text:
\section{Introduction}
    \paragraph{Motivation.}
Academic and commercial process mining tools aim to find the root causes of performance or compliance problems in processes. Mainly, a classifier, say a decision tree, is created using the data gathered from the process and then the rule mining is done using that decision tree~\cite{de2016general}. However, this approach may lead to diagnoses that are not valuable. In some cases, the main cause of the problem is already known and essentially cannot be altered. Also, due to the strong correlation of the known main cause and the problem, it may become impossible to see the other minor but probably more practically valuable causes of the problem.
 Consider the following two scenarios:
\begin{itemize}
	\item [(i)] there is a bottleneck in the process and it is caused by the most busy employee, or
	\item [(ii)] there are deviations caused by the most experienced resources taking the most difficult cases.
\end{itemize}
In these scenarios, it is likely that the most busy employees or the most experienced resources are declared the main reasons for the bottleneck or deviations in the process. This is not just unfair but also does not provide novel insights (just stating the obvious). Even if we remove the attribute conveying the employee or the resource, still rules that proxy these attributes would be revealed as the result of the traditional rule mining~\cite{zemel2013learning}. In these cases, it is essential to make inference about the less trivial root-causes of the problem in the process

As another application, consider that for a given process we are interested in questions which are related to investigating the process while ignoring the effect of different values of a particular attribute. ``Following the progress of career paths while eliminating gender differences" is one example of these sorts of situations where we need to remove the correlation between two attributes in the data. Here we need to infer the rules governing the process ignoring the correlation between the effect of different values of that particular attribute and the process attribute of interest.

\paragraph{Discrimination-aware data mining.} Each population can be partitioned into several subgroups according to the properties of its members, e.g., race, age, or academic degree. \emph{Discrimination} means treating a subgroup of people, called \emph{sensitive group}, in an unfair way merely because of being a member of that subgroup. There is a possibility that negligent usage of new advanced technologies, especially in the field of data mining and machine learning, inadvertently cause discrimination. To avoid these phenomena,
detecting discrimination and designing fair predictors have been studied intensively(see~\cite{bolukbasi2016man,hardt2016equality,kleinberg2016inherent,kusner2017counterfactual,zemel2013learning,zliobaite2015survey}). This paper is the first paper to address this problem in the context of process mining.

	\emph{Demographic parity} is one of the most studied definitions of fairness that indicates the portion of people in the sensitive subgroup who receive the desired result must be the same as the whole population. To maintain this criterion, in some approaches the training data is manipulated. This is done by changing the labels of some items in  the training data~\cite{kamiran2010classification,kamiran2012data,zemel2013learning}, or by applying carefully designed re-sampling methods~\cite{kamiran2012data}. In another approach,~\cite{zemel2013learning}, the representation of the data is changed, and the fairness is maintained as a side-effect of fair representations. In~\cite{kamiran2010discrimination}, demographic parity in a decision tree is retained by taking into account the information gain of the sensitive attribute as well as the class attribute as the criteria used for splitting the internal nodes. In~\cite{kamiran2010discrimination}, the relabeling technique is also used to modify leaves of the resulting decision tree.

Besides demographic parity, other notions of fairness have been formalized in the literature. For example, \emph{equal opportunity} and \emph{equal odds} have been introduced in~\cite{Hardt:2016:EOS:3157382.3157469}, and \emph{individual fairness} has been introduced in~\cite{dwork2012fairness}. We refer the interested readers to~\cite{berk2018fairness} for a review of various fairness criteria.
		\paragraph{Process mining. }Process mining is the link between model-based process analysis and data-oriented analysis techniques; a set of techniques that support the analysis of business processes based on event logs. In this context, several works have been dedicated to decision mining and finding the correlation among the process data and making predictions~\cite{sani2017subgroup,leemans2014process,de2016general,rozinat2006decision}.

		Ethical and legal effects of process mining can be considered in two categories; confidentiality and fairness issues. Confidentiality in the process mining has recently receiving attention~\cite{RafieiWA18}. To the best of our knowledge, there is no work in the area of process mining dedicated to investigating fairness issues. This is the first publication considering discrimination within a given process.
\paragraph{Our Results.}
We provide a solution for the previously mentioned problems. Specifying a problem in the process, we propose an approach by adopting the techniques available in data mining for removing discrimination from classifiers in the area of process mining to avoid unfair or obvious conclusions in such scenarios. We do that by declaring the attribute that indicates the existence of the problem in the given situation as the \emph{class attribute} and the attribute that we want to decrease its dependency to the class attribute as the \emph{sensitive attribute}. We consider the class attribute to be binary with the following two values: $+$ indicates the desirable result conveying the problem of interest has not been faced while $-$ has the opposite meaning. The sensitive attribute is also assumed to be binary, where $\pg$ convey belonging to the sensitive group while $\fg$ convey belonging to the rest of the population (favorable group). In the previous examples, we can consider the existence of the delay or deviation in the cases as the class attribute and the employee being busy or highly experienced as the sensitive attribute. Now, we can consider the problem as a discriminatory case and remove the dependency of the class and the sensitive attributes in the resulting classifier by creating a fair classifier. Doing so, the resulting rules would not be discriminatory against the sensitive group. Also, this technique masks some of the causes of that problem and focus on the other ones. Consequently, we can aim at finding more feasible solutions for the problem in the process by finding other possibly less obvious or even hidden causes of the problem.

We have implemented this approach as a ProM plug-in. In this plug-in, we create two decision trees using the data gathered about the process. The first one is a standard decision tree (using algorithm C4.5) describing the correlations between dependent and independent attributes as they are. The second one is a fair decision tree, in which the dependency between the sensitive and the class attributes is decreased or removed.
The goal is to create a decision tree with no unacceptable discrimination and the maximum possible accuracy. A snapshot of the plug-in implemented in ProM is depicted in Figure~\ref{pic::snapshot}.

The remainder of the paper is organized as follow. In Section~\ref{problem}, we present the problem statement. A high-level overview of the proposed approach is presented in Section~\ref{approach}. The experimental results of applying the implemented method on some real event logs are presented in Section~\ref{result}. Finally, in Section~\ref{conclution}, we summarize our approach and discuss directions for further research.
\begin{figure}\label{pic::snapshot}
	\includegraphics[width=120mm]{prom.jpg}
	\caption{The two decision trees created by the implemented plug-in. The left one is a standard decision tree and the right one is a fair decision tree. The yellow leaves are the ones that have been relabeled to ensure fairness.}
\end{figure}

\section{Problem Statement}\label{problem}
To analyze conformance and performance problems, we use event data and process models (discovered or hand-made).\footnote{We assume the reader to be familiar with the concepts like set, multi-set, and function. Given a non-empty set $X$, we denote all the non-empty subsets of $X$ by $\mathbb{P}(X)$. Given two sets $A$ and $B$, a partial function $f: A \not \mapsto B$ is defined as a function $f:A'\mapsto B$ for some $A'\subseteq A$. We say $f(a) = \bot$ if $a \not\in A'$.} An event log is a collection of traces and each trace is a collection of events related to the same case. Also each trace may be associated with some attributes. Consider $\mathcal{U}_{act}$ as the universe of all possible \emph{activity names},  $\mathcal{U}_{time}$ the universe of all possible \emph{time stamps}, $\mathcal{U}_{att}$ the universe of all possible \emph{attribute names}, $\mathcal{U}_{val}$ the universe of all possible \emph{values}, and, $\mathcal{U}_{map}:\mathcal{U}_{att} \not \mapsto \mathcal{U}_{val}$. Also, let $values: \mathcal{U}_{att} \mapsto \mathbb{P}(\mathcal{U}_{val})$ be the function that returns the set of all possible values for each attribute name. We define an event log as follow:
\begin{definition}[Event Log]\label{def:universe}
	An \emph{event} is an element of $\mathcal{U}_{act} \times \mathcal{U}_{time} \times \mathcal{U}_{map} $ and the universe of all possible \emph{events} is denoted by $\mathcal{E}$. A \emph{log} is an element of $\mathbb{P}(\mathcal{U}_{map}\times \mathbb{P}(\mathcal{E}))$ and the universe of all possible logs is denoted by $\mathcal{L}$. We call each $t\in L$, where $L \in \mathcal{L}$, a \emph{trace}.
\end{definition}
To work with event logs, we need the following helper functions:
\begin{itemize}
	\item Given an event $e = (act,time,map)\in \mathcal{E}$, $\pi_{act}(e) = act$, $\pi_{time}(e) = time$, and, $\pi_{map}(e) = map$.
	\item Given $t= (map, E) \in L$, where $L\in \mathcal{L}$, then $\pi_{map}(t) = map$ and $\pi_{events}(t) = E$.
	\item Given $E \in \mathbb{P}(\mathcal{E})$, then $\pi_{maxtime}(E) = \argmax_{e \in E} \pi_{time}(e)$, $\pi_{act}(E) = \{ e \in E | \pi_{act}(e) = act\}$, and, $E_{\leq time} =\{e \in E| \pi_{time}(e) \leq time\}$. This function returns the set of events of a trace whose time stamps are at most equal to a given time stamp.
\end{itemize}
We assume that each event in a given log $L$ is unique and also has a unique time stamp. In other words, $\forall t,t'\in L \forall e\in \pi_{events}(t) \forall e'\in \pi_{events}(t') \big( \pi_{time}(e) =\pi_{time}(e')\implies (e=e'\land t=t')\big)$.

If the problem in the process is about the traces, like delay in some cases, then for a given trace all the values of its trace and event level attributes might be relevant. However, if the problem is related to a specific activity, like a bottleneck in activity $act$, then we need to extract the data from the trace attributes plus the attributes of a subset of its events that occur before the occurrence of that specific event. Also, the class attribute may occur several times in a given trace. We define the notion of a \emph{situation} to handle such cases as follows:
\begin{definition}[Situation]\label{def:situation}
	We define a \emph{situation} as an element in $(\mathcal{U}_{map}\times \mathbb{P}(\mathcal{E}))$. The set of all possible situations is denoted by $\mathcal{U}_{sit}$. Given a log $L\in \mathcal{L}$, we define the set of all situations derived from it as:
	$$S_L=\bigcup_{(map,E)\in L}\ \big( \bigcup_{e \in E}\ \ \ \{(map,E_{\leq \pi_{time}(e)})\} \big).$$
	It is obvious that $L \subseteq S_L$. Any $S \subseteq S_L$ is called a \emph{situation subset} of $L$. For a given log $L$, there are two main types of situation subsets. The first one is the \emph{trace situation subset} which is $S_{L,\bot} = L$. The second type is the \emph{event specified situation subsets} which includes all $S_{L,act} =\{ (map , E)\in S_L| \pi_{act}(\pi_{maxtime}(E))=act\}$, where $act\in \mathcal{U}_{act}$ and $S_{L,act} \neq \emptyset$.
\end{definition}

An attribute may be a trace or an event level attribute. So, it may happen several times with different values in a trace. To specify an attribute, besides the name of the attribute, we need to know if it is a trace or an event attribute and if it is an event attribute, we need to know to which events does it belong. To concretely specify an attribute, we use the \emph{situation feature} notion defined as follows:
\begin{definition}[Situation Feature]\label{def:sf}
	For any given $ a \in \mathcal{U}_{act}\cup \{\bot \}$ and $att \in \mathcal{U}_{att}$, we call $ \sitf_{a,att}:\mathcal{U}_{sit}\not \mapsto \mathcal{U}_{val}$ a \emph{situation feature}. Given a situation $(map, E)$ we define $\sitf_{a,att}((map,E))$ as follows:
	$$\sitf_{a,att}((map,E)) = \begin{cases} map(att) & a = \bot\\ {\pi_{map}}(\pi_{maxtime}(\pi_{a}(E)))(att) & a \in \mathcal{U}_{act}\end{cases}.$$
	We denote the universe of all possible situation features by $\mathcal{U}_{\sitf}$.	Given a situation feature $\sitf_{a,att}$, we define $values(\sitf_{a,att})=values(att)$. Also, for a given $n \in \mathbb{N}$, $\EP \in \mathcal{U}_{\sitf}^n$ is a \emph{situation feature extraction plan} of size~$n$, where $\mathcal{U}_{\sitf}^n$ is defined as $ \underbrace{\mathcal{U}_{\sitf} \times \dots \times \mathcal{U}_{\sitf}}_{n\ times}$.
\end{definition}
A situation feature extraction plan can be interpreted as the schema, the tuple composed of those situation features that are relevant to the given problem in the process.

The first step of solving any problem is concretely specifying the problem. We call such a problem description a \emph{situation specification} which is defined as follows:
\begin{definition}[Situation Specification]\label{def::ss}
	A situation specification is a tuple $\Ss =(\EP,\ssf,\csf,\epsilon)$ in which
	\begin{itemize}
		\item [(i)] $\EP \in \mathcal{U}_{\sitf}^n$, where $n \in \mathbb{N}$, is the situation feature extraction plan which includes all the situation features for which we are going to investigate their effect on the given problem.
		\item [(ii)] $\ssf \in \mathcal{U}_{\sitf}$, the sensitive situation feature where $values(\ssf) = \{\fg,\pg \}$ and $\ssf \not\in \EP$.
		\item [(iii)] $\csf \in \mathcal{U}_{\sitf}$, the class situation feature where $values(\csf) = \{+,-\}$, $\csf \not\in \EP $, and $\csf \neq \ssf$.
		\item [(vi)] $\epsilon \in [0,1]$, indicating the acceptable level of discrimination against $\ssf$ (the amount of acceptable dependency between $\ssf$ and $\csf$).
	\end{itemize}
\end{definition}

For a given situation specification, we go through the following three steps;
	\begin{enumerate}
		\item \textbf{Enriching the log:} The event log is enriched with several attributes that are driven from the given event log or other sources of information.
		\item \textbf{Extracting the data:} The relevant independent situation features, sensitive and class situation features values are driven from the enriched log.
		\item \textbf{Learning fair classifier:} Two decision tree classifiers are created by the C4.5 algorith
		. Then a relabeling technique is used to remove the unacceptable discrimination from one of the decision trees.
	\end{enumerate}
	\begin{figure}\label{pic::general}
		\includegraphics[width=130mm]{generalApproach1.jpg}
		\caption{The general framework proposed for fair root-cause analysis. First, according to the situation specification the event log is enriched by preprocessing the log and other sources of information. Then, the data is extracted from the enriched event log. Finally, two standard and fair classifier are created. Based on the analysis result, it is possible to adapt the situation specification to gather additional insights.}
	\end{figure}

	The general approach of our method is depicted in Figure~\ref{pic::general}. In this approach, the first two steps are related to the data extraction and the third one aims at creating a fair classifier.

	\section{Approach}\label{approach}
	We go through every one of the steps for creating a fair classifier for a given situation specification, mentioned in the previous section, in more details.
	\paragraph{1. Enriching the log.} Let $\Ss =(\EP,\ssf,\csf, \epsilon)$ be the given situation specification. If $\EP$ includes situation features that can not be directly extracted from the given log $L$, we enrich the log by augmenting each trace of it.
	In this step, we add some attribute values to the traces and its events. These added attributes can be related to any one of different process characteristics; time perspective, data flow-perspective, control-flow perspective, conformance perspective, or resource organization perspective. They may be driven from the given log, conformance checking results from replaying the traces on a given Petri-net model, or any external information resource like the weather information. Here, we assume that we have access to the log, Petri-net modeling the process of the log and the conformance checking results of replaying the log on the given model. The range of possible attributes that can be used to enrich the log is broad. We have implemented some of them. For example, some of the implemented trace attributes are \emph{trace-duration}, \emph{trace-delay}, \emph{sub-model-duration }, and, \emph{deviation}. Some of the implemented event attributes are \emph{next-activity-in-the-trace}, \emph{previous-activity-in-the-trace}, \emph{total-workload}, and \emph{resource-workload}.

	 The formal definitions of these attributes are beyond the scope of this paper\footnote{Refer to~\cite{de2016general} for the formal definition of some of the properties.}.
	\paragraph{Extracting the data.} To discover meaningful dependency results by the decision tree, we need to capture the data such that the causality relations among them and the class attribute are not violated. To do so, given  $\csf =\sitf_{a,att}$, we apply the following two rules while extracting the data;
	\begin{enumerate}
		\item If $a \in \mathcal{U}_{act}$, each trace may map to several situations and the data should be extracted from that part of the trace that happens before the occurrence of $\csf$. If $a =\bot$, then $\csf$ is related to a trace level attribute and the data should be extracted from the whole trace.
		\item The value of the independent situation feature with the closest occurrence time to the occurrence of $\csf$ must be collected.
	\end{enumerate}
	The second rule is valid assuming that if one of the independent situation features has happened several times before the occurrence of $\csf$ in a trace, the one that is closest to the occurrence of $\csf$ regarding the time, has the most effect on its value.

	To follow the first rule, for the given log $L$ and the situation specification $\Ss=(\EP,\ssf,\csf,\epsilon)$, where $\csf =\sitf_{a,att}$, we set $S=S_{L,\bot}$ if $a=\bot$ and we set $S=S_{L,act}$ if $a = act$. If $S=S_{L,\bot}$, then each situation is a trace in the log. However, if $S=S_{L,act}$, then for all $s \in S$ we have $\pi_{maxtime}(\pi_{events}(s)) = act$. i.e., $S$ is the set of all situations in $S_L$ for which the activity name of the event with maximum time stamp is $act$.

	The final step for extracting the data is creating a data table and annotating each row of the table by adding the values of sensitive and class situation feature to it. We create the data table and annotate it as specified in the following definition. Note that each situation is mapped to a row of the table.
		\begin{definition}[Situation Feature Table]	\label{def:table}
		Given a situation feature extraction plan $\EP=(\sitf_{a_1,att_1}, \dots , \sitf_{a_n,att_n})$, and a situation set $S \subseteq \mathcal{U}_{sit}$, a \emph{situation feature table} is a multi-set which is defined as:
		$$T_{S,\EP}=[ ( \sitf_{a_1,att_1}(s),\dots , \sitf_{a_n,att_n}(s) )| s \in S].$$
		For a log $L\in \mathcal{L}$, $S\subseteq S_L$, we call $T_{S,\EP}$ a \emph{situation feature table of $L$}.

		For a given situation feature table $T_{S,\EP}$ and $\csf,\ssf \in \mathcal{U}_{\ssf}$ for which $\ssf \neq \csf$ and $\csf,\ssf \not\in \EP$ and $\forall_{s \in S}\ (\csf(s)\neq \bot \land \ssf(s) \neq \bot) $, we define an \emph{annotated situation table} $\AT_{S,\EP,\ssf,\csf}$ as:
		$$\AT_{S,\EP,\ssf,\csf}=[( \sitf_{a_1,att_1}(s),\dots , \sitf_{a_n,att_n}(s), \ssf(s), \csf(s) ) | s \in S].$$
		We call each element of $\AT_{S,\EP,\ssf,\csf}$ an \emph{instance}.
		For a given instance  $inst_s= ( \sitf_{a_1,att_1}(s), \dots , \sitf_{a_n,att_n}(s) , \ssf(s), \csf(s))$ we define $\pi_{\EP} (inst_s)=\big(\sitf_{a_1,att_1}(s),\\ \dots , \sitf_{a_n,att_n}(s) \big)$, $\pi_{\ssf}(inst_s)=\ssf(s)$, and, $\pi_{\csf}(inst_s)=\csf(s)$.
	\end{definition}
	Here, $\ssf$ is the sensitive and $\csf$ is the class (label) situation feature. Also, each member of $inst_s \in \AT_{S,\EP,\ssf,\csf}$ where $s \in S$
	can be seen as a row of the data table in which $\pi_{\EP}(inst_s)$ is the tuple including independent attribute values and $\pi_{\csf}(inst_s)$ is the class attribute value of $inst_s$.
	\paragraph{Learning fair classifier.}
	We define a classifier as follow:
	\begin{definition}[Classifier]\label{def:classifier}
		Let $S$ be a set of situations and $\EP= ( \sitf_{a_1,att_1}, \dots ,\\ \sitf_{a_n,att_n})$ be a situation extraction plan and $\csf\in \mathcal{U}_{\sitf}$ such that $\forall_{1\leq i \leq n} \sitf_{a_i,att_i} \neq \csf$, then a \emph{classifier} is a function $class:T_{S,\EP} \mapsto values(\csf)$.
	\end{definition}
Given a classifier $class$ and an annotated situation table $\AT_{S,\EP,\ssf,\csf}$, then the accuracy of $class$ over $\AT_{S,\EP,\ssf,\csf}$ is measured as:
$$acc(class,\AT_{S,\EP,\ssf,\csf}) = \frac{|[inst \in \AT_{S,\EP,\ssf,\csf}| class(\pi_{\EP}(inst))=\pi_{\csf}(inst)]|}{|\AT_{S,\EP,\ssf,\csf}|}.$$
	For fairness, we use demographic parity as the main concept. To measure the discrimination in the data, we use the measure mentioned in~\cite{kamiran2010discrimination}, which is:
	$$disc(\AT_{S,\EP,\ssf,\csf})= \frac{|[inst \in \AT_{S,\EP , \ssf , \csf} |\pi_{\ssf}(inst)=\fg \land \pi_{\csf}(inst)=+]|}{|[inst \in \AT_{S,\EP , \ssf , \csf}| \pi_{\ssf}(inst)=\fg]|}- $$
	$$\frac{|[inst \in \AT_{S,\EP , \ssf, \csf}| \pi_{\ssf}(inst)=\pg \land \pi_{\csf}(inst)=+)]|}{|[inst \in \AT_{S,\EP , \ssf , \csf}| \pi_{\ssf}(inst)=\pg]|}.$$

   By replacing $\pi_{\csf}(inst)$ with $class(\pi_{\EP}(inst))$ in this equation, we can measure the discrimination imposed by the classifier $class$.

For the classifier, we use decision trees. It is worth mentioning that both the classifier and the measure of discrimination can be changed according to the given application.

The first step toward removing the discrimination has already been taken during the creation of the classifier by not considering the sensitive situation feature for the classification purpose (Definition~\ref{def:classifier}). As mentioned in many works, e.g.~\cite{zemel2013learning}, this is not enough due to the existence of correlation among different situation feature values in a given situation feature table. The discrimination in the classifier can be further eliminated by the relabeling technique. In this paper, we relabel leaves in the decision tree to balance accuracy and fairness. However, other discrimination free classifiers can be used~\cite{aghaei2019learning,zafar2017fairness,zemel2013learning}.

As mentioned before, in the implemented plug-in two classifiers are generated. The first one is a tree classifier that is generated by J48 tree classifier implementation of C4.5 algorithm in WEKA package. Then, if the discrimination in the resulting decision tree is more than an acceptable threshold $\epsilon$, the leaves of the decision tree are relabeled to create a fair classifier. For the relabeling, we use an algorithm similar to the one mentioned in~\cite{kamiran2010discrimination}. In~\cite{kamiran2010discrimination}, the leaves of the tree are ordered in descending order of the ratio of the discrimination gain and accuracy lose of relabeling each leaf. Then according to this order, leaves are relabeled until the discrimination in the classifier tree is lower than $\epsilon$. As mentioned in~\cite{kamiran2010discrimination}, the problem of finding the set of leaves to be relabeled such that the discrimination in the decision tree is lower than a given threshold $\epsilon$ with the lowest possible negative effect on the accuracy of the decision tree is equivalent to the knapsack problem. In the relabeling algorithm implemented in the ProM plug-in, we use dynamic programming and rounding to choose approximately the best possible set of leaves to be relabeled.

Note that in the context of process mining and root cause analysis, changing the class label from + to - and from - to + at the same time may not be desirable. Consider a case where the problem in the process is the delay in some cases. Then + label of a situation means being on-time. Changing the label of some leaves from + to - means considering some delay where they do not exist. So in some cases we may need to restrict the relabeling technique to just desirable or just undesirable labeled leaves of the tree. Note that if we restrict the relabeling, there might be cases where the discrimination of the fair tree is higher than given $\epsilon$. In these cases the discrimination in the fair decision tree would be close $\epsilon$.

		\section{Implementation and Experimental Results}\label{result}
		The approach presented in Section \ref{approach} has been implemented as a plug-in of ProM which is an open source framework for process mining. The implemented plug-in is available under the name \emph{Discrimination aware decision tree}.

		The inputs of the plug-in are the event log, the Petri-net model of the process, and, the conformance checking results of replaying the given log on the given model. Using these inputs, several derivative attributes for enriching traces have been implemented.

		The current implementation focuses on three types of problems in a given process:

		\begin{itemize}
			\item \textbf{Routing problems: }When there is a choice in the model of the process, what was the reason that some cases took one choice while the others took another?
			\item \textbf{Deviation problems: }This category refers to the questions like why a specific activity has been skipped or what was the reason that some traces do not comply with the given model.
			\item \textbf{Performance: }This category includes questions like what was the reason for the delay in the cases or why a specific activity takes more time in some cases.
		\end{itemize}
		To illustrate the fair analysis of these problems we use two real data logs, the \emph{hospital billing}\footnote{https://data.4tu.nl/repository/collection:event\_logs\_real} event log and \emph{receipt phase of an environmental permit application process (WABO) CoSeLoG project}\footnote{https://data.4tu.nl/repository/uuid:a07386a5-7be3-4367-9535-70bc9e77dbe6} (receipt log for short). We use the last 20000 traces of hospital billing log in the experiments which include 71188 activities. The receipt log includes 1434 traces and 8577 activities. In this initial evaluation, we created a controlled experiment with a known ground truth. The discrimination is added to the event logs artificially and then the altered logs are used to evaluate the method and investigate the effect of removing discrimination on the accuracy of the created fair decision trees. In all the experiments the same setting has been used. For example in all the experiments $\epsilon =0.05$ and there was no limit for applying relabeling technique. Also for each event log, the same set of independent situation features has been chosen and all the parameters for creating the decision tree were the same. 60 percent of the data has been used for training, and 40 percent of the data has been used for testing the classifier.
	\begin{figure}
		\includegraphics[width=120mm]{resultall.jpg}
		\caption{The result of applying the implemented ProM plug-in on two real event logs. In all these charts, the blue curve exhibits the level of discrimination in data, the orange curve shows the level of discrimination in standard decision tree, the gray curve shows the level of discrimination in a fair decision tree, the yellow color curve exhibits, and, the green color curve exhibits the accuracy of the fair tree. The first three pictures respectively show the results of applying our technique when there is (a) a performance problem, (b) a routing problem, and (c) a conformance problem in the receipt process. (d), (e) and (f) show the same results when the same problems are observed in the hospital billing process. In all these experiments $\epsilon =0.05$ and the level of discrimination in the data is depicted by the $x$-axis. In all these experiments, the level of discrimination in the fair classifiers are less than the given threshold $\epsilon$. Also, as the level of discrimination increases in the data, the difference between the accuracy of the fair decision tree and standard decision tree increases. Also in part (f), the fair decision tree demonstrates a better performance than the standard decision tree in terms of accuracy.}\label{pic:resultall}
	\end{figure}
	\begin{SCfigure}

		\caption{The result of applying implemented plug-in with different values for parameter $\epsilon$ which is depicted in purple in the chart. In this chart, the value of $\epsilon$ shown by the pink curve. The level of discrimination in the data in all these experiments are the same. In all these experiments, the level of discrimination in the fair decision tree is lower than the given threshold $\epsilon$. Also, the accuracy of the fair decision tree tends to be lower for the lower values of $\epsilon$.}\label{pic:epsilon}
		\includegraphics[width=6.5cm]{epsilon.jpg}
	\end{SCfigure}
	The results of our experiment are depicted in Figure~\ref{pic:resultall}. The first three charts present the results for the receipt process and the other three charts present the results for the hospital billing process regarding the three problems that have been mentioned above. Chart (a) and chart (d) show the results of applying our technique when there is a performance problem in the process for which we consider the delay in the traces. Chart (b) and chart (e) show the results of applying our technique when there is a routing problem in the process for which we consider the choice between ``T05 Print and send confirmation of receipt" and skipping this transition in the receipt process and the choice between ``BILLED" and skipping this transition in the hospital billing process. Chart (e) and chart (f) show the results of applying our technique when there is a conformance problem in the process for which we consider the existence of deviation in the traces in both processes.

	In each chart, the level of discrimination in the data is depicted on the $x$-axis (and also by the blue curve for the sake of easy comparison). In each experiment the level of discrimination in data (denoted by `discrimination data', the blue curve, in the charts), the level of discrimination in standard decision tree (denoted by `discrimination standard tree', the orange curve, in the charts), the level of discrimination in fair decision tree (denoted by `discrimination fair tree', the gray curve, in the charts), the accuracy of the standard tree (denoted by `accuracy standard tree', the yellow color curve, in the charts), and, the accuracy of the fair tree (denoted by `accuracy fair tree', the green color curve, in the charts) are recorded. For example the two trees shown in Figure \ref{pic::snapshot} are related to the red vertical line in Figure \ref{pic:resultall}(a), where the discrimination in the data is 0.48, the discrimination by the standard decision tree is 0.26, the discrimination by the fair decision tree is 0.05, and the accuracy of standard and fair decision tree are 0.84 and 0.79, respectively ($\epsilon = 0.05$ and we set no limit for applying relabeling technique).

	As is depicted in Figure~\ref{pic:resultall}, we can reduce the discrimination on the sensitive group at the expense of some reduction at the accuracy of the classifier. As expected, as the level of discrimination increases in the data, the amount of the accuracy of the classifier that needs to be sacrificed for removing the discrimination increases. We need to be careful using this technique, as there are occasions where discrimination may be put on the favorable group. This phenomenon is also unfair. Surprisingly, in some cases like Figure~\ref{pic:resultall}(f), the fair decision tree outperforms the standard decision tree in terms of accuracy. This phenomenon has been reported in~\cite{kamiran2010discrimination} as well. Note that in all the experiments the relabeling technique with no limitation on the label of the leaf have been used, which means, both leaves with + and - labels may get relabeled.

	The chart in Figure~\ref{pic:epsilon}, demonstrates the level of discrimination in the fair decision tree and its accuracy for different settings of parameter $\epsilon$. As expected, the accuracy of the fair decision tree is lower when $\epsilon$ is smaller. Here, we use the receipt log and delay in traces as the class situation feature.

	To demonstrate that a fair classifier may affect the results of the root cause analysis of a given problem in a process, an example based on an experiment on a real event log is given in the Appendix \ref{appendix}.

	\section{Conclusion}\label{conclution}

	The first step toward enhancing a process by removing one of its performance or compliance problems is diagnosing the root causes of that problem. By using standard data mining techniques for detecting the causes, the results might be obvious and mainly regarding those parts of the process that can not be altered. The other less obvious roots of the given problem are sometimes the most precious diagnosis where the real improvement can be applied. To reveal these less vivid causes of the problem we need to mask the obvious ones. We did so by looking at the cause that we need to ignore its effect on the problem as the sensitive attribute. Then removing the dependency between the sensitive and the class attributes from the created classifier, we allow other causes to shine. This is done at the expense of a small reduction in the accuracy of the resulting classifier.

	This research has several applications; detecting the discrimination within a process, removing the discrimination from the process by replacing the fair classifier with the current one, making more accurate and realistic judgments about the root causes of the problem at hand.

	This research can be extended in several directions. The first one is to add new derived attributes to the log when enriching the log. The other one is altering the fairness criteria, the classification method, or the technique for creating the discrimination-free classifier depending on the application.

	\bibliographystyle{splncs04}

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text:
Q: Target a specific version of Android with Meteor Yes, I have read the Meteor Cordova PhoneGap Integration Guide but I couldn't find any information on this issue.
I am trying to set up Meteor to develop for Android.
My phone runs Android 4.2.something and a lot of our potential users probably run some JellyBean (4.1.x-4.3.x), ICS (4.0.x) or Gingerbread (2.3.x) - but probably not KitKat (4.4.x).
Meteor by default targets only android-19 (4.4.x), un-installing this target even makes it think that the ADK is not installed at all. (I used meteor configure-android for that, which appearantly only configures the ADK and not Meteor itself.)
How can I make both my emulator and my builds target older/different versions of Android?

A: This is a bit outdated but if anyone else runs into this this same question, The answer by Mário is valid, but if like myself one does not use Android Studio this can also help.
Add this line to your mobile-config.js:
App.setPreference('android-targetSdkVersion', '17');
replace 17 with your desired version.
This might also help: https://github.com/meteor/meteor/issues/3434#issuecomment-97953738

A: You can do this in mobile-config.js.So no need to struggle with android studio.
App.setPreference('android-minSdkVersion', '14');**
App.setPreference('android-targetSdkVersion', '23');
App.setPreference('android-versionCode', '3');**

A: You need to use Eclipse or Android Studio. I had a similar issue with the iOS version. It's using the iOS 8 and with this SDK the iPhone 4 is not supported. So in my case, I opened the generated project file and changed it to iOS 7 SDK.
I'm not an Android Developer but the process should very very similar I guess.

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What is a trademark?
Written by Philip Newman
Published in Business management on December 24, 2018
0 comments | Tags: company names, Trademarks
A trademark is something that identifies the products or services of a specific business. It is, therefore, a valuable business asset that needs to be protected to prevent others trying to use it to sell their products and services. The trademark can just be the name of the company or product but does not need to be and can be:
Words – eg company name or product name
Designs – eg shapes, symbols, patterns, logos and signs
Sounds – eg jingles
Shapes of goods or packaging – eg three dimensional (3d) shapes
Movement – eg a sequence of images
Smells, although these will be very hard to register
A combination of any of the above
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A trademark is just one type of intellectual property that can be protected. Other types are copyright, patents and designs.
A trademark can be unregistered or registered. The symbol ® can be used after the name to indicate a registered trademark. TM can be used after the name to indicate an unregistered trademark.
Registered trademarks in the UK are those accepted by the Intellectual Property Office ('IPO') or the European Union Intellectual Property Office where a EU wide registration has been made. A registration lasts for ten years at the end of which it can be renewed.
The oldest UK registered trademark is the Bass triangular shape in an oval with the words Bass & Co's pale ale registered on 1 January 1876. This is still a registered trademark although no longer owned by Bass & Co. An example of a design that is a registered trademark is the Burberry check pattern owned by Burberry Limited.
Unregistered trademarks are those that are not included in the IPO register but are still used to identify the business's products or services.
It is possible to stop someone using the same or similar trademark to a pre-existing registered or unregistered trademark (known as 'passing off') but it is harder to demonstrate this for an unregistered trademark. For an unregistered trademark the claimant will need to demonstrate that they:
own the trademark for the appropriate goods and services;
have built up goodwill associated with the trademark; and
have been harmed in some way by that person using the trademark.
Rules around trademarks
Trademarks in the UK are covered by The Trade Marks Act 1994 as amended by subsequent legislation and most recently by The Trade Marks Regulations 2018 which is in force from 14 January 2019.
Trademarks are recognised if:
they can be clearly and precisely represented in the register of trademarks to enable others to determine what is protected; and
they sufficiently distinguish the products and services of one person from those of another person.
The business registering the trademark must have some form of separate identity (eg individual person, company, LLP, partnership or trust). Once a trademark has been registered this should help stop another business using that trademark for the same or similar products or services. This does not stop another business selling the same or similar products or services provided a different trademark is used or using a similar trademark for totally different products or services.
What can be accepted as a registered trademark
As detailed above there are numerous forms that a trademark can take. There are, however, also rules that mean a trademark:
cannot be offensive, eg cannot contain swear words or obscene images;
cannot only describe the type of goods or services it relates to, eg cannot use hosepipe for a company that makes hosepipes;
cannot be misleading, eg cannot use 'natural' for goods that are man-made;
cannot be a 3-dimensional shape associated with the goods, eg cannot use the shape of an egg for eggs;
cannot be too common and non-distinctive, eg cannot be a simple statement like 'the best';
cannot be a protected designation of origin or geographical indication;
cannot be a protected traditional term for wine or specialities guaranteed;
cannot be a protected plant variety;
cannot be the same as or too similar to an existing registered trademark for the same class;
cannot look too similar to symbols, eg flags, emblems or hallmarks of countries or international organisations.
The last of these is covered by the World Intellectual Property Organisation guidelines. This sets out the terms, emblems and symbols that cannot be included in a registered trademark that are protected under an international agreement. The main agreement is Article 6ter of the Paris Convention for the Protection of Industrial Property ('Article 6ter'). This prevents the registration and use of trademarks that are identical or very similar to:
armorial bearings;
flags;
other emblems;
official signs;
hallmarks;
abbreviations; or
of any country, international intergovernmental organisation ('IIG'), a program or institution established by an IIG, or an international convention to which one or more country is party.
An example of an acceptable three dimensional shape as a registered trademark is the Borer biscuit packaging. The BT theme mnemonic is an example of an acceptable sound as a registered trademark.
Web domain, company and LLP names
A web domain name is separate from an organisation's trademark and just because someone has a registered trademark it does not mean that they have the right to a web domain that includes their trademark. It could be that the other party is using the term for different goods or services so is also legitimately using that domain name. Similarly having a web domain does not mean that this is their trademark, especially if someone else has an earlier trademark that is too similar.
It is, however, possible to have someone's domain name registration removed if that domain name has been registered unlawfully or maliciously.
As for domain names just having a registered company or LLP does not stop a person from using a trademark that is the same as or similar to the company's name, especially if the trademark was in use before the company was incorporated. In addition, the company name may not be a trademark as it might:
be a descriptive word or term;
indicate geographical origin;
already be registered by someone else.
Alternatively, a business might have a trademark, which is a word or term, that is not allowed by Companies House as it is not allowed by the legislation covering company names.
Types of trademark
There are certain types of trademark that only certain organisations can register:
Certification marks – marks owned by trade associations, government departments, technical institutes or similar bodies that indicate a guarantee that the goods or services are of a certain standard or have a certain characteristic.
Collective marks – marks that indicate the goods or services are from members of a trade association, rather than just one business.
Register of trademarks
The IPO maintains a register of trademarks and there is a facility to search IPO's database online. A search of this database should be carried out when deciding on the name of a business or name of a company and before an application to register a trademark is made. This should show if there is an existing registered trademark the same as or too similar to the proposed name for the same class of goods or services. If a search does not find a match this does not mean that the IPO will accept the trademark as not being the same as or too similar to an existing trademark.
It is possible to use someone else's existing trademark if that person has licensed the use.
Trademark classes
A registered trademark is specific to a class of business, although the same entity can register the same trademark in more than one applicable class. The trademark classification system groups together similar goods (products) or services into 45 different classes:
goods are listed in classes 1 to 34;
services are listed in classes 35 to 45.
These are consistent across the world and the list is referred to as the Nice classification. All classes have an overall heading explaining what is covered in that class. Each class then has a list of approved terms.
When applying to register a trademark it is important that all the correct classes and approved terms within each class for the business's goods and services are chosen. New classes or approved terms cannot be added to an existing registered trademark.
The trademark class search function of the TMclass website can be used to find out which class or classes the business's goods or services fall within.
Selling, transferring, licensing or mortgaging trademarks
As for other assets the following can be done with trademarks:
Sale – full sale of the trademark or partial sale relating to some of the goods and/or services;
Transfer – full transfer of the trademark or partial transfer relating to some of the goods and/or services;
License – full or partial licensing of the trademark to another person;
Mortgage or charge of the trademark – the mortgagor or chargor then has an interest in the trademark until the debt is settled.
Any change of ownership, new licensing agreement or new security should be notified to the IPO on the appropriate form.
Objecting to other peoples' trademarks
The procedures and rules for objecting to trademarks differ depending on whether the trademark is unregistered, has been entered in the IPO's register or published by the IPO in its online journal.
Objecting to a published trademark
When a trademark application is first accepted by the IPO it is initially published in its online journal. There is then a period of two months (extendable to three months) when objections to the registration can made. This can be by either:
third party observations – where someone thinks the application has been accepted by the IPO in error, eg the trademark is descriptive of, or a generic term for, the goods or services, is not unique, is potentially misleading to consumers or is too similar to an existing trademark. The IPO will only consider the observations; or
opposition – where someone tries to stop a published trademark becoming registered. The entire application or only some of the goods or services can be opposed. This can be on:
absolute grounds where there is an alleged problem with the trademark, eg it is:
descriptive of the goods and/or services;
generic for those goods/services; or
non-distinctive and should be free for everyone in that line of trade to use
relative grounds as there exists an earlier trademark (which does not have to be registered) owned by the opponent with which the applicant's trademark would conflict if it were used.
Anyone can oppose an application on absolute grounds but only the owner of an earlier trademark may oppose on relative grounds.
Opposing a registered trade mark
Once a trademark has been registered any of the following action can be taken to challenge it:
Invalidation – apply to remove a trademark, in entirety or only some of the goods or services, from the register, eg where:
it is not unique to the owner; or
the same or similar trademark (registered or unregistered) is owned by another party.
Revocation – apply to remove a trademark from the register, in entirety or only some of the goods or services, eg where:
it has not been used by the registered owner;
it has become the common name for the registered goods or services; or
it is likely to mislead the public, particularly as to the nature, quality or geographical origin of the goods or services.
Rectification – apply to correct an error or omission in the recorded details of a registered trademark, eg where:
words or terms used to describe items in the list of goods or services are self-evidently wrong;
the registered owner's name and/or address is incorrect;
there was a genuine mistake of identity where the intention was always to name one person as the owner when another person was named in error.
Rectification cannot be used to:
amend the trademark;
add to the list of goods and services; or
change the owner of the trademark.
The costs of opposing or objecting to a trademark can be high and may be more than awarded in a successful claim.
In addition to the IPO the European Union Intellectual Property Office can make enforcement orders in relation to UK registered trademarks.
Professional advice and mediation
To help in obtaining and defending trademarks there are numerous legal professionals who specialise in Intellectual Property law. The following organisations can be contacted to help find suitable professionals:
Chartered Institute of Trade Mark Attorneys;
Mediation is often a cheaper and quicker way of resolving disputes than going to court and the outcome can be beneficial to both parties. Mediation services are also provided by the IPO.
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The Authority's Engineering Division staff is comprised of licensed professionals supported by sophisticated computer monitoring programs and equipment. Their expertise is used internally to ensure the Development Authority is compliant with all federal, state and local safety and regulatory requirements, and externally by providing contract services at competitive rates for value-added services to municipalities in the North Country region.
With a solid track record of helping communities understand their technical challenges and working in partnership to develop cost effective solutions, requests for technical assistance by municipalities have increased significantly over the last several years. Authority staff work with communities to devise solutions for a wide range of technical challenges, including water and wastewater treatment system projects. On an as-requested contractual basis staff also provide project management and infrastructure facility oversight, host Geographic Information Systems (GIS) for communities, provide Supervisory Control and Data Acquisition (SCADA) system access, and conduct municipal efficiency studies for communities interested in exploring shared service opportunities.
The Development Authority has partnered with the Village of Lyons Falls and others to clean up the Lyons Falls Pulp & Paper site in Lewis County in preparation for redevelopment.


Anti-static conductive, ESD safe hypo allergenic tweezers made from Vommaloy. Special stainless steel, anti-magnetic and acid resistant.
Static dissipative, ESD Safe hypo allergenic tweezers made from Vommaloy.
Special stainless steel, anti-magnetic and acid resistant.


All About Air received the American Standard Heating & Air Conditioning recognition as a American Standard Customer Care Dealer for outstanding customer service. Let us take care of your needs today!
Our normal business hours are Monday thru Friday 7:30AM-4:00PM. However there is always at least one of our NATE certified technicians available for after-hours emergencies. We offer same day service for most requests, based on availability. We offer 24 Hour Emergency Service, 7 days a week for heating systems and the quickest response available for cooling systems.
Saturday & Sunday: Available as Needed.
Want to join our family team? We are offering a career opportunity to become one of our outstanding technicians. Read the JOB DESCRIPTION by clicking the career title link below.
CLICK HERE to APPLY TODAY by emailing your resume.
At All About Air we focus on providing a range of energy efficient solutions and the highest quality heating and cooling equipment for our customers. This allows us to give our customers many options when choosing a heating and cooling system for their home. When choosing All About Air for your energy efficient products, you will save your family money and time because our staff will give you the knowledge to operate the equipment to maximize your energy bill.
At All About Air we are always in search of the most innovative technology on the market and we continually educate our staff on any new developments in the industry. With consistent training and certifications for our technicians and installers, we will always be able to service our customers and offer the newest equipment and heating and cooling solutions available.
At All About Air we striving to provide competitive pricing and high quality affordable service, maintenance and equipment to save you money! We offer a wide variety of brands with a range of products and services to fit any budget. We also offer maintenance agreements that can save our customers money over time. Call us now for more info and find out how we can lower your energy bills and make your home comfortable for your entire family.
Lenny Hewitt, Owner of All About Air Heating & Cooling, was born in the great Northwest and has more than 30 years experience in the Heating and Cooling Industry. Over the years Lenny has developed close relationships with his customers and vendors and always strives to please; his personalized service, advice and product options are truly unique and trustworthy. His experience in the field has given him all the knowledge to handle each customer as an individual and his continued training and education allow All About Air provide the best possible solutions for each and every home. Lenny enjoys taking road trips on his Gold Wing motorcycle together with his wife and Office Manager, Ann Hewitt. He also teaches pitching to many aspiring fastpitch softball players in and around the Vancouver area; his involvement in the girls' softball community started when his daughter and Marketing Manager, Jennifer was nine years old.
In the Office: As Office Manager, Ann brings her skills and knowledge to the All About Air office from her 25 years of experience in the Banking Industry. Her confidence in the fields of finance and accounting, plus her drive to continually educate the team and herself keep the team together and at the top of their game. As Marketing Manager, Jennifer holds a BA in Marketing from Seattle University and taps into her six years of experience in business and consumer sales, marketing and customer service to provide complete office support for the team.
In Summary, the All About Air team is there to provide you with outstanding personalized service and a customer experience that is unmatchable.


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Richard Anthony Movie Cast, Review, Wiki, Story, Trailer, Release date and more Richard Anthony is a Kannada language movie. The movie release date is 12 March 2022. It includes Rakshit Shetty in the cast. Story The plot revolves around the…


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More than 300 people were killed by more than 180 tornadoes in late April, 2011. This historic outbreak, like many that came before it, now ranks as the most thoroughly-documented tornado outbreak in United States history. With ever-advancing technology including GPS, high-resolution satellite imagery, and instant communication, such documentation of the outbreak spans far beyond the limits of just one newspaper column or one television account.
Visible satellite imagery reveals the scaring of the earth by the April, 2011 tornado outbreak. Destruction of trees and other vegitation reveals the barren earth beneath the once-dense forest.
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Waldron Learning Center
Right: Kristi Ritter and Miguel Flores enjoy the day.
The Waldron Learning Center opened in 1978, and was named after Marjorie Waldron, a local resident, who had a great love for special education students.
The school provides educational services for students with moderate to severe impairments and is operated by the Branch Intermediate School District.
In addition to the classrooms, the Waldron Center has a conference room, gymnasium with locker rooms, computer lab, cafeteria, and a designated outside play area. Swimming facilities are also available to our students at the Coldwater Aquatic Center within walking distance of our facility.
The building is WiFi accessible and equipped with security cameras, secured entrance, and a 911 emergency ID phone system.
The Waldron Center has been identified as a MI HeartSafe school. Members of this school know how to recognize the signs of a sudden cardiac arrest and have met the following requirements: written cardiac emergency plan; cardiac emergency response team and trained at least 10% of staff in CPR/AED; have accessible AEDs with identifying locations; and performs one cardiac drill per year.
Bulldog T-Shirt Day!
Waldron staff and students shared a little school spirit and everyone showed off their version of the Waldron Bull Dog t-shirt! Way to Go Waldron Bulldogs!!!
"AmeriGas School Days Program"
Pictured above, Special Education Director Darren Adair receives a check for $927.77 from AmeriGas District Manager Blaine Sprinkle, left, and Lisa Sorgenfrei. Waldron Learning Center has partnered with AmeriGas for their School Days Program with great success! Thanks to Asama Coldwater Manufacturing, the company and its employees have donated an average of $1,250 each…
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Effective as a stand-alone system and also functions well with other preservatives.
Offers effective protection against bacteria, yeast and mould growth while imparting emolliency.
Performs best in formulations below 6.0 pH, but is also proven effective at pH levels above 6.0.
May solidify at temperatures below 77F, if this occurs gently heat at 86 – 104F and stir to ensure homogeneity.
Oil-soluble. Limited solubility in water-based formulations because sorbic acid is insoluble in water. But, manufacturer confirms that Optiphen Plus is effective in creams, lotions, shampoo, balms, salves & scrubs.
Recommended usage: 0.75 – 1.5%.


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A Class of Its Own: Everett Wins Super Bowl Title
by Independent Staff • December 8, 2011 • 2 Comments
Everett line coach Mike Milo and Everett players celebrate the team's Super Bowl victory Saturday at Bentley University.
The Everett High football team was ranked the best team in the state in September but preseason polls have never mattered much to Everett High head coach John DiBiaso, his staff, and the players in his vaunted program.
Led by a record-breaking performance by quarterback Jonathan DiBiaso, Everett stormed through the regular season undefeated while solidifying its No. 1 ranking.
A 46-0 rout of Lynn English in the semifinals left Everett with one more challenge to conquer in its quest for perfection.
And with one quarter remaining in the Division 1A Super Bowl Saturday night before a capacity crowd at Bentley University, Lincoln-Sudbury High School was threatening to spoil Everett's bid for a second straight Super Bowl championship.
But Everett showed its determination, character, and talent in outscoring its opponent 15-0 in the final ten minutes. The biggest play and one of the best moves you'll see was turned in by junior Jakarrie Washington, whose 63-yard run down the sidelines helped Everett continue on to a 36-13 victory over Lincoln-Sudbury.
Everett claimed its second consecutive Super Bowl championship and extended its winning streak to 25 games.
Coach John DiBiaso told his team in a huddle at midfield after the game, "To everybody, I'm so proud of you – I can't say enough how much I thank you for the hard work and dedication."
Lincoln-Sudbury coach Tom Lopez said Everett's big-play capability was the difference.
"I'm proud of the way our kids played," said Lopez. "You don't want to lose but we thought we could compete with them. They made some big plays. They have a lot of speed on offense. [Jonathan] DiBiaso ran the ball a lot better than we thought he could."
Lopez praised the Crimson Tide for their phenomenal season.
"They deserve credit – they're the best team in the state," he told reporters after the game. "It was nice to stick with them for three quarters – maybe I'll feel better tomorrow or next week or next year about that, but right now I don't feel good about that."
There were offensive stars throughout the Everett lineup. DiBiaso, who was hampered by a stomach virus and did not participate in the pre-game calisthenics, was 11-for-20 for 108 yards passing, but it was his excellent running (16 carries for 70 yards, but none bigger than a 27-yard scramble with Everett pinned at its own 2-yard-line) and decision making that sparked the Tide offense.
Vondell Langston, the game's leading rusher with 126 yards on 13 carries, raced 62 yards following DiBiaso's key run. DiBiaso had an 11-yard keeper before scoring the first touchdown on a 1-yard run with 2:48 left in the first quarter.
Langston scored on a 2-yard run to culminate a 9-play 56-yard drive to make it 14-0 with 8:24 left in the second quarter. Washington had a 22-yard reception to help set up Langton's TD.
Lincoln-Sudbury closed the gap to 14-6 on a 61-yard interception return for a touchdown.
Jalen Felix took center stage with three fine plays in succession. After blocking the PAT, he returned the ensuing kickoff 57 yards to the Warriors' 26-yard line. He scored on a 2-yard run and when Gilly Desouza booted the PAT, Everett led 21-6.
Lincoln-Sudbury struck pay dirt in the third quarter on a 14-yard touchdown pass, closing the gap to 21-13.
But Washington took the wind out of the Warriors' sails with a sensational 63-yard run. Joey White's run for the 2-point conversion made it 29-13. DiBiaso's second touchdown run and Desouza's PAT finalized the 36-13 victory and championship season.
Everett's defense, notably the front group of Ralph Jonathas, Milton Lopez, Paul Marckini, Jeff Soulouque, and Omar Graciano and linebackers Angel Duarte and Vondell Langston, was outstanding in limiting Lincoln-Sudbury to one touchdown.
Coach John DiBiaso was pleased with the entire team's effort against a tough opponent.
"I'm thrilled for my seniors and the entire program," said DiBiaso. "Winning the title was much more difficult the second time in a row because of the bull's-eye on our back and the fact that the previous year we were very motivated coming in to the season having lost to Xaverian. The players had to stay self motivated because we had won the year before."
DiBiaso praised his team's mental toughness.
"We had to overcome a lot of adversity [including a season-ending injury to star linebacker Chris McCarthy] and the kids stayed mentally tough and responded when Lincoln-Sudbury cut the lead to a touchdown and they did what they have done all year."
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2 comments for "A Class of Its Own: Everett Wins Super Bowl Title"


"Return to the Temple of Elemental Evil" is a very good adventure that requires a lot of prepping, a lot of gaming time, and a lot of dice rolling. Those who remember the original "Temple of Elemental Evil" will love this campaign, as will newcomers to D&D 3E.... 19/08/2013 · Return to the Temple of Elemental Evil – in 4E My Greyhawk 4E campaign continued on Friday with the group continuing to explore the ruined Moathouse near the Village of Hommlet. They'd just discovered a priestess examining some artefacts which the group recognised as being part of the cult of Tharizdun, and they were unsure of what else they would find.
8/02/2015 · Wizard's announcement that the Temple of Elemental Evil was going to be a feature of this season's campaign theme strikes me both as a sign of the merits of this series and also as a maybe an unwelcome travelling companion on this journey.
This item presents modifications to Return to the Temple of Elemental Evil to adapt that product to RPGA Living Greyhawk, Tournament Play. It was run at GenCon UK, 2001, as a special event. It was run at GenCon UK, 2001, as a special event.


Help, I Have Nothing To Wear!
Is Anyone Watching, Swimming lessons anyone?
The children were lined up in the cafeteria of the Catholic elementary school for lunch.
Moving further along the lunch line, at the other end of the table was a large pile of chocolate chip cookies.
As I enjoyed watching the Olympics the last few weeks, the discipline and commitment that each Olympian strives to achieve are amazing.
Every time I see or hear about the Olympics, I think about my daughter who became intrigued with the swimmers. I would find her in the living room practicing the butterfly stroke with the swimmers on our shag carpet. Every day she came home after school to practice. And at night I would find water on the bathroom floor as she perfected her strokes.
We soon learned that she be going on a kindergarten school trip. The class was going to a swimming pool, and she did not know how to swim. And the trip was only a week away. So we went and bought a new swimsuit for the occasion, but caution our daughter not to go into the deep end because swimming around the living room does not make her a swimmer. But, she kept insisting that she knew how to swim, after all, she had practiced!
The day arrived, and Lyle took her to school that morning and again cautioned her not to go into the deep end. He got a smile, hug, and a wave as she headed for the bus and her friends.
We waited impatiently until the time arrives to pick her up, and Lyle raced down to the school to get her.
She was so convinced that she knew how to swim. After all, she learned all the strokes and had practiced with the best. The only thing she was missing though was the water!!!
Although we laugh at remembering this story, I wonder how many times do I think I know what's best for myself too?
I look up to the mountains— does my help come from there?
My help comes from the Lord, who made heaven and earth!
He will not let you stumble; the one who watches over you will not slumber.
Do yo ever feel like God has too much to do to take care of your little problems?
Thank you, dear friends for spending your time with me, Have a wonderful weekend be safe. Hugs to all.
Previous Post: It's Time To Practice Your Music!
Where did you learn this?
Copyright © 2019 Karan K. Gleddie. All rights reserved.


Kincraig Castle Hotel is owned by a family of golfers and boasts its own golf practice facility with a 4 holes pitch & putt course within the castle grounds, a feature which is unique among hotels in the Scottish Highlands. There are 20 golf courses within a 20 miles radius of the hotel, the most famous of which are the Royal Dornoch, Castle Stuart and The Nairn Golf Club championship courses.
The Scottish Highlands conjure up images of breathtaking scenery and the stunning landscape is used to great effect when it comes to the Highland region's golf courses. The challenges of the golf courses allow the seasoned professional and the occasional amateur golfer to enjoy a round of golf in Scotland: 'The Home of Golf' - and what is more Scottish than the stunning Highland Region? Make your golf visit a proper 'hole in one' by exploring and enjoying the golf courses of the Highland Region using Kincraig Castle Hotel as your base.
We'll be happy to arrange tee times for our guests on any golf courses and we can offer special 'Stay & Play' golfing packages.
Royal Dornoch Golf Club, currently ranked 1 in Scotland and 6 in the world by Golf Digest.
Brora Golf Club, James Braid's 1891 seaside Masterpiece which Five-time Open Champion Peter Thomson stated was "the best traditional links course in the world".
Carnegie Links at Skibo Castle, the exclusive course located at Andrew Carnegie's Skibo Castle.
Golspie Golf Club, James Braid 1889 seaside classic rumoured to have the finest greens in Scotland.
Tain Golf Club Old Tom Morris clever 1890 design hosted Scottish Ladies Amateur Championship in 2012.
Castle Stuart Golf Links, Scottish Open Venue and voted best new course in Scotland.
Nairn Dunbar offers a Championship Links course that challenges players of all abilities.
We strive to ensure your stay with us exceeds your expectations, making an unforgettable visit.


text: Q: Kendo Grid & Rich text Editor of Sharepoint 2013 I'm using custom grids (Kendo UI Core free version) into an sharepoint 365 APP. I would like to use the Rich text Editor of sharepoint 2013 when I edit some fields. is this possible?


Wikipedia: The Hostel is situated next to the Master's Lodge. It was built in a neo-Georgian style in 1926 from designs by Thomas Henry Lyon. The Hostel was intended to be part of a larger complex but only one wing was built. It currently houses undergraduates and some fellows. During World War II the London School of Economics was housed in The Hostel and nearby buildings, at the invitation of the Master and Fellows.


I am selling my slightly used Wii concole white and it includes Wii Sports. The retail price alone for a Wii console is $209 but I am throwing a Wii Sports along with the console. This is a very great deal!


Drew Barrymore is the queen of quirky Romantic Comedies and she's back at it again in her new flick, 'Fever Pitch'. This marks the 8th Film made by her production company, Flower Films, since their introduction in 1995. In this picture she stars opposite Jimmy Fallon in a tale of a love-triangle romance between Lindsey Meeks (Barrymore), Ben Wrightman (Fallon), and the Boston Red Sox.
Lindsey is a successful business professional who thinks she's found the one, when she starts dating school teacher Ben. The relationship seems perfect until the summer rolls around and she discovers his other love (or shall I say obsession): Baseball. Lindsey finds herself competing for Ben's time and attention, which drives her into losing sleep, slacking at her job, and risking her chance at a promotion.
During a "romantic" dinner, Lindsey (Drew Barrymore) is taken aback by the antics of new boyfriend Ben (Jimmy Fallon).
Barrymore is as charming as ever in this new comedy. Her character has some great comedic lines that are only enhanced by her over-the-top, goofy facial expressions and her unmasked, radiant sweetness.
Although Fallon has yet to prove himself as a strong actor on the big screen, this movie has been one of his best roles yet. His character provides some good laughs and repeatable one-liners, but he pulls off some unrealistically sweet feats, that are a little too much. The script is funny, quirky, and sweet, but not very involved. This movie is definitely a chick flick that you wouldn't want to drag your boyfriend or a date to. Instead make it a girls' night out with your close friends; and for all you students out there strapped for cash, save your pennies, this one will be just as good on video.


text:
Возвы́шенский райо́н — административная единица на севере Казахской ССР и Казахстана в составе Северо-Казахстанской области, существовавшая в 1967—1997 годах.

История
Возвышенский район был образован в составе Северо-Казахстанской области Казахской ССР согласно Указу Президиума Верховного Совета Казахской ССР от 2 января 1967 года. Центром района стало село Возвышенка.

23 мая 1997 года Указом № 3528 Президента Республики Казахстан Возвышенский район упразднён. Его территория вошла в состав Булаевского района Северо-Казахстанской области.

Главы района

Первые секретари Возвышенского райкома компартии Казахстана
 Магазов Килаш Магаэович (1967—1970)
 Зенченко Василий Дмитриевич (1970—1978)
 Бубнов Геннадий Михайлович (1978—1985)
 Викторов Евгений Иванович (1986—1990)

Председатели исполкома Возвышенского районного совета
 Жузенов Енсеген Едилович (1967—1969)
 Магазов Килаш Магазович (1970—1978)
 Смагулов Шаймурат Смагулович (1978—1981)
 Кудасбаев Амантай Букольбаевич (1981—1987)
 Уразалин Аманбол Мухаметкалиевич (1987—1992)

Главы Возвышенской районной администрации
 Уразалин Аманбол Мухаметкалиевич (1992—1995)

Акимы Возвышенского района
 Уразалин Аманбол Мухаметкалиевич (1995—1996)
 Амрин Кеменгер Кожахметович (1996—1997)

Секретари Возвышенского районного маслихата
 Скибский Николай Васильевич (1994—1997)

Примечания

Ссылки
 Справочник по истории административно-территориального деления Северо-Казахстанской области (29 июля 1936 г. — 1 января 2007 г.). — Петропавловск, 2007. — 361 с. — ISBN 978-601-7029-04-3.

Упразднённые районы Казахстана

redpajama_set_name: RedPajamaWikipedia


text:
Butler County officials warn of scams during coronavirus fear, uncertainty
In this March 19, 2020 photo, a shopper looks for toilet paper at a Stop & Shop supermarket during hours open daily only for seniors in North Providence, R.I. Federal law enforcement is warning that scam artists are preying on older people's fears by peddling fake tests for the coronavirus to Medicare recipients. (AP Photo/David Goldman)
News | Updated April 18, 2020
By Ed Richter, Staff Writer
Officials throughout Butler County and Ohio are warning residents to watch out for scams during the time of the cornoavirus pandemic, when suspects will use fear and uncertainty to seek money.
Ohio Attorney General Dave Yost recently warned that fear and uncertainty surrounding the COVID-19 outbreak could spawn an outbreak of scams, which could come by phone, internet, email or your doorstep.
"COVID-19 is not the only thing we have to protect ourselves against," Yost said. "Thieves and crooks prey on fear and uncertainty. Ohioans need to inoculate themselves against an outbreak of scams with knowledge and good practices."
Explore MORE: Census 2020: How to participate in Butler County despite coronavirus slowdown
Last Monday, U.S. Attorney David M. DeVillers said the public is urged to report suspected fraud schemes related to COVID-19 by contacting the National Center for Disaster Fraud at 1-866-720-5721 or disaster@leo.gov.
"This U.S. Attorney's Office is open for business and we still have plenty of room in jail for those who try to line their own pockets through fraud during this pandemic," DeVillers said. "I've instructed my district to ignore dollar amount or victim number thresholds usually needed for federal prosecutions. Those reprehensible enough to take advantage of our worried citizens will pay the price."
Explore MORE: 8 Butler County residents have recovered from coronavirus, cases up to 22
The NCDF can receive and enter complaints into a centralized system. The NCDF coordinates complaints with 16 additional federal law enforcement agencies, as well as state Attorneys General and local authorities.
Local law enforcement agencies said there have not been any spikes in scams since the coronavirus COVID-19 began in late January.
Butler County Sheriff Richard Jones said Friday he had not heard of any scams being reported, noting that more people are home and are paying more attention to their homes and neighborhoods.
"Everyone is watching out for each other," he said. "There are more eyes and ears out there right now."
Explore MORE: Butler County governments brace for big drop in revenues: What they're saying
Butler County Prosecutor Michael Gmoser said he has heard from fellow prosecutors across the nation that there have been some spikes in scams being reported.
"It's the same thing I've always been preaching," he said. "Scammers will take advantage of whatever situation is happening… And like the virus, it's going to get worse before it gets better."
He said scammers have seasons. During spring break, they target the elderly with a scam that indicates their grandchild is in need of emergency cash. During income tax season, scammers act as IRS agents demanding money to prevent someone from being arrested for not paying back taxes. In addition, do not buy gift cards, money orders, wire transfers, pre-paid money cards, etc. or use cash to pay for any of these items, officials said.
Explore MORE: Living through coronavirus: No baseball means no Reds players, no revenue for memorabilia store owner
Gmoser said he's heard some anecdotal reports about some trying to sell access to a doctor, sell a ventilator or other device or sell medication for COVID-19.
"Education is key," he said. "Don't give out any banking information, Social Security numbers or any other personal information over the phone. That's like giving someone the key to the bank."
Gmoser said if someone isn't sure about something or if it's deal that's too good to be true, it probably isn't and people should reach out to family members or other trusted sources to verify the offer or information.
Duke Energy issued a warning to customers to watch out for scammers trying to take advantage of the coronavirus pandemic. Duke Energy officials said, "we will never ask for personal information over the phone or demand payment using money orders or gift cards."
In addition, Duke Energy on March 21 temporarily suspended disconnections for nonpayment and waived late fees as many customers may be facing unusual financial hardships as a result of COVID-19.
MORE: Butler County seniors, parents fear coronavirus threatens high school proms, graduations
Middletown police Chief David Birk said there are scams on social media. One scam asks a target to make a donation of $500 to get a return of $5,000, while another asks people to pay money up front for coronavirus vaccines and cures, medical equipment and supplies.
According to the Federal Trade Commission, "There currently are no vaccines, pills, lotions, lozenges or other prescription or over-the-counter products available to treat or cure Coronavirus disease 2019 (COVID-19) online or in stores."
In addition, the FTC said there are no home test kits for the coronavirus and asked the public not to respond to texts, calls, click on unknown links, pop-ups or emails about COVID-19 deals or products.
At a news conference Friday, Butler County Commissioner Don Dixon and Ken Wilson, Council on Aging vice president for program operations, both said this is a time when senior citizens in the community need to be protected the most.
"Be on guard," Wilson said. "Double-check emails and phone calls."
"If it doesn't feel legitimate, call the (Butler County) prosecutor's, sheriff's or commissioner's offices," Dixon said. "Be safe. Call first to protect yourselves."
How to prevent being scammed during the coronavirus pandemic
Watch out for emails claiming to be from the Centers for Disease Control and Prevention (CDC) or other expert sources with special advice or information about the coronavirus. Legitimate information is available for free on the CDC's website.
Ignore online advertisements promoting cures for the coronavirus. According to the Federal Trade Commission, "There currently are no vaccines, pills, lotions, lozenges or other prescription or over-the-counter products available to treat or cure Coronavirus disease 2019 (COVID-19) online or in stores."
Know who you're buying from. Online sellers may claim to have in-demand products, like cleaning, household, and health and medical supplies when, in fact, they don't.
Research nonprofit organizations and crowdfunding campaigns before donating. A database of registered charities is available on the Ohio Attorney General's website. Avoid groups that pressure you into donating and never donate via cash, gift cards, wire transfer or prepaid money card. These are the preferred payment methods of scammers.
Be cautious of anyone going door to door offering coronavirus testing or temperature readings and requesting personal information. Call law enforcement immediately if you see a suspicious person. Never let strangers into your home.
Beware of emails and other attempts to "phish" for your personal, financial and medical information. When in doubt, do not share. If the source claims to be your bank or a government agency, confirm they are legitimate by calling the organization at a phone number you have verified.
Amid discussions of providing working families with government checks, those details are being finalized. For more information, visit the Federal Trade Commission website and stay tuned for updates from reliable news sources.
When online, avoid clicking on unknown links or pop-ups and never download any suspicious email attachment. Doing so could infect your devices with malicious software designed to steal your personal information or lock your computer until you pay a ransom.
Hang up on robocalls. Don't press any numbers. Scammers are using illegal robocalls to pitch everything from scam Coronavirus treatments to work-at-home schemes. The recording might say that pressing a number will let you speak to a live operator or remove you from their call list, but it might lead to more robocalls, instead.
Fact-check information. Scammers, and sometimes well-meaning people, share information that hasn't been verified. Before you pass on any messages, contact trusted sources.
Consumers who suspect an unfair or deceptive sales practice should contact the Ohio Attorney General's Office at www.OhioProtects.org or 800-282-0515.
SOURCES: OHIO ATTORNEY GENERAL'S OFFICE, FEDERAL TRADE COMMISSION


For example, we all know InDesign is better than Photoshop—graphically and typographically—for many advanced layouts. So you may use InDesign to build the structure and form of a large poster—getting the text just right—and then finish it by applying crazy graphic or color effects in Photoshop. Similarly, you might want to design something in InDesign that will end up on the web or in a video. Photoshop is far better at exporting for web and other onscreen formats.


I am a fourth year PhD student in the Department of Computer Science at Rice University, advised by Prof. Moshe Vardi. I obtained a BS in Computer Science and a BA in Mathematics from Rice University as well.
Masters Thesis, Rice University, April 2017.
Boolean Satisfiability (SAT) is fundamental in many diverse areas such as artificial intelligence, formal verification, and biology. Recent universal-hashing based approaches to the problems of sampling and counting crucially depend on the runtime performance of specialized SAT solvers on formulas expressed as the conjunction of both k-CNF constraints and variable-width XOR constraints (known as CNF-XOR formulas), but random CNF-XOR formulas are unexplored in prior work. In this work, we present the first study of random CNF-XOR formulas. We prove that a phase-transition in the satisfiability of random CNF-XOR formulas exists for k=2 and (when the number of k-CNF constraints is small) for k>2. We empirically demonstrate that a state-of-the-art SAT solver scales exponentially on random CNF-XOR formulas across many clause densities, peaking around the empirical phase-transition location. Finally, we prove that the solution space of random CNF-XOR formulas 'shatters' at all nonzero XOR-clause densities into well-separated components.
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2017.
Recent universal-hashing based approaches to sampling and counting crucially depend on the runtime performance of SAT solvers on formulas expressed as the conjunction of both CNF constraints and variable-width XOR constraints (known as CNF-XOR formulas). In this paper, we present the first study of the runtime behavior of SAT solvers equipped with XOR-reasoning techniques on random CNF-XOR formulas. We empirically demonstrate that a state-of-the-art SAT solver scales exponentially on random CNF-XOR formulas across a wide range of XOR-clause densities, peaking around the empirical phase-transition location. On the theoretical front, we prove that the solution space of a random CNF-XOR formula 'shatters' at all nonzero XOR-clause densities into well-separated components, similar to the behavior seen in random CNF formulas known to be difficult for many SAT algorithms.
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2016.
The runtime performance of modern SAT solvers on random k-CNF formulas is deeply connected with the `phase-transition' phenomenon seen empirically in the satisfiability of random k-CNF formulas. Recent universal hashing-based approaches to sampling and counting crucially depend on the runtime performance of SAT solvers on formulas expressed as the conjunction of both k-CNF and XOR constraints (known as k-CNF-XOR formulas), but the behavior of random k-CNF-XOR formulas is unexplored in prior work. In this paper, we present the first study of the satisfiability of random k-CNF-XOR formulas. We show empirical evidence of a surprising phase-transition that follows a linear trade-off between k-CNF and XOR constraints. Furthermore, we prove that a phase-transition for k-CNF-XOR formulas exists for k = 2 and (when the number of k-CNF constraints is small) for k > 2.


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The Inner Sanctum es el décimo séptimo álbum de estudio de la banda británica de heavy metal Saxon, publicado en 2007 por SPV/Steamhammer Records. Es el primer trabajo donde participa el baterista Nigel Glockler, tras su retorno a la banda a mediados de 2005. Además cuenta con la participación especial de los vocalistas Lemmy Kilmister de Motörhead, Angry Anderson de Rose Tattoo y Andi Deris de Helloween, quienes cantaron junto con Biff Byford en la canción «I've Got to Rock (to Stay Alive)».

El 3 de abril del mismo año la discográfica volvió a lanzar el disco en una edición limitada, la cual incluía un DVD con diez canciones grabadas en distintas ciudades en Europa.

Lista de canciones
Todas las canciones escritas por Saxon.

Posicionamiento en listas semanales

Miembros
 Biff Byford: voz
 Paul Quinn: guitarra eléctrica
 Doug Scarratt: guitarra eléctrica
 Nibbs Carter: bajo
 Nigel Glockler: batería
 Músicos invitados
 Lemmy Kilmister, Angry Anderson y Andi Deris: voz en «I've Got to Rock (to Stay Alive)»
 Matthias Ulmer: teclados

Referencias

Álbumes de Saxon
Álbumes de 2007
Álbumes de heavy metal
Álbumes producidos por Charlie Bauerfeind
Álbumes de SPV/Steamhammer Records
Álbumes en inglés


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What Facebook's updates mean for your business
Facebook has had a particularly busy start to 2018, announcing a string of updates that have the potential to significantly alter news feeds and advertising. While Facebook's commitment to improving the user experience seems unwavering, questions remain around what these changes mean for brands and businesses.
Those that have long relied on the platform as a means of promotion and a way to engage directly with consumers are likely to feel the impact most. But what exactly has been announced, and what do Facebook's updates mean for your business in 2018?
News feed algorithm alterations
Perhaps the biggest update of all came in January, when Facebook announced it would be rolling out an amended news feed algorithm that favours content from friends and family, as opposed to brands, businesses and news outlets. In a seemingly direct response to the criticism the platform has faced around the 'fake news' controversy, founder Mark Zuckerberg outlined the plans in a blog post at the beginning of the year.
"You'll see less public content, including news, video and posts from brands", Zuckerberg explained. "After this change, we expect news to take up roughly 4% of News Feed – down from roughly 5% today". Such statistics seem small, but consider that 510,000 comments, 293,000 statuses and 136,000 pictures are uploaded to the site every minute, and it becomes clear just how significant this percentage drop will be.
Page insights updates
Another update was announced last month that has potentially big results for brands and advertisers using the platform. Facebook plans to remove as many as 20 metrics from their business analytics pages, deeming them to be "redundant, outdated, not actionable or infrequently used".
Image Source: Facebook
Facebook has marketed the plans as a step towards increased transparency for brands and business owners, giving users the tools needed to hone in on the metrics that really matter. Regardless of your feelings towards the removal of long-standing metrics such as page mentions and social impressions, the update serves as another sign that Facebook is radically altering its approach towards businesses that use the platform.
What the updates mean for businesses
While every business is likely to view the changes differently, there's no shying away from the fact that they promise to have a significant impact across all sectors.
John Ridding, Chief Executive of the Financial Times, criticised the update, commenting that "quality content will no longer be an option" if no sustainable solution is found to the problems posed by the rise of so-called 'fake news'. Instead of the changes proposed, Ridding suggests implementing "a valuable subscription model on platforms that enables publishers to build a direct relationship with readers and to manage the terms of access to their content".
In many ways, the updates mirror changes that are happening across the wider web. Search engines including Google have been taking steps to promote quality content that's of the highest value to its audience. Facebook is following suit in its attempts to put user experience front and centre, adopting a mantra of 'quality over quantity' with regard to the content they promote.
In theory, brands who actively create engaging, shareable content should still be able to thrive under the new algorithm, however there are of course some additional limitations to consider. Posts that encourage organic engagement are still likely to perform better than those that don't. However, getting the content seen in the first place is likely to pose a bigger problem than before.
How to make the updates work for you
Focus on producing quality content that appeals directly to your audience. It's no longer enough to rely on a big budget and let boosted ads do the hard work for you. Instead, everything you produce should meet the needs of your audience in order to encourage valuable organic engagement.
Alternate the ways that you use Facebook. Consider channels such as Facebook Live, which the brand's Head of News Feed, Adam Mosseri, promotes as a valuable way for brands to boost engagement. "Live videos often lead to discussion among viewers on Facebook – in fact, live videos on average get six times as many interactions as regular videos". Facebook has been quick to confirm that posts generating conversation will continue to rank most favourably, so it's worth exploring different avenues in a bid to encourage engagement.
Of course, there's always the option of investing in alternative channels and methods to help boost growth. While many businesses rely on social as a means of connecting with audiences, exploring other avenues such as PR and content marketing can certainly help to bridge the gap for those who have historically used the platform for brand awareness and promotion.
For more information or to discover how Ad-Rank can help to protect the interests of your business, get in touch today.
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