not ask him any more questions.

6."I don't remember having told you my name,"– remarked the girl.

4.Перепишите и переведите предложения, обращая внимание на причастные и герундиальные обороты:

1.A new generation of computers having been designed, the new era of global communication began.

2.There are several hundred universities in Great Britain, Oxford and Cambridge being the oldest and the most famous.

3.His having taken part in the discussion surprised us greatly.

4.He mentioned his having shown these slides at the conference.

5.She noticed somebody following her.

6.I dislike my relatives' interfering in my affairs.

7.They were heard discussing a new plan of reconstructing the museum.

5. Перепишите и переведите предложения, обращая внимание на инфинитив и инфинитивные обороты:

1. We didn’t expect him to come so early.

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2.You are not allowed to wear overcoats, smoke, drink alcohol inside the cinema house.

3.The sad ending of this film made half the audience weep.

4.The programme seems to have forgotten one of the computer languages.

5.The lecturer pretended to be listening attentively to the students.

6.Перефразируйте предложения, используя инфинитив цели, как в приведенном примере:

Model: We entered the University because we want to study. – We entered the University to study.

1.They moved quietly because they did not want to wake up their parents.

2.If you need to understand the meaning of an unknown word, use the dictionary.

3.He worked hard because he thought he could get a promotion.

4.She bought a new dress in order to impress him.

5.They left home for the airport three hours before the flight, as they did not want to be late.

7.Поставьте глаголы в скобках в форму либо инфинитива, либо герундия:

1.He’ll never forget (meet) Mel Gibson.

2.Do you fancy (go) to the theatre.

3.He is so shy, he prefers (be) alone rather than in a company.

4.I can’t help (be) nervous before the exams.

5.The students can’t wait to go on holiday.

8.Переведите текст письменно:

Robots with new algorithms

New intelligent algorithms could help robots to quickly recognize and respond to human gestures. Researchers at A*STAR Institute for Infocomm Research in Singapore have created a computer program which recognizes human gestures quickly and accurately, and requires very little training. Many works of science fiction have imagined robots that could interact directly with people to provide entertainment, services or even health care. Robotics is now at

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a stage where some of these ideas can be realized, but it remains difficult to make robots easy to operate. One option is to train robots to recognize and respond to human gestures. In practice, however, this is difficult because a simple gesture such as waving a hand may appear very different between different people. Designers must develop intelligent computer algorithms that can be «trained» to identify general patterns of motion and relate them correctly to individual commands.

Now, Rui Yan and co-workers at the A*STAR Institute for Infocomm Research in Singapore have adapted a cognitive memory model called a localist attractor network (LAN) to develop a new system that recognize gestures quickly and accurately, and requires very little training. «Since many social robots will be operated by non-expert users, it is essential for them to be equipped with natural interfaces for interaction with humans», says Yan. «Gestures are an obvious, natural means of human communication. Our LAN gesture recognition system only requires a small amount of training data, and avoids tedious training processes».

Yan and co-workers tested their software by integrating it with Shape Tape, a special jacket that uses fibre optics and inertial sensors to monitor the bending and twisting of hands and arms. They programmed the Shape Tape to provide data 80 times per second on the three-dimensional orientation of shoulders, elbows and wrists, and applied velocity thresholds to detect when gestures were starting. In tests, five different users wore the Shape Tape jacket and used it to control a virtual robot through simple arm motions that represented commands such as forward, backwards, faster or slower. The researchers found that 99. 15% of gestures were correctly translated by their system. It is also easy to add new commands, by demonstrating a new control gesture just a few times.

The next step in improving the gesture recognition system is to allow humans to control robots without the need to wear any special devices. Yan and co-workers are tackling this problem by replacing the Shape Tape jacket with motion-sensitive cameras. «Currently we are building a new gesture recognition system by incorporating our method with a Microsoft Kinect camera», says Yan. «We will implement the proposed system on an autonomous robot to test its usability in the context of a realistic service task, such as cleaning»!

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СОДЕРЖАНИЕ ЗАЧЕТОВ И ЭКЗАМЕНОВ

Итоговый контроль знаний студентов осуществляется в форме зачета (I, II, III семестры) и экзамена (IV семестр) на очной форме обучения. На заочной форме обучения студенты сдают экзамены в I, III семестрах и зачет во II семестре. Успешное выполнение письменных контрольных работ по лексико-грамматическому материалу обеспечивает студенту допуск к зачету, который включает следующие вопросы:

1.Чтение фрагмента профессионально ориентированного текста объемом 800 печатных знаков и его пересказ (или ответы на вопросы преподавателя по тексту).

2.Устное изложение одной из изученных тем (или ответы на вопро-

сы).

Содержание итогового устного экзамена по курсу «Английский язык»

1.Чтение, письменный перевод со словарем профессионально ориентированного текста объемом 1500–2000 печатных знаков, обсуждение его содержания на английском языке.

2.Ознакомительное чтение и пересказ текста на английском языке.

3.Устное изложение одной из изученных в течение курса тем по общеэкономической специальности.

Пример типового задания для зачета (очная/заочная форма обучения)

1. Read and translate the text using the dictionary:

Hard Drives: A Bit of Progress

Information in most computer memories is stored in the form of «bits» represented by the polarization of tiny magnets on the surface of memory devices such as the computer's hard drive. The capacities of these devices have increased exponentially over the last 30 years, a feat made possible by progressively reducing the area taken up by the magnets storing the information. In modern machines, these magnets are so small that reducing their size any further risks creating unstable data, due to random flipping of the direction of polarization of the magnets at higher densities. Now, Mojtaba Ranjbar and

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colleagues at the A*STAR Data Storage Institute have honed a key technology, called bit-patterned media, to overcome this problem and allow data to be stored at previously unattainable densities.

Bit-patterned media technology replaces the continuous magnetic film traditionally used in hard drives with an array of small, patterned magnetic dots (see image), each of which stores a bit of data. By carefully designing the size and shape of these dots, data can be stored at very high densities without the instability that would be encountered if a continuous film were used. Using bit-patterned media, however, is not without its own difficulties, chief among which is a problem known as «switching field distribution», whereby the magnetic field required to write or erase data in each dot differs slightly and by an unknown amount. As a result, the magnetic field applied by a hard drive write head may be too small, or too large, resulting in data errors. Previous work by other researchers sought to minimize the switching field distribution problem by covering all of the magnetic dots with a continuous magnetic film placed on top of the dots, which alters the magnetic interactions between individual dots. The approach called «capped bit-patterned media» traditionally requires different magnetic materials for the dots and film, introducing additional fabrication complexity.

Ranjbar and co-workers used the same material for the film and dots, and positioned the dots above the film rather than below it. This approach allowed a particularly simple fabrication process, in which dots were etched in a controlled fashion, leaving a continuous, unattached film underneath and obviating the need for a separate deposition step to introduce a new magnetic material. The researchers found that this simplified process successfully reduced switching field distribution, and also lowered the field strengths necessary for writing data. Ranjbar comments, «Combined with the ease of fabrication, this technology should prove useful in bit-patterned media for next-generation hard disk drives».

2.Answer the questions on the text:

1.Where is information stored in computer?

2.What is a bit-pattern media technology?

3.What are the advantages of this approach?

3.Speak on the topic:

Do you know good food tips? What are they?

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Пример типового экзаменационного билета (очная/заочная форма обучения)

1. Read and translate the text using the dictionary:

History of keyboard

While typewriters are the definitive ancestor of all key-based text entry devices, the computer keyboard as a device for electromechanical data entry and communication derives largely from the utility of two devices: teleprinters (or teletypes) and keypunches. It was through such devices that modern computer keyboards inherited their layouts.

As early as the 1870s, teleprinter-like devices were used to simultaneously type and transmit stock market text data from the keyboard across telegraph lines to stock ticker machines to be immediately copied and displayed onto ticker tape. The teleprinter, in its more contemporary form, was developed from 1903-1910 by American mechanical engineer Charles Krum and his son Howard, with early contributions by electrical engineer Frank Pearne. Earlier models were developed separately by individuals such as Royal Earl House and Frederick G. Creed.

Earlier, Herman Hollerith developed the first keypunch devices, which soon evolved to include keys for text and number entry akin to normal typewriters by the 1930s.

The keyboard on the teleprinter played a strong role in point-to-point and point-to-multipoint communication for most of the 20th century, while the keyboard on the keypunch device played a strong role in data entry and storage for just as long. The development of the earliest computers incorporated electric typewriter keyboards: the development of the ENIAC computer incorporated a keypunch device as both the input and paper-based output device, while the BINAC computer also made use of an electromechanically-controlled typewriter for both data entry onto magnetic tape (instead of paper) and data output.

From the 1940s until the late 1960s, typewriters were the main means of data entry and output for computing, becoming integrated into what were known as computer terminals. Because of the lack of pace of text-based terminals in comparison to the growth in data storage, processing and transmission, a general move toward video-based computer terminals was affected by the 1970s, starting with the Datapoint 3300 in 1967.

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The keyboard remained the primary, most integrated computer peripheral well into the era of personal computing until the introduction of the mouse as a consumer device in 1984. By this time, text-exclusive user interfaces with sparse graphics gave way to comparatively-graphics-rich icons on screen. However, keyboards remain central to human-computer interaction to the present, even as mobile personal computing devices such as smartphones and tablets adapt the keyboard as an optional virtual, touchscreen-based means of data entry.

2. Read the text. Define the main idea of the text and give its summary:

Network society

The term Network Society describes several different phenomena related to the social, political, economic and cultural changes caused by the spread of networked, digital information and communications technologies. A number of academics (see below) are credited with coining the term since the 1990s and several competing definitions exist. The intellectual origins of the idea can be traced back to the work of early social theorists such as Georg Simmel who analyzed the effect of modernization and industrial capitalism on complex patterns of affiliation, organization, production and experience.

The term network society was coined in Dutch by Jan van Dijk in his book De Netwerkmaatschappij (1991) (The Network Society), and used by Manuel Castells in The Rise of the Network Society(1996), the first part of his trilogy The Information Age. In 1978 James Martin used the related term «The Wired Society» indicating a society that is connected by massand telecommunication networks. Barry Wellman and the team of Roxanne Hiltz and Murray Turoff also have done work on the concept of network society.

Van Dijk defines the network society as a society in which a combination of social and media networks shapes its prime mode of organization and most important structures at all levels (individual, organizational and societal). He compares this type of society to a mass society that is shaped by groups, organizations and communities ('masses') organized in physical co-presence.

Wellman studied the network society as a sociologist at the University of Toronto. His first formal work was in 1973, «The Network City» with a more comprehensive theoretical statement in 1988. Since his 1979 «The Community Question», Wellman has argued that societies at any scale are best seen as networks (and «networks of networks») rather than as bounded groups in hierarchical structures. More recently, Wellman has contributed to the theory of

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