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  • How can a non-technical person learn to write a spec for small projects?

    - by Joseph Turian
    How can a non-technical person learn to write specs for small projects? A friend of mine is trying to outsource some development on a statistics project. In particular, he does a lot of work in excel, and wants to outsource the creation of scripts to do what he now does by hand. However, my friend is extremely non-technical. He is poor at writing technical specs. When he does write a spec, it is written the way you would describe doing something in excel (go to this cell and then copy the value to that cell). It is also overly verbose, and does examples several times. I'm not sure if he properly describes corner cases. The first project he outsourced was a failure. I think he overdescribed some details, but underdescribed corner cases. That and/or the coder he hired didn't think through the corner cases and ask appropriate questions. I'm not sure. I got on IM with him and it took me half an hour to dig out a description that should have taken five minutes or less to describe. I wrote the scripts for him at the end, but didn't examine why his process with the coder failed. He has asked me for help. However, I refuse to get involved, because taking his spec and translating it into clear requirements is 10x more work than executing on a clearly written spec. What is the right way for him to learn? Are there resources he could use? Are there ways he can learn from small, low-pressure practice projects with coders? Most of his scripts are statistical and data processing oriented. e.g. take this column and run an average over it. Remove these rows under these conditions. So the challenge is different than spec'ing a web app.

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  • Why doesn't Unity's OnCollisionEnter give me surface normals, and what's the most reliable way to get them?

    - by michael.bartnett
    Unity's on collision event gives you a Collision object that gives you some information about the collision that happened (including a list of ContactPoints with hit normals). But what you don't get is surface normals for the collider that you hit. Here's a screenshot to illustrate. The red line is from ContactPoint.normal and the blue line is from RaycastHit.normal. Is this an instance of Unity hiding information to provide a simplified API? Or do standard 3D realtime collision detection techniques just not collect this information? And for the second part of the question, what's a surefire and relatively efficient way to get a surface normal for a collision? I know that raycasting gives you surface normals, but it seems I need to do several raycasts to accomplish this for all scenarios (maybe a contact point/normal combination misses the collider on the first cast, or maybe you need to do some average of all the contact points' normals to get the best result). My current method: Back up the Collision.contacts[0].point along its hit normal Raycast down the negated hit normal for float.MaxValue, on Collision.collider If that fails, repeat steps 1 and 2 with the non-negated normal If that fails, try steps 1 to 3 with Collision.contacts[1] Repeat 4 until successful or until all contact points exhausted. Give up, return Vector3.zero. This seems to catch everything, but all those raycasts make me queasy, and I'm not sure how to test that this works for enough cases. Is there a better way?

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  • Understanding normal maps on terrain

    - by JohnB
    I'm having trouble understanding some of the math behind normal map textures even though I've got it to work using borrowed code, I want to understand it. I have a terrain based on a heightmap. I'm generating a mesh of triangles at load time and rendering that mesh. Now for each vertex I need to calculate a normal, a tangent, and a bitangent. My understanding is as follows, have I got this right? normal is a unit vector facing outwards from the surface of the triangle. For a vertex I take the average of the normals of the triangles using that vertex. tangent is a unit vector in the direction of the 'u' coordinates of the texture map. As my texture u,v coordinates follow the x and y coordinates of the terrain, then my understanding is that this vector is simply the vector along the surface in the x direction. So should be able to calculate this as simply the difference between vertices in the x direction to get a vector, (and normalize it). bitangent is a unit vector in the direction of the 'v' coordinates of the texture map. As my texture u,v coordinates follow the x and y coordinates of the terrain, then my understanding is that this vector is simply the vector along the surface in the y direction. So should be able to calculate this as simply the difference between vertices in the y direction to get a vector, (and normalize it). However the code I have borrowed seems much more complicated than this and takes into account the actual values of u, and v at each vertex which I don't understand the need for as they increase in exactly the same direction as x, and y. I implemented what I thought from above, and it simply doesn't work, the normals are clearly not working for lighting. Have I misunderstood something? Or can someone explain to me the physical meaning of the tangent and bitangent vectors when applied to a mesh generated from a hightmap like this, when u and v texture coordinates map along the x and y directions. Thanks for any help understanding this.

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  • How Can You Get More Productive In Life Sciences Sales?

    - by charles.knapp
    Only half of all doctors will meet with pharmaceutical sales reps, and that percentage continues to decrease. Furthermore, when reps are granted an opportunity to share information, the average interaction is only about a minute and a half. Concurrently, call quotas continue to increase. What does this matter? Sales reps need to spend less time on traditional planning and after-call reporting, more time making calls, and make more productive use of short presentation times. Fortunately for sales reps, Oracle offers the first life sciences CRM that is designed to double sales time and halve reporting time. In particular, our new Life Sciences Edition Offline Client is designed so that you can actually turn the screen around, so that your CRM is useful for presentations and not just reporting, whether you are connected to cloud or working offline such as in restricted clinical environments. Watch Piers Evans, Industry Strategy Director, show what this looks like in the day of a typical pharmaceutical sales representative. By use of this code snippet, I agree to the Brightcove Publisher T and C found at https://accounts.brightcove.com/en/terms-and-conditions/. -- This script tag will cause the Brightcove Players defined above it to be created as soon as the line is read by the browser. If you wish to have the player instantiated only after the rest of the HTML is processed and the page load is complete, remove the line. -- brightcove.createExperiences();

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  • SQLAuthority News – Whitepaper Download – Using Star Join and Few-Outer-Row Optimizations to Improve Data Warehousing Queries

    - by pinaldave
    Size of the database is growing every day. Many organizations now a days have more than TB of the Data in their system. Performance is always part of the issue. Microsoft is really paying attention to the same and also focusing on improving performance for Data Warehousing. Microsoft has recently released whitepaper on the performance tuning subject of Data Warehousing. Here is the abstract about the whitepaper from official site: In this white paper we discuss two of the new features introduced in SQL Server 2008, Star Join and Few-Outer-Row optimizations. These two features are in SQL Server 2008 R2 as well.  We test the performance of SQL Server 2008 on a set of complex data warehouse queries designed to highlight the effect of these two features and observed a significant performance gain over SQL Server 2005 (without these two features). The results observed also apply to SQL Server 2008 R2.  On average, about 75 percent of the query execution time has been reduced, compared to SQL Server 2005. We also include data that shows a reduction in the number of rows processed and improved balance in parallel queries, both of which highlight the important role the Star Join and Few Outer-Row features played. I encouraged all of those interested in Data Warehouse to read it and see if they can learn the tricks. Using Star Join and Few-Outer-Row Optimizations to Improve Data Warehousing Queries Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority News, T SQL, Technology

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  • SEO Keyword Research Help

    - by James
    Hi Everyone, I'm new at SEO and keyword research. I am using Market Samurai as my research tool, and I was wondering if I could ask for your help to identify the best key word to target for my niche. I do plan on incorporating all of them into my site, but I wanted to start with one. If you could give me your input on these keywords, I would appreciate it. This is all new to me :) I'm too new to post pictures, but here are my keywords (Searches, SEO Traffic, and SEO Value / Day): Searches | SEO Traffic | PBR | SEO Value | Average PR/Backlinks of Current Top 10 1: 730 | 307 | 20% | 2311.33 | 1.9 / 7k-60k 2: 325 | 137 | 24% | 822.94 | 2.3 / 7k-60k 3: 398 | 167 | 82% | 589.79 | 1.6 / 7k-60k I'm wondering if the PBR (Phrase-to-broad) value of #1 is too low. It seems like the best value because the SEOV is crazy high. That is like $70k a month. #3 has the highest PBR, but also the lowest SEOV. #2 doesn't seem worth it because of the PR competetion. Might be a little too hard to get into the top page of Google. I'm wondering which keywords to target, and if I should be looking at any other metric to see if this is a profitable niche to jump into. Thanks.

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  • How can a non-technical person can learn to write a spec for small projects?

    - by Joseph Turian
    How can a non-technical person learn to write specs for small projects? A friend of mine is trying to outsource some development on a statistics project. In particular, he does a lot of work in excel, and wants to outsource the creation of scripts to do what he now does by hand. However, my friend is extremely non-technical. He is poor at writing technical specs. When he does write a spec, it is written the way you would describe doing something in excel (go to this cell and then copy the value to that cell). It is also overly verbose, and does examples several times. I'm not sure if he properly describes corner cases. The first project he outsourced was a failure. I think he overdescribed some details, but underdescribed corner cases. That and/or the coder he hired didn't think through the corner cases and ask appropriate questions. I'm not sure. I got on IM with him and it took me half an hour to dig out a description that should have taken five minutes or less to describe. I wrote the scripts for him at the end, but didn't examine why his process with the coder failed. He has asked me for help. However, I refuse to get involved, because taking his spec and translating it into clear requirements is 10x more work than executing on a clearly written spec. What is the right way for him to learn? Are there resources he could use? Are there ways he can learn from small, low-pressure practice projects with coders? [edit: Most of his scripts are statistical and data processing oriented. e.g. take this column and run an average over it. Remove these rows under these conditions. So the challenge is different than spec'ing a web app.]

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  • SEM & Adwords: How many click without a sale before i should pause a keyword

    - by Thomas Jönsson
    I wonder how many clicks I optimally should let pass through every new keyword I try in Adwords before I find out that it's not making a profit and it should be paused! It's actually four question. 1: At which likelihood percentile should I pause a word? 2: How many clicks should I let through before I pause a word for those word which do not generate any lead? 3: How many clicks should I let through after one sale to consider the word not to be profitable? 4: Does the likelihood of the word becoming profitable affect the above? Conditions: -The clicks is normally distributed. (correct?) -A CR of 1% is break even, everything above is profit (1 sale/100 clicks=break even) Cost per Click(cpc) = 4$ -Marginal (profit per sale) = 400$ -Paybacktime = 1 year -Average click per word = 0,333 per day (121 + 2/3 per year) Exampel: After 1 click and no sale the keyword still has a high probability to be profitable. After 500 clicks and no sale it has almost no likelihood to not be profitable and should probably be paused. Thanks in advance!

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  • Suitability of ground fog using layered alpha quads?

    - by Nick Wiggill
    A layered approach would use a series of massive alpha-textured quads arranged parallel to the ground, intersecting all intervening terrain geometry, to provide the illusion of ground fog quite effectively from high up, looking down, and somewhat less effectively when inside the fog and looking toward the horizon (see image below). Alternatively, a shader-heavy approach would instead calculate density as function of view distance into the ground fog substrate, and output the fragment value based on that. Without having to performance-test each approach myself, I would like first to hear others' experiences (not speculation!) on what sort of performance impact the layered alpha texture approach is likely to have. I ask specifically due to the oft-cited impacts of overdraw (not sure how fill-rate bound your average desktop system is). A list of games using this approach, particularly older games, would be immensely useful: if this was viable on pre DX9/OpenGL2 hardware, it is likely to work fine for me. One big question is in regards to this sort of effect: (Image credit goes to Lume of lume.com) Notice how the vertical fog gradation is continuous / smooth. OTOH, using textured quad layers, I can only assume that layers would be mighty obvious when walking through them -- the more sparse they were, the more obvious this would be. This is in contrast to where fog planes are aligned to face the player every frame, where this coarseness would be much less obvious.

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  • B.S.in Computer Science, weak eyes => career change

    - by Prometheus
    So I am going to earn B.S. in Computer Science soon. I like computers. I like programming. The problem is that my eyes are very weak. Depending on their condition, I can only put in about 6 hours in front of computer a day. If I push myself, I have trouble even keeping my eyes open because of soreness/pain, consequently headaches. My eyes do not have medical conditions. I was just born with weak eyes. I tried many different approaches to work around this problem - better monitor, breaks every 10 minutes, supplements... I even memorized a lot of shortcuts to reduce my time on computers! But I am finally giving up. I do not think I can be a programmer for the rest of my life. I was the top of my class in high school because all works were paper-based, I did average in college due to the nature of my eyes and the difficulty of the material. So what do you recommend I do? Or, Is there a career that is similar to programming but requires interacting with computers less?

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  • Bad at math, feeling limited

    - by Peter Stain
    Currently I'm a java developer, making websites. I'm really bad at math, in high school I got suspened because of it once. I didn't program then and had no interest in math. I started programming after high school and started feeling that my poor math skills are limiting me. I feel like the programming's not that hard for me. Though web development in general is not that hard, i guess. I've been doing Spring and Hibernate a lot. What i'm trying to ask is : if I understand and can manage these technologies and programming overall, would it mean that I have some higher than average prerequisite for math and details? Would there be any point or would it be easy for me to take some courses in high school math and get a BSc in math maybe? This web development is really starting to feel like not my cup of tea anymore, i would like to do something more interesting. I'm 25 now and feel like stuck. Any help appreciated.

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  • Global vs. Local Monthly Searches in Adwords keyword tool

    - by Gregory
    I'm trying to learn how to use a keyword tool in Adwords. Here's what I entered: Country- Russia Language-Russian Desktop and laptop devices And the keyword was ???? ? ??????? (tours to Israel in Russian Cyrillic letters) . As a broad match type... Now... the results that I got were: Global monthly: 60,500 Local monthly: 40,500 If I got it right..."Global monthly" means in this context : worldwide average monthly searches for this search term in ANY language in any Google search site (google.ru, google.com.ua, google.com, google.fr etc.). It's all nice, BUT... Then I made an query for tours to Israel in English in the US...And I got: Global monthly: 60,500 Local monthly: 27,100 That doesn't make any sense to me though! How come the total sum (the global) is actually a smaller number than a combined sum of just TWO countries??? (27,100+40,500=67,60060,500) By "any language" they mean a translation of the term into ANY possible language???Or maybe by "language" Google means the language of searchers' operating system? or their browsers' language?

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  • Hardware settings reviewing

    - by dino99
    Get some hardware related errors logged into dmesg: oem@dub:~$ dmesg | grep ata10 [ 1.007989] ata10: PATA max UDMA/133 cmd 0xa800 ctl 0xa480 bmdma 0xa408 irq 18 [ 1.691664] ata10: prereset failed (errno=-19) [ 1.691670] ata10: reset failed, giving up oem@dub:~$ dmesg | grep ata2 [ 0.990290] ata2: SATA max UDMA/133 abar m1024@0xfebfb800 port 0xfebfb980 irq 45 [ 1.688011] ata2: SATA link up 3.0 Gbps (SStatus 123 SControl 300) [ 1.688055] ata2.00: unsupported device, disabling [ 1.688057] ata2.00: disabled As I understand, its related to my old Seagate SATA HDD, and the PATA CDROM. These errors are quite new, so I feel that their settings (dma, write-cache, ...) have been modified by some upgrades. I've already used hdparm to set write-cache off on the HDDs. But it seems like I need to review some other setting(s) too. With oldest distro it was easy to know about the hardware settings, but now on Quantal/Precise its deeply hidden for the average user. So i would like to know how to view/modify these settings. About the CDROM reader, the problem is different: - the system don't identify it with an UUID; but only with ATAPI or by-id oem@dub:~$ dmesg|grep 'ATAPI' [ 1.308611] ata3.00: ATAPI: TSSTcorp CDDVDW SH-S203D, SB00, max UDMA/100 oem@dub:~$ ls -l /dev/disk/by-id/ ...... lrwxrwxrwx 1 root root 9 oct. 1 06:42 ata-TSSTcorp_CDDVDW_SH-S203D -> ../../sr0 .....

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  • How to create per-vertex normals when reusing vertex data?

    - by Chris Smith
    I am displaying a cube using a vertex buffer object (gl.ELEMENT_ARRAY_BUFFER). This allows me to specify vertex indicies, rather than having duplicate vertexes. In the case of displaying a simple cube, this means I only need to have eight vertices total. Opposed to needing three vertices per triangle, times two triangles per face, times six faces. Sound correct so far? My question is, how do I now deal with vertex attribute data such as color, texture coordinates, and normals when reusing vertices using the vertex buffer object? If I am reusing the same vertex data in my indexed vertex buffer, how can I differentiate when vertex X is used as part of the cube's front face versus the cube's left face? In both cases I would like the surface normal and texture coordinates to be different. I understand I could average the surface normal, however I would like to render a cube. Also, this still doesn't work for texture coordinates. Is there a way to save memory using a vertex buffer object while being able to provide different vertex attribute data based on context? (Per-triangle would be idea.) Or should I just duplicate each vertex for each context in which it gets rendered. (So there is a one-to-one mapping between vertex, normal, color, etc.) Note: I'm using OpenGL ES.

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  • probability of trouble-free upgrade

    - by intuited
    One of the problems with recommending Ubuntu to potential future users, especially those not particularly given to technical endeavours, is that there is a chance that upgrades will break their machine, and they'll have to pay or otherwise coerce some knowledgeable person into fixing them. In my limited experience of running successive versions of Ubuntu since 8-something on a couple of different laptops, this chance is quite high. I'm not sure if I'm just unlucky with the hardware that I'm using, or if it's a result of the higher-than-average number of packages I have installed, or if upgrades are just typically problematic. So I'd like to know the likelihood, for a casual user, of doing a release upgrade, for example from 10.04 to 10.10, without experiencing any regression bugs. Obviously this is dependent on the hardware that people are running. Canonical seems to be making some efforts towards collecting data on this, for example with the "I am affected by this bug" checkbox on their issue tracker, and with the laptop compatibility reports, but I've not seen anything comprehensive. I'm hoping for an objective reference here, for example a study carried out by relatively unbiased individuals. However, anecdotal evidence is probably useful too.

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  • How Can I Know Whether I Am a Good Programmer?

    - by Kristopher Johnson
    Like most people, I think of myself as being a bit above average in my field. I get paid well, I've gotten promotions, and I've never had a real problem getting good references or getting a job. But I've been around enough to notice that many of the worst programmers I've worked with thought they were some of the best. Bad programmers who are surrounded by other bad programmers seem to be the most self-deluded. I'm certainly not perfect. I do make mistakes. I do miss deadlines. But I think I make about the same number of bonehead moves that "other good programmers" do. The problem is that I define "other good programmers" to mean "people who are like me." So, I wonder, is there any way a programmer can make some sort of reasonable self-evaluation? How do we know whether we are good or bad at our jobs? Or, if terms like good and bad are too ill-defined, how can programmers honestly identify their own strengths and weaknesses, so that they can take advantage of the former and work to improve the latter?

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  • Easily Close All Tabs in Google Chrome

    - by Asian Angel
    Do you find yourself with a lot of tabs open but dread closing all but one manually? Now you can close all of your tabs with a single click, and have just one ready to go with the Close all Tabs extension. Before We all find ourselves with a lot of tabs open sooner or later. That is not so bad until we realize that we need to close all of them and get back to work. A person could open a new tab and manually close the rest or close the entire window and restart Chrome. But a single click solution would be a lot more convenient. After There it is…the single click solution. Just click the Toolbar Button and BOOM! One fresh window with a single new tab page showing. Now if you could only take the rest of the day off… Conclusion The Close all Tabs extension may not be something that everyone would use, but if you are tired of manually closing all of those tabs then you will definitely like it. Links Download the Close all Tabs extension (Google Chrome Extensions) Similar Articles Productive Geek Tips Focused New Tabs Quick-Fix for Google ChromeVisually Browse Through Your Open Tabs in Google ChromeMake Google Chrome Open with Pinned TabsStupid Geek Tricks: Compare Your Browser’s Memory Usage with Google ChromeEasily Control a Large Amount of Tabs in Google Chrome TouchFreeze Alternative in AutoHotkey The Icy Undertow Desktop Windows Home Server – Backup to LAN The Clear & Clean Desktop Use This Bookmarklet to Easily Get Albums Use AutoHotkey to Assign a Hotkey to a Specific Window Latest Software Reviews Tinyhacker Random Tips Acronis Online Backup DVDFab 6 Revo Uninstaller Pro Registry Mechanic 9 for Windows Fun with 47 charts and graphs Tomorrow is Mother’s Day Check the Average Speed of YouTube Videos You’ve Watched OutlookStatView Scans and Displays General Usage Statistics How to Add Exceptions to the Windows Firewall Office 2010 reviewed in depth by Ed Bott

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  • Linux to Solaris @ Morgan Stanley

    - by mgerdts
    I came across this blog entry and the accompanying presentation by Robert Milkoski about his experience switching from Linux to Oracle Solaris 11 for a distributed OpenAFS file serving environment at Morgan Stanley. If you are an IT manager, the presentation will show you: Running Solaris with a support contract can cost less than running Linux (even without a support contract) because of technical advantages of Solaris. IT departments can benefit from hiring computer scientists into Systems Programmer or similar roles.  Their computer science background should be nurtured so that they can continue to deliver value (savings and opportunity) to the business as technology advances. If you are a sysadmin, developer, or somewhere in between, the presentation will show you: A presentation that explains your technical analysis can be very influential. Learning and using the non-default options of an OS can make all the difference as to whether one OS is better suited than another.  For example, see the graphs on slides 3 - 5.  The ZFS default is to not use compression. When trying to convince those that hold the purse strings that your technical direction should be taken, the financial impact can be the part that closes the deal.  See slides 6, 9, and 10.  Sometimes reducing rack space requirements can be the biggest impact because it may stave off or completely eliminate the need for facilities growth. DTrace can be used to shine light on performance problems that may be suspected but not diagnosed.  It is quite likely that these problems have existed in OpenAFS for a decade or more.  DTrace made diagnosis possible. DTrace can be used to create performance analysis tools without modifying the source of software that is under analysis.  See slides 29 - 32. Microstate accounting, visible in the prstat output on slide 37 can be used to quickly draw focus to problem areas that affect CPU saturation.  Note that prstat without -m gives a time-decayed moving average that is not nearly as useful. Instruction level probes (slides 33 - 34) are a super-easy way to identify which part of a function is hot.

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  • How to advertise (free) software?

    - by nebukadnezzar
    I'm not sure if this fits on SO, but other SE sites don't seem to fit either, so I understand when this question gets moved, Although I'd like to avoid getting it closed due to being offtopics, since I think that this question might fit, considering this part of the FAQ: Stack Overflow is for professional and enthusiast programmers, ... covers … a specific programming problem ... matters that are unique to the programming profession Sorry for the lengthy Introduction, though. When Software is advertised, it is usually Software for one (or more) specific purpose, such as: Mozilla Firefox - A Web Browser Ubuntu - An Operating System Python - A Programming Language Visual Studio - A Development Studio ... And so on. But when writing Libraries, that is, Software that doesn't necessarily serve one specific purpose, but instead multiple purposes, which are usually supposed to be used inside an application, such as: Irrlicht - A 3D Engine Qt - An Application Framework ... The process of advertisement gets a little more difficult. I'm a developer of the latter kind of Software, and I naturally want to advertise my Software. It's not commercial Software; It's not GPL either. It's completely free (Licensed under the MIT License :-)). I naturally host my stuff at github, which technically makes it very easy to access the software, and I thought that these might be possible options, although I have no experience with them: Submit the Software to Freshmeat, and hope for the best Submit the Software to Sourceforge, and hope someone accidently stumbles over it Write spammails, and get death threats via Mail ... But something tells me that these methods are probably not the best Methods. So, my final question would be, How does the Average Joe Hobby Programmer advertise his/her Software Library?

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  • CRM Evolution 2014: Mediocrity is the New Horrible in Customer Service

    - by Tuula Fai
    "Mediocrity is the new horrible in customer service," Blair McHaney, Gold's Gym Almost everyone knows that customers' expectations have risen. But, after listening to two days of presentations at CRM Evolution, I think it’s more accurate to say that customers' expectations have skyrocketed. Fortunately, most companies have gotten the message and are taking their customer service to a higher level. For those who've been hesitant to 'boldly go where their customer service organization has not gone before,' take heart. I’ve got some statistics that will encourage you to take those first few steps. Why should I change? By engaging customers online, ancestry.com achieved a 99.5% customer satisfaction score (CSAT) while improving retention and saving millions on greater efficiency, including a 38%-50% drop in inbound calls and emails.1 By empowering employees to delight customers, Gold’s Gym achieved a 77.5% Net Promoter Score (NPS) and 22% customer churn rate. No small feat when you consider the industry averages are 40% NPS and 45% churn.2 By adapting quickly to social media, brands like Verizon have benefited from social community members spending 2.5x-10x more than average customers.3 ‘The fierce urgency of now’ is upon us in customer service. You can take your customer service to a higher level! To find out more, click here CRM Evolution Customer Service Experience Footnotes: 1. Arvindh Balakrishnan, Is Your Customer Service Modern?2. Blair McHaney, Wire Your Organization with Customer Feedback3. Becky Carroll, The Power of Communities for Improving the Service Experience and Building Advocates

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  • An Interesting Perspective on Oracle's Mobile Strategy

    - by Carlos Chang
    Oracle’s well known for being an acquisitive company. On average, I think we acquire about 1 company a month. (don’t quote me, I didn't run the numbers)  With all the excitement around mobile, mobile and wait for it… mobile, well, you know...what' s up with that? Well, just to be clear and quote Schultz from Hogan's Heroes "I know nothing! Nothing! "  But I did recently run across this blog by Kevin Benedict over at mobileenterprisestrategies.com covering this very topic, Oracle Mobility Emerges Prepared for the Future,  a little (fair use) snippet here:"History, however, may reward Oracle's patience.  While veteran mobile platform vendors (including SAP) have struggled to keep up with the fast changing market, R&D investment requirements, the fickle preferences of mobile developers, and the emergence of cloud-based mobile services, Oracle has kept their focus on supporting mobile developers with integration services and tools that extend their solutions out to mobile apps.”It’s an interesting read, and I would encourage you to check it out here.   BTW, if you’re a Twitter user, follow our new account @OracleMobile To the first ten thousand followers, I bequeath you my sincere virtual thanks and gratitude. :)  For the dedicated mobile blog, go to blogs.oracle.com/mobile.

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  • Would this be viewed poorly amongst the programming community?

    - by Eric P
    So one of my responsibilities at work is to build an internal tool that helps the workers enter in all their information. It's an enterprise application that is similar to a Windows forms database tool. So it's not much different than like developing a Word + Excel combo application, but the average person in this workgroup is a 20-40 year old woman or a random chatty male type. Plus I know all of these people are heavily involved with Facebook on a daily basis. How bad would it be if I styled my new interface to be similar to what Facebook does. People could get award points and stuff when they fill out different types of forms and basically compete against each other like it was a game. When people had completed one, it would be posted on their wall and everyone could comment/like stuff just like in Facebook. And it would be like they are doing peer reviewing for fun. The rewards would be outstanding I would imagine. These people are so into Facebook and Facebook games that productivity would rise due to them trying to compete and earn points and achievements. Would this be taking advantage of the people by 'tricking them into working harder by giving them a game' or would it be viewed as something that would improve happiness at work?

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  • SEO Keyword Research

    - by James
    Hi Everyone, I'm new at SEO and keyword research. I am using Market Samurai as my research tool, and I was wondering if I could ask for your help to identify the best key word to target for my niche. I do plan on incorporating all of them into my site, but I wanted to start with one. If you could give me your input on these keywords, I would appreciate it. This is all new to me :) I'm too new to post pictures, but here are my keywords (Searches, SEO Traffic, and SEO Value / Day): Searches | SEO Traffic | PBR | SEO Value | Average PR/Backlinks of Current Top 10 1: 730 | 307 | 20% | 2311.33 | 1.9 / 7k-60k 2: 325 | 137 | 24% | 822.94 | 2.3 / 7k-60k 3: 398 | 167 | 82% | 589.79 | 1.6 / 7k-60k I'm wondering if the PBR (Phrase-to-broad) value of #1 is too low. It seems like the best value because the SEOV is crazy high. That is like $70k a month. #3 has the highest PBR, but also the lowest SEOV. #2 doesn't seem worth it because of the PR competetion. Might be a little too hard to get into the top page of Google. I'm wondering which keywords to target, and if I should be looking at any other metric to see if this is a profitable niche to jump into. Thanks.

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  • iFrame content pageviews not matching parent page pageviews

    - by surfbird0713
    I have a page with content hosted in an iFrame, both using the same GA account ID. When I look at the pages report, the parent page has about 9000 unique views, but the iFrame content only has 3700. Anyone have an idea what could cause that kind of discrepancy? My only guess is that it would be caused by people moving on before the iFrame content has a chance to load, but the average time on page for the host page is 56 seconds, so that doesn't seem possible. This is the page in question: http://cookware.lecreuset.com/cookware/content_le-creuset-lid_10151_-1_20002 The flipbook is hosted in the iFrame on a separate domain. I have each page of the flipbook triggering a virtual pageview to try to evaluate engagement with the book - when the flipbook loads, it fires a pageview for the page it is on, so that is the page I'm using for the 3700 number. I also looked at the source of the iFrame in the pages report, and that number just about matches the virtual pageviews so that piece is consistent. Any ideas on this are much appreciated. Thanks!

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  • Combined Likelihood Models

    - by Lukas Vermeer
    In a series of posts on this blog we have already described a flexible approach to recording events, a technique to create analytical models for reporting, a method that uses the same principles to generate extremely powerful facet based predictions and a waterfall strategy that can be used to blend multiple (possibly facet based) models for increased accuracy. This latest, and also last, addition to this sequence of increasing modeling complexity will illustrate an advanced approach to amalgamate models, taking us to a whole new level of predictive modeling and analytical insights; combination models predicting likelihoods using multiple child models. The method described here is far from trivial. We therefore would not recommend you apply these techniques in an initial implementation of Oracle Real-Time Decisions. In most cases, basic RTD models or the approaches described before will provide more than enough predictive accuracy and analytical insight. The following is intended as an example of how more advanced models could be constructed if implementation results warrant the increased implementation and design effort. Keep implemented statistics simple! Combining likelihoods Because facet based predictions are based on metadata attributes of the choices selected, it is possible to generate such predictions for more than one attribute of a choice. We can predict the likelihood of acceptance for a particular product based on the product category (e.g. ‘toys’), as well as based on the color of the product (e.g. ‘pink’). Of course, these two predictions may be completely different (the customer may well prefer toys, but dislike pink products) and we will have to somehow combine these two separate predictions to determine an overall likelihood of acceptance for the choice. Perhaps the simplest way to combine multiple predicted likelihoods into one is to calculate the average (or perhaps maximum or minimum) likelihood. However, this would completely forgo the fact that some facets may have a far more pronounced effect on the overall likelihood than others (e.g. customers may consider the product category more important than its color). We could opt for calculating some sort of weighted average, but this would require us to specify up front the relative importance of the different facets involved. This approach would also be unresponsive to changing consumer behavior in these preferences (e.g. product price bracket may become more important to consumers as a result of economic shifts). Preferably, we would want Oracle Real-Time Decisions to learn, act upon and tell us about, the correlations between the different facet models and the overall likelihood of acceptance. This additional level of predictive modeling, where a single supermodel (no pun intended) combines the output of several (facet based) models into a single prediction, is what we call a combined likelihood model. Facet Based Scores As an example, we have implemented three different facet based models (as described earlier) in a simple RTD inline service. These models will allow us to generate predictions for likelihood of acceptance for each product based on three different metadata fields: Category, Price Bracket and Product Color. We will use an Analytical Scores entity to store these different scores so we can easily pass them between different functions. A simple function, creatively named Compute Analytical Scores, will compute for each choice the different facet scores and return an Analytical Scores entity that is stored on the choice itself. For each score, a choice attribute referring to this entity is also added to be returned to the client to facilitate testing. One Offer To Predict Them All In order to combine the different facet based predictions into one single likelihood for each product, we will need a supermodel which can predict the likelihood of acceptance, based on the outcomes of the facet models. This model will not need to consider any of the attributes of the session, because they are already represented in the outcomes of the underlying facet models. For the same reason, the supermodel will not need to learn separately for each product, because the specific combination of facets for this product are also already represented in the output of the underlying models. In other words, instead of learning how session attributes influence acceptance of a particular product, we will learn how the outcomes of facet based models for a particular product influence acceptance at a higher level. We will therefore be using a single All Offers choice to represent all offers in our combined likelihood predictions. This choice has no attribute values configured, no scores and not a single eligibility rule; nor is it ever intended to be returned to a client. The All Offers choice is to be used exclusively by the Combined Likelihood Acceptance model to predict the likelihood of acceptance for all choices; based solely on the output of the facet based models defined earlier. The Switcheroo In Oracle Real-Time Decisions, models can only learn based on attributes stored on the session. Therefore, just before generating a combined prediction for a given choice, we will temporarily copy the facet based scores—stored on the choice earlier as an Analytical Scores entity—to the session. The code for the Predict Combined Likelihood Event function is outlined below. // set session attribute to contain facet based scores. // (this is the only input for the combined model) session().setAnalyticalScores(choice.getAnalyticalScores); // predict likelihood of acceptance for All Offers choice. CombinedLikelihoodChoice c = CombinedLikelihood.getChoice("AllOffers"); Double la = CombinedLikelihoodAcceptance.getChoiceEventLikelihoods(c, "Accepted"); // clear session attribute of facet based scores. session().setAnalyticalScores(null); // return likelihood. return la; This sleight of hand will allow the Combined Likelihood Acceptance model to predict the likelihood of acceptance for the All Offers choice using these choice specific scores. After the prediction is made, we will clear the Analytical Scores session attribute to ensure it does not pollute any of the other (facet) models. To guarantee our combined likelihood model will learn based on the facet based scores—and is not distracted by the other session attributes—we will configure the model to exclude any other inputs, save for the instance of the Analytical Scores session attribute, on the model attributes tab. Recording Events In order for the combined likelihood model to learn correctly, we must ensure that the Analytical Scores session attribute is set correctly at the moment RTD records any events related to a particular choice. We apply essentially the same switching technique as before in a Record Combined Likelihood Event function. // set session attribute to contain facet based scores // (this is the only input for the combined model). session().setAnalyticalScores(choice.getAnalyticalScores); // record input event against All Offers choice. CombinedLikelihood.getChoice("AllOffers").recordEvent(event); // force learn at this moment using the Internal Dock entry point. Application.getPredictor().learn(InternalLearn.modelArray, session(), session(), Application.currentTimeMillis()); // clear session attribute of facet based scores. session().setAnalyticalScores(null); In this example, Internal Learn is a special informant configured as the learn location for the combined likelihood model. The informant itself has no particular configuration and does nothing in itself; it is used only to force the model to learn at the exact instant we have set the Analytical Scores session attribute to the correct values. Reporting Results After running a few thousand (artificially skewed) simulated sessions on our ILS, the Decision Center reporting shows some interesting results. In this case, these results reflect perfectly the bias we ourselves had introduced in our tests. In practice, we would obviously use a wider range of customer attributes and expect to see some more unexpected outcomes. The facetted model for categories has clearly picked up on the that fact our simulated youngsters have little interest in purchasing the one red-hot vehicle our ILS had on offer. Also, it would seem that customer age is an excellent predictor for the acceptance of pink products. Looking at the key drivers for the All Offers choice we can see the relative importance of the different facets to the prediction of overall likelihood. The comparative importance of the category facet for overall prediction might, in part, be explained by the clear preference of younger customers for toys over other product types; as evident from the report on the predictiveness of customer age for offer category acceptance. Conclusion Oracle Real-Time Decisions' flexible decisioning framework allows for the construction of exceptionally elaborate prediction models that facilitate powerful targeting, but nonetheless provide insightful reporting. Although few customers will have a direct need for such a sophisticated solution architecture, it is encouraging to see that this lies within the realm of the possible with RTD; and this with limited configuration and customization required. There are obviously numerous other ways in which the predictive and reporting capabilities of Oracle Real-Time Decisions can be expanded upon to tailor to individual customers needs. We will not be able to elaborate on them all on this blog; and finding the right approach for any given problem is often more difficult than implementing the solution. Nevertheless, we hope that these last few posts have given you enough of an understanding of the power of the RTD framework and its models; so that you can take some of these ideas and improve upon your own strategy. As always, if you have any questions about the above—or any Oracle Real-Time Decisions design challenges you might face—please do not hesitate to contact us; via the comments below, social media or directly at Oracle. We are completely multi-channel and would be more than glad to help. :-)

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