Cooperating to Change Learning

There is a signal that indicates the need to change education.

While elementary and secondary schools have progressed considerably in the last years, the coming days of the field are focused to get much more impactful improvements.

Technological innovations in big data analytics, mobile expansion in and beyond of schools, and the breakthroughs in cloud-based smart content are making significantly accurate tools to define what academic procedures would show the optimum and drastically change present educational methods.

Such innovative analytics and cloud-based smart content can assist educators to uncover deep insights which will change procedure to learning and help shift the classrooms from assembly-line to a totally individualized setting – environments which motivate and involve students at any level from kindergarteners learning to university.

But for a real revolution to happen, you will find a demand of smooth collaboration between teachers, parents and pupils that build a learning environment which fosters knowledge progress and uses technology to enhance students involvement which undoubtedly boosts results. Public-private along with cooperating with private sectors are necessary to shift classrooms to a setting which motivates and engages learners at any level and delivers an environment in which success is not produced in a vacuum but collectively.

The view on the classroom of the future cannot reach a more essential time. Research has shown that on a worldwide level nearly 2 of 3 adults have not accomplished the equal of high-grade education. This result is inappropriate in a century where a secondary degree is usually the minimum necessary for a person to efficiently get into the employed pool.

Against that background for today, it has been introduced a new collaboration with the University of South Carolina to utilize their recently minted Center for Applied Innovation to develop technology basis and information needed for customized education which boosts results for students.

Incorporating with USC we are searching solutions for using big data and analytics to help arrange smart content, student evaluations and information inside as well as outside the University. Since the project advances our alliance would make USC a global center of experience for educational institutions applying the same products all over the world.

Collaborations with educational institutions are essential whether we intend to be focused on changing education.

Collectively we can make an improvement. Together we can develop and apply technology to change education and be sure that limitations to learn are getting a smaller reason for accomplishment globally.

New available build notification (72048)

This is our spam / virus filter update for customers on the mx1 / filtering cluster. The information is brought to you by our vendor, Spam Experts.

OS update

SpamExperts is preparing to upgrade all Local Cloud servers from Debian 6 (LTS) to Debian 7.0 over the next weeks. This is generally done as part of your regular automatic update, however it may be executed manually during off-peak times (before 8am or after 7pm server time during working days, or throughout the day during the weekend). This process is monitored carefully by our engineers, and we’ll ensure only a single system is upgraded at a time so your clients will not notice any of the changes. Generally, the update will complete quickly. In case you prefer to be part of our early upgrade rounds, please let us know via email and we’ll manually upgrade your systems as soon as possible. In case you’d like to schedule a specific date/time for the upgrade, please let us know and we’ll put you on the exclusion list to agree on a date/time first. In general, we expect no issues here. If you’re running a virtual machine, please ensure your environment and kernel are up to date. It is not required to reboot the machine after the upgrade, however you may wish to schedule a reboot on your end some date after the upgrade to ensure the boot process is working as expected.

For more information, please do not hesitate to contact us.


Filtering (services):

  • This build includes general filtering/performance updates only

Front-end / GUI:

  • Resolved issue with non-existent API call being used by the WHMCS addon (#21638)
  • Adjusted spacing before ‘error details’ option of the log search (#21611)
  • Resolved issue with Archive search functionality when recipients with quotation marks are used (#21609)

Plugins & Integration tools:

  • cPanel: Resolved binary errors after installing the cPanel addon (#21597)
  • DirectAdmin: Resolved issue with “Comments starting with ‘#’ are deprecated” (#21434)

In case you have items you would like to discuss in more detail, please inform support and these topics will be included in the next quarterly CTO webinar. This next webinar be held on the 2nd October 2014.

Mobile Activity Recognition

This article could be useful to know how to identify and monitor the physical activity of users of smartphones. We will share some methods of cleaning education information, feature choice, proper selecting classification algorithm and model validating. You will see the process of development of the recognition system for mobiles.


The stunning activity smartphones apps could do is to sense present users physical activity like walking, driving, or staying. Identification of activities features numerous apps from fitness or health monitoring to context-based marketing and staff tracking. Context-aware apps are able to personalize activities depending on present process. As an example, if the user is looking for nearby companies it could use bigger radius when driving, and smaller when walking.

One of the apps created for training data accumulating is the Sensor Logger by IBM. It extracts the accelerometer appr. 50 times a second and writes results into local file. Also, it writes captured from GPS present speed. This application was presented to 20 volunteers to install on their mobile phones and use this app for data reporting in the process of exercises.

Feature Recovery

Files recorded by Sensor Logger were examined with the feature recovery program. This program divided logged information into parts of 3 sec, then computed representative feature of every part. These features were commonly determined by frequency analysis of the accelerometer data. It was calculated with a fast Fourier transform (FFT). The FFT functions were after that measured by splitting the series of FFT coefficients into subranges and getting the sums of coeffs in every range. To illustrate, one among the arrangements was the sums of coeffs in a range of 1 Hz, 2 Hz so on. It was calculated a few optional sets of features in the range of minimum and maximum frequencies.

Other characteristics used together with FFT were the mean, the variation, and the energy of the signal, and additionally velocity provided by GPS. The measurements were saved in the database, records were made every 3 second-part with acquired features and the activities from which information was taken. Data connected with log file was also saved in the database. It consisted of a phone model, the OS, the username, and the track name.

Cleaning Data

Before accepting a new log file into the testing data it should be examined for accuracy of classifying with decision tree which was created using validated data. In case the classification accuracy of a new log file is lower an obtained from random partitioning it needs to be audited. To fix the issue the log file should be edited and all wrong data which is determined from inspection should be deleted.

Model Validating

Log files of every track are randomly split between training and testing sets while randomly partitioned. Consequently, training and testing sets may be related, because of consist of records of the same file. Data which is part of the same track logged by the same user with the same phone on the same situation, are usually near each other regarding a variety of features. For that reason examination with random partitioning could be not really indicative and could not show overfitting. A preferable method to define the accuracy is to divide by users – delete several users from training information and apply the data recorded by these users for testing. In a minimum one instance, one of the feature sets provided by random partitioning showed good results, however, results were not as positive with dividing by users, possibly because of overfitting happened by random partitioning.

OS Independency Validating

If you accumulate data from Android, iOS and Windows Phone OS it is crucial to check the possibility to mix the results and create one common decision tree. We did some testing with dividing data by operating systems, just to illustrate the creation of a decision tree from information obtained from Android and use it for iOS. Results proved the same behavior of accelerometer in every OS, no big contrast between data obtained from these operating systems. This approval granted mixing the data and creating a single decision tree.

The tree could be exported as text, HTML or a PMML file. The text file is a basic textual depiction of the tree in which internal nodes are shown by the predicate which is connected with the node and line indentation is needed for branching structure.
Recognition model begins with collecting data from accelerometer and GPS. Every 3 seconds the information is calculated for feature extracting by using FFT. Eventually, the original decision tree code is used to get a classification result – the recognition activities.

YARN Review

Apache Hadoop is regarded as the most in-demand applications for big data handling. It is installed proficiently by a lot of companies for quite a while. Although Hadoop is known as a trusted, scalable and inexpensive option, it is repeatedly receiving upgrades from a big network of builders. Consequently, the version 2.0 gives some innovative functions, one of them is Yet Another Resource Negotiator (YARN), HDFS Federation, and a highly accessible NameNode, it makes Hadoop cluster far more efficient, robust and trustworthy. You will get information on the features and advantages of YARN in this article.

Apache Hadoop 2.0 contains YARN, which splits the resource handling and processing elements. The YARN-based configuration is not limited to MapReduce. The article represents YARN and its benefits. You can get details on how to improve your clusters with YARN’s scalability, performance, and flexibility.

Overview of Apache Hadoop

Apache Hadoop is an open-source application framework which could be deployed on a cluster of computers so the devices are able to interact and collaborate to keep and handle huge volumes of information in an extremely syndicated way. First of all, Hadoop contains two basic elements: HDFS and a distributed computing engine which gives you the ability to execute applications as MapReduce tasks.

MapReduce is an easy software model spread by Google. It is really useful for handling big data in a parallel and scalable manner. It is encouraged by functional programming on which people show their computation in the form of a map and reduce services which handle info as key-value couples. Hadoop also offers the application system for executing MapReduce tasks in the form of a string of map and reduce jobs.
On an important note, the Hadoop system handles all the involved elements of syndicated processing: parallelization, planning, resource supervision, internal interacting, dealing with soft and hard malfunctions or others.

Best Time of Hadoop

However there have been some open-source implementations of the MapReduce model, Hadoop MapReduce rapidly evolved into most favored. Hadoop is likewise among the most interesting open-source projects in the world resulting from a number of great benefits: a high-level API, near-linear scalability, open-source license, capability to be executed on asset hardware and failing persistence. It was installed on a huge number of servers of thousands of companies, and nowadays it is a must for large-scale syndicated storage and processing. Several premature adopters like Yahoo and Facebook constructed huge clusters ranging on 4000 machines to fulfill their continuously increasing data processing demands. Once they created clusters, they have noticed restrictions of the Hadoop MapReduce framework.

The significant limitation of MapReduce is mostly connected with scalability, resource usage, support of workloads distinct from MapReduce. Application execution is regulated by 2 systems:
JobTracker – a single master process. It coordinates any running task and assigns map and reduce jobs for running on TaskTracker. TaskTracker process is secondary, it runs given tasks and regularly informs to the JobTracker. Yahoo technicians in 2010 started to work on a totally new structure of Hadoop which handles all the limitations and add new features.

YARN – Next Generation of Hadoop

The following terms have changed in YARN:

  • ResourceManager in place of cluster manager.
  • ApplicationManager in place of a separate and short-lived JobTracker.
  • NodeManager in place of TaskTracker.
  • A distributed application in place of a MapReduce job.

The YARN structure is consisted of a global ResourceManager, which runs a primary service, generally on a dedicated computer. ResourceManager monitors the number of live nodes and resources obtained on the cluster and matches applications with resources. The ResourceManager is a unique task which obtains info, therefore, it is able to distribution selections in a shared, protected and multi-tenant way.
Once a user runs an application, an instance of a portable process named ApplicationMaster initiated coordination of functioning for all the jobs within the application. This consists of task monitoring, failed jobs restart, speculatively slow tasks execution and determining the number of job counters. These duties were formerly allocated to one JobTracker. The ApplicationMaster and jobs that fit in are executed on resource containers managed by the NodeManager.

The NodeManager is usually a more common and effective form of the TaskTracker. As an alternative to acquiring a limited number of map and reduce slots, the NodeManager possesses several dynamically generated resource containers. The containers size is determined by the volume of resources it consists of, like memory, CPU, HDD, network IO. At present only memory and CPU are included. The quantity of containers on a node is a result of setting specifications and the number of node resources outside devoted to the slave daemons and OS.

Once the ResourceManager takes a new syndication of the task, one of the primary choices the Scheduler does is picking a container where ApplicationMaster would execute. Just when ApplicationMaster is starting it is getting responsibility under the total life cycle of the application. In the first instance, it will deliver resource queries to the ResourceManager to request needed containers. A resource request means a request to get a number of containers to fulfill the demands of the application.


YARN is a totally rebuilt architecture of Hadoop. It appears to be a revolution for the way distributed programs are installed on a cluster of commodity computers. YARN provides evident perks in scalability, effectiveness, and flexibility in comparison to traditional MapReduce in the initial version of Hadoop. Either minor or big Hadoop cluster gets advantages from YARN. For the end-users, the difference is barely visible. You won’t find any explanation not to move from MRv1 to YARN. Nowadays YARN is effectively applied in development by lots of companies like Yahoo, Xing, eBay, Spotify etc.

Machine Data Harvesting

Machine data is available in various forms. Temperature sensors, health trackers, and also air-conditioning systems deliver large volumes of information. But it is hard to know which information is important. In this article, you will know some ways of supporting the usage of big data sets using Hadoop.

Keeping and providing the data

You should consider the ways and terms of information storage prior to examine fundamental methods of data keeping.
One of the main problems with Hadoop is that it delivers append-only information for big volumes of data. However, this technique appears perfect for keeping machine data. This case turns into an issue because the amounts of information contribute needless load into an environment just when turning to live and useful.
You will need mindful management if you want to use Hadoop for storing big data. You will need a strategy to use it. If you want to use the information for live alerts, you wouldn’t like to sift for years to choose the latest info. You should select consciously what to store and for how long.
To know the volume of information you need to store you’ll have to calculate the size of your records and periods when data renews. Basing on these calculations you can get knowledge of amounts of created data. As an example, a three-field data unit is small, however, saved every 15 seconds, creates about 45 KB of data. If you multiply this to 2000 computers you’ll get 92 MB per day.
You should ask yourself: how long do you want your data to be available? By-the-minute information is not really used during a week, because the importance of this info is weak when the problem is solved.
You should also define the baseline knowing the context. The baseline is a data point or matrix shows standard operation. You can much more easily recognize aberrant trends or spikes with available baseline. Baselines are comparison values you keep to identify when a new amount is beside of standard level. Baselines have 3 types:

  • Pre-existing baselines – that is already known baseline if you monitor a big data.
  • Controlled baseline – for the units and systems which need control. Determine baseline with the comparison of the controlled and monitored value
  • Historical baselines – this type is applied to systems where the baseline is calculated by existing value.

Historical baselines certainly modified with time and, apart from exceptional conditions, never specified to a hard figure. It should be changeable based on the information you get from the sensor and the value. Baselines should be computed based on past values. As a result, you have to figure out the amount you want to compare and how far back to go.
You may keep and generate graphical representations of information, however just like with basic storage, you are improbable to return a certain moment of time. But you should keep the minimum, maximum and the standard every 15 minutes to create the graph.

Storing the data in Hadoop

Usually, Hadoop is not good enough for live database for big data. However it is a reasonable solution for appending the information into the system, a near-line SQL DB for data storage is a much preferable option. A reasonable way to load information into the database is using a permanent write into the Hadoop Distributed File System (HDFS) by adding to current. Hadoop is able to work as a concentrator.

One technique is to record every diverse information into a particular file for a time and copy this data into HDFS for handling. Also, you may write straight into a file on HDFS which is available from Hive.

Within Hadoop, various small files significantly less effective and practical compared to a small quantity of bigger files. Largest files are spread across the cluster with better effectiveness. For this reason, the information is better to spread across several nodes of the cluster.

Assembling the information from various data points to the numerous bigger files is more effective.
You have to be sure that the data is extensively transferred within the system. With a 30-node cluster, you’d like data to be split over the cluster for better efficiency. This allocation leads to the most effective transactions and replies time. It is crucial if you want to utilize the info for monitoring and alerts.
Those files may be attached via one concentrator that writes the data into these bigger files by gathering from various hosts. Separating the information in this way means you may begin to divide your data systematically in accordance with the host.

Generating Baselines

First of all, if you don’t have hard of controlled baselines, you should generate its statistics to define which is normal. This data will probably modify with time, so you desire to be capable to know what the baseline is over this time by examining available information. You could analyze the data with Hive by applying an appropriate query to create minimum, maximum and average research.

To save reexamining every time write the data into a new table to compare the definition of the issue in the incoming streams. For constant tests calculate the items you evaluate the latest and the current. Research the whole table and compute a proper value across the entire data. You also could calculate additional values like standard deviation or precision.

The important step for generating baselines is archiving the old data. First of all, you need to compress the data, then mark them appropriately. This method needs creating a modified form of the baseline query to summarizes the data by applying a set of maximum and minimum basis.
Ensure that you keep your information which could be summarized to create the data and values beyond.


When you utilize and handle raw computer data, obtaining and keeping the information into Hadoop is actually the slightest of your troubles. Instead, you have to define what data shows and the way you would like to review and report the data. When you have the raw data and is able to run queries on it within Hive you should compute your baselines. After that operate a query which primary signifies the baseline and then issues against it to locate the data beyond the baseline limits. This article contains some of the methods for handling, defining, and then eventually determining those live exceptions to find errors which should be reported and alerted and then shown to a control application.

Building a Behavioral Profile Model


The majority of us go shopping. We purchase all kinds of things, starting from simple essentials such as meals to various entertainment venues, for example, music. While we are shopping, we’re not simply discovering stuff for using in our everyday life, also we reveal our involvement in different social institutions. Our behavior and choices on the internet create our behavioral profiles.
When we purchase an item it has some features which can differ or make the same from each other stuff. For instance, the value of product, dimensions, or kind are instances of various characteristics. Additionally those numerical or itemized arranged characteristics, also there are text characteristics which are not itemized. As an example, the text of item information or consumer testimonials is also a type of various characteristics.
Analysis of textual content along with other natural language processing procedures could be really useful for extraction of interpretation from those unstructured textual content, which generally is beneficial in duties such as behavioral profiling.
This post presents an example of the way to build a behavioral profile model with text classification. It tells how to use SciKit, effective Python-based machine learning program for creating models and analysis for implementing this model to simulated consumers and their product or service buying history. In this particular scenario, you’ll build a model that assigns to customers one of the listed music-listener profiles, such as raver, goth or metal. The task is founded on the particular products every customer buys along with the interacting textual product info.

The Scenario

Take a look at the listed scenario. You possess info allocation which contains various consumer profiles. Every profile consists of a selection of brief, natural language-based information for any product which customer bought. Listed is an example of product info for a boot.

Description: Rivet Head offers the latest fashion for the industrial, goth, and darkwave subculture, and this men’s buckle boot is no exception. Features synthetic, man-made leather upper, lace front with cross-buckle detail down the shaft, treaded sole and combat-inspired toe, and inside zipper for easy on and off. Rubber outsole. Shaft measures 13.5 inches and with about a 16-inch circumference at the leg opening. (Measurements are taken from a size 9.5.) Style: Men’s Buckle Boot.

The objective is to classify every one existing and upcoming customer into one of the behavioral profiles, according to product info. Here is demonstrated the example: the curator is using product samples for building a behavioral profile, a behavioral model, a customer profile and last of all a customer behavioral profile.

The primary step would be to consider the function of a curator and grant the system a concept of every behavioral profile. One method for this is manual seeding the system with samples of every item. These samples will assist in the definition of a behavioral profile. In terms of this argumentation we’ll categorize the users into one of the musical behavioral profiles:

  • Punk
  • Goth
  • Hip hop
  • Metal
  • Rave

Provide types of products defined as appearing punk like information of punk albums and music groups, for instance, “Never Mind the Bollocks” by the Sex Pistols. Other items should consist of things regarding hairstyle or clothes.

Software Setup

Every needed information and source code could be obtained from the bpro project on JazzHub. When you’ll get the data to make sure you have installed Python, Skikit Learn and all the dependencies.
Once you unpack tar you will see two YAML files which contain profile information. The product descriptions are artificially created by using a body of docs. Periodicity of word occasions in product descriptions has recognized the process of creation.
Two data files are provided for analysis:

  • customers.yaml — Contains a list of consumers, for every customer included a list of products descriptions and also correct behavioral profile. The correct behavioral profile is that which you know it is truly right. For instance when you are reviewing data of user goth to verify that these buys show that the customer is definitely a goth user.
  • behavioral_profiles.yaml— Contains a list of the profiles (punk, goth, etc.), as well as example list of products descriptions which explain that profile.

Building a behavioral profile model

You should begin with creating a term-count-based depiction of the body by using SciKit’s CountVectorizer. The body object is a basic listing of strings containing product descriptions.
The next step is to tokenize product descriptions into personal words and create a phrase dictionary. Every phrase located by the analyzer throughout the procedure of setting is assigned a unique integer catalog that tells a column in the output matrix.

You can get an output of some items to check which was tokenized. Just use command print vectorizer.get_feature_names()[200:210].
Keep in mind that existing vectorizer is without “stemmed” words. Stemming is a procedure of receiving a basic source or origin form for inflected or derivative words. As an illustration, big is a basis stem for the word bigger. SciKit is unable to manage more engaged tokenization, like stemming, lemmatizing and compound splitting, however, you are able to use specialized tokenizers, for example from Natural Language Toolkit library.

The procedures of tokenization like stemming make it easier to decrease the number of needed training samples, due to the fact numerous forms of a word don’t demand statistical depiction. You may utilize additional tips to decrease training demands, like applying a dictionary of types. As an example: in case you have a selection of goth musical group names, you are able to build basic word token, like goth_band, and include it to the description before creating functions. Considering this even if you’ll meet a band initially in a description, the model manages in the process where it manages alternative groups which types it knows.

In the computer learning, monitored specification troubles like this are posed by initial definition a set of features along with a matching target. Then the selected algorithm tries to locate the model with the best suitability to information, it reduces faults against an identified set of data. Consequently, the next step is to create the characteristic and target label vectors. It is usually wise to randomize the monitoring just in case verification procedure doesn’t do that.

At this point, you are in a position to select classifier and educate your behavioral profile model. Before this, it will be wise to examine the model to make sure it works.
Once you’ve built and tested the model, you will be able to test on many user profiles. You may use the MapReduce framework and transfer trained profiles to work nodes. Every node then receives a set of client profiles with their buying history and applies the model. Next, when the model is applied, your clients are placed on a behavioral profile. You are able to use profile assignments for many purposes. For instance, you can use it for targeting promotions or use the recommendation system for your customers.

Watson Sensory Input Overview

Watson’s pros and cons

Plenty of features that allowed Watson successfully participate in its Jeopardy! performance also help it become extremely suitable for typical jobs that require massive segments of natural language information. Lots of aspects make understanding and discourse concerning natural language problematic. Due to Watson relies on plenty of these points, it gives completely new process to the style computer systems may add benefit to our life. This article explains a method for improving Watson with the ability to automatically detect relevant non-textual information. You may consider these upgrades as providing to Watson “eyes and ears”.

Watson is notably effective in:

  • Proper performing on unstructured content, especially text – However multiple systems allow for the computers to work with natural language data, majority of systems finish with what volumes to little more than the capability to index different phrases. Watson can take synonyms, puns, sarcasm and much more forms of speech. Watson is able to absorb and efficiently work on content starting from technical documentation to blogs and wiki articles.
  • Effective operating with big amounts of reference material – one way computers have successfully resolved difficulties is to use their performance for assistance on dealing with big volumes of data. Exploring a database with millions of records will happen in a flash. For medical doctors it is physically extremely difficult to read and keep in mind all the relevant data being generated daily. The system should have ways to understand which data belongs to each other among billions of records. Watson provides such technology which is helpful for this aims.
  • Learning potential – world is changing. To keep relevancy of developing troubles and increasing information bases, solution should be dynamically fit and study. Watson’s skills to learn and modify via basic user communication retains technology relevant and constantly enhancing.
  • Human interaction – During the history of computer machines users have had to accommodate to interact with the system on its conditions. Though that solution is good for individuals that are ready and want to learn different idiosyncrasies for every new solution. With the development of Watson human-computer interaction is shifting to the level where the system is able to effectively and, in common, interact chatting with human users on their conditions. This human way of conversing is now a normal practice. The ability to automatically structure and require follow-up questions in natural language is an effective technique for user interaction.

Even after its Jeopardy! success Watson received lots of improvements. The size and power of the footprint have been significantly decreased together its functionality are reguraly increasing. But while Watson is optimized to work with natural language, content, context, and interaction, it is not able to deal with sensory input. Watson has no sensory interface to function as eyes and ears. It will just reply on a context which has been defined in textual form.

The “meaningful ask”

To communicate with Watson sensory information should be translated into an understandable form such as text. For instance, suitable picture can be a medical X-ray image. A human radiologist will understand this image by using a contextual explanation.

The technology to have the computer automatically create this explanation is in truly initial phases. But for lots of other types of sensory handle – such as sound recognition that a sound from a specific breed of dolphin or that heart beat waveform tells about particular form of tachycardia. We can automatically transform obtainable data into a text description.

Dr. Alex Philp of GCS researching represents this translation procedure as transforming sensory information into a meaningful ask. Due to the reason Watson can’t recognize sound, it is not able to listen to it directly and explain to you the meaning of the sound. But in case when sound is processed and converted into descriptive phrase included in query or as a context to question, Watson could reply properly. This process of translation generates the meaningful ask.


While computer systems are consistently getting more clever and smart, people perform an essential role through application developing and deployment. Contrary to lots of back-office or machine-to-machine programs, the majority of Watson-based products are tailored for human interaction.

In the process of creation, professionals work with Watson to determine which sets of information needs including in its data body so to tweak the direction in which information is applied. Watson continue to rely on a long-term training stage where human specialists regularly communicate with it by improving desired reasoning routes, de-emphasizing the unwanted ones, and determining resources which need to be included to body.

When a method is installed for usage, humans interact with it via interface received from application. An effective factor of Watson is its potential to have an open, constant dialog with user. Watson can remember where it stays in dialog and constantly monitor the full set of conversation relevant context. This behaviour allows to prevent the need to continuously re-enter similar data, and it enhances the precision of answers. In the case explained in this article the obtainable sensory data gets to be component of interaction. For example, if a physician interacts with Watson for a certain patient, the sensory system automatically involves into the context, related materials to patient history and present status. Examples of medical telemetry could include specifically sensed information like heart rate, blood pressure, temperature, blood-oxygen saturation, brain wave patterns etc. Additionally the real-time analytics of the stream-processing method could produce artificial telemetry by detecting conceivably faintly discernible patterns or correlations.

Take into account advantages of improving Watson therefore it could get straight sensory input and data from electronic medical systems. This feature will eradicate require for the medical professional to specifically explain lots elements of the patient status so to concentrate on other activities which couldn’t be automated with present technologies. Preferably, a doctor could ask the question: “What is the reason of the slow respiration for the patient in bed 32?” and receive all the needed contextual data. Watson will reply with a selection of potential reasons and levels of accuracy.

In the far future, computers may have advantageous intelligence than people. Someone could debate that from that point, they are getting more humans than we are. However, the best strategy is by using computers for that they are ideal at, like lurking throughout large volumes of data to provide objective results, and to keep opinion regarding those results in hands of people. This method is practical for 2 factors: computers are not enough precise to invariably believe them, and our life experience, mixed of the route our brain is wired, deliver another level of understanding and judgement. Thinking of humans and machines is differ but complementary. Collectively they are an effective mixture. Including sensory input to Watson improves this combination with delivering extra data to shared context.

Noticing and gaining knowledge from results

Contrary to Jeopardy! game where every question and reply were self-contained, majority of solutions created for Watson nowadays are aimed at a ensuing activity, for example advice of medical care or an item to buy. In certain circumstances the inclusion of sensory input to Watson may allow to instantly monitor and study from results of its suggestions.

User Request Web Content Prognostication

Discover ways to examine web server log files to learn ways of users website browsing and forecast next browsed content. This article explains applying extensible Markov model to cluster web pages on a website and predict the place user will move next. The algorithm utilizes InfoSphere® Streams and R for regular issue prognostications based on model.


Webserver log files are used to examine users surfing behaviour. As an illustration, in “Predicting Web Users’ Next Access Based on Log Data”, Rituparna Sen and Mark Hansen have utilized combination of first-order Markov models to examine clusters of pages on a website. They applied these models for prognostication which webpage user supposed to visit next. They suggested implementing this information to pre-fetch a resource before a real request by user. This article will explain how to use IBM InfoSphere Streams, combined with R to run an identical analysis of webserver logs.
This solution is implementing extensible Markov models (EMMs), initially released in 2004 by Margaret Dunham, Yu Meng, and Jie Huang, to mix a stream clustering algorithm with a Markov chain. A Markov chain is a mathematical system that reviews transformations from one state to other, in which the following state is relying only on present and not the sequence of proceedings that came before.
The states of Markov chain are aggregation specified by stream clustering algorithm. The EMM can transform eventually by including new states since they are discovered and also damping or trimming current states with time. Consequently, the model is able to make adjustments eventually. This opportunity is particularly crucial in systems with dynamic usage style that changes over the time. As an example, website will probably display dynamic usage pattern, as well as improvements in structure, in some time.

Advantages of integration

The majority of machine learning models designed for forecasting are performed offline on big amounts of training info. Right after the model are properly trained, prediction could be done right away. This technique is suitable for numerous sorts of issues, however if the patterns for prediction are changing regularly, this method could create models that drop behind the system they are attempting to forecast. Since EMM could be educated dynamically, they are effective for modelling systems like network traffic, auto traffic, or another system in which clustering patterns can transform eventually. Web server traffic is one of those sphere. Server logs deliver an infinite source of streaming information to educate the model when the system is already performing forecasting.

Prognosticating content requests from web server logs

Internet servers are keeping logs of resource queries. Every log entry consists IP address of user, timestamp for request, and the destination for requested data. All this information characterize user and requests to website.


This article shows how to forecast users actions on a website to predict content requests using webserver log files. The modelling and prognosticating are completed by applying EMM. The solution represented here is a testament to concept. Upcoming work is essential for developing a genuine solution. Next actions involve enhancing overall performance by clustering sets of webpages, incremental studying, and using InfoSphere Streams to carry several cases of R.

15 Practices of SEO Copywriting for 2014

I’ll bring disappointment to businessmen and marketeers: SEO Copywriting practices won’t give you guaranteed SERPs. Easy and quick way to increase your sales and conversion doesn’t exist. Even if you’ll find tips and genuine tricks which are placed periodically you won’t get any idea what exactly will make your ranks increase in near future.

However, market best methods remain really valuable to use. These are trustworthy copywriting and SEO strategies – efficient techniques that in the long run have shown efficient in ranking, traffic raise, sales, click-through rates. They might furthermore strengthen trustworthiness and reputation.

We’ve posted list of 15 best techniques for 2014 year. All are following SEO and content guidelines. Use them to increase possibility of high ranking.

1. Write Initially

2014 Search engine optimization copywriting is focused on quality – nicely-written optimized material that attracts people. It stands to reason to write initially and then fix for search engines. Simply sort keywords after writing and this method will save you considerable time.

2. Attract

Attracting your audience as a article writer implies interacting with them. What will you do to engage your readers? You should begin with catchy title. When it is done you should keep your audience attracted. You can do this with creating connection on emotional level.

3. Apprise

Information is a gold. Once you’re promoting something people don’t want you to tell them it’s best product to increase sales. They want to know what benefits they will get from your service or product.

4. Relevance

Relevance belongs to aspects which influence on quality, so stay tuned. You shouldn’t bother if an article is irrelevant to your company. If your website, for example, provides domain name registration filling your content with information about internet marketing will only bring you to loss of audience.

5. More Than 300 Words

Though search engines have never mentioned about definite post length webmasters made conclusion that longest articles with 300 or more words length usually engage better. Longer articles are more attractive, therefore it is wise.

6. Remember Precepts

Definitely this tip is one of the best. You may seeking a way to influence on search engines, but keep in mind what copywriting is all about: marketing and selling. Your aim is to get conversion. And you’ll get it if you’ll remember commandments.

 7. Create Skimmable Articles

To reach best benefit your article must be quickly read through. To explain, you should involve headings, subheads, bullet points and enough free space while writing content optimized for search engines. Outline main points with bold and italics.

8. Study Keyword Research

Keywords remain a nutshell of search, therefore it is important to understand how to find those which are bringing traffic. After Google swapped their old tool with new Planner, multiple SEO writers discovered it became harder to do keyword research. As an alternative you can use Moz Guide and SEJ Guide.

9. Keyword per Page

Proper search engine optimized article considers one topic per page. Also good SEO is targeting one main keyword per page, usually phrase with big number of searches and less competition. Other used must be associated to main or long tail keywords.

10. Optimize Body

Apply your main keyword 2-5 time according to length of article all over body copy. “Keyword density” have stopped being relevant to SEO and copywriting, but “over-optimization” still actual. So you should take care of number of times your keywords appear in article.

11. Start with a Question

Rhetorical questioning is a good method to start your article, it calls curiosity and attract readers. That might be your first sentence. By the way Google’s Hummingbird appears to like keyword-rich questions.

12. Hyperlink Keywords

While linking to your article from other pages or linking out from your content it can be useful to create link with text keyword. However, while performing this it is crucial not to use same phrase constantly, particularly within one page.

13. Read Your Article Aloud

Reading you article aloud could lead you to another point of view. Lots of good content writers do the same. If you think this is strange or don’t like your voice try text-to-speech software. Or ask your relatives to do this.

14. Call to Action

No matter blog or copywriting most likely you’d want your visitors to perform actions on your website, so ask them to do this. Call-to-action like “click here to register now” is working great even nowadays, also properly planned CTA that include keywords can raise conversion rates.

15. Go Social

The planet is social. If you’re not benefiting from social media networks you’re loosing a lot. Professional copywriting is insufficient. Motivate your audience to share your articles via social website with social buttons on your website.

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