1 Powerful Way to Boost Your Creativity.

What is common between Pablo Picasso, Johannes Kepler, and Elon Musk?
· They are Powerful Innovators.
· They are all “Expert-Generalists”.

And Being an Generalist is one of the most powerful ways to boost your creativity.

Let us explain…

The term, expert-generalist means:

(2) Drill deep to focus and perfect the thinking.

So what we mean here is that the one thing that separates the great innovators from everyone else is that they seem to know a lot about a wide variety of topics.

And their wide knowledge base supports their creativity.

Albert Einstein was trained in physics, but to formulate his law of general relativity, he taught himself an area of mathematics far removed from his expertise, Riemannian geometry.

James Watson and Francis Crick combined discoveries in X-ray diffraction technology, chemistry, evolutionary theory, and computation to solve the puzzle of the double helix.

Steve Jobs, of course, drew on insights from his study of calligraphy and a rich understanding of design to create a new breed of computing devices.

And so on……………

Sounds Hard?

But being an expert-generalist is not hard at all. All this requires is devoting a concentrated block of time on your personal development every day.

As you go further, you broaden your knowledge horizons and you start connecting the seemingly unconnected dots from various disciplines. And that is when you see a surge in your creativity and your analytical abilities to being able to compare analogies between disciplines.

And here are some simple ways to be an expert-generalist.

Learn a Completely New Skill.
Noted psychologist Vygotsky argued in 1978 argues that learning is most effective within the “zone of proximal development.” I know this sounds like a psychology lecture so, in simple terms, it is the space slightly beyond a learner’s current knowledge base and skills level, but a place where learning is still within a person’s reach.”

In simple words, we are talking about discomfort here. Constructive discomfort. And this is one of the prerequisites to be creative. After all, being creative requires doing something that has not been done before. If you are not willing to do something new, then it’s hard to be creative.

Similarly, Johannes Kepler struggled to understand how the planets could move around the sun and drew on his knowledge of light and magnetism to try to understand the force that moved the planets.

So, in short, learn something new and learn it deep so that its analogies can be mined later in a completely different field. This new perspective of looking at things is what leads to creativity.

Be an Open Networker
Here’s the amazing insight we discovered: the one variable that explains 65% of the variance in someone’s career success is having an open network.
And the most successful people tend to be exceptional networkers.

Networkers tend to belong to multiple networks and are able to speak the insider lingo and translate to help move information or make introductions of people across those network lines. And this dissipation and assimilation of knowledge in social circles is what makes them insanely creative in their respective fields.

Always remember if you want to be a successful networker, you must decide to be open to different experiences, however uncomfortable they can be.

And Lastly, be a Voracious Reader.
To be an expert-generalist, people at least need 3 skills and the value lies at the intersection of those. One must combine a hard skill, a soft skill, and a perspective to become an expert-generalist.

For example, Elon Musk combines programming, reading, and existential philosophy. Warren Buffett combines value investing, consumer psychology, and humanities. Marissa Mayer combines computer science, management, and linguistics.

And one of the ways this can be done is to be a voracious reader. Warren Buffet reads 500 pages a day. Elon Musk completes 2 books a week. Charles Munger never goes to sleep without reading 50 pages. The list is endless.

What’s more, you increase your value as a professional. Those who can bridge the gap between silos of knowledge are far more in demand than others who have specialized knowledge in one field.
And knowledge happens to be the only resource which grows by sharing. The more you share, you more you break the glass ceiling of your capacity and conquer new frontiers. Simple as that!

As Michel Eyquem de Montaigne has rightly said.


In Memoriam: Jack Widowsky

In Memoriam: Jack Widowsky

Jack Widowsky

We are sad to report the passing of our friend, Manhattan Project veteran Jack Widowsky. Widowsky passed away on March 22, 2018 at the age of 95. A military veteran, he was one of the last surviving members of the atomic bombing missions.

He was born in Newark, New Jersey on September 10, 1922. Widowsky was sworn into the United States Air Force on October 22, 1942 and was called for active duty on January 30 of the following year. After a series of tests, he qualified to become a navigator. Widowsky reflected on how pleased he was with this result in an oral history interview with the Atomic Heritage Foundation: “I just felt that I could do a good job at that. I wasn’t interested in being a bombardier or a pilot. That was my first choice.”

After graduating from navigation school in Louisiana, Widowsky became an official aerial navigator and was promoted to the rank of second lieutenant. He was assigned to a crew that was part of the 393rd Bomb Squadron.

He and his crew were a part of the 509th Composite Group under the command of Lieutenant Colonel Paul Tibbets. Widowsky regularly served as the navigator on the B-29 Top Secret at the Wendover Air Base in Utah and later on Tinian Island. The crew also flew long-range navigation training assignments in Cuba. He recalled, “We went to Havana a fairly good amount. On weekends, we went in. Went to bars, had a few drinks, but I’m not a big drinker.”

One of the biggest challenges Widowsky had to face in his work was the wind. “What affects the direction of the airplane is the wind velocity and the wind direction,” he remembered. “Sometimes you can run into some strong winds that can push you off course. I had to be on top of that every minute of the day.”

Widowsky participated in the Hiroshima and Nagasaki missions. He served as the navigator aboard The Big Stink, which was the backup strike plane on Iwo Jima during the Hiroshima mission. He described learning about the atomic bombing of Hiroshima: “We heard on the radio President Truman announcing we had dropped the atomic bomb on Hiroshima…we were all charged up for what we did. I say to this day, I am honored and proud that I participated in that. One reason is because we saved thousands of American lives. They had tens of thousands of GIs on Okinawa ready to invade Japan.”

On the Nagasaki mission, Widowsky and his crew flew on the Laggin’ Dragon, one of the weather reconnaissance planes. He recalled, “Then there was another mission coming up. We were to be an advance weather plane to Nagasaki, which was not the primary target. We went to Nagasaki, we radioed our weather report, went back to Tinian. I think it was Kokura was the initial target, but by the time the Bockscar got there, it was socked in [covered by clouds]. They were ordered to drop the bomb visually, not by radar. So, they went to Nagasaki and dropped the bomb on Nagasaki.”

Widowsky concluded in the interview, “Everyone can’t fly in the number one plane. The other planes had jobs to do, and you had to do it right. Otherwise the whole mission would have been cancelled, or not run the way it should have been.”

After the war, Widowsky became a salesman for candy and toy distributors, remaining in his home state of New Jersey. He retired in 2013. He and his wife were married for over seventy years.


The Latest Tips for ERP Implementation

  • 18 January 2017
  • CTND
Categories: ERP

Someone working at their desk with charts and paperwork

Manufacturers are flocking to ERP solutions. In 2016, 1 in 4 manufacturers surveys said ERP investments were their top priority, according to the recent Hennik Research’s Annual Manufacturing Report. It makes sense. The right ERP system can provide seamless communication between the various departments of your manufacturing company.

With more and more ERP solutions available, it’s not always easy to determine the right factors to consider when choosing a product for your own business. That’s why we’ve provided a list of the latest tips for ERP implementation, to get you started.

1. Understand Your Own Company Goals

For any technology to benefit your company, you must first understand what you want it to accomplish. This means documenting every existing business process. How is order information currently passed to production and inventory? What reports do departments rely on? Too often, management decides on an ERP system without knowing how the company processes run. This can result in an ERP selection that isn’t entirely compatible with your company’s production needs.

Once you have processes documented, it will be much easier to identify areas for improvement; this will guide you in defining the scope for your project. Look particularly for unnecessary repetition of tasks, or objectives that are difficult to deliver on regularly. If order information is re-entered several times as it moves from production through delivery, perhaps data sharing is priority. If one area of production sees frequent waves of intense production that it can’t handle, maybe a system that can handle sales forecasts is key.

2. Research Several Vendors

Every ERP system is different. Finding the right system for your company requires research, and strong communication with potential providers. If an ERP system requires a major overhaul of business operations, for example, it’s likely not for you. While researching, make sure that you:

  • Check out the vendor’s reputation and history. Does their ERP software meet industry standards? Has their software been recognized by any industry organizations?
  • Reach out to other companies that use the potential ERP. How was their experience? Are they looking to switch to another system?
  • Understand the system’s data collection and reporting capabilities. Can the ERP measure and track the most important metrics at your company?
  • Understand the ERP’s customization capabilities. Is the system an out-of-the-box solution? If so, does the system address all your company’s goals?

3. Find the Right Company Fit

Choosing the right ERP provider is not just about the ERP system itself; it also means ensuring the vendor is able to support your company’s needs, in everything from implementation to ongoing support. Company reps should be able to answer all your questions, and make you feel comfortable when talking with them. This could be based on their experience with companies within your industry, or of the same business size. Ideally, the reps will understand your company culture and values (i.e. you can easily establish a rapport with them).

Without proper vendor support, you risk getting stuck during implementation, or training. Aside from the fact that it’s simply frustrating to encounter delays, this can also slow down or interrupt your business production. This means a massive loss in the investment spent on the ERP system; and the time spent on research, meetings, and partial implementation.

Finding the right ERP provider for your company will not only ensure a smooth ERP implementation, but the right vendor will also act as a comprehensive resource so you don’t experience delays or interruptions.

4. Test Everything

Before going live with your ERP system, it’s important to test every component to avoid bugs or errors that could hurt your company. While testing, document everything you do, the results of your tests, and all processes for using the system. This will not only help with future employee onboarding and training, but your documentation allows you to compare the new ERP system with the previous way of doing things. Are reports reflecting accurate data in a more concise manner? Is accurate information moving from department to department as expected?

Testing provides the opportunity to find and fix any bugs in the software before they cause damage to your production process. Bring in employees from all departments that will use the ERP system; ask them to go through the instructions for using the system, and see where they get stuck. Understand that some aspects of the ERP may not be as intuitive as others, so it’s okay if there’s a learning curve for some tasks. Identifying pain points beforehand allows you to prepare training documentation to help employees better transition.

5. Welcome the Changes from ERP

Introducing a new ERP system requires employee training, and a modification of processes. This is inevitable. The sooner everyone embraces these changes, the sooner your company can embrace the benefits of the new system. The new ERP system may take steps out of report generation, or data entry — it may reduce redundant tasks, or take some previously manual tasks to an automatic format. Even though ERP solutions are meant to make things easier, keep in mind that it’s possible employees will stick to old processes or find workarounds that keep the new ERP system out of the picture. This isn’t because your employees want to do things the “hard way,” but more likely because they are resisting the change to their workflow, and they haven’t fully bought into the new system.

Management needs to lead the charge on this major change. Show visible enthusiasm for the new system, to motivate employees to feel the same. ERP systems are meant to help companies improve processes, communication, and more. Only with full company support can the system bring the benefits leadership is looking for.

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Rare white giraffes caught on camera for the first time in history

After local residents tipped them off, conservationists found the giraffes in the Ishaqbini Hirola Conservancy in Kenya’s Garissa county.

The two white giraffes in Kenya — spotted by rangers at the Hirola Conservation Program in early August — have a genetic condition called leucism. Leucism results in the partial loss of color from an animal’s skin, hair, or scales, but not in other organs like the eyes, for instance. Other animals are white because they’re albinos — they have a congenital disorder that inhibits the formation of color in the animal’s skin, hair, scales, and eyes. That’s why leucistic animals can have dark eyes, while albino animals usually have pink eyes. (The pink color is actually from the blood vessels inside the eyes, seen through colorless irises.)


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This Mandarin Chinese vocabulary list covers the names of objects, places, and people found in the hospital. Complete with an audio pronunciation guide and animated graphics showing the stroke order of the Chinese characters.

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Gerbera greenhouse

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Example 03: Moment Capacity of a Timber Beam Reinforced with Steel and Aluminum Strips

Steel and aluminum plates are used to reinforced an 80 mm by 150 mm timber beam. The three materials are fastened firmly as shown so that there will be no relative movement between them.

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Allowable Bending Stress, Fb
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Problem 1004 | Increase in moment capacity due to aluminum plate reinforcement

Problem 1004
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Darrell Sapp / Pittsburgh Post-Gazette
A bag of homemade Everything Seasoning can add spice to many different dishes.

You know you have a keeper when you make a recipe and can’t keep your hands away from the finished dish.

So it is with this Everything Seasoning mix. I want to put “Everything” on everything. So far, I’ve put it on toast, scrambled eggs, vegetables, chicken and fish.

With purposeful irony, I sprinkle it on top of a cream cheese-schmeered plain toasted bagel.

I keep an open container of Everything in my personal seasoning basket along with kosher salt, hot sauce, soy sauce, pepper flakes and pepper mill.

If the sunflower seeds are too sharp for your tummy, leave them out.

Find nigella seeds (aka charnushka and kalonji) at Indian markets. Or just borrow some from an Indian friend.

Everything Seasoning

1/4 cup sesame seeds

1/4 cup sunflower seeds

2 tablespoons poppy seeds

2 teaspoons nigella seeds

1/2 teaspoon caraway seeds

2 teaspoons dried onion flakes

2 teaspoons dried garlic flakes

2 teaspoons sea salt

Preheat oven to 325 degrees. Spread sesame and sunflower seeds in a pie pan and bake, shaking twice, until light golden, 7 to 8 minutes. Pour into a bowl.

In a frying pan over medium heat, toast the following in turn until fragrant and slightly darker, and add to bowl: poppy seeds, 5 minutes; nigella seeds, 2 minutes; caraway seeds, 1 minute.

Stir in dried onion flakes, dried garlic flakes and sea salt. Stir before using.


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Five challenges for intelligent text entry methods.

A writing system is a culture-preserving device and a defining feature of civilization. The continual improvements and refinements of writing systems throughout history are a testament to their importance. In the last 10,000 years, the media of writing systems have changed from cave walls, rune stones, and sand to papyrus, wax tablets, parchment, paper, and--in our age--networked computers. Carvings, pieces of scribble, and elaborate texts have been generated from tools ranging from stones, iron rods, and fingers to styli, quills, pens, typewriters, and keyboards. In parallel the very heart of writing systems has also changed and adapted. The original logographic writing systems, such as the hieroglyphs, have throughout the course of Western civilization gradually been replaced by refined alphabetic systems. Between the 16th and 19th centuries the need for fast and accurate transcriptions of proceedings in courts and parliaments motivated research into various shorthand systems, culminating in the Pitman and Gregg shorthand writing systems that are still with us today.

This article is about the latest generation of writing systems--intelligent text entry methods. Such text entry methods use AI techniques to improve our writing systems. The high-performing speech and handwriting recognition systems we have available today would be impossible without decades of research and optimization. However, there is more to these systems than engineering. I explain why research on intelligent text entry methods is important in the next section. Then I sample selected text entry methods and set them into their historical context. I argue that for text entry methods to make a difference in society, we need to leave the narrow objective functions of high entry rates and low error rates. To that end I raise five challenges for intelligent text entry: localization, error correction, editor support, feedback, and context of use.

Beyond the Desktop Keyboard

Our desktop keyboard has been with us with its familiar QWERTY layout since the typewriter was invented in the late 19th century. Contrary to popular belief, the main motivation behind the QWERTY layout was probably not to reduce typing speed (touch-typing was not invented at the time). Instead it solved an engineering problem: how to minimize the risk of mechanical jamming of the keys. Old typewriters worked much like pianos. When the user hit a key a hammer fell down onto an ink tape, which in turn caused an imprint on the paper. If two neighboring keys were hit in close succession there was a risk the two corresponding hammers would jam. The QWERTY layout solved this by distributing frequent letter-pair combinations to the left and right hand sides of the keyboard. This minimized the probability that two keys next to each other would be hit at the same time. As it turned out, such a bimodal distribution was reasonably efficient for touch-typing with the left and right hands, which is also why it provides us with a rather high entry rate and reasonably low error rate. This, in combination with society's tendency to keep technologies that are "good enough," is probably the primary reason why the desktop keyboard is still the de facto text entry method today (David 1985, Gould 1992).

However, some users suffer from repetitive strain injury (RSI) and prefer to avoid using a keyboard and mouse. Other users have a severe disability, such as paralysis, that prevents them from entering text except by using their eyes or perhaps by activating a few muscles. These users need alternative text entry methods.

Modern mobile phones easily support web browsing, e-mail, blogging, and so on. These applications all require a text entry method. Since the desktop keyboard is too large for a phone it is necessary to conceive practical alternative text entry methods that offer as high text entry rates as possible. If we also consider that users might want to enter text while walking outside on a windy and noisy street, the problem becomes even more complex. Is it possible to engineer efficient text entry methods that work well even under such difficult circumstances?

Leaving the mobile world aside for a moment, emerging mainstream technologies, such as wall-sized displays, multitouch tables, and volumetric displays, do not lend themselves to the use of desktop keyboards. For all these reasons, it is worth exploring text entry beyond the desktop keyboard. And let's not forget, it takes many hours of practice to become a skilled typist. Perhaps a more intuitive and efficient text entry method lies ahead of us and will replace the desktop keyboard altogether. If we don't search, we will never know if we are currently at a local or global optimum.

Exploring AI in Text Entry

AI opens up unique possibilities to create new text entry methods or improve upon existing ones. The main reason is that human languages are highly redundant (Shannon 1948). These redundancies can be captured in language models. Intelligent text entry methods take advantage of language redundancies in multiple ways. For example, they can reason about users' input and deduce users' intended words; or they can display predictions in a way that allow users' input to be used more efficiently.

Curiously, language redundancy has been exploited since at least the creation of Nova Ars Notaria ("the new note art") in 12th century Europe. This was a shorthand system attributed to the monk John of Tilbury. In it, letters were simplified into simple line marks. To enable faster writing of common word stems, they were compressed into sequences of simple line marks and dots. These word stems were found by frequency analysis of the Book of Psalms. (Melin 1927).

Despite being eight centuries old Nova Ars Notaria neatly illustrates the basic principles behind efficient text entry. First, users' efforts in articulating their intended words should be minimized. Second, language redundancies should be modeled and exploited (Kristensson 2007, Melin 1927). In this vein, there is a long history of AI contributions to text entry design, particularly in the early work on handwriting and speech recognition.

However, as this special issue illustrates, making efficient use of AI techniques in user interfaces is often easier said than done. Jameson alludes in the introduction of this special issue to the need for a binocular view that takes both AI and HCI perspectives into account. This article takes this perspective by presenting five challenges for intelligent text entry: localization, error correction, editor support, feedback, and context of use. To set the stage, we start by sampling the state of the art in intelligent text entry.

Intelligent Text Entry Methods

The usefulness of text entry methods is directly linked to entry and error rates. Unfortunately, the literature is inconsistent in how to measure text entry performance, and there are not even standardized measures of entry and error rate. In this article entry rate is measured in words per minute, where a word is defined as five consecutive characters. Unless otherwise specified, error rate is measured as the character-level edit distance between the stimulus text and users' response text, divided by the number of characters in the stimulus text. This provides an error rate between zero and one.

Text entry research has resulted in literally hundreds of systems, and as a consequence it is not feasible to survey them all. What follows is a sampling of recent intelligent text entry methods. Also note that entry and error rates cannot be readily compared across different studies because experimental setups are rarely consistent across studies, and participants' general performance may vary dramatically across samples. The accepted procedure to definitely conclude one method is better than another is to compare the methods directly against each other in the same experiment. Nevertheless, it is informative to get a sense of the text entry performance of various techniques, even if those performance measures are not directly comparable. So with that caveat, the sampling below lists entry rate measurements for all techniques, if such data is available.

Speech and Handwriting Recognition

Perhaps the most natural form of input besides a keyboard is to enable users to write text using speech or handwriting recognition.

Speech recognition has the potential to be very fast. A user can speak up to 200 words per minute (Rosenbaum 1991). In practice, individual performance, fatigue, recognizer design, accents, and noise characteristics of the environment and the microphone dramatically affect recognition performance, which in turn affects users' entry and error rates. Encouragingly, our recent findings indicated that novice users could speak at a mean entry rate of 13 words per minute while walking around outdoors in a windy environment. Ignoring recognition delay, an expert user had a mean entry rate of 45 words per minute while walking around outdoors. The corrected error rate (on the word level) was less than 2 percent. This indicates that speech recognition has the potential to be an efficient mobile text entry method (Vertanen and Kristensson 2009).

Another possibility is to use handwriting recognition. Handwriting recognition has evolved tremendously (Tappert, Suen, and Wakahara 1990; Plamondon and Srihari 2000). Our recent longitudinal user study showed that state-of-the-art handwriting recognition results in a text entry rate of about 25 words per minute after several hours of practice (Kristensson and Denby 2009). In addition, both entry and error rates were almost identical to what was provided by a de facto QWERTY on-screen keyboard. This means that a modern handwriting recognizer can be regarded as a reasonably fast text entry method for pen-based interfaces.

Intelligent On-Screen Keyboards

One of the most common text entry methods on today's mobile touch-screen devices is an onscreen keyboard. This is an image of a keyboard layout rendered on a touch-sensitive screen. The typical on-screen keyboard uses the familiar QWERTY keyboard layout, and this is the only keyboard layout that will be considered in this article. (1) On-screen keyboards are easy to learn and use as users benefit from transfer learning from their experience with their desktop keyboard counterparts. Since they lack tactile sensation feedback, users do not have a sense of the difference of being between two keys or at the center of one. This causes users to easily mistakenly hit an unintended neighboring key. In addition, unlike desktop keyboards, users are not provided any tactile feedback when a key press is registered by the system. This may cause users to mistakenly believe a key press was registered when it in fact was not.

On-screen keyboards are not very fast when used with a stylus or a single finger, such as the index finger. The mean entry rate varies depending on the study but is typically in the range of 15-30 words per minute (MacKenzie and Soukoreff 2002). This is in part because of the bimodal distribution of frequent letter key pairs, as mentioned earlier in this article.

However, both entry and error rates offered by on-screen keyboards can be improved by making two observations. First, the landing points on an on-screen keyboard pixel grid are of a much higher resolution than registered key presses on a physical keyboard. Second, human languages are highly redundant, and the regularities can be captured and exploited by a language model (Kristensson and Zhai 2005).

These two observations make it possible to infer a user's intended letter key, even though the user mistakenly hit a neighboring key. As we discussed previously above, such errors are easy to make on an on-screen keyboard. Further, Fitts's law (Fitts 1954) tells us that the time it takes to hit an individual key is proportional to both the size of a key and the distance to it. By relaxing the constraint that a user must hit an intended key exactly within its boundary we in effect temporarily artificially magnify the user's intended key. According to Fitts's law this increases the mean entry rate if users exploit the extra tolerance by pointing at keys faster (and therefore less precisely) than on a regular on-screen keyboard.

Goodman et al. (2002) present an on-screen keyboard that achieves this effect by combining a probabilistic model of users' landing points with a character-level language model. Their system uses this model to infer users' intended letter keys, as they are writing the text letter by letter.

We have previously explored an alternative approach that pattern-matches users' tapping traces against ideal point templates that represent entire words. We call it the elastic stylus keyboard (ESK). This system performs corrections by geometric pattern matching. A correction occurs on the word level and is triggered when the user slides the pen (or finger) from the left to the right. In figure 1, the user intended to tap the letter keys T, H, and E in succession (indicated by filled rhombi centered on the keys). However, the user accidentally missed all the intended keys and instead hit the neighboring keys R, J, N, and E in succession (landing points indicated by outlined circles). Despite the user missing all the intended keys, and despite a spurious extra tap when trying to hit the H key, the system would correctly infer that the user intended to hit the word the. In our experiments we used a lexicon with about 57,000 words. In a small formative expert study (involving the authors only) we found we could reach an entry rate of 45 words per minute using this system (Kristensson and Zhai 2005).

ShapeWriter (Zhai and Kristensson 2003, Kristensson and Zhai 2004) uses a radically different approach (figure 2). Instead of serially tapping the keys, ShapeWriter lets users gesture the shapes of words over an on-screen keyboard. Each word in a large lexicon has a corresponding template gesture that is defined as the connected lines that sequentially intersect the center points of the letter keys of the word. For example, the word the is a v-like shape on the QWERTY keyboard layout (see figure 3). The user's intended word is found by a pattern recognizer that primarily looks at the scale-translation invariant shape similarity between the user's gesture and the set of template gestures. In our user studies we found that ShapeWriter enabled novice users to write at a mean entry rate of 25 words per minute. In an accelerated learning study, where users repeatedly wrote multiple phrases, the same novice users reached a mean entry rate of 44 words per minute. The peak entry rate was 99 words per minute (Kristensson 2007).

Other Methods

A plethora of intelligent text entry methods have been proposed beside speech, handwriting, and on-screen keyboards. Darragh, Witten, and James (1990) present a system for typing prediction for desktop keyboards, and Masui (1998) uses such a system for a mobile text entry system.

Intelligent text entry methods are about inference, and inference is closely related to compression. Taking this thought one step further, one realizes that instead of predicting users' text one can try to decompress users' abbreviations. Abbreviations have a long history. Monks in medieval European monasteries often abbreviated words when copying bibles. For example, the Latin word uerbum ("word") was often abbreviated into ubu (Janson 2004). Shieber and Nelken (2007) propose an intelligent text entry method that enables users to enter abbreviated words, thereby reducing the number of letter keys that have to be pressed by the user to write words. Their system automatically decompresses the abbreviations to full words as the user is typing. Unfortunately their experiments did not indicate that their system provided any higher entry rates in comparison to typing out the full words.

Clawson et al. (2008) correct physical thumb keyboard typing errors using a machine-learning algorithm. Physical thumb keyboards have previously been shown to be very fast, up to 60 words per minute. However, at such a high entry rate the error rate exceeds 8 percent (Clarkson et al. 2005). Therefore, an effective automatic error correction mechanism would have the potential to improve efficacy.

AI techniques have also been explored to improve text entry in eye-tracking interfaces. Salvucci (1999) presents a system that recognizes words in a small vocabulary by decoding eye-tracking traces over an on-screen keyboard. This system enabled study participants to write at a mean entry rate of 15 words per minute (derived from their reported results of 822 milliseconds per character).

A completely different approach to writing using eye-trackers is Dasher (Ward and MacKay 2002). In Dasher the user navigates a cursor through graphical boxes that contain the desired characters. The boxes' sizes and positions are based on a language model. In a sense, with Dasher the user is driving to the intended destination--the desired sequence of words. The driving is efficient for common word sequences because the system provides the most likely letter sequences with the largest areas on the display. For less common words, the driving will be more involved and the user has to steer carefully toward smaller areas (less likely letters). This system enabled one of the expert users to reach an entry rate up to 25 words per minute.

As is illustrated in the above sampling, intelligent text entry methods span many areas, some of which have transformed into their own specialized research fields, such as handwriting and speech recognition. The fact that some of the above intelligent text entry methods are commercially available for desktop computers and mobile phones shows that decades of research within the intersection of AI and HCI have to some extent succeeded.

Nevertheless, there are still many issues that need to be solved. Below are five major challenges that I believe we need to tackle: localization, error correction, editor support, feedback, and context of use.

Challenge 1: Localization

Depending on your country, the topology of your desktop keyboard layout will vary. For example, the first six top-left letter keys read QWERTZ on a German keyboard and AZERTY on a French keyboard. However, since desktop keyboards are typed one character at a time, a different keyboard layout simply means that the operating system needs to switch key map.

In contrast, any text entry method that relies on a list of words (a lexicon) will need to collect such a list for every language supported. Collecting and verifying high-quality lexicons is a labor-intensive task that may require access to native speakers of each language.

Text entry methods that require higher-order language models, for example unigram, bigram, or trigram models, also require sufficient text data (corpora) to train those models. For language models to be useful the corpora need to be representative of what users are intending to write. It is currently difficult to get access to high-quality corpora for different languages that properly model what users intend to write on their mobile phones.

Some intelligent text entry methods, such as handwriting and speech recognition, also require models of users' articulations. For example, a speech recognizer needs at the very least an acoustic model to function. This model needs to be rebuilt for different languages.

In the literature, the issue of localization tends to be underestimated, particularly when completely new text entry methods are proposed. Nevertheless, a good example of a novel text entry method proposed by researchers that quickly supported many different languages is Dasher (Ward and MacKay 2002). Currently Dasher supports more than 60 languages. Since Dasher models each language as an adaptive character-level language, it can be seeded with a relatively small corpus and still function acceptably while it gradually adapts to a user's writing.

Challenge 2: Error Correction

With or without a proper language model, in text entry, errors are unavoidable. Here we divide users' errors into two broad categories: cognitive errors and motor errors. Cognitive errors are due to the user having an improper model of an intended word. For example, a user may not be able to spell or pronounce an intended word correctly. Motor errors are due to mistakes that occur due to tremor or stress. Both classes of errors, but particularly motor errors, tend to increase as the task becomes more complex. For example, a user who is simultaneously walking and interacting with a touch-screen will find it more difficult to precisely hit targets on the screen (Crossan et al. 2005).

Another class of errors is not due to the user but to the system. (2) If the recognizer has an incorrect model of the user, then some errors are inevitable. In practice, it is difficult to design a perfect model. For example, the standard tri-gram language model of a speech-recognition system makes many assumptions of users' speech that are clearly not true, such as the assumption that all users' utterances are only dependent on the two last spoken words (that is, a trigram model). Another obvious model mismatch exists in word recognizers, such as the one used in ShapeWriter. Here the system uses a dictionary of words. If users' intended words are not in the dictionary the system cannot possibly infer the correct words (so-called out-of-vocabulary errors).

Since errors are unavoidable, users need support to correct them. At the very least, some mechanism to correct errors is required. Furthermore, error correction may be carried out in the same or a different modality. Karat et al. (1999) observe that correcting speech-recognition errors using speech alone is problematic since it may lead to cascading errors when utterances intended to correct errors are themselves misrecognized. One solution is to use a correction interface that uses a different modality, such as a touch-screen. Figure 4 shows a word confusion network correction interface for a mobile speech-recognition system (Vertanen and Kristensson 2009). The best hypothesis is shown at the top row. The individual words in the best hypothesis can be changed by touching alternative words in the corresponding row positions. A word can be deleted by touching the X button in the corresponding row. The words represent the best hypothesis in the recognition result

If it is desirable to support hands-free error correction in speech recognition the problem becomes more challenging. McNair and Waibel (1994) propose a technique that enables users to correct speech-recognition errors by simply respeaking the intended words. In previous systems in the literature, users had to first select the error region in the text by speaking the misrecognized text. Thereafter, users replaced the text by respeaking their intended words. McNair and Waibel (1994) explain that their one-step method better resembles how we resolve misunderstandings in our daily human-human communication.

Depending on the circumstances, efficient error correction techniques can be critical for the success of a system. As an example, we carried out a formative study where users dictated text while they walked around outdoors. Our users were able to enter text at 15 words per minute with a corrected word-level error rate of about 2 percent, despite the fact that the word-level error rate of the speech recognizer was 26 percent (Vertanen and Kristensson 2009).

In conclusion, we are still searching for efficient error correction interfaces. Well implemented, they improve entry rates and reduce frustration among users.

Challenge 3: Editor Support

Effective error correction methods ultimately depend on good editor support. Modern operating systems are designed for text entry by means of a desktop keyboard. Whenever a user types an individual key the keyboard's scan code is translated into a keyboard-independent key code that is stored in a queue and sequentially dispatched to the window the user is currently working on. If the user makes a mistake the user presses the Delete and Backspace keys, which are processed and dispatched in the same manner as all other key presses.

The aforementioned process is one directional and there is no reliable way for a text entry method to retrieve text that has previously been dispatched to the user's working window. When text entry is operating on a unit more complex than a single character, two issues arise.

First, a number of intelligent text entry methods calculate the probability of a recognized word based on previous words already written by the user. If there is no reliable way to detect these previous words in the user's document this cripples practical implementations of these methods.

Second, an intelligent text entry method may output several word candidates. However, only the highest ranked word candidate is dispatched to the user's document. Since recognition is error prone, it is convenient to enable users to revisit previously written words and let them explore the alternative choices. Such interaction requires support by operating systems. Unfortunately, this support is lacking. As a result, text entry systems have to resort to manually monitoring and modeling all user interface messages (Watanabe, Okada, and Ifukube 1998). Such mechanisms are error prone and may not be possible to implement on all operating systems.

This issue is not likely to disappear until intelligent text entry methods, such as speech recognition, become mainstream text entry methods on both desktop and mobile computers. Better toolkits and input method frameworks would mitigate the current situation.

Challenge 4: Feedback

Another aspect that requires attention is user feedback. The immediate feedback users receive from a recognizer is the recognized text presented to them by the system. The quality of recognition is most likely a highly influential feedback mechanism in itself. Curiously, the effect of immediate versus withheld feedback on users' performance with, and perception of, a text entry method does not appear to have been formally studied. Recognition-based text entry methods have a degree of tolerance toward imprecise "sloppy" input. This can result in a positive feedback loop that causes users to write increasingly sloppily as they observe how much the recognizer tolerates. Such an effect may be positive, as it drives users to find their own optimal operating point (Kristensson and Denby 2009). (3) Of course, at a certain point the input will be too noisy for any recognizer, and the system will fail with a recognition error. Figure 5 exemplifies how we tried to preempt this situation by visualizing to users how close their input gestures are to the recognized ideal templates in ShapeWriter (Kristensson and Zhai 2004).

Annotating Words Based on Confidence.

In most modern word processing applications a spelling checker annotates words not found in a dictionary. The popularity of spelling checkers indicates that users perceive them as effective when using a text entry method, such as the desktop keyboard. With a character-per-character text entry method, it is easy to make mistakes on the character level. Such errors can be reliably detected by a spelling checker.

In contrast, intelligent text entry methods often work by recognizing and outputting entire individual words into a document. This effectively eliminates spelling errors and the need to detect them. However, it does not necessarily mean that words outputted by the recognizer are words intended by the user. Since these unintended words are undetectable by spelling checkers, researchers have tried to use other sources of information to detect them. Aside from a grammar checker, another source of information is the recognizer's confidence scores. If these confidence scores are sufficiently accurate, they can be used as a basis to annotate words. An early example of the use of confidence visualization, possibly the first, is described by Schmandt (1981). He presents a speech-recognition system that displayed recognized key words in different shades depending on the recognizer's confidence scores.

It is plausible that annotating low-confidence words would help users detect more misrecognized words. (4) To investigate this hypothesis, we built a speech-recognition user interface that annotated recognized words based on the speech recognizer's confidence values (figure 6). Our experiment showed that annotating words based on their confidence did not result in a measurable difference in the number of errors users were able to detect and correct. Examining the material further, we found that misrecognized words were indeed found significantly more often when they were annotated in comparison to a baseline where no words were annotated at all. However, unannotated misrecognized words in the annotation condition were also discovered less often than in the baseline. In other words, users tended to look only at the annotated words. Unless confidence scores are highly accurate, this means that users will not be able to identify that many misrecognized words, since these will often fail to be correctly annotated by the system (Vertanen and Kristensson 2008).

Galletta et al. (2005) reached a similar conclusion when they investigated the efficacy of spelling checkers. They found that when spelling checkers correctly annotated misspelled words users indeed benefited. However, they also found that users tended to trust spelling checkers too much. This meant that when the spelling checker made a mistake users tended to believe the spelling checker and made incorrect changes prompted by it. In addition, if the spelling checker was unable to detect a spelling error, users tended to not detect the error themselves.

In conclusion, until confidence scores are more accurate they are unlikely to aid users. We have shown that if confidence scores are accurate enough they do indeed benefit users (Vertanen and Kristensson 2008). However, to reach such levels it may be necessary to incorporate external knowledge outside the speech recognizer. Since speech recognizers already use statistical knowledge of the preceding word context, an alternative source might for example be commonsense reasoning. Commonsense reasoning has earlier been explored as a means to disambiguate between speech-recognition hypotheses (Lieberman et al. 2005).

Challenge 5: Context of Use

The last challenge is to design for the heterogeneous contexts that users of mobile devices are exposed to on a daily basis. The by far most common experimental situation is to let the participant sit down in a quiet office and enter a set of given phrases or sentences. This experimental set-up has completely dominated mobile text entry research, with very few exceptions. However, as modern mobile phones and their text entry methods become increasingly sophisticated, users are probably going to expect that they will be able to enter text at reasonable speeds, even on a shaky bus or when walking down a street. Suddenly a high entry rate and a low error rate is not enough. A text entry method will be required to also understand and support users' needs in particular situations. For example, if the user is walking toward the departure gate, a speech recognizer may be the most effective text entry method. On the other hand, if the user is attending a committee meeting a completely silent text entry method is desired. A context-aware system could model such situations and automatically switch input modality.

Oulasvirta et al. (2005) carried out one of the first comprehensive studies of users interacting with their mobile devices while walking around outdoors. They discovered that users cope with their environment in fragmented bursts, constantly shifting attention between their device and their surroundings. This finding inspired the design of our static error correction interface in our mobile speech-recognition system Parakeet (Vertanen and Kristensson 2009).

Proper modeling of different contexts of use raises many currently unanswered questions. We do not have sufficient knowledge of how users use, and would like to use, mobile text entry methods today. Nor do we know which modalities they prefer in different situations and contexts. A seamless multimodal text entry system needs to be able to accurately sense and reason about its surrounding based on models of both the environment and the user's preferences. It will require a user interface that enables users to easily monitor, understand, and control its behavior. Such multimodal adaptive text entry systems provide users with more flexible options than what is currently offered.

Walking the Last Mile

It is worth noting that a clever algorithm does not guarantee a successful intelligent text entry method. Even though it may be possible to show theoretical quantitative advantages with certain approaches, there is always a risk that the user interface is cumbersome, or that the algorithm is perceived as brittle by users.

An example is our work on designing the elastic stylus keyboard that was presented earlier in this article (Kristensson and Zhai 2005). The design of this system followed an iterative design process. Two user studies were conducted using an early incarnation called the linear correction system. The primary difference between the linear correction system and the final design was that the former could not cope with insertion and deletion errors (the user omitting a tap, or introducing a spurious extra tap). The efficacy of the linear matching algorithm was evaluated in a user study. Theoretically, it should enable users to write more quickly than a traditional system since it provides a certain degree of error tolerance. However, in the user study the system achieved almost the same entry rate as a baseline system that performed no corrections at all. When analyzing the material further, we discovered that while the system did automatically correct the majority of users' errors, it also caused a couple of errors that most likely made users trust the system less. It is well established that interactive systems that do not behave as expected undermine users' trust in them (Tseng and Fogg 1999). Thus, this deficiency resulted in a system that was perceived as brittle by users. For example, if a single tap was accidentally omitted a completely different word would be outputted, which puzzled users. (5) Since users did not trust the system they chose to be overly careful. As a result, the text entry rate did not increase as expected.

In contrast, the final elastic stylus keyboard system took into account the fact that users may omit or accidentally add spurious taps. As a result, the final system eliminated almost all errors detected in the first user study. This indicates that marginal improved performance of the AI algorithm can dramatically determine the end-user benefits of the intelligent text entry method. (6) It also shows that theoretical benefits remain theoretical until they are empirically verified. In the end, engineering and evaluation have to go hand in hand if we hope to create better systems.

During the last decades, several intelligent text entry methods have been conceived, improved upon, and evaluated by researchers from both the AI and HCI communities. Unlike perhaps most other research on interactive systems, the text entry research field has maintained close connections to AI and engineering. A possible explanation is that text entry methods have always had clear objective functions insofar that a good text entry method has a high entry rate and a low error rate.

However, these objective functions have been optimized to a point where further progress is most likely incremental. In this article, I have presented five challenges that set a broader view on intelligent text entry. It is not enough to design a clever algorithm and show empirical benefits. The motivation behind text entry research is to ensure we have practical writing systems available for everyone, everywhere. Thus, successful text entry methods need to work in the real world. Researchers need to walk the last mile and ensure their methods have a purpose in our society. In this process, several neglected, but often critical, challenges arise that may very well be deciding factors in the success of a text entry method. By addressing these challenges, intelligent text entry methods will have greater impact in our society.

I thank Keith Vertanen and David MacKay for their assistance. The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreement number 220793.

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(1.) The use of a keyboard layout optimized for efficient one-finger typing can increase the asymptotic performance of expert users, but it creates a learning hurdle for beginning users. The usability side-effects theme article in this issue discusses the special importance of avoiding excessive learning demands during early use. Offering a familiar, though less efficient, keyboard with a new text entry technique is consistent with this point.

(2.) The remainder of this section provides concrete illustrations of many of the general points made in the section on errors in the theme article on usability side effects.

(3.) The attempt of users to find their own "ideal operating point" can be seen as an example of a type of learning discussed in the usability side-effects theme article: learning which of various alternative ways of using the system works best for the user in question (and perhaps in particular situations). The novelty of this learning problem in the case of ShapeWriter is typical, in that it is due to the novelty of the intelligent technology being introduced. The visualization shown in the figure is an ingenious way of speeding up the learning by providing pertinent feedback.

(4.) The need to learn to make appropriate use of the confidence values of an interactive intelligent system is another example of the difficulty that users face in figuring out how to take advantage of what is offered by an intelligent system. It illustrates the worst case, discussed in the section on learning in the usability side-effects theme article, in which even extensive experience may not enable a user to discover the best method. Note that the researchers cited here had to perform a controlled study to determine the consequences of relying on confidence values; normal users could hardly be expected to reach reliable conclusions on their own.

(5.) This response of users to unexpected system behavior illustrates a point made in the section on comprehensibility in the usability side-effects theme article: a user's operation of a system can be influenced by the user's ability to understand and predict the system's responses to his or her actions. In this example, lack of predictability and comprehensibility led to overly cautious user behavior that did not fully exploit the potential of the system's intelligent interpretation algorithms.

(6.) This example illustrates a point made in the section on errors in the usability side-effects theme article: instead of globally maximizing accuracy according to some standard metric, researchers and designers should find out what particular errors are likely to occur and what consequences are associated with them, so as to be able to ensure that the system's performance parameters and correction mechanisms handle the most important cases effectively.

With Notes by Anthony Jameson

Per Ola Kristensson is a junior research fellow at the University of Cambridge and a cofounder of ShapeWriter, Inc. He received his Ph.D. in computer science from Linkoping University in 2007.
COPYRIGHT 2009 American Association for Artificial Intelligence
Copyright 2009 Gale, Cengage Learning. All rights reserved.

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Date:Dec 22, 2009
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Wednesday, 30 December 2015

Brothers' Fury - Giles Kristian

My holiday fiction reading has been the second volume in Giles Kristian’s 'Bleeding Land' trilogy, Brothers’ Fury - set in the English Civil War.

The main characters come from the Rivers family. One brother, Mun, fights for the King and the other, Tom, for Parliament - mainly due to a feud with another local family. They also have a sister Bess who tries to find her brother and bring him back to the family.

The book mostly focuses on the small war, so typical of this conflict. Small units interdicting supplies, attacking columns of reinforcements and supporting or relieving sieges. In addition, we have subterfuge and plenty of internal feuds. The stories come together at different times as their respective units of horse almost clash. In Cornwellian style the book ends at at a major battle - in this case the first Battle of Newbury in 1643.

As other reviewers have said, this is on the grimmer, gorier, grittier side of historical fiction. The characters are certainly gritty, not really likeable, and certainly not in the heroic school of historical fiction. However, none of that makes this book any the less readable.

Many of the actions would make good skirmish wargames using rules like Donnybrook. I might dust down my ECW figures for a game before the holiday is over.

Monday, 28 December 2015

Somua S-35 in German service

Given the growing pile of metal (and plastic) on my painting table, my Christmas list was very restrained this year. However, one addition was some 28mm armoured support for my German WW2 units serving in the Balkans.

I thought I would start with a PKW III, however, the Warlord model is part of their link up with the kit maker Italeri. If you are a model kit builder this is fine, but they are not good wargame models. They are too fiddly and time consuming to build, and more importantly, simply not robust enough for the wargames table. The tracks in particular are not nearly strong enough, even if you manage to construct them.

My pre-Christmas trip down to my Mum's included a stop over in what is probably my idea of heaven - the book town of Hay on Wye. A castle, good pubs, and over 20 second hand bookshops. One of my many purchases was a MMP publication on the 'Prinz Eugen' Division. This superbly put together publication has masses of photographs of the division in action in the Balkans. What caught my eye was the extensive use of captured French armour. I was aware of the H38 and R35 being used, but not the much heavier Char B1. 17 of these heavy tanks went to the Balkans.

However, I settled on the Somua S-35, not least because the Warlord model is one of their much more robust resin models. Just a few parts that go together quickly, even the gun barrel has a half decent lug, one of my pet hates with poor tank model design.

German forces in the Balkans had 43 tanks of this type used by Panzer Battalion 202 and 212 as well as Special Purpose Armoured Company 12. They were known as Panzerkampfwagen Somua 35S(f) 739 in German service.

The S-35 was built between 1936 and 1940 for the French Cavalry Divisions. For its time it was a well designed tank, better than most of its opponents. The problem was a complex and expensive production methodology and poor strategic and tactical deployment. Around 440 were built. It had a 47mm main gun and MG, with a petrol engine that managed 25mph on roads and around 20mph cross country. I have seen originals at Saumur, Bovington and in the USA. A few in Bulgarian service survived in post-war police roles.

I painted this model Panzer Grey, added some German markings and dirtied it up with plenty of Balkan mud. A very decent model that does the job well.

Friday, 25 December 2015

Dragon Rampant

Having played Lion Rampant I decided to give the recently published fantasy version a go. Also an opportunity to dust down my box of fantasy figures.

The essence of the rules are the same, so I won't repeat my explantation of the game mechanics. The rules use the same basic unit structure and game mechanisms for movement, shooting and combat. Retinues become warbands.

In this version you get some additional units, including two sizes of war beasts for those big models. The big addition is a section called fantastical rules. Here you can add hate, terror etc and of course magic. You can pay for different types of spellcasters who can use a range of spells, the more powerful of which require higher dice throws to activate.

As with Lion Rampant, you get a number of scenarios and sample army lists. Although again you are encouraged to make up your own.

So, onto the tabletop. We have an Orc Warband with a Big Boss, an Orc Shaman with a few limited spells, and several units of warriors classed as Bellicose Foot. They all have Wild Charge, so that's what they do once in attack range.

Their opponents are Goblins led by a Daemon Prince, with a Shaman, wolf riders, archers and several units of spear armed warriors.

The Orcs rather predictably stormed across the table, scattering the Goblins whose shooting abilities caused some damage.

The Daemon Prince was left a bit isolated, but difficult to kill. The solution was to use a spell from the Orc Shaman that leaves units 'battered', fighting at reduced strength, and then attack with another unit. With the death of their leader that was game over for the Goblins.

As with Lion Rampant, the rules provide for a quick and enjoyable game. Unlike some fantasy rules the magic element isn't dominant, it provides a bit of longer range artillery and other benefits. You can use any figures in these rules you want and Warbands are limited only by your imagination.

I enjoyed these rules and will certainly use them again.

Tuesday, 22 December 2015

Lion Rampant

The holiday break is an opportunity to catch up on some outstanding projects, one of which is trying out the Lion Rampant rules. These are pretty popular at my club, even eclipsing SAGA at present.

Lion Rampant is published by Osprey and written by Daniel Mersey. They are a simple set of rules for the medieval period based around a retinue of typically around 50 models in six or so units of 6 or 12 figures, although you can do smaller and bigger actions. It's not quite skirmish, but it is part of trend towards smaller games that don't require a year's painting before you get a game on the table. A 6' by 4' table is standard.

Each retinue has a leader, who is important, but not a super hero. Try not to get him killed though, as the courage tests can be painful. The activation sequence starts with Leaders issuing challenges, rallying battered units and testing for wild charges by your Knights. However, the key is ordered activations that require different dice throws to move, shoot or attack. This is very clever as it reflects a units willingness do undertake different actions. For example, knights are difficult to get moving but easy to charge. If you fail an activation, your turn ends, a bit like Black Powder. This means you need to decide which activations are most important to your battle plan and have lower dice requirements, so the risk of failing is lower.

The shooting and combat mechanisms are very straightforward, based on the ability of the unit and the armour class to survive hits. You remove models and that weakens the unit's impact once you reach half strength. Losing figures is even more important in the courage (morale) tests that you have to take after losing casualties, because they get deducted from the dice roll.

The game is usually played to a number of scenarios, but you can make your own up. These are typically small action scenarios around raiding, convoys and collecting taxes.

There are a few sample army lists, but you are encouraged to write your own, with some limitations to stop super army design. There are eleven troop types, all typical Western European medieval types, but you can rename them for other parts of the world. These are adequate, but might need a bit of tweaking for units like Janissaries, who don't fit well.

So, onto the tabletop. My retinues, you will be surprised, are from the Balkans! Bosnians and Ottomans for this play test. The Ottomans are the attackers and made it across the table quickly (horses move 10/12" and foot 6/8"), while the Bosnians kept failing activations and largely hugged the baseline.

This helped the Ottomans who are strong in shooters and they peppered the Bosnians causing courage tests and consequential retreats.

The Bosnian Knights eventually got a wild charge in but were clevely lured onto rough ground that made them less effective. When they routed it was game over and mass conversions followed!

These are definitely my type of rules. Simple, easy to remember game mechanics, yet plenty of subtle game play. Recommended.

Tuesday, 15 December 2015

Strathclyde and the Anglo-Saxons in the Viking Age

Precious little wargaming or related reading recently, as work has taken over all waking hours. Roll on Xmas!

One book I have finished is Tim Clarkson's 'Strathclyde and the Anglo-Saxons in the Viking Age'. This has been on the reading shelf for some time and I picked it up knowing very little of the kingdom that that covered the area where I live today. I suspect my solitary knowledge was the partial chapter in the WAB Age of Arthur book and the stones at Govan Old Parish Church in Glasgow.

Strathclyde is the modern name for Strat Clud, indicating a kingdom based on the strath or lower valley of the River Clyde. They were also known as Cumbrians and their kingdom stretched from Glasgow down the Solway Firth and then on into modern day Cumbria.They were ethnically Britons or North Welsh, but as becomes clear in the book, we know very little about them. The limited sources almost all come from mentions in the annals of other nations.

The book covers the period from the middle of the 8th Century to the beginning of the 12th. From the capture of their original capital on Dumbarton Rock by the Vikings, until they were overrun by the Scots and the Scottish parts of Strathclyde became part of Scotland.

It covers conflicts with the Vikings, the Anglo Saxons and less frequently the Kingdom of Alba. They were probably present at the great battle of Brunanburgh in 937 when the Saxons defeated the Norse and their Scots and Strathclyde allies - even if no one is quite sure where the battle site is.

The other great battle was Carham in 1016 or 1018, between the English and the Scots. The English or more properly the Northumbrians, were probably commanded by Earl Uhtred of Bamburgh. Yes, that's Bebbenberg. For those watching 'The Last Kingdom' or reading the Bernard Cornwell books, a Uhtred did actually exist. He was obviously a renowned warrior and leader, but perhaps not quite as well travelled as Cornwell's character!

This book fills a gap in our understanding of a Kingdom that was an important part of Dark Ages Britain. I will look that bit more carefully at the stones on my next visit to Govan.


Multi-threading and the Operating System

On most modern operating systems, many different users can run programs (processes) at what appears to be the same time. If the operating system is hosted by a hardware system with a single Central Processing Unit (CPU) then these processes are not running at exactly the same time - it just appears that they are. The operating system allocates the CPU and other system resources to the processes that need them and (preemptively) switches these resources from one running program to another. This switching of resources gives the appearance that the programs are running at the same time.

In a very similar way, many modern operating systems allow multiple threads to exist within each process. Again, the operating system handles the allocation of the CPU and various resources to individual threads and switches these allocations from one running thread. Again, this gives the appearance that the threads are running at the same time.

If process and threads are handled in a similar way by the operating system, what distinguishes them from each other?

All processes have distinct address spaces, open files and other system resources; this enables each process to run with minimal or no effect on other processes running at the same time. However, this separation of resources has a run-time cost:

Because threads run within a single process, all of the threads associated with a process share the same data and code address space, the same open files and most other resources. Each thread also has its own register set and stack space. Creation of a thread, therefore, does not require extensive system memory. When the operating system finds it has to switch execution context from one thread to another within the same process, about the only thing the operating system needs to do is switch CPU ownership and register sets. This is the reason that threads are often called `light-weight processes'.


What causes left shoulder pain medguidance electricity deregulation in california

Left shoulder pain can be extremely concerning, especially when the cause is unknown. The pain may occur when you breathe deeply, making it very difficult to function throughout the day. Left shoulder pain may be the result of normal activities of daily living to serious health conditions. In this article we will describe some common causes of this pain, the symptoms associated with each cause, and what you can do to relieve the discomfort. Causes of Left Shoulder Pain Poor Computer Habits

Spending extensive periods of time in front of a computer may lead to acute left shoulder pain. When a person uses a computer their posture is typically slouched with hunched shoulders, leading to left shoulder pain. Working on a computer may also lead to inflammation of the muscles, muscle spasms, and severe headaches. This discomfort can easily be remedied and prevented in the future by improving poor computer habits.

• The most effective method is to leave the computer and take a brief walk every few hours. This can be a short walk around the office or a lengthier walk outdoors. The main objective is to allow your muscles to stretch out. Some specialists recommend breaking for 3 minutes every 30 minutes.

• A comfortable chair makes for a comfortable body, so be sure to invest in a well-made chair that supports the body’s most susceptible regions (neck, shoulders, and back). Your chair should also be adjusted for your comfort. It should be low enough so that your feet comfortably touch the ground, thighs are parallel to the floor, and the back of the chair should support your entire spine.

• The act of switching your mouse to opposite sides of the keyboard is extremely beneficial for centralized left shoulder pain, as it prevents you from overusing one side of your body. It instead equally distributes your movement to both the right and left sides of your body.

An injury to the shoulder or surrounding region can cause pain and discomfort. Injury related left shoulder pain is usually quite obvious to a person, particularly if it manifests itself right after the accident that caused the injury. In some cases the pain may not develop immediately after an injury. This is most common among athletes. For instance, a baseball pitcher may cause a problem by straining their tendons and then gradually exacerbate the shoulder by overusing it because they never realized it was damaged. By the time they notice the pain they may have been incurring more damage for months.

Other symptoms that may coincide with this discomfort are swelling, bruising, tenderness when touched, and in some incidents a bulge will form under the skin. However, the type of symptoms that will be experienced by the victim is completely dependent upon the type of injury. If the accident or injury was quite severe then a broken bone may be the cause of the left shoulder pain. A broken collarbone or shoulder blade will be extremely painful, and will be followed by severe swelling, bruising, and the affected bone may protrude from the body.

Treatments – A broken bone will require immediate medical attention. However, if the injury/accident was mild and you are only experiencing minor symptoms, then you may be able to mend the injury by yourself. The first thing that you will need to do is to apply an ice pack to the region. If you do not own an ice pack, then a bag of peas or ice wrapped in a towel will suffice. This should be kept on the area for enough time to prevent excessive swelling, typically around 20 minutes to avoid over-chilling the skin. After this, the area should be compressed with a bandage in order to give it the support it requires. Once the area has been iced and compressed, rest the affected area by avoiding strenuous activities for a few days. Disease Conditions

• A herniated disc – This can affect both the legs and arms if it is severe enough. The pain may be accompanied by numbness and tingling of the arms and legs. This type of shoulder pain will require medical intervention, typically in the form of medication and physical therapy.

• Osteoarthritis – This affects an individual’s joints causing pain, discomfort, localized swelling, warmth, and creaking of the joints. This syndrome is a result of natural aging, so if you are older and have shoulder pain this may be the cause of your discomfort. There is no effective treatment for this condition. There are only some ways to ease the pain. These include avoiding stress on the inflected joints, pain medication, swimming, and seeing a physical therapist.

If left shoulder pain and any accompanying symptoms plague you for a prolonged period of time, seek medical attention. Treatment for left shoulder pain can be either medical or homeopathic. Medical treatment usually involves the administration of pain and anti-inflammatory medication and strapping the inflected shoulder. If you would prefer a more natural approach, investigate acupuncture, which can be highly effective for shoulder pain.