text:
Predicting Breakdowns in Cloud Services (with SPIKE)
05/15/2019 ∙ by Jianfeng Chen, et al. ∙ NC State University ∙ IEEE ∙ 0 ∙ share
Maintaining web-services is a mission-critical task. Any downtime of web-based services means loss of revenue. Worse, such downtimes can damage the reputation of an organization as a reliable service provider (and in the current competitive web services market, such a loss of reputation causes extensive loss of future revenue). To address this issue, we developed SPIKE, a data mining tool which can predict upcoming service breakdowns, half an hour into the future. Such predictions let an organization alert and assemble the tiger team to address the problem (e.g. by reconfiguring cloud hardware in order to reduce the likelihood of that breakdown). SPIKE utilizes (a) regression tree learning (with CART); (b) synthetic minority over-sampling (to handle how rare spikes are in our data); (c) hyperparameter optimization (to learn best settings for our local data) and (d) a technique we called "topology sampling" where training vectors are built from extensive details of an individual node plus summary details on all their neighbors. In the experiments reported here, SPIKE predicted service spikes 30 minutes into future with recalls and precision of 75 than other widely-used learning methods (neural nets, random forests, logistic regression).
READ FULL TEXT VIEW PDF
Jianfeng Chen
Joymallya Chakraborty
Philip Clark
Kevin Haverlock
Snehit Cherian
Tim Menzies
Data Mining definition services in Cloud Computing with Linked Data
In recent years Cloud Computing service providers have been adding Data ...
06/18/2018 ∙ by Manuel Parra-Royon, et al. ∙ 0 ∙ share
Service Wrapper: a system for converting web data into web services
Web services are widely used in many areas via callable APIs, however, d...
10/17/2019 ∙ by Naibo Wang, et al. ∙ 0 ∙ share
Predicting SLA Violations in Real Time using Online Machine Learning
Detecting faults and SLA violations in a timely manner is critical for t...
09/04/2015 ∙ by Jawwad Ahmed, et al. ∙ 0 ∙ share
DeepTriage: Automated Transfer Assistance for Incidents in Cloud Services
As cloud services are growing and generating high revenues, the cost of ...
11/25/2020 ∙ by Phuong Pham, et al. ∙ 0 ∙ share
Fast Context-Annotated Classification of Different Types of Web Service Descriptions
In the recent rapid growth of web services, IoT, and cloud computing, ma...
05/31/2018 ∙ by Serguei A. Mokhov, et al. ∙ 0 ∙ share
JSRT: James-Stein Regression Tree
Regression tree (RT) has been widely used in machine learning and data m...
10/18/2020 ∙ by Xingchun Xiang, et al. ∙ 0 ∙ share
This week in AI
Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.
Managing cloud services is an important problem. Mismanaging such services results in server downtime and an associated loss of revenue, particularly for organizations with penalty clauses in their service contracts. Even down times of just a few hours each month can be detrimental to the professional reputation of an cloud-service provider. This is a concern since organizations with a poor reputation for reliability have a harder time attracting and retaining clients.
This paper explores one kind of breakdown– specifically, service spikes that can freeze up a cloud server. In Figure 1, such spikes are clearly visible (see the higher values). At first glance, such spikes seem relatively infrequent and very small (the y-axis of Figure 1 is in milliseconds). However, it should be remembered that a modern web page shows results from dozens of microservices, each of which uses dozens of other queries to the underlying databases. Spikes like those shown in Figure 1 can lead to frustratingly slow systems performance (e.g. very slow displays of new web pages). Hence, such spikes are critical business events that can damage an organization's reputation as a reliable cloud service provider.
Predicting service spikes is hard since they can occur rarely and may occur as sudden extreme outliers. For example, in Figure
1, the large 11am and 8:30pm spikes might be anticipated by the steady build-up in the proceeding hour. However, between 6pm and 3am, we count six other spikes that are not proceeded by any apparent build-up.
Figure 1. Web service response time; one cloud compute node, 11/26/2018.
Another factor complicating spike prediction is the rapidly changing nature of cloud environments. For example, consider LexisNexis (the organization that funded this research). In the year or two, LexisNexis has retired its locally managed CPU farms in favor of CPU farms managed by multiple major cloud vendors. But cloud instance managements tools are evolving rapidly. Hence, like every other user of cloud-based services, LexisNexis anticipates that, in the near future, it will change its web architecture yet again.
Due to this relentless pace of change, much of the prior operational history is not relevant to current or future operations. Hence, methods that work in prior studies may not work for future studies. This means that data science teams working on cloud clusters are forced to constantly update their model.
Further, at each update, new learning technologies may be necessary. For example, the authors of this paper started with established methods for predicting service spikes (Syu et al., 2018) and when those did not work, we moved on to other methods (described later in this paper). In all, we spent three months building and discarding a dozen different predictors111During this trial-and-error period, we often took solace from Thomas Edison's famous quote "I have not failed. I've just found 10,000 ways that won't work." before finding one that could handle the specifics of the LexisNexis environment.
Initially, we imagined that we would be building a recommender system that would suggest the number and type of cloud server instances that should be added or deleted in order to maintain service availability (at minimum cost). In theory, such a recommender system could be learned from the historical logs of prior nominal and off-nominal behavior.
However, once we realized how fast the cloud services were changing, we also realized that much of the historical log was no longer relevant to current practice. So we changed track and asked "what are the major pain points of running the LexisNexis cloud service?". This new question prompted our subject matter experts to recounted various war stories about what happens when a service spike occurs. One issue with those events is gathering together the response team. "It can take five to ten minutes to realize we have a problem", we were told, "after which it can take another few minutes of calling/texting to get everyone we need into a conference call".
From remarks such as this, we realized our goal needed to be "early warning". Accordingly, the goals of this project were set to be:
Build comprehensible and effective predictors for service spikes, 30 minutes into the future.
As to what constitutes a "service spike", using the results of Section 4, we defined that to be values over 470 ms/query. Such a predictor would allow an organization to reduce their response time to forthcoming incidents. Further, in some cases, it would be possible to remove the cause the spike, thus preventing that incident from occurring in the first place.
Note that the above goal includes comprehensible models
. Our experts required some report of the lessons learners that they can read, understand and audit. Hence, we need to use data mining methods that produce human readable models (e.g. not Naive Bayes classifiers, not neural networks, not instance-based learners, not random forests).
1.1. Organization of this Paper
The rest of this paper explores methods for building comprehensible and effective predictors for service spikes of over 470 milliseconds per query, 30 minutes into the future. Our paper is structured as follows. Section 2 first briefly introduces some background of the LexisNexis information retrieval system, features of search engine we used, as well as the motivations of the project. Section 3 goes over all machine learning techniques explored during the project. Section 4 presents the data we collected for the prediction in the project. Section 5 reveals the exploration of all steps, leading to the managerial or technical summary. Finally we conclude the project in section 6 as well as the future work in section 7.
2.1. Business Context
LexisNexis is a corporation providing computer-assisted legal research (CALR) as well as business research and risk management services (Vance, 2010; lnn, 1994) . During the 1970s, LexisNexis pioneered the electronic accessibility of legal and journalistic documents (Miller, 2012) . As of 2006, the company has the world's largest electronic database for legal and public-records related information (Miller, 2012) .
LexisNexis provides regulatory, legal, business information and analytics to the legal community. Legal and research professionals use the Lexis Advance platform to find relevant information more easily and efficiently (lnl, 2019) . It helps them to prepare legal cases and drive better legal outcomes. LexisNexis operates across multiple major cloud vendors . As a large distributed system, LexisNexis is mindful of costs and wants to scale the database and search service when demand is anticipated or predicted to be needed. Hence, this paper.
2.2. System Architecture
The LexisNexis database contains over 100 billion documents and records. Records are added at the rate of nearly two million new items daily from over 50,000 sources. In all, over 20 million legal documents are processed daily from legal jurisdictions throughout the world. In addition, the databases contain over 320 million company profiles with content archives dating back 40 years.
To support all this, the Lexis Advanced product suite is a modern web application consisting of a central monolithic application, supported by hundreds of micro-services. Individual micro-services perform a wide array of functions, including content loading and enrichment, document search and retrieval, user authentication, and notifications. Many of these services are shared by multiple other services, forming an interconnected web of dependencies. When service disruptions occur, they are felt as a cascade of failures in which one service effectively disables multiple other services.
LexisNexis utilizes a number of technologies to constantly monitor the state of the application environment. Application logs, server logs, host metrics, web traffic, infrastructure status, and user activity are all monitored using various automated tools and in-house software. One of them 222In this paper, some terms are anonymized for proprietary business reasons. is primarily used to ingest, parse, and visualize data from log files, while the other tool is used to monitor application performance metrics such as response time and throughput. Both tools allow users and automated scripts to monitor and react to quickly changing conditions in near real time.
2.3. Document Storage and Search
Currently, LexisNexis makes extensive use of some multi-model NoSQL database for document storage and searching. Such document search engine stores and queries data in documents, graph data, or relational data, providing incredible flexibility. It includes a search engine that is especially suitable for full-text searches (most documents in LexisNexis are in full-text).
The database in LexisNexis supports massive horizontal and can support installations with hundreds of nodes, petabytes of data, and billions of documents (which still processing tens of thousands of transactions per second). The system also is a trusted database having all the enterprise features required to handle sensitive enterprise data:
Advanced Security: It offers granular security controls at the document and even element/property level, redaction and anonymization for safe sharing, as well as advanced encryption.
ACID Transactions: It has multi-document ACID transactions that provide data consistency even with large-scale transaction applications.
Cloud Neutral: It has been successful running in the cloud for over a decade and it is compatible with any public cloud provider.
However, such trusted database can lead to operational issues. At its core, it is a twin instance system where data is stored on Node1 and indexed on Node2. This twin architecture increases survivability of the system against insult (if the index node goes down, it can be rebuilt elsewhere). On the other hand, experience has shown that this twin model can complicate the operation of such database system. Nodes cannot be simply added (if more performance is required) or removed (if we want to save operational costs if the CPUs are under-utilized). There is also an additional operational cost of such a system– after a crash (when data has to be rebuilt), some expert human supervision is required to appropriately partition the data across the servers.
In the meanwhile, LexisNexis may soon be running its database servers on some open-sourced container orchestration – at which time, the expertise needed to run a database server will need to be updated, yet again. Because of this relentless pace of change in cloud services. we made the design decision to build SPIKE without using detailed knowledge of the current internal database system. Instead, as discussed in section 4, when we did data collection, we restricted ourselves to measurements we might reasonably expect to see in a wide range of future cloud environments.
3. Data Mining Technology
We adopted four widely used machine learning models as system spike learners, including logistic regression (LG), classification and regression trees (CART)/random forest(RF), artificial neural network (ANN) and long-short term memory network (LSTM). We chose these four learners since a recent survey of predicting service spike
(Syu et al., 2018)
listed LG, CART and ANN as the most common machine learning models to predict the web server response time. They also suggest several other ML approaches applicable to time series modeling, including some more complicated ANNs developed and used in deep learning (e.g., LSTM).
3.1. Logistic Regression
Logistic regression analyzes the relationship between multiple independent variables and a categorical dependent variable, and estimates the probability of occurrence of an event by fitting data to a logistic curve
(Park, 2013) . Two kinds of logistic regression are binary or binomial logistic regression and multinomial logistic regression:
Binary logistic regression is used when the dependent variable is dichotomous and the independent variables are either continuous or categorical.
When the dependent variables are not dichotomous and is comprised of more than two categories, a multinomial logistic regression can be employed.
Unlike linear regression, logistic regression can directly predict probabilities, i.e. the odds of a dependent variable happens. The most essential assumption to the logistic regression is that the independent variables are linearly related to the log odds, that is the logit of the probability defined as
logit(p)=logp1−p(0<p<1).
Logistic regression is used in various fields, including the system maintenance. For example, Hoffert et al. (Hoffert et al., 2009) trained the logistic regression models to predict the response time of a search and rescue (SAR) operations system and therefore simplify the configuration of middleware and adaptive transport protocols.
3.2. Cart
The decision tree, or specifically, the classification and regression trees
CART (Rutkowski et al., 2014) is another common type of supervised learning algorithm. It works for both categorical and continuous input and output variables. CART splits the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter/differentiator in input variables. The leaf nodes of the tree contain an dependent variable which is used to make a prediction.
There are two types of trees:
Classification Tree which serves problems with categorical target variables;
Regression Tree which is applicable to problems with continuous target variables.
In our work, when applying the decision tree, we treated the target (service spikes indicator) as continuous target, instead of binary category. An estimation of service spike values let operation team staffs have the sense how urgent the further actions should take.
In terms of generating comprehensible models, classification and regression trees are our preferred choice. In our experience, if they can be kept under a few dozen nodes, decision/regression trees are fast to read and understand. Using the trees, engineers can figure out the key factors that contribute the targets. That information is constructive in further system refactoring. For example, we can adjust more storage and bandwidth resource if the I/O is the key factor to system performance to some microservice.
3.3. Other Learners
In order to assess the impact of our "comprehensability" requirement on learner performance, as described in this section, we also explored some learning models that can produce somewhat opaque results.
Random Forests construct multiple trees at the training time. The prediction of such a forest comes from the majority view of all its trees. Random forest was first introduced by Tin Kam Ho (Ho, 1995) and has been applied in many ML applications (Alexander et al., 2014; Grömping, 2009; Belgiu and Drăguţ, 2016) . Random forests can produce a very large set of trees which an be hard to read and understand.
Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use (Dormehl, 2019) . The most basic type of neural net is something called a feedforward neural network, in which information travels in only one direction from input to output. A more widely used type of network is the recurrent neural network (RNN), in which data can flow in multiple directions.
Neural network models represent their knowledge in a somewhat arcane distributed manner which is not human comprehensible.
Long-short term memory networks (Hochreiter and Schmidhuber, 1997) , or LSTM for short, is a special kind of RNN, capable of learning long-term dependencies. Figure 2 shows a simple recurrent network. In RNN, the model is interpreted not as cyclic, but rather as a deep network with one layer per time step and shared weights across time steps. This algorithm is called back propagation through time (Werbos, 1990)
. Recurrent Neural Networks suffer from problems with short-term memory. During back propagation, recurrent neural networks suffer from the vanishing gradient problem (gradients are values used to update a neural networks weights). The vanishing gradient problem is when the gradient shrinks as it back propagates through time. If a gradient value becomes extremely small, it does not contribute too much learning. LSTM was created as the solution to this short-term memory problem. The long short-term memory block is a complex unit with various components such as weighted inputs, activation functions, inputs from previous blocks and eventual outputs. The block is called a long short-term memory block because RNN is using a structure founded on short-term memory processes to create longer-term memory.
Figure 2. A Simple Recurrent Neural Network (Lipton et al., 2015)
Learner Parameters Default Best Descriptions
CART min samples split 2 0.61 The minimum number of samples required to split an internal node.
max depth None 5 The maximum depth of the tree.
RF n_estimators 10 20 The number of trees in the forest.
min samples split 2 0.1 The minimum number of samples required to split an internal node.
Table 1. List of hyperparameters tuned.
3.4. Data Pre-Processing with SMOTE
As mentioned above, our training data is very imbalanced. Specifically, from Nov 2018 to Jan 2019, the service spikes happened at only 3.4% of the time. Such imbalanced training data makes it hard for classification models to detect rare events (Sun et al., 2009) .
There are several ways to apply resampling to mitigate for class imbalance (Chawla et al., 2002; Walden et al., 2014; Wallace et al., 2010; Mani and Zhang, 2003) :
Over-sampling to make more of the minority class;
Under-sampling to remove majority class items;
Some hybrid of the first two.
Machine learning researchers (Haixiang et al., 2017) advise that under-sampling can work better than over-sampling if there are hundreds of minority observations in the datasets. When there are only a few dozen minority instances, over-sampling approaches are superior to under-sampling. In the case of large size of training samples, the hybrid methods would be a better choice.
The Synthetic Minority Oversampling Technique(SMOTE) (Chawla et al., 2002) is a hybrid algorithm that performs both over- and under-sampling. SMOTE calculates the k nearest neighbors for each minority class samples. Depending on the amount of oversampling required, one or more of the k-nearest neighbors are picked to create the synthetic samples. This amount is usually denoted by oversampling percentage (e.g., 50% by default). The next step is to randomly creating a synthetic sample along the line connecting two minority samples.
3.5. Parameter Tuning with Differential Evolution
In machine learning, model hyperparameter are values in machine learning models that can require different constraints, weights or learning rates to generate different data patterns, e.g., the number of neighbours in k
-Nearest Neighbours (KNN)
(Keller et al., 1985) . Such hyperparameters are very important because they directly control the behaviors of the training algorithm and also impact the performance of the model being trained. Therefore, choosing appropriate hyperparameters plays a critical role in the performance of machine learning models. Hyperparameter tuning is the process of searching the most optimal hyperparameters for machine learning learners (Biedenkapp et al., 2018; Franceschi et al., 2017) .
Recent studies have shown that hyperparameter optimization can achieve better performance than using "off-the-shelf" configurations in several research areas in software engineering, e.g., software defect prediction (Fu et al., 2016a; Tosun and Bener, 2009; Osman et al., 2017; Fu and Menzies, 2017; Krishna et al., 2017) and software effort estimation (Xia et al., 2018) . To the best of our knowledge, we are first to apply hyperparameter optimization in response time prediction.
Hyperparameter optimization can be implemented in many ways:
Grid search (Bergstra et al., 2011)
loops through all combinations of all parameters. Although Grid search is a simple to implement, it suffers if data have high dimensional space called the "curse of dimensionality". Previous work has shown that grid search might miss important optimization
(Fu et al., 2016b) and is a time-wasting process since only a few of the tuning parameters really matters (Bergstra and Bengio, 2012) .
Random search (Bergstra and Bengio, 2012)
randomly samples the search space and evaluates sets from a specified probability distribution. Such random searches do not use information from prior experiment to select the next set and also it is very difficult to predict the next set of experiments.
Bayesian optimization (Pelikan et al., 1999) works by assuming the unknown function was sampled from a Gaussian Process and maintains a posterior distribution for this function as observation are made. However, it might be best-suited for optimization over continuous domains with small number of dimensions (Frazier, 2018) .
This paper uses Differential evolution (DE) for hyperparameter optimization. DE has proven useful in prior SE tuning studies (Fu et al., 2016a) . Also, our reading of the current literature is that there are many advocates for differential evolution like Vesterstrom et al. (Vesterstrøm and Thomsen, 2004)
showed DE to be competitive with particle swarm optimization and other genetic algorithms.
The premise of DE is that the best way to mutate the existing tunings is to extrapolate between current solutions. DE builds a population P from a small number of randomly selected solutions of size np. Then, each member of the population is compared against a mutant built as follows. Three solutions (a,b,c)∈P are selected at random. For each tuning parameter k, at some probability cr, we replace the old tuning xk with yk as yk=ak+f×(bk−ck) where f is a parameter controlling differential weight. The main loop of DE runs over the population P of size np, replacing old items with new candidates (if new candidate is better). This means that, as the loop progresses, the population is full of increasingly more valuable solutions (which, in turn, helps extrapolation).
For pragmatic reasons we did not tune all parameters of all learners. LSTMs took 30 minutes to test each tuning. Given our DE settings, that would have required 6000 hours of CPU; i.e. 25 weeks. Table 1 shows the parameters that we did tune. During that tuning process, we asked our optimizers to maximize recall and precision. As to the control parameters of DE, using advice from Storn and Fu et al. (Storn and Price, 1997; Fu and Menzies, 2017) , we set {np,f,cr}={20,0.75,0.3}. Also, the number of generations gen was set to 10 to test the effects of a very CPU-light optimizer.
For the set of parameters we did tune, see Table 1.
4. Data
For this analysis, we collected data from the LexisNexis N-document database searching microservice. This N-document database contains 20+ million documents.
Figure 3. Independent features histogram. x-axis shows sorted all monitoring values. y-axis is the corresponding frequency.
Figure 4. Histogram of Web Service Response Time. x-axis is the service response time in milliseconds. y-axis is the frequency in the training set. Note that this plot shows a dependent variable that is different to the THRt independent variable discussed in the text.
Recall from the introduction that the technology used in LexisNexis' cloud systems is changing rapidly. Accordingly, when we did data collection at one-minute intervals, and restricted ourselves to measurements we might reasonably expect to see in a wide range of future cloud environments. Specifically, we say that each node x connects to multiple nodes y1,y2,… (and by "connects" we meant that these node yi reads or writes data from/to node x). That is, our training data has different columns for:
Intra-node data from each node x in the cloud;
Inter-node data that samples the topology of the network; i.e. that includes any information about nodes y that reads/writes from/to node x.
For the intra-node data from node x, for each of the following attributes, we created 5 columns of data showing mean values seen in the past t∈{10,30,60,300,1440} minutes:
All Logged Errors Per Minute (EEt);
Total Physical Memory Used in MB (MPt);
Web Transaction Throughput (THRt).
The application performance index Apdex Score (ASt). The Apdex score is defined as the number of satisfied samples plus half of the tolerating samples plus none of the frustrated samples, divided by all the samples in unit minute. That is:
Apdex = (Satisfactory samples + 0.5×Tolerating samples + 0×Frustrated samples)/Total samples
Here, satisfactory, tolerating and frustrating are defined in the standard way, as per (apd, 2019) .
Percentile EE AS MP THR
0% (min) 0 0.53 67800 2.83
25% 0.12 0.96 105000 15.90
75% 3.88 0.97 111000 102
100% (max) 347 0.99 272000 243
Table 2. Statistics of monitored data after the final steps in the pipeline.
For the inter-node data, for each node y that connects to x, we collected mean values for (EMt, MPt, THRt, ASt)333Some monitoring data may be missing due to system architecture reasons., as well as their service response time, over the last t=60 minutes. For the case study of this paper, we were exploring a system of 12 nodes. In total, our collected data comprises 75 columns (variables):
From node x, there were 20 columns holding data from 5 time stamps of four variables (EEt, MPt, TRt, ASt) for t∈{10,30,60,300,1440} minutes;
There was also 54 additional columns holding data for the 12 nodes y that read/write data from/to x. For each node y, we record the mean value for (EEt, MPt, TRt, ASt) and service response time as seen in the last t=60 minutes. Please note that the response time in y nodes are independent variables;
The dependent column web service response time of x node, i.e. the node serving the N-document search microservice.
The columns of our collected data had the distributions of Table 2. Figure 3 and 4 show the histograms of attributes(independent features) and class (dependent feature) respectively. From these Figure 3 and 4, we have the following observations:
All our variables are highly non-evenly distributed, i.e. all of them have large standard deviation.
In statistics, a long tail of some distributions of numbers is the portion of the distribution having a large number of occurrences far from the "head" or central part of the distribution (Bingham and Spradlin, 2011) . We see this pattern among all attributes.
From Figure 4 we can see that in majority time, the system response time varied from 0-470. By selecting for x>470, we could focus this study on the most outstanding service spikes.
5. Predicting with Spike
LexisNexis is serving customers from hundreds of areas. Their behavior pattern may be changing at all times, therefore lead to various situations of service spikes. For example, the way customers from Wall Street using the LexisNexis service is different to those from Silicon Valley – at least, they are interested in distinct group of documents. As a result, model to predict "financial news" spikes is not applicable to predict "tech scandal" spikes. Therefore it is important to train SPIKE on recent local data that is specific to a particular web service.
That said, deciding what data mining method to apply is a time-consuming and CPU expensive process. Therefore, our work was divided into two stages:
Model selection and tuning where we ruled out many options to select one promising method. For this stage, we used one month of data divided into an 80% train phase and a 20% test phase (where the test data was selected from the last week of that month).
Testing our most promising method. For this stage, we used a different month of data to test the method selected during stage one. We stepped through this data in "windows" of ten minutes. At each step i, we trained the model (found by model selection and tuning) using the next 24 hours of data (i.e. windows i to i+144). This model was then tested using data from the next half hour of data (i.e. windows i+j,j∈{145,146,147}). This means that the stage2 results (reported below) come from 4317 different train/test pairs.
For details on these two stages, see below.
5.1. Stage One: Model Selection and Tuning
In this stage, we sorted our one month data by time then trained on the first 80% and tested on the last 20% 444We did not use a randomized strategy to produce train/test sets since it makes more methodological sense to predict the future from the past.. The models were trained to get the real-time web service response time in this stage.
Not all treatments were applied to all data sets. For example, as mentioned above, the neural nets were too slow to tune. As for the other learners, when optimizing hyperparameters, at the request of our business users, we optimized for maximizing recall. Recall is defined for a two-class classifier so if this stage, to guide the hyperparameter optimization, we defined "spike" as per Figure 4; i.e. greater than 470ms.
Important point: SMOTE or hyperparameter optimization never used information from the test data. If we applied SMOTE or hyperparameter optimization, these algorithms were used only on the training data; i.e. our results are not over-fitted to test data.
a b c d r=aa+c p=aa+b
Learner SMOTE? TUNE? TP FP FN TN Recall(%) Precision(%)
ANN(10×4) 504 1756 27 799 95 22
Regression Tree (CART) 493 868 38 1687 93 36
Random Forest 489 764 42 1791 92 39
Random Forest 428 439 103 2116 81 49
Decision Tree (CART) 294 432 237 2123 55 40
LSTM 53 78 478 2477 10 40
Logistic Regression 42 36 489 2519 8 54
ANN( 5×4) 40 44 491 2511 8 48
Logistic Regression 31 9 500 2546 6 77
LSTM 30 23 501 2532 6 57
ANN(10×4) 31 33 500 2522 6 48
Table 3. Learning on first 80% of data, testing on the more recent 20%. Results sorted by recall (and higher values are better). ANN=Artificial Neural network, LSTM=Long-short Term Memory. In this table higher values for recall, precision.
Figure 5. Model generated from CART ret, lo and sh-synr are node names. Branches leading to spikes (with service response time >470ms/query) are highlighted in red.
In all, we explored the 14 treatments of Table 3. As shown by the rows highlighted in gray, three methods achieved very high recalls of over 90% (Random Forests, CART, ANN). Initially, off-the-shelf CART performed poorly. However, when augmented with SMOTE and tuning, CART achieved very high recalls of over 90% (the associated precisions are not good– which is a problem solved by the sensitivity analysis of the next section).
Applying the comprehensibility criteria, our summary of Table 3 is that SMOTE+ Regression Trees (CART) + hyperparameter optimization performs best. While ANN offered marginally better recalls, it is hard to read those models. CART, on the other hand, produced the simple regression tree of Figure 5.
Since this tree is easy to comprehend, it is easy to extract important business knowledge about LexisNexis cloud machines. For example:
Among all 12 nodes studied, only three were found to be important by this tree: ret, lo and sh-synr. Prior to this study, the importance of these nodes to healthy operations at LexisNexis has not been realized.
To avoid spikes, engineers are advised to take action that avoids the the red branches of Figure 5.
To understand the precision and recalls, consider the threshold
370ms that triggers an alert: Among all actual future spikes(response time >470ms), 49.2% of them have predicted value >370ms (therefore trigger the alarm), which shows as point A; Among all alarms triggered (i.e. predicted future response time >370ms), 93.8% of them did exceed the threshold (having response time >470ms), indicated as point B.
Figure 6. Precision and recalls under different sensitivity to predict services spikes half an hour ahead. x-axis is the threshold value used to trigger an alarm.
5.2. Stage Two: Testing our Most Promising Method
Stage one found that the tree learner (CART) with specific hyperparameters (as shown in Table 1) was comparatively better than several other methods.
This second stage tests if that model is useful on real-world data. To predict the web service response time
at the moment (t+.5)hr,
SPIKE trained the data from [t−Lhrs, t), where L is the training window size. We explored L∈{0.25,0.5,1,5,24,48} and found that best results come from training from the last L=24hrs of prior data.
We found that the precisions and recalls achieved by CART+ SMOTE+ optimization were sensitive to our threshold for recognizing a spike. Hence, we show results where we declared a "spike" being defined as more than from 370ms to 490ms.
Figure 6 shows results seen while adjusting the threshold for predicting a spike. As shown in this figure, at a threshold of 404ms, can achieve precisions and recalls of 75% or higher.
6. Conclusions and Lessons Learned
Using regression trees (e.g CART) and synthetic over-sampling of rare events (e.g. via SMOTE) and hyperparameter optimization (e.g. using DE), optimizers it is possible to build effective and comprehensive predictors for service spikes. SPIKE can predict with reasonable recall and precisions if a spike will occur in the next 30 minutes. Further, SPIKE can report its reasoning via a very small and easily comprehensible tree, from which we can learn important (and previously unknown) aspects about this domain.
The factors that lead to service spikes are highly context specific. Much time was spent in this work trying solutions from other sites (Syu et al., 2018) , which proved less-than-satisfactory for this problem (neural nets, logistic regression). There exist tools for exploring a large number of options within data miners. If we had our time again, we would first commission a hyperparameter optimization (a tool to explore all those options) before secondly use those optimizers to faster explore different data mining options.
But even with hyperparameter optimizers, building predictors is a complex tasks (certainly, much more than running one query, then glancing at a simple data dashboard). There is considerable creativity required in how to design the inputs to a learning problem and how to find tune the resulting models. For example, in this work, we made poor progress until we somewhat serendipitously decided to:
Add inter-node information to the training set (see Section 4);
Conduct a sensitivity analysis (see previous section).
More generally, a modern cloud environment can generate petabytes of operational logs, every day. For example, LexisNexis constantly monitors the state of its cloud services, collecting data from many microservices at one-minute intervals. A data science team exploring the problem of service spikes needs considerable business knowledge to "slice and dice" the data. In all, the results of this paper took three months to generate:
1 month of a LexisNexis data engineer generating our training data by writing complex joins across large datasets.
1 month of inductive engineering, applying different data mining methods to the data. As mentioned above, this proved to be a tedious task that required developed and discarding a dozen very bad predictors before finding one that achieved useful results
1 month of a senior LexisNexis engineer serving as a liaison between our team and the rest of LexisNexis. The importance of the liaison cannot be overstated. That person (a) maintained senior management's awareness and enthusiasm for this project; (b) organized access to numerous subject matter experts.
When staffing similar efforts in the future, we recommend a similar "three-sided" team comprising inductive engineers, data engineers, and business knowledge experts.
7. Future Work
We believe SPIKE is a general method for managing rare, but critical, CPU issues in complex cloud environments:
For each node, SPIKE trains models using (a) intra-node details about the recent history of that node as well as (b) some inter-node knowledge about connected nodes.
For rare events, it is important to use class rebalancing tools (like SMOTE).
Also, since the factors that lead to service spikes are highly context specific, it is useful to employ hyperparameter optimization (like DE).
In theory, these three principles should apply to other services at LexisNexis and other organizations. In future work we aim to test that conjecture using more data.
Also, after prediction comes diagnosis and repair. If we build trees like Figure 5 from more data (covering more months and more LexisNexis services) then we would be able to uncover critical thresholds for critical nodes that most effect LexisNexis services. Using that knowledge, plus more subject matter expertise, we should then be able to propose spike reduction policies.
This work was partially funded by (a) a gift from LexisNexis managed by Phillpe Poignant; and (b) an NSF CCF grant #1703487.
lnn (1994) 1994. Company News; A Name Change is Planned for Mead Data Central. The New York Times. (1994). https://www.nytimes.com/1994/12/02/business/company-news-a-name-change-is-planned-for-mead-data-central.html?src=pm
lnl (2019) 2019. LEGAL. www.relx.com (2019). https://www.relx.com/our-business/market-segments/legal
apd (2019) 2019 (accessed Apr 1, 2019). Apdex Alliance. http://www.apdex.org/.
Alexander et al. (2014) Daniel C Alexander, Darko Zikic, Jiaying Zhang, Hui Zhang, and Antonio Criminisi. 2014. Image quality transfer via random forest regression: applications in diffusion MRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 225–232.
Belgiu and Drăguţ (2016) Mariana Belgiu and Lucian Drăguţ. 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114 (2016), 24–31.
Bergstra and Bengio (2012) James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research 13, Feb (2012), 281–305.
Bergstra et al. (2011) James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011. Algorithms for hyper-parameter optimization. In Advances in neural information processing systems. 2546–2554.
Biedenkapp et al. (2018) Andre Biedenkapp, Katharina Eggensperger, Thomas Elsken, Stefan Falkner, Matthias Feurer, Matilde Gargiani, Frank Hutter, Aaron Klein, Marius Lindauer, Ilya Loshchilov, et al. 2018. Hyperparameter Optimization. Artificial Intelligence 1 (2018), 35.
Bingham and Spradlin (2011) Alpheus Bingham and Dwayne Spradlin. 2011. The Long Tail of Expertise. Pearson Education ISBN 9780132823135 (2011). https://books.google.com/books?id=9qLfsonmwhAC&pg=PT5#v=onepage&q&f=false
Chawla et al. (2002) Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16 (2002), 321–357.
Dormehl (2019) Luke Dormehl. 2019. What is an artificial neural network? Hereś everything you need to know. EMERGING TECH (2019). https://www.digitaltrends.com/cool-tech/what-is-an-artificial-neural-network/
Franceschi et al. (2017) Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil. 2017. Forward and reverse gradient-based hyperparameter optimization. arXiv preprint arXiv:1703.01785 (2017).
Frazier (2018) Peter I Frazier. 2018. A tutorial on bayesian optimization. arXiv preprint arXiv:1807.02811 (2018).
Fu and Menzies (2017) Wei Fu and Tim Menzies. 2017. Easy over hard: A case study on deep learning. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. ACM, 49–60.
Fu et al. (2016a) Wei Fu, Tim Menzies, and Xipeng Shen. 2016a. Tuning for software analytics: Is it really necessary? Information and Software Technology 76 (2016), 135–146.
Fu et al. (2016b) Wei Fu, Vivek Nair, and Tim Menzies. 2016b. Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors? arXiv preprint arXiv:1609.02613 (2016).
Grömping (2009) Ulrike Grömping. 2009. Variable importance assessment in regression: linear regression versus random forest. The American Statistician 63, 4 (2009), 308–319.
Haixiang et al. (2017) Guo Haixiang, Li Yijing, Jennifer Shang, Gu Mingyun, Huang Yuanyue, and Gong Bing. 2017. Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications 73 (2017), 220–239.
Ho (1995) Tin Kam Ho. 1995. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, Vol. 1. IEEE, 278–282.
Hochreiter and Schmidhuber (1997) Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.
Hoffert et al. (2009) Joe Hoffert, Daniel Mack, and Douglas Schmidt. 2009. Using machine learning to maintain pub/sub system qos in dynamic environments. In Proceedings of the 8th international workshop on adaptive and reflective middleware. ACM, 4.
Keller et al. (1985) James M Keller, Michael R Gray, and James A Givens. 1985. A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics 4 (1985), 580–585.
Krishna et al. (2017) Rahul Krishna, Tim Menzies, and Lucas Layman. 2017. Less is more: Minimizing code reorganization using XTREE. Information and Software Technology 88 (2017), 53–66.
Lipton et al. (2015) Zachary C. Lipton, John Berkowitz, and Charles Elkan. 2015. A Critical Review of Recurrent Neural Networks for Sequence Learning. (2015). arXiv:cs.LG/1506.00019
Mani and Zhang (2003) Inderjeet Mani and I Zhang. 2003. kNN approach to unbalanced data distributions: a case study involving information extraction. In Proceedings of workshop on learning from imbalanced datasets, Vol. 126.
Miller (2012) Stephen Miller. 2012. For Future Reference, a Pioneer in Online Reading. The Wall Street Journal (2012). https://www.wsj.com/articles/SB10001424052970203721704577157211501855648?KEYWORDS=lexisnexis
Osman et al. (2017) Haidar Osman, Mohammad Ghafari, and Oscar Nierstrasz. 2017. Hyperparameter optimization to improve bug prediction accuracy. In Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE), IEEE Workshop on. IEEE, 33–38.
Park (2013) Hyeoun-Ae Park. 2013. An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain. Korean Society of Nursing Science 43, 2 (2013), 1–5. http://synapse.koreamed.org/DOIx.php?id=10.4040%2Fjkan.2013.43.2.154
Pelikan et al. (1999) Martin Pelikan, David E Goldberg, and Erick Cantú-Paz. 1999. BOA: The Bayesian optimization algorithm. In
Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1
. Morgan Kaufmann Publishers Inc., 525–532.
Rutkowski et al. (2014) Leszek Rutkowski, Maciej Jaworski, Lena Pietruczuk, and Piotr Duda. 2014. The CART decision tree for mining data streams. Information Sciences 266 (2014), 1–15.
Storn and Price (1997) Rainer Storn and Kenneth Price. 1997.
Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces.
Journal of global optimization 11, 4 (1997), 341–359.
Sun et al. (2009) Yanmin Sun, Andrew KC Wong, and Mohamed S Kamel. 2009. Classification of imbalanced data: A review.
International Journal of Pattern Recognition and Artificial Intelligence
23, 04 (2009), 687–719.
Syu et al. (2018) Yang Syu, Chien-Min Wang, and Yong-Yi Fanjiang. 2018. A Survey of Time-Aware Dynamic QoS Forecasting Research, Its Future Challenges and Research Directions. In International Conference on Services Computing. Springer, 36–50.
Tosun and Bener (2009) Ayse Tosun and Ayse Bener. 2009. Reducing false alarms in software defect prediction by decision threshold optimization. In Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement. IEEE Computer Society, 477–480.
Vance (2010) Ashlee Vance. 2010. Legal Sites Plan Revamps as Rivals Undercut Price. The New York Times. (2010). https://www.nytimes.com/2010/01/25/technology/25westlaw.html?_r=1&ref=reedelsevier
Vesterstrøm and Thomsen (2004) Jakob Vesterstrøm and Rene Thomsen. 2004.
A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In
Evolutionary Computation, 2004. CEC2004. Congress on, Vol. 2. IEEE, 1980–1987.
Walden et al. (2014) James Walden, Jeff Stuckman, and Riccardo Scandariato. 2014. Predicting vulnerable components: Software metrics vs text mining. In Software Reliability Engineering (ISSRE), 2014 IEEE 25th International Symposium on. IEEE, 23–33.
Wallace et al. (2010) Byron C Wallace, Thomas A Trikalinos, Joseph Lau, Carla Brodley, and Christopher H Schmid. 2010. Semi-automated screening of biomedical citations for systematic reviews. BMC bioinformatics 11, 1 (2010), 55.
Werbos (1990) P. J. Werbos. 1990. Backpropagation through time: what it does and how to do it. Proc. IEEE 78, 10 (Oct 1990), 1550–1560. https://doi.org/10.1109/5.58337
Xia et al. (2018) Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, and Tim Menzies. 2018. Hyperparameter Optimization for Effort Estimation. arXiv preprint arXiv:1805.00336 (2018).


text:
North Metro Denver SBDC Wins Regional Excellence, Innovation Award
By John Feeley April 06, 2017
The North Metro Denver Small Business Development Center (SBDC), housed and sponsored by Front Range Community College, is the SBDC Excellence and Innovation Award winner for a six-state region.
The award recognizes the center's efforts to assist small-business owners and the center's innovative approach to serving small manufacturers throughout Colorado.
Glenn Plagens
Glenn Plagens, who has been senior director of the North Metro Denver SBDC since 2011, has grown consulting over 500 percent. Last year, the center, which has satellite offices in Brighton, Broomfield, and Commerce City, provided over 2,600 hours of consulting for over 560 clients, $9.2 million of capital formation, 25 business starts, and 126 jobs created.
The center has over 30 consultants and trainers working with clients to provide the best possible experience.
In an effort to better serve the state's small manufacturing clients, Plagens created a "Beta Manufacturing" program, working intensively with four small businesses over two and a half years, which led to the creation of 23 jobs, $5.2 million in capital formation, and $4 million increased sales.
The North Metro Denver SBDC is housed at FRCC's Westminster Campus, 3645 W. 112th Ave. Contact the office at (303) 440-1032 or northmetro.sbdc@frontrange.edu.
Plagens has more than 25 years of experience as a small-business owner. In 2014, Plagens was the winner of the State Star Award from America's Small Business Development Center organization. He is certified in Economic Gardening, which is a "grow from within" strategy targeting existing growth companies and offering them critical strategic information customized to their needs. He has a bachelor's degree in business administration with a marketing major from Eastern Michigan University.
An SBDC helps existing and new businesses grow and prosper by providing free and confidential consulting and no-cost or low-cost training programs. The SBDC combines information and resources from federal, state and local governments with those of the education system and private sector to meet the specialized and complex needs of the small business community. Consulting experts work in partnership to provide entrepreneurs with crucial information that can mean the difference between success and failure.
An SBDC is funded in part by Congress through a partnership with the U.S. Small Business Administration (SBA). Region 8 of the SBA encompasses Colorado, Wyoming, Montana, North Dakota, South Dakota, and Utah.
See all Press Releases.
About Front Range Community College
FRCC offers nearly 100 degree and certificate programs from locations in Boulder County, Larimer County, Westminster, and Brighton, and online. FRCC is a member of the Colorado Community College System.
About the Colorado Community College System
The Colorado Community College System (CCCS) is the state's largest system of higher education, serving more than 144,000 students annually at 13 colleges and 39 locations across Colorado. Our open access mission ensures all Coloradans who aspire to enrich their lives have access to quality higher education opportunities. CCCS students save time and money with affordable tuition and fees, concurrent enrollment, and guaranteed transfer to any public university in Colorado. Online courses are available via Colorado Community Colleges Online. CCCS also oversees career and technical programs in our 13 colleges, more than 160 school districts, and six other post-secondary institutions. By partnering with business and industry, CCCS helps employers meet workforce needs and prepares students for direct entry to their chosen career pathway. The vast majority of our students are Colorado residents and, after graduation, 97 percent of our students stay in state, becoming the workforce that powers the Colorado economy and the heart of our neighborhoods, businesses, and communities.
North Metro Denver SBDC: 16 Businesses, 38 New Jobs in First Six Months of 2015
North Metro Denver SBDC Joins in Creating Adams County Microloan Fund
Plagens wins State Star Award from SBDC as FRCC Sweeps State Recognitions
Economics Professor Wins Award for Excellence in Teaching with Technology
Interior Design Program Recognized with Excellence in Education Award
Larimer SBDC Releases 2015 Economic Impact Results
John Feeley Retired in 2018 as director of public relations at Front Range Community College.

redpajama_set_name: RedPajamaCommonCrawl


Stretch film is a highly stretchable plastic film made of polyethylene (PE) based materials and is widely used for pallet wrapping to secure loading for efficient transit handling and storage.
Our stretch film plant is strategically located at Port Klang, Selangor, Malaysia and within the West Port vicinity. This facility ranks as the world's largest stretch film facility in a single location. This strategic and proximate location to the port has given us a competitive advantage with quick turnaround time of raw materials importation and export shipments of our stretch film products to our global customers and markets.
As for our Klang Hock Plant located at Klang, we produce a wide range of blown and cast stretch film. We are also capable of producing coloured films.
With our well-equipped customer service centre housed at our new head office at Shah Alam, Selangor, all operations ranging from order entry to logistic planning are seamlessly linked via our comprehensive SAP system to enable us to cut down the response time to all our valued customers with timely and accurate information of their orders and delivery status at all times.
The robust demand for eco-friendly stretch film products especially Nano 6 film and others thin gauge films ranging from 8 micron to 12 micron thickness has encouraged Scientex to expand its production capacity not only at its Malaysia plant at West Port, Port Klang but also its new overseas plant at Phoenix, Arizona, USA.
The first phase of the USA plant which began operations in January 2018 has a yearly production capacity of 30,000 MT, while phase two will see the capacity double up to 60,000 MT by 2019. The huge demand for stretch film in America shall see our USA plant serve the readily available market demand through such capacity expansion.


The best way to understand Making Great Leaders is to experience it for yourself. Call us today to learn more.
are not law-related services and are separate and distinct from the legal services provided by Ennis Britton Co., LPA. The relationship between the client and EBC is not protected by the attorney–client privilege.


text:
"The function of music is to release us from the tyranny of conscious thought."
Thomas Beecham
Home / Our School / People / Rob Hall
Rob Hall
Rob Hall studied music on the joint course between Manchester University and the Royal Northern College of Music prioritising composition and clarinet, and later, saxophone. As well as a degree and two diplomas, he received numerous composition and performance awards. He then won a scholarship to study at Berklee College of Music USA to pursue jazz studies.
Tours, performances and recordings, including radio and television, have taken him throughout the UK, and abroad, and he is in demand as a session musician. He plays Sopranino, Soprano, Alto & Tenor saxophones, Eb/Bb & Bass Clarinets, EWI (wind synthesiser) and Piano.
Rob has performed and recorded across many genres with leading artists including Kenny Wheeler, Dan Gottlieb, Jim Mullen, Julian Arguelles, BBC Scottish Symphony Orchestra, Scottish Ballet, Paragon Ensemble Scotland, Manchester Camerata, Steven Osborne, Matthew Herbert, Christine Getty, David Patrick Octet, Ingrid Henderson and Simon Thoumire Orchestra.
Festival appearances include Edinburgh International and Jazz festivals, Bath, Cheltenham, Celtic Connections, Prague International, Atina Jazz Italy, BBC Proms, Liverpool Jazz festival, the Highland Festival, and Glastonbury Jazz Stage.
He has released eight CDs of original & improvised music for the British contemporary music label FMR, five of which are in collaboration with Scottish pianist and composer Chick Lyall and all of which have achieved national media broadcasts. His music has also been aired on BBC Radio2, BBC Radio3, JazzFM, BBC Scotland and BBC regional channels.
As composer Rob has written music for a wide variety of settings - including commissions for choirs, chamber orchestra, jazz orchestra, solo and small groups - with numerous compositions for small jazz ensembles and multi-media work with visual artists James Hawkins and John McGeogh.
He is published by Spartan Press and ABRSM Publishing (2017-18 Piano exam syllabus) and his music has been performed in such venues as London's Royal Festival Hall and Barbican, Glasgow Royal Concert Hall and RNCM Manchester.
His commitment to music education over more than 20 years extends to a number of self-initiated projects such as his national company Jazz Course UK and his own venue The Music Workshop (Scotland). He has extensive experience as a peripatetic teacher, workshop leader and CPD presenter to numerous music organisations.
He teaches saxophone at Dollar Academy and in 2018 was appointed to St Mary's Music School Edinburgh teaching saxophone, clarinet, and ensemble work. For 8 years Rob lectured in jazz and acted as specialist examiner at the RSAMD (now Royal Conservatoire Scotland) and for 3 years he was Musician in Residence in Ayr with WEA Scotland.
As an ABRSM examiner in both jazz and classical disciplines, Rob works throughout the UK and internationally, with past tours throughout Asia and to Turkey, Arab States and New Zealand. He has presented courses and seminars for ABRSM both in the UK and abroad.
www.robhall.co.uk
Clarineo


text:
Why the Warriors value Andre Iguodala's…
SportsNBAGolden State Warriors
Why the Warriors value Andre Iguodala's intangibles, downplay his low shooting numbers
By Area News, News Group and Mark Medina |
PUBLISHED: February 25, 2018 at 1:39 pm | UPDATED: May 18, 2018 at 9:42 am
Click here if you're having trouble viewing the gallery on your mobile device.
NEW YORK – Before he even heard the first question, Warriors forward Andre Iguodala declined the interview.
He knew the line of inquiry. Iguodala has averaged the fewest points (5.4), 3-point field-goal percentage (22.9) and playing time (25.5 minutes) in his 14-year NBA career.
"Sometimes I feel like, 'Why interview me?'" he said.
Ultimately, Iguodala relented. He then set out to explain why, at age 34, he does not feel concerned about his play.
"I haven't shot the ball well at all, but I've been in a good groove," Iguodala told Bay Area News Group. "And I've felt better than I have felt in a long time."
The Warriors (46-14) may enter Monday's game against the New York Knicks (24-37) with Iguodala showing poor marks from 3-point range in December (13 percent), January (20 percent). But he has improved those numbers in February (25 percent). Iguodala may have gone scoreless without a shot or free-throw attempt despite playing 29 minutes last week in Portland. But he posted seven points, seven assists and six rebounds against Oklahoma City on Saturday in a game that demonstrated his versatility.
So as Warriors forward Kevin Durant observed, "Andre is a guy that you just have to watch; you can't look at the stats with him." Therefore, the Warriors still have argued Iguodala's various intangibles on defense, playmaking and on-court leadership offset his low statistical contributions. Those qualities partly prompted Warriors coach Steve Kerr to allow Iguodala to organize morning shootaround and lead the team in huddles two weeks ago against Phoenix.
"He sees the game as well as anybody I've ever been around. He knows what's happening before it happens on both ends," Kerr said. "If he makes shots, great. If he doesn't, that's okay. We don't win or lose games based on Andre making shots. We do win or lose games based on having him out there and doing the things I just mentioned."
Under that context, Iguodala does not look at the two additional seasons remaining on his contract as his career expiration date. In other words, Iguodala has been facetious this season when he has said he should retire because of his poor shooting numbers.
"I can play until I'm 40," Iguodala said. "I can get numbers somewhere else, real good numbers. You get in a rhythm and you know where your shot is coming from. The game works in mysterious ways. But everyone takes a little bit less to have more success. We all accept that. That's what makes us good."
What makes the Warriors good: they have four All-Stars that can score pretty well in Durant, Stephen Curry, Draymond Green and Klay Thompson. What makes Iguodala's role difficult: he remains mindful that he does not have many opportunities to shoot himself out of a slump. He said he also resists doing so because the Warriors' offensive balance bodes more importance than his offensive production.
"You need those guys to have that confidence," said Iguodala, referring to the Warriors' star players. "So, you might have to sacrifice in order for those guys to get where we need to be so we can play at the highest level."
The Warriors believe they will reach their highest level partly because of Iguodala, who first became cemented in franchise lore for accepting a bench role and then winning the 2015 Finals MVP for his stellar defense on LeBron James. Therefore, Durant argued, "we have to have Andre in the game because he's so smart with everything."
"Sometimes he may score, make jump shots and knock down shots. But when he doesn't, he's still in the game," Durant said. "That's the beauty of Andre."
How has Iguodala exerted his beauty? He has offered various qualities that Warriors reserve guard Shaun Livingston likened to a "Swiss Army knife."
Iguodala often directs teammates on the floor on how to run the offense. His poor 3-point shooting aside, Iguodala has improved his accuracy through eight games in February (58.6 percent) after going 44.9 percent in 11 games in January and only 33.8 percent in 12 games in December. And the Warriors said he exerts a strong balance between leading with words and action.
"He's being more aggressive," Livingston said. "He's understanding when the playoffs roll around, that's when we show our value as far as being able to have an impact on the game."
Nonetheless, Iguodala has admitted he has not always provided the impact he desired.
He has missed seven games this season for various minor ailments, and has sat in five others for rest purposes. After stressing, "I don't like to make excuses," Iguodala revealed he "was very sick this year earlier when I had a bad stretch." And he shared there are rare moments he has not agreed with Kerr's analysis of his game.
"About 95 percent of the time, he gets it," Iguodala said.
The other five percent of the time, Iguodala said Kerr has told him to play more aggressively. Iguodala said he often answers, "'Are you serious?'"
Regardless, the Warriors maintain such issues will evaporate as the playoffs approach. So much that Livingston argued Iguodala's shooting numbers are "first-world problems" that do not concern the team.
Neither the Warriors nor Iguodala expect he will mirror the prime of his career in Philadelphia (2004-2012) when he usually led his team in scoring (15.3 points per game) and landed an All-Star appearance (2012). Yet, that foundation partly explains Iguodala's defiance that his poor shooting numbers mean little.
"Things like that can affect the majority of people, but I've been around for so long. I've accomplished so much that I can say, 'Screw you,'" Iguodala said. "I've accomplished more than 90 percent maybe more than all players in the history of the game. So who can tell me what I can and can't do? I got that mindset, whether it's right or wrong. This league is all about confidence, so you need to have that mindset sometimes."
And it is that mindset that the Warriors value from Iguodala more than anything else.
3 charged in Ridge murder
Town offering low-interest rebuild and repair loans
Paradise to be power-less for two hours Monday
Mark Medina


Micro summary: This McDonnell Douglas MD-83 experienced an in-flight upset and crashed.
Executive Summary: Abstract: This report explains the accident involving Alaska Airlines flight 261, a McDonnell Douglas MD-83, which crashed into the Pacific Ocean about 2.7 miles north of Anacapa Island, California. Safety issues discussed in this report include lubrication and inspection of the jackscrew assembly, extension of lubrication and end play check intervals, jackscrew assembly overhaul procedures, the design and certification of the MD-80 horizontal stabilizer trim control system, Alaska Airlines' maintenance program, and Federal Aviation Administration (FAA) oversight of Alaska Airlines. Safety recommendations are addressed to the FAA.
Executive Summary On January 31, 2000, about 1621 Pacific standard time, Alaska Airlines, Inc., flight 261, a McDonnell Douglas MD-83, N963AS, crashed into the Pacific Ocean about 2.7 miles north of Anacapa Island, California. The 2 pilots, 3 cabin crewmembers, and 83 passengers on board were killed, and the airplane was destroyed by impact forces. Flight 261 was operating as a scheduled international passenger flight under the provisions of 14 Code of Federal Regulations Part 121 from Lic Gustavo Diaz Ordaz International Airport, Puerto Vallarta, Mexico, to Seattle-Tacoma International Airport, Seattle, Washington, with an intermediate stop planned at San Francisco International Airport, San Francisco, California. Visual meteorological conditions prevailed for the flight, which operated on an instrument flight rules flight plan.
The National Transportation Safety Board determines that the probable cause of this accident was a loss of airplane pitch control resulting from the in-flight failure of the horizontal stabilizer trim system jackscrew assembly's acme nut threads. The thread failure was caused by excessive wear resulting from Alaska Airlines' insufficient lubrication of the jackscrew assembly.
Contributing to the accident were Alaska Airlines' extended lubrication interval and the Federal Aviation Administration�s (FAA) approval of that extension, which increased the likelihood that a missed or inadequate lubrication would result in excessive wear of the acme nut threads, and Alaska Airlines' extended end play check interval and the FAA's approval of that extension, which allowed the excessive wear of the acme nut threads to progress to failure without the opportunity for detection. Also contributing to the accident was the absence on the McDonnell Douglas MD-80 of a fail-safe mechanism to prevent the catastrophic effects of total acme nut thread loss.
The safety issues discussed in this report include lubrication and inspection of the jackscrew assembly, extension of lubrication and end play check intervals, jackscrew assembly overhaul procedures, the design and certification of the MD-80 horizontal stabilizer trim control system, Alaska Airlines' maintenance program, and FAA oversight of Alaska Airlines. Safety recommendations are addressed to the FAA.


Do Storms Threaten Your Electrical System? | FB Electrical Services Inc.
Even a small electrical surge can cause damage to the electrical equipment and appliances you rely on each and every day. And most people do associate power surges with storms. It is certainly possible for a lighting storm to create a power surge that knocks out power to each of your electrical appliances all at once.
This may be due to a downed power line or some other event that has to do with this utility. However, conventional power surges do not originate from stormy weather.
That's right. Up to 80% of power surges are actually generated within your home on a small scale, every day! This means that lightning storms or winter blizzards are not the biggest threat to your home's electrical system. In fact, you probably won't even know when a power surge begins to effect your electrical equipment. Something as seemingly benign as a motor starting can pull in a lot of amps, and an overvoltage can send a power surge back through other equipment in your home.
So, does this mean you only need surge protection at appliances that pull in a lot of amps—like your HVAC systems? Well, no. Even if your HVAC systems have surge protection by way of a power strip, the system itself can send a surge back to the other appliances and equipment in your home that aren't protected.
You'll end up having to replace all kinds of systems throughout your home months or years earlier than you otherwise would have had to. This means a lower return on investment for you.
We alluded to this above, but it's important that you're aware—a power strip along might not necessarily provide the surge protection your home needs. This is because many of your most important appliances are wired into the home and not attached to a power strip. Additionally, not all power strips are surge protectors! Be sure to read the label when you purchase one.
That's not to say there isn't a benefit to power strips—they allow you to shut off power to each appliance connected to them in one click when you're not using those appliances (think your computer, printer, and desk lamp). But for that double layer of protection, it would serve you very well to consider the installation of a whole-house surge protection system.
When you're seeking professional and highly experienced electricians in Newton, MA, look no further than FB Integrated Solutions Inc. Contact us today!


design-dinterieur.net -Living Room Ideas Tumblr Find inspirational living room decorating ideas here. We feature pictures of living room designs, foyers, furniture and occasionally tips and tricks on how you can make your living room more beautiful.
Living Room Ideas Tumblr . Find inspirational living room decorating ideas here. We feature pictures of living room designs, foyers, furniture and occasionally tips and tricks on how you can make your living room more beautiful.
most popularLiving Room Ideas Tumblr ideas for 2018. BrowseLiving Room Ideas Tumblr designs and decorating ideas. Discover inspiration for yourLiving Room Ideas Tumblr remodel, including colors, storage, layouts and organization.


Fear is a funny thing. It could be as motivating as it is crippling; as confusing as it is definitive; as disenchanting as it is familiar. Most people walk away from fear, but I like to make sure there's a certain amount of it in my life.
My parents always taught me to work hard, so I did, and I was successful. I kept my eye on the ball, followed through, and work never failed me. Years before I joined Barrett-Jackson, I took the leap into the luxury real estate market. The group was booming and my background in stocks and finance gave me the conviction to have a crack at it, but something strange happened. For the first time in my life, work did fail me. I woke up before the sun came up and gave every day my all, but my success got replaced with adversity.
It was the onset of the global financial crisis and the collapse of the real estate market. I was afraid, but it was in the face of this crisis that the opportunity to learn the meaning of resilience appeared. Joan Rivers was once explaining to her daughter that being successful in show business is all about rejection. She used the analogy of a storm, and said that most people would give up and come in out of the rain, but a few people who stay out long enough to get struck by lightning. I learned through that hardship that sometimes, the worst thing occurs on your path to the best thing. Not only did I stick with my real estate career long enough to bounce back, but closing my biggest deal is what led me to where I am today.
As you might imagine, collector cars doesn't seem like an asset class that attracts too many women, unlike art, diamonds and wine. To be fair, at Barrett-Jackson, there aren't a lot of us on the surface. However, behind the scenes, you will find a population of passionate, hard-working women, with the tenacity and determination of a committed and dynamic team.
As Barrett-Jackson's Brand Manager, I have the unique ability to do work I love, along the man I love, with a team I love. The cars are fun, but it's all about the people. And although I hold a top position, the last thing I'm willing to do is speak more than I listen. There isn't a single person within the organization who I can't learn something from. And when I need expertise outside of the office, I turn to a peer group or a mentor. Growth doesn't occur in a vacuüm. For me, it's synonymous with learning.
Sometimes I'm asked for advice on how to be a successful female leader, and the answer is simple: act like one.
Are you going into a meeting where your merit and authority could be questioned? Come over-prepared. Your position doesn't make you powerful, your knowledge does. Do you have an idea? Present it with confidence and conviction. Even in an executive role, many of us—men and women alike—need to get others on board with our goals to turn them into actions. Sometimes we can get shot down because of our gender, our stature, or the fact that we're simply not the one who's been listened to until now, but change has to start somewhere, so be ready for it. The more equipped you are, the harder it is to knock you down. Leadership isn't a circumstance; it's a state of mind. Often people think that you need legions of followers to lead, but if you're not leading already, you'll never cultivate a following.
People inherently want to be liked. I tell women all the time to get over this need. As fortunate as I am to have warm, rewarding relationships across my team, I have zero desire to be liked. I'm much more focused on being respected, which for me, is a non-negotiable. Of course, respect is a two-way street (automotive pun intended). In order to work here, you have to show respect, but I wouldn't be doing my job if I didn't display that same respect in return. I respect everyone I work with. If I didn't, they wouldn't work here! However, it's a balance. Shedding the desire to be liked isn't an excuse to be abrasive and crude. There is a difference between being aggressive and assertive. I urge our team to be assertive.
I would get bored if I didn't have new goals to accomplish, new feats to achieve. Challenges and achievement do coexist. In fact, one makes the other one sweeter. On the heels of an overwhelmingly successful flagship auction in our home base of Scottsdale marking 45 years of Barrett-Jackson, (which is an event I often describe as Herculean and Sisyphean in nature), we've tasked ourselves with bringing the event to the Northeast. In June, the Barrett-Jackson brand will touch down in Mohegan Sun, and will hopefully not only be a new way to bring people together, but will become a multiplier of our once-lofty philanthropic goals. We have had the privilege to offer the platform for the generosity of our bidders, consignors and the automotive manufacturers who have powered this philanthropy. To date, our charity cars have brought in more than $88 million for those who need it, and within the next five years I'll remain committed to bringing that number up to $100 million. You can't do that by simply doing what you're told. So get out there, women, and lead. It's all in the mindset.
Executive Brand Manager of Barrett-Jackson, Carolyn oversees the company's image as The World's Greatest Collector Car Auctions®, working alongside her husband Craig.
Did You Take All of Your Vacation This Year?


I urge your readers to support this, although it's not as far-reaching as everyone involved wanted or expected, at least it's a good two steps up the ladder.
If you want local feelings taken into account on planning issues, you need to support this.
I'm certainly not saying it will stop all development, but it will give more value to local opinion and give planning officers and councillors, who sit on planning committee, more ammunition to resist applications.
You may well have the opinion, which I understand, of "all these houses have been granted, so why bother? It's a waste of time".
Please don't let this idea control your feelings.
In a few years' time, and even now, Ribble Valley Borough Council officers have to keep reviewing the Core Strategy & Housing Numbers.
This Neighbourhood Plan can only help in giving some control back to the local area. The greater the support given to the plan, the more weight can be attributed to its value.
Hence, when the next real review comes for the Ribble Valley Borough Council Core Strategy before 2028, possibly as early as 2023, only four years away, there will then be a base plan to work to and, hopefully, the Neighbourhood Plan Team will add greater input.
Without a base to work from, it is literally starting from the beginning again.
The real benefits might not be seen for the best part of 10 years, but that is not an excuse for ignoring it now.
If you value Longridge for your children or grandchildren, please support this to help bring some control back to local people.


text:
Q: why my footer is not increasing from its edges in html? please help me i am writing html document and having a problem. can you tell why my footer is not increasing from its edges even if i select width 100% please help
this code is not compelte i have not given css please click link at bottom to see my problem
enter code

About just do it

                Home
                |
                Blog
                |
                About
                |
                Contact

      copyright © 2021 kaustubh krishna

find about us at our social media

support@justdoit.com

                About the company
                We offer freelance services for you and your company

 here

link of document:  https://codepen.io/kaustubh0711/pen/XWNdNrv?editors=1100

A: Your .small-container div has a max-width css style keeping it from taking up the entire width of the screen.  You'll also have to remove the left and right padding from it or else you'll still have gaps on each side of your footer.

A: You have to close the .small-container and .row, they affect the footer.

    ... content ...

redpajama_set_name: RedPajamaStackExchange


text:
Interactive Education
Art Sphere's Online Classes
ASI Books
Become an Intern!
Site Locations
Transforming Lives Through The Arts
The Guitar is a wooden instrument with a fretted fingerboard and curved in sides that usually has six strings.
The Guitar was invented in 1500 BC. The six-string acoustic guitar was invented by Gaetano Vinaccia however the more modern design that we know today was created by Antonio Torres Jurado and originates from Spain.
Rocked with Star!
Let's enjoy the sound of this instrument through this wonderful performance by Eddie Van Halen .
Learn more about Eddie Van Halen
Eddie Van Halen was born on January 26th 1955, in Amsterdam, Netherlands. Despite one of his parents being a pianist and even taking lessons from a teacher, he was never actually taught to read music but rather would listen to recordings of Bach and Mozart only to then improvise. Whilst his parents wanted him to get into classical music, Van Halen was drawn towards rock music inspired by bands like the Bettles and the Dave Clark Five.
Eddie and his brother formed the band that would become Van Halen in 1972 with the name change happening 2 years later. It became a staple of the Los Angeles music scene rather quickly and in 1977 Warner Records had offered Van Halen a recording contract. The band won the 1992 Grammy Award for Best Hard Rock Performance with Vocals for the album For Unlawful Carnal Knowledge.
Bok Building 1901 S 9th Street, Studio #502, Philadelphia, PA 19148
Message from ASI's Director
Staff 2023
ASI Board
Collaborative Public Murals
Community Art Events
Conflict Resolution Through Art
Cross-Cultural Art Exchanges
Themed Workshops
©2023 Art Sphere, Inc.
info@artsphere.org • Log In • Register
Teacher Parent Volunteer Student Partner
I consent to Art Sphere sending me emails
*YOU MUST BE 13 YEARS OR OLDER TO SUBSCRIBE*


text:
Nightclubs start asking for NHS Covid pass in England
By BBC News Last updated Jul 30, 2021 13
Some nightclubs in England have begun requiring an NHS Covid pass for entry, amid a demand for MPs to debate their use.
NHS Covid passes allow users to show proof of their vaccination, test or immunity status at venues.
They are available through England's NHS app, which is separate from the NHS Covid-19 app in England and Wales.
The Lib Dems called for ministers to scrap the passes for good or at least allow MPs to debate them in Parliament.
Liberal Democrat home affairs spokesman Alistair Carmichael accused ministers of committing to the passports "by stealth".
"We now have a new ID card snuck onto our phones without even as much as a whisper from the government," he said.
Pings rise again to new record in latest week
How can I prove I've had both my jabs?
What is the EU vaccine 'passport' and can I use it?
"Businesses, events and venues have been encouraged to use the Covid pass to help reduce transmission of Covid-19."
Prime Minister Boris Johnson announced earlier this month that people attending nightclubs and other large venues where crowds gather would be required to be fully vaccinated from the end of September.
Some nightclub owners previously told the BBC Covid passes were "unworkable" and that they would not be requiring them.
But the O2 Ritz in Manchester and Electric Ballroom in Camden will both require guests to present a valid NHS Covid pass as a condition of entry to the Ultimate Power club night on Friday evening.
"For our initial London and Manchester events, we are requesting Covid Passes at point of entry to our clubs," the organisers said on the event's website.
Other venues under the O2 brand and run by Academy Music Group, which has around 18 venues in England, tell guests they will be required to show their Covid status on entry.
A "domestic" pass option on the app explains there are four ways to get an NHS Covid pass – through "testing, full vaccination, natural immunity, or an exemption".
It says: "You may need to show you NHS Covid Pass at places that have chosen to use the service." People can also request a paper pass to prove their vaccination status.
image copyrightGoogle
The wording on the NHS Covid app was changed on Monday after trials of the domestic pass for events ended, the Department of Health confirmed.
Other updates made this week include linking the pass to Apple Wallet and allowing those who took part in the trial of the Novavax vaccine to obtain a pass.
Differing approaches to vaccination status are emerging across the UK, amid legal, privacy and discrimination concerns.
In Scotland, Deputy First Minister John Swinney has said barring people who refuse a coronavirus vaccination from certain large events is the wrong approach.
In England, Cabinet Office minister Michael Gove warned those who refuse to take a vaccine may be unable to access mass attendance venues such as football stadia or nightclubs.
Foreign Secretary Dominic Raab has said the government was in favour of making Covid passes compulsory in more settings.
Test or jab
To access their NHS Covid pass, users must first link their NHS number with other personal information to verify their identity.
According to the terms of the service, a domestic NHS Covid pass allows a person to demonstrate their Covid-19 lateral flow, PCR test or vaccine status for events or "other domestic uses which the government may approve from time to time".
The terms add that the service is not intended to be used by employers to check their staff have been vaccinated.
It also allows an exemption to apply, for example, when a person has taken part in an official trial for a Covid vaccine that has yet to receive approval.
A travel NHS Covid pass allows a person to demonstrate their vaccination status for international travel.
The NHS app is described as being "a simple and secure way to access a range of NHS services on your smart phone or tablet".
It is available to people aged 13 and over who are registered with a GP service and can be accessed on the Apple App Store and Google Play Store, as well as on desktop devices.
Other functions on the app include appointments, prescriptions as well as allowing users to access an overview of their medical history and records held by their GP.
A Department for Health and Social Care spokesperson said: "Businesses, events and venues have been encouraged to use the Covid pass to help reduce transmission of Covid-19."
Covid vaccines have prevented 60,000 deaths in England – Jonathan Van-Tam
Pregnant women urged to get jab as majority unvaccinated
Time limit is exhausted. Please reload the CAPTCHA. + six = eight
Time limit is exhausted. Please reload the CAPTCHA. 3 + eight =


text:
Ibn Ezra on Exodus 16:13
ויהי בערב. בסוף היום בעצמו. AND IT CAME TO PASS AT EVEN. At the end of that very day.
ותעל השליו. אולי עלתה מפאת ים: THAT THE QUAILS CAME UP. Possibly from the direction of the sea.46Came up indicates that they came from the sea. Compare, Come ye up from the Jordan (Josh. 4:17) (Filwarg).
שכבת הטל. כמו ונבלי שמים מי ישכיב. טעם ירידה. ובמקום אחר כתוב וברדת הטל. ירקב שם חוי שאמר כי המן הוא הנקרא בלשון פרסי תרנגבי"ן ובלשון ערבי מ"ן ובל' לע"ז מנ"א. כי קושיות רבות עמדו עליו.האחת כי איננו יורד היום במדבר סיני כי ההר ידוע ואני ראיתי זה הדומה למן במלכות אלצכי"ר. והוא יורד בניסן ובאייר ולא בחדשים אחרים. ועוד אם תשימהו לשמש לא ימס. ועוד כי בלילה לא יבאש ועוד כי איננו חזק ואין צורך שידוכנו אדם במדוכה שיעשה ממנו עוגות. ועוד כאשר יושם בלשון יומם ועוד כי איננו משביע שיוליד דם טוב רק הוא נכנם ברפואות. ועוד כי ביום הששי היה יורד משנה ועוד כי לא היה יורד בשבת ועוד כי ירד לכל מקום שיחנו. ועוד כי עבר עמהם את הירדן. ולא שבת עד חצי ניסן על דרך הפשט: A LAYER OF DEW. Shikhvat (a layer) is similar to yashkiv (bring down) in Or who can bring down (yashkiv) the bottles of heaven (Job 38:37).47Translated according to I.E. They both come from the root shin, kaf, bet. It means a descent.48According to I.E. shikhvat ha-tal (a layer of dew) means a dew fall. Scripture elsewhere writes, u-veredet ha-tal (And when the dew descended)49Translated according to I.E. (Num. 11:9). May the name of Chivi50See I.E. on Ex. 14:27 and the notes thereto. rot for saying that the manna is to be identified with what is known in the Persian tongue as tarnagbin, in Arabic as man, and in the vernacular as manna. There are many questions raised against him. First of all, no manna falls today in the wilderness of Sinai,51If the Biblical manna is to be identified as the substance mentioned by Chivi then it should still be found in Sinai today. It is not. Therefore it was not there in the days of Moses. for we know the location of the mountain.52Mount Sinai. Hence we can ascertain that no manna falls there today. Now I have seen that which resembles manna in the kingdom of Al tzakir,53This place cannot be identified. and it only comes down in Nisan and Iyyar and in no other months.54Whereas the manna appeared daily for 40 years. Additionally, if you place it in the sun it does not melt.55Whereas the manna did. See verse 21. Furthermore, it does not rot in the evening.56Whereas the manna did. See verse 20. Moreover, it is not very hard and one does not have to grind it in a pestle57As one had to do with the manna. See Num. 11:8. if one wants to make cakes out of it. Furthermore, it melts when placed upon the tongue.58Which I.E. assumes the manna did not. If it did Scripture would have informed us of this. In addition, it does not satisfy59Which the manna did. because it does not produce good blood. It is only used as a medical ingredient.60It cannot serve as food. Furthermore, a double portion of manna came down on Friday.61Which shows that it is not the substance spoken of by Chivi. Moreover, the manna did not come down on the Sabbath. Furthermore, the manna came down wherever Israel camped.62It came down only in the places where Israel camped. When Israel moved on, it ceased to appear there. Thus it cannot be identified with the substance mentioned by Chivi. In addition, the manna crossed over the Jordan with the Israelites and did not cease to descend, according to the plain meaning of Scripture, until half the month of Nisan,63However, after the 15th of Nisan it ceased to come down. It thus cannot be identified with the substance mentioned by Chivi. had passed.


If everyone agreed all the time, there would be no reason to consider different perspectives or look for new ways to handle situations. The best ideas often emerge from the creative problem-solving process that has to occur to reach conflict resolution.
1. As team members work through conflicts, they are able to make progress toward accomplishing goals that need to be reached.
This is true in terms of project-specific goals, as well as the goal of becoming a stronger, more unified team. Team members will grow by working through problems and dealing with challenges.
2. Working through conflict with colleagues can provide a forum for unifying the group.
After all, people are much more likely to buy into the outcome of decisions that are made if they were involved in the decision-making process. Making sure everyone has a voice — even if it's a voice of dissent — is a vital component of an effective team.
3. By getting involved in efforts to resolve conflict, members of a workgroup can actually become more productive.
Rather than spending times dwelling on the fact that disagreement exists, taking action to deal with conflict can get co-workers focused on taking actions that lead to greater productivity.
4. If problems and disagreements are ignored and avoided rather than being handled in a constructive way, things will stay the same or get worse.
When co-workers air their differences and work through them, the stage is set for positive change to occur within the group. Keeping the status quo might be the path of least resistance, but if you can persevere through the struggle and come out better on the other side, it's worth it.
5. Engaging in effective conflict management can lead to the resolution of problems that naturally arise when people with different goals, opinions, and attitudes work together.
Only through acknowledging differences and engaging in constructive communication can problems be resolved. And you, or an aspiring manager on your team, has to learn to manage conflict in order to harness its potential.
While conflict is often viewed as something to be avoided at all costs, that is not necessarily the case. Managed effectively, conflicts in the workplace can actually have positive outcomes.


text:
Q: Downloading multiple similar URLs using braces #!/bin/sh
orig=$1

var=${orig%.jpg}
var=`echo "$var" | grep -o "[0-9][0-9]$"`

cutten=${orig%[0-9][0-9].jpg}

sequence='{1,'
for i in `seq 2 $(( $var - 1 ))`; do
sequence=$sequence$i","
done
output=$cutten$sequence$var"}.jpg"

echo "$output"
#wget "$output"

It takes an img link, eg: http://foo.bar/some-image-20.jpg and returns
http://foo.bar/some-image-{1,2,3,4,5,…,18,19,20}.jpg,
that is, files *1.jpg through *20.jpg
While on echo it works as expected (the output is as above) when run with wget:
-- (Date/Time) -- http://foo.bar/some-image-%7B1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20%7D.jpg
(Proceeds to resolve this whole one link, and fails)

Pretty much it replaces '{' with '%7B' and '}' with '%7D'. Any Ideas?

A: I don't know of any web server that supports wildcard downloads, you'll have to fetch each URL individually.
Keeping as much of your original script as possible, that would be something like:
#!/bin/sh
orig=$1

var=${orig%.jpg}
var=$(echo "$var" | grep -o "[0-9][0-9]$")

cutten=${orig%[0-9][0-9].jpg}

for i in $(seq 1 $(($var - 1))) ; do
      echo $cutten$i.jpg
done | wget -i -

This makes use of wget's -i option to read a list of URLs to fetch from a file, in this case stdin, so we can just pipe the list of files to wget.
(if you want to see what it is going to download before you run it, replace wget -i - with cat)

A: http://foo.bar/some-image-{1,2,3}.jpg is bash source syntax to represent the list of words http://foo.bar/some-image-1.jpg, http://foo.bar/some-image-2.jpg, http://foo.bar/some-image-3.jpg. Source syntax means that it's something you type in your script, it isn't something that happens on a variable expansion. echo "$output" shows you the command you'll be running, which is
wget http://foo.bar/some-image-{1,2,3}.jpg

Since braces are not valid in URLs, wget encodes them as %7B and %7D. This is unrelated to your problem: the server just sees one URL containing what is as far as it's concerned an URL that doesn't refer to an existing file.
You need to pass 3 (in my example, 20 in yours) different URLs on the command line of wget. You could use eval to make bash parse the content of a variable as source code to make use of its brace expansion feature, but it would be easier to build the list in the first place — this isn't any more difficult than building the string with the braces.
#!/bin/sh
suffix=${1##*[0-9]}
prefix=${1%"$suffix"}
last=${prefix##*[!0-9]}
prefix=${prefix%"$last"}

while [ "$last" -gt 1 ]; do
  last=$((last-1))
  set -- "$prefix$last$suffix" "$@"
done

wget "$@"


text:
I Am Ready: Early Learning and the Arts
When Quynn Johnson steps into the Pre-K and Kindergarten classrooms at Margaret Brent Elementary School in Baltimore City, she is met with hugs and big, bright smiles. The students love that they've been learning to tap with the artist and they've been waiting patiently for her to return. "What do I do?" the artist asks. "You make music with your feet and you keep a steady beat!" the students respond.
Quynn is a multi-award-winning performing artist, choreographer, author, and the co-director of SOLE Defined, a percussive dance company. Though she has performed tap dance for national and international audiences, on this morning, she is captivating our state's youngest learners—not on a stage, but in a classroom—through rhythm, imagination, and dance. Already a professional teaching artist on the Young Audiences roster, Quynn is now training to also become a Maryland Wolf Trap Artist.
As the Maryland regional affiliate of Wolf Trap, Young Audiences is proud to be expanding access to the arts for Maryland's youngest students during the critical early learning years.
High-quality early childhood education is absolutely essential to giving children the best possible start in school and in life. Decades of practice and research prove that integrating the arts into classroom experiences contributes to greater academic achievement and social/emotional development for our youngest students. The creative collaboration between teachers and artists in the Wolf Trap residency builds foundations and fuels momentum in arts-integrated early learning classrooms.
The children sing along with the artist to the tune of Frère Jacques, "I am ready, You are, too, Eyes on the teacher, We're going to learn." Everyone knows the song and everyone is excited to sing it—first in English, then in Spanish.
Estoy listo
Estás tambien
Ojos en la profe
Vamos a aprender
Quynn made up the song while teaching in the Summer Arts & Learning Academy last summer, but now sings it with students in lower elementary grades at every school she visits. Mrs. Jager, the Kindergarten teacher, likes it, too. She uses "I am ready" as a transition song all the time—and her students will remind her if she forgets!
The artist is not only here to teach the young students how to tap, she is here to build the teachers' skills and knowledge and demonstrate how to integrate the art form into their lessons throughout the year. This embedded professional development is something that distinguishes the Wolf Trap Early Learning Residency from other artist residencies.
As Quynn leads the students in an exercise stomping out beats, another skill is being fortified. They are identifying patterns and practicing word sounds like "Apple, apple, ah, ah, ah," and "Bat, bat, buh, buh, buh." The classroom teachers and the artist work together to tailor the residency to each particular classroom—deciding how refined the dance movements should be for each age group and which literacy skills to focus on.
"The process is pretty new and different to me but I like it," said Quynn. The artist pays particular attention to teach the elements of her art form that educators will feel comfortable utilizing in the future—and not just off the cuff, but with some artistic integrity. "I think the embedded PD portion is a great way to keep the teachers involved and shows them how it doesn't have to be its own lesson but can be incorporated within what they're already doing. I never want them to feel like it's unreachable."
By the end of Quynn's residency in the Pre-K and Kindergarten classrooms, the students know how to keep a steady beat on their feet, and they remember a beat pattern and two basic tap steps. And the teachers can connect the art form to literacy—leading students to sound out words and sounds with their feet. "They both felt great and comfortable with everything we did in class and I could hear them using it after our time was over." They were ready.
Eighty-five percent of brain development occurs during the first five years of a child's life. Participation in the arts encourages positive growth in a child's emotional, physical, intellectual, creative, and social development. As the Maryland affiliate of the Wolf Trap Institute for Early Learning Through the Arts, Young Audiences expands access to the arts for Maryland's youngest students during the critical early learning years. Bring a Maryland Wolf Trap 16-Session Residency into your school.
Tagged: artist-in-residence, arts integration, Baltimore City Public Schools, dance, early learning, kindergarten, literacy, Margaret Brent Elementary Middle School, Maryland Wolf Trap, pre-kindergarten, professional development, professional development for educators, Quynn Johnson, roster artist, tap dance, teaching artist, Wolf Trap