题目内容
（请给出正确答案）
Modern technology provides us with a lot of benefits except______.
A．more production and a higher standard of living
B．extended life span
C．a better pumping system
D．more food available
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更多“Modern technology provides us …”相关的问题
A.indication
B.innovation
C.information
A.keep up
B. keep on
C. keep off
D. keep away
Which of the following is the main topic of the passage？
A．History of the arts.
B．Basic questions of the arts.
C．New developments in the arts.
D．Use of modern technology in the arts.
Will mankind continue to live longer and have a higher quality of life？ In large measure the answer depends on technology and our ability to use it wisely. If we keep making progress as we have over the past fifty years, the answer is definitely yes. (82) The advancement of technology depends upon research and development, and the latest statistics show that the United States is continuing to pump billions of dollars annually into such efforts. So while we are running out of some scarce resources, we may well find technological substitutes for many of them through our research programs.
Therefore, in the final analysis the three major factors of production (land, labor, capital) are all influenced by technology. When we need new skills or techniques in machinery, people will start developing new technology to meet these needs. As equipment proves to be slow or inefficient, new machines will be invented. Technology responds to our needs in helping us maintain our standard of living.
What is the best title for the passage？
A．The Definition of Technology
B．Modern Technology
C．The Application of Technology
D．The Development of Technology
The world of business is one area in which technology is isolating us. Experts say, for example, that many people will soon be able to work at home. With access to a large central computer, employees such as office clerks, insurance agents, and accountants could do their jobs at display terminals in their own homes. They would never have to actually see the people they're dealing with. In addition, the way employees are paid will change. Workers' salaries will be automatically paid into their bank accounts, making paper checks unnecessary. No workers will stand in line to receive their pay or cash their checks. Personal banking will change, too. Customers will deal with machines to put in or take our money from their accounts.
Another area that technology is changing is entertainment.
Music, for instance, was once a group experience. People listened to music at concert halls or in small social gatherings. For many people now, however, music is an individual experience. Walking along the street or sitting in their living rooms, they wear headphones to build a wall of music around them. Movie entertainment is changing, too. Movies used to be social events. Now, fewer people are going out to see a movie. Many more are choosing to wait for a film to appear on television or are borrowing videotapes to watch at home. Instead of laughing with others, viewers watch movies in their own living rooms.
(1)After work, Michael likes to ______.
A、listen to music at the concert hall
B、watch a movie in his living room
C、run a program on his computer in his office
D、play baseball with his workmates
(2)The sentence “Michael is imaginary, but his lifestyle. is very possible” means ______.
A、Michael is a person full of imagination and he can make his dreams come true
B、Michael is not a real person but probably the lifestyle. does exist
C、Michael has ambitions but he can't make his dreams come true
D、Michael is a person full of imagination and his lifestyle. is common nowadays
(3)In the modern world, people will
A、stand in line to receive their pay or cash their checks
B、see the people they' re dealing with
C、listen to music at concert halls or in small social gatherings
D、watch movies in their own living rooms
(4)What is the main idea of the passage？
A、We may no longer need to communicate with other human being.
B、Modern technology seems to be separating human beings.
C、We may no longer need to work in the office.
D、Modern technology makes it possible for us to work and entertain ourselves at home.
(5)What will the author most probably discuss after the last paragraph？
A、Games and sports.
B、Personal banking.
C、Music and films.
D、International business.
Technology surrounds us and is a part of our daily lives. We can find technological innovations at home , our workplaces , our schools , and our playgrounds. (1)________technology has improved our lives in some ways , it has also brought many negative(2)________.
Modern technology has made life very (3) ________. We have everything we need and our inventions take care of so many unpleasant details in our day-to-day lives. Foods from all over the world are available at local supermarkets. International travel is now fast and easy. We can talk to friends on cell phones any time. anywhere. and we can catch a TV show from many countries far away.
(4)________. these conveniences have a price. We consume too much energy and too many resources , and we create too much waste. The world is overflowing with pollution and waste. We cannot continue like this forever. We should be more responsible about protecting our world while still enjoying conveniences. (5)________our natural world is destroyed. The conveniences will be useless.
It is fairly common for an industrial cluster to break up and for production to move to locations with lower wages when the technology of the industry is no longer rapidly improving--when it is no longer essential to have the absolutely most modern machinery，when the need for highly skilled workers has declined，and when being at the cutting edge of innovation conveys only a small advantage．Explain this tendency of industrial clusters to break up in terms of the theory of external economies．
Parallel Computer Models
并行模式
Parallel processing has emerged as a key enabling technology in modern computers, driven by the ever-increasing demand for higher performance, lower costs, and sustained productivity in real-life applications. Concurrent events are taking place in today's high- performance computers due to the common practice of multiprogramming, multiprocessing, or multicomputing.
Parallelism appears in various forms, such as lookahead, pipelining, vectorization, concurrency, simultaneity, data parallelism, partitioning, interleaving, overlapping, multiplicity, replication, time sharing, space sharing, multitasking, multiprogramming, multithreading, and distributed computing at different processing levels.
In this part, we model physical architectures of parallel computers, vector super- computers[1], multiprocessors, multicomputers, and massively parallel processors. Theoretical machine models are also presented, including the parallel random-access machines (PRAMs)[2]and the complexity model of VLSI (very large-scale integration) circuits. Architectural development tracks are identified with case studies in the article. Hardware and software subsystems are introduced to pave the way for detailed studies in the subsequent section.
The State of Computing
Modern computers are equipped with powerful hardware facilities driven by extensive software packages. To assess state-of-the-art[3]computing, we first review historical milestones in the development of computers. Then we take a grand tour of the crucial hardware and software elements built into modern computer systems. We then examine the evolutional relations in milestone architectural development. Basic hardware and software factors are identified in analyzing the performance of computers.
Computer Development Milestones
Computers have gone through two major stages of development: mechanical and electronic. Prior to 1945, computers were made with mechanical or electromechanical parts. The earliest mechanical computer can be traced back to 500 BC in the form of the abacus used in China. The abacus is manually operated to perform decimal arithmetic with carrying propagation digit by digit.
Blaise Pascal built a mechanical adder/subtractor in France in 1642. Charles Babbage designed a difference engine in England for polynomial evaluation in 1827. Konrad Zuse built the first binary mechanical computer in Germany in 1941. Howard Aiken[4]proposed the very first electromechanical decimal computer, which was built as the Harvard Mark I[5]by IBM in 1944. Both Zuse's and Aiken's machines were designed for general-purpose computations.
Obviously, the fact that computing and communication were carried out with moving mechanical parts greatly limited the computing speed and reliability of mechanical computers. Modern computers were marked by the introduction of electronic components. The moving parts in mechanical computers were replaced by high-mobility electrons in electronic computers. Information transmission by mechanical gears or levers was replaced by electric signals traveling almost at the speed of light.
Computer Generations
Over the past five decades, electronic computers have gone through five generations of development. Each of the first three generations lasted about 10 years. The fourth generation covered a time span of 15 years. We have just entered the fifth generation with the use of processors and memory devices with more than 1 million transistors on a single silicon chip.
The division of generations is marked primarily by sharp changes in hardware and software technologies. Most features introduced in earlier generations have been passed to later generations. In other words, the latest generation computers have inherited all the nice features and eliminated all the bad ones found in previous generations.
Elements of Modern Computers
Hardware, software, and programming elements of a modern computer system are briefly introduced below in the context of parallel processing.
Computing Problems
It has been long recognized that the concept of computer architecture is no longer restricted to the structure of the bare machine hardware. A modern computer is an integrated system consisting of machine hardware, an instruction set, system software, application programs, and user interfaces. These system elements are depicted in Fig. 1. The use of a computer is driven by real-life problems demanding fast and accurate solutions. Depending on the nature of the problems, the solutions may require different computing resources.
For numerical problems in science and technology, the solutions demand complex mathematical formulations and tedious integer or floating-point computations. For alphanumerical problems in business and government, the solutions demand accurate transactions, large database management, and information retrieval operations.
For artificial intelligence (AI) problems, the solutions demand logic inferences and symbolic manipulations. These computing problems have been labeled numerical computing, transaction processing, and logical reasoning. Some complex problems may demand a combination of these processing modes.
Algorithms and Data Structures
Special algorithms and data structures are needed to specify the computations and communications involved in computing problems. Most numerical algorithms are deterministic, using regularly structured data. Symbolic processing may use heuristics or nondeterministic searches over large knowledge bases.
Problem formulation and the development of parallel algorithms often require interdisciplinary interactions among theoreticians, experimentalists, and computer programmers. There are many books dealing with the design and mapping of algorithms or heuristics onto parallel computers. In this article, we are more concerned about the resources mapping problems than the design and analysis of parallel algorithms.
Hardware Resources
The system architecture of a computer is represented by three nested circles on the right in Fig. 1. A modern computer system demonstrates its power through coordinated efforts by hardware resources, an operating system, and application software. Processors, memory, and peripheral devices form the hardware core of a computer system. We will study instruction-set processors, memory organization, multiprocessors, supercomputers, multicomputers, and massively parallel computers.
Special hardware interfaces are often built into I/O devices, such as terminals, workstations, optical page scanners, magnetic ink character recognizers, modems, file servers, voice data entry, printers, and plotters. These peripherals are connected to mainframe computers directly or though local or wide-area networks.
In addition, software interface programs are needed. These software interfaces include file transfer systems, editors, word processors, device drivers, interrupt handlers, network communication programs, etc. These programs greatly facilitate the portability of user programs on different machine architectures.
Operating System
An effective operating system manages the allocation and deal-location of resources during the execution of user programs. We will study UNIXE[6]extensions for muhiprocessors and muhicomputers later. Mach/OS kernel and OSF/1[7]will be specially studied for muhithreaded kernel functions, virtual memory management, file subsystem, and network communication services. Beyond the OS, application software must be developed to benefit the users. Standard benchmark programs are needed for performance evaluation.
Notes
[1] vector super-computers: 向量巨型机体系机构。向量巨型计算机的体系机构，目前大多数仍为多流水线结构，也有的采用并行处理机构。
[2] parallel random-access machines(PRAMs)：并行随机存取机器具有任意多个处理器，以及分别用于输入、输出和工作的存储器的机器模型。
[3] state-of-the-art：最新技术水平；当前正在发展的技术，或者在当前应用中保持领先地位的技术。
[4] Howard Aiken: Mark I计算机的设计者。
[5] Harvard Mark I：哈佛Mark I计算机。Mark I计算机是一种在30年代末40年代初由(美国)哈佛大学的Howard Aiken设计并由IBM公司制造的机电式计算器。
[6] UNIX：UNIX操作系统。
[7] Mach/OS kernel and OSF/1：Mach操作系统/OS操作系统，Kernel核心程序。在操作系统中，实现诸如分配硬件资源、进程调度等基本功能的程序，是与硬件机器直接打交道的部分，始终驻留内存。OSF/1开放软件基金会/1。
Choose the best answer for each of the following:
Questions 31 to 35 are based on the following passage:
Telephone, television, radio, and telegraph all help people communicate with each other. Because of these devices, ideas and news of events spread quickly all over the world. For example, within seconds, people can know the results of an election in another country. An international football match comes into the homes of everyone with a television set.News of a disaster such as an earthquake or a flood can bring help from distant countries within hours, help is on the way. Because of modern technology like the satellites that travel around the world, information travels fast.
How has this speed of communication changed the world？ To many people,the world has become smaller. Of course this does not mean that the world is actually physically smaller. It means that the world seems smaller. Two hundred years ago,communication between the continents took a long time. All news was carried on ships that took weeks or even months to cross the ocean. In the seventeenth and eighteenth centuries,it took six weeks for news from Europe to reach America.This time difference influenced people's actions. For example, one battle, or fight, in the War of 1812 between England and the United States could have been avoided. A peace agreement had already been signed. Peace was made in England, but the news of peace took six weeks to reach America. During these six weeks, the large and serious Battle of New Orleans was fought. Many people lost their lives after a peace treaty had been signed.They would not have died if news had come in time.In the past,communication took much time than it does now.
There was a good reason why the world seemed so much larger than it does today.
31. News spreads fast because of____.
A.modern transportation B.new technology C.the change of the world D.a peace agreement
32. According to this passage,____is very important to people in a disaster area.
A.fast communication B.modern technology C.latest news D.new ideas
33. Which of the following statements is true？
A.The world now seems smaller because of faster communication.
B.The world is actually smaller today.
C.The world is changing its size.
D. The distance between England and America has changed since the War of 1812
34. Two hundred years ago,news between the continents was carried____.
A.by telephone and telegraph B.by land C.by air D.by sea
35. The New Orleans Battle could have been avoided if the peace agreement had been signed____.
A.by both sides B.in time C.in America D.in England
Speech Recognition
语音识别系统
Automatic recognition of speech by machine[1]has been a goal of research for more than four decades and has inspired such science fiction wonders as the computer HAL in Stanley Kubrick's famous movie 2001—A Space Odyssey[2]and the robot R2D2 in the George Lucas classic Star Wars[3]series of movies. However, in spite of the glamour of designing an intelligent machine that can recognize the spoken word and comprehend its meaning, and in spite of the enormous research efforts spent in trying to create such a machine, we are far from[4]achieving the desired goal of a machine that can understand spoken discourse on any subject by all speakers in all environments. Thus, an important question is, What do we mean by "speech recognition by machine". Another important question is, How can we build a series of bridges that will enable us to advance both our knowledge as well as the capabilities of modern speech-recognition systems so that the "holy grail"[5]of conversational speech recognition and understanding by machine is attained？
Because we do not know how to solve the ultimate challenge of speech recognition, our goal here is to give a series of presentations on the fundamental principles of most modern, successful speech-recognition systems so as to provide a framework from which researchers can expand the frontier. We will attempt to avoid making absolute judgments on the relative merits of various approaches to particular speech-recognition problems. Instead we will provide the theoretical background and justification for each topic discussed so that the reader is able to understand why the techniques have proved valuable and how they can be used to benefit practical situations.
One of the most difficult aspects of performing research in speech recognition by machine is its interdisciplinary nature[6], and the tendency of most researchers to apply a monolithic approach to individual problems. Consider the disciplines that have been applied to one or more speech-recognition problems.
1. signal processing—the process of extracting relevant information from the speech signal in an efficient, robust manner. Included in signal processing is the form of spectral analysis used to characterize the time-varying properties of the speech signal as well as various types of signal preprocessing (and postprocessing) to make the speech signal robust to the recording environment (signal enhancement).
2. physics (acoustics)—the science of understanding the relationship between the physical speech signal and the physiological mechanisms (the human vocal tract mechanism)[7]that produced the speech and with which the speech is perceived (the human hearing mechanism).
3. pattern recognition—the set of algorithms used to cluster data to create one or more prototypical patterns of a data ensemble, and to match a pair of patterns on the basis of feature measurements of the patterns.
4. communication and information theory—the procedures for estimating parameters of statistical models; the methods for detecting the presence of particular speech patterns, the set of modern coding and decoding algorithms used to search a large but finite grid for a best path corresponding to a "best" recognized sequence of words.
5. linguistics—the relationships between sounds, words in a language, meaning of spoken words and sense derived from meaning. Included within this discipline are the methodology of grammar and language parsing.
6. physiology—understanding of the higher-order mechanisms within the human central nervous system that account for speech production and perception in human beings. Many modern techniques try to embed this type of knowledge within the framework of artificial neural networks (which depend heavily on several of the above disciplines).
7. computer science—the study of efficient algorithms for implementing, in software or hardware, the various methods used in a practical speech-recognition system.
8. psychology—the science of understanding the factors that enable technology to be used by human beings in practical tasks.
Successful speech-recognition systems require knowledge and expertise from a wide range of disciplines, a range far larger than any single person can possess[8]. Therefore, it is especially important for a researcher to have a good understanding of the fundamentals of speech recognition (so that a range of techniques can be applied to a variety of problems), without necessarily having to be an expert in each aspect of the problem. The purpose is to provide this expertise by giving in-depth discussions of a number of fundamental topics in speech-recognition research.
A general model for speech recognition begins with a user creating a speech signal (speaking) to accomplish a given task. The spoken output is first recognized in the speech signal that is decoded into a series of words that are meaningful according to the syntax, semantics and pragmatics[9]of the recognition task. A higher-level processor that uses a dynamic knowledge representation to modify the syntax, semantics, and pragmatics according to the context of what it has previously recognized obtains the meaning of the recognized words. In this manner, things such as non-sequitors are omitted from consideration at the risk of misunderstanding, but at the gain of minimizing errors for sequentially meaningful inputs. The feedback from the higher-level processing box reduces the complexity of the recognition model by limiting the search for valid input sentences (speech) from the user. The recognition system responds to the user in the form of a voice output, or equivalently, in the form of the requested action being performed, with the user being prompted for more input.
A Brief History of Speech-Recognition Research
Research in automatic speech recognition by machine has been done for almost four decades. To gain an appreciation for the amount of progress achieved over this period, it is worthwhile to briefly review some research highlights[10]. The reader is cautioned that such a review is cursory, at best, and must therefore suffer from errors of judgment as well as omission.
The earliest attempts to devise systems for automatic speech recognition by machine were made in 1950s, at Bell Laboratories. Davis, Biddulph, and Balashek built a system for isolated digit recognition for a single speaker. The system relied heavily on measuring spectral resonances during the vowel region of each digit. Another effort of note in this period was the vowel recognizer of Forgie and Forgie, constructed at MIT Lincoln Laboratories[11]in 1959, in which 10 vowels embedded in a /b/-vowel-/t/ format were recognized in a speaker-independent manner. Again, a filter bank analyzer was used to provide special information and a time-varying estimate of the vocal tract resonances was made to decide which vowel was spoken.
In the 1960s several fundamental ideas in speech recognition surfaced and were published. However, the decade started with several Japanese laboratories entering the recognition arena and building special-purpose hardware as part of their systems. In the 1960s three key research projects were initiated that have had major implications on the research and development of speech recognition for the past 20 years. The first of these projects was from the effort of Martin and his colleagues at RCA[12]Laboratories, beginning in the late 1960s, to develop realistic solutions to the problems associated with nonuniformity of time scales in speech events. At about the same time, in the Soviet Union, Vintsyuk proposed the use of dynamic programming methods for time aligning a pair of speech utterances. Although the essence of the concepts of dynamic time warping, as well as rudimentary versions of the algorithms for connected word recognition, were embodied in Vintsyuk's work, it was largely unknown in the West and did not come to light until the early 1980s; this was long after the more formal methods were proposed and implemented by others.
A final achievement of note in the 1960s was the pioneering research of Reddy in the field of continuous speech recognition by dynamic tracking of phonemes. Reddy's research eventually spawned a long and highly successful speech-recognition research program at Carnegie Mellon University, which, to this day, remains a world leader in continuous- speech-recognition systems.
In the 1970s speech-recognition research achieved a number of significant milestones. First was the area of isolated word or discrete utterance recognition. The Japanese research showed how dynamic programming methods could be successfully applied; and the American research showed how the ideas of linear predictive coding (LPC)[13], which had already been successfully used in low-bit-rate speech coding, could be extended to speech- recognition systems through the use of an appropriate distance measure based on LPC spectral parameters.
Another milestone of the 1970s was the beginning of a longstanding, highly successful group effort in large vocabulary speech recognition at IBM, in which researchers studied three distinct simple database queries, the laser patent text language for transcribing laser patents, and the office correspondence task, called Tangora, for dictation of simple memos.
Speech research in the 1980s was characterized by a shift in technology from template- based approaches to statistical modeling methods—especially the hidden Markov model approach. Although the methodology of hidden Markov modeling (HMM)[14]was well known and understood in a few laboratories, it was not until widespread publication of the methods and theory of HMMs, in the mid-1980s, that the technique became widely applied in virtually every speech-technology that was recognition research laboratory in the world.
Another "new" technology that was reintroduced in the late 1980s was the idea of applying neural networks to problems in speech recognition. Neural networks were first introduced in the 1950s, but they did not prove useful initially because they had many practical problems. In the 1980s, however, a deeper understanding of the strengths and limitations of the technology was obtained, as well as the relationships of the technology to classical signal classification methods. Several new ways of implementing systems were also proposed.
Finally, the 1980s was a decade in which a major impetus was given to large vocabulary, continuous-speech-recognition systems by the Defense Advanced Research Projects Agency (DARPA) community[15], which sponsored a large research program aimed at achieving high word accuracy for a 1,000-word, continuous-speech-recognition, database management task. The DARPA program has continued into the 1990s, with emphasis shifting to natural language front ends to the recognizer, and the task shifting to retrieval of air travel information. At the same time, speech-recognition technology has been increasingly used within telephone networks to automate as well as enhance operator services.
Notes
[1] automatic recognition of speech by machine: 计算机自动语言识别。
[2] science fiction wonders as the computer HAL in Stanley Kubrick's famous movie 2001—A Space Odyssey: 斯坦利·库布里克著名的科幻电影《 2001年太空漫游 》(1968),也有人译成《 2001年太空奥德赛 》。荷马史诗《奥德赛 》(Odyssey)中的主人公奥德修斯(Odysseus)从特洛伊战争回来的途中花了20年时间才最终到家。他经历了不可思议的冒险，甚至还到阴间同死者对话。因此，Odyssey指任何追求某个目标漫长、复杂的旅途，也可指一个精神上或心理上长期追求、探索的历程。
[3] in the George Lucas classic Star Wars：乔治·卢卡斯的经典科幻电影《星球大战》(1977)。Star Wars，美国战略防御系统的别称。此系统规划于20世纪80年代，得到里根总统支持。系统采用可在太空运行的激光武器来击落敌方导弹。此别称来源于“星球大战”的科幻片，该片反映了这个系统的高科技特点。
[4] far from: 远不是，远非，如：His work is far from good.(他的工作很不好。)
[5] holy grail: 耶稣在最后的晚餐时用的杯(或盘)；长期以来梦寐以求的东西。
[6] its interdisciplinary nature：它的跨学科特性。
[7] the physiological mechanisms (the human vocal tract mechanism)：生理机制(人的声道发声机制)。
[8] a range far larger than any single person can possess: far larger是形容词比较级短语，作定语修饰名词range，此句是意思是：比任何一个人的知识面都要广得多。
[9] the syntax，semantics and pragmatics：句法、语义学和语用学。
[10] is worthwhile to briefly review some research highlights. 简单回顾研究上的重大进展是有益的。
[11] MIT Lincoln Laboratories:(美国)麻省理工学院林肯实验室(Massachusetts Institute of Technology)。
[12] RAC: 美国无线电公司(Radio Corporation of America)。
[13] linear predictive coding(LPC)：线性预测编码法，一种语音压缩技术，它将语音产生的机理模型化为一个离散的、时变的、线性递归滤波器，该滤波器又进一步模型化为一个正反馈回路中的自适应线性预测器。
[14] Hidden Markov modeling HMM: 隐式马尔可夫模型。
[15] Defense Advanced Research Projects Agency (DARPA) community: (美国)国防部高级研究系统计划署。
Choose the best answer:
