Evan A. Sultanik, Ph.D.

Evan's First Name @ Sultanik .com

Chief Scientist
Digital Operatives, LLC

Adjunct Professor
Drexel University College of Computing & Informatics
Department of Computer Science

Recent Content:

Service Discovery on Dynamic Peer-to-Peer Networks Using Mobile Agents

or, How Bumper Cars Relate to Computer Science

The absurd realization that it has almost exactly been a decade since I defended my master’s thesis took me by surprise. It seems like yesterday. That work has had some decent exposure over the years, but, like most creative works produced toward the beginning of one’s career, I do not look back on it as a paragon.

Moshe Kam, upon reading a preliminary draft, summarized it very well:

[It] reads like Tristram Shandy, but devoid of humor.
He was referring to my organization of one particular chapter: First I described the overall technique, then I proceeded to iteratively break the problem into successively smaller chunks. A bit like taking apart a matryoshka doll. Or maybe a more apt analogy would be: peeling away the layers of a rotten onion only to find a miniscule, barely salvageable core, calling into question the cost/benefit of the whole excavation. Moshe was right. With that said—and unlike the eponymous biographee of the aforementioned book—I think I can at least claim that my ideas are fully “born” in the first volume.

Despite somehow managing to get that chapter of my thesis published in a book, I feel like it always suffered from my cumbersome presentation. Therefore, perhaps inspired by Stetson hats and hip flasks, I have since devised an analogy that I hope at least makes the problem (if not my solution) more accessible. That’s what I am going to describe in the remainder of this post.

Consider, for a moment, that you are piloting a car in a huge bumper car arena.

CC BY 2.0, adapted from here

You have some control over your own movement, but there are so many others driving around in the arena that there are constant, unavoidable collisions that throw you off course. From afar, everyone’s movement seems random.

The challenge is that you need to know the time, but you do not have a watch. In fact, there are very few others that have a watch. How do you find the time?

The naïve solution is to simply yell out, “Who has the time‽” The problems with this solution are:

  1. If everyone needs to know the time at once, there will be very many yelling people.
  2. What if no one with a watch can hear you? Bumper car arenas tend to be loud.

A slightly more intelligent approach might be to:

  1. Take a piece of paper and write a request on it for the current time.
  2. Pass the piece of paper to the next person who bumps into you.
  3. If one who is watch-less receives such a piece of paper, he or she passes it on to the next person that bumps into him or her.
  4. When the note eventually reaches someone with a watch, he or she will write the current time on the piece of paper and send it back to you.

The obvious shortcomings to this approach, however, are that:

  1. When the note eventually gets back to you, the time will be incorrect!
  2. What if, in fact, no one has a watch? How long do you have to wait without receiving a response before you can be sure that no one has a watch?
  3. What if not everyone speaks and/or reads English?

My realization is that: If one roughly knows the topology of the network (i.e., the locations of all of the cars), and if the messages are truly passed randomly through the network, one can use ergodic theory and random graph theory to accurately predict the frequency of message arrivals. Assuming knowledge of the network topology is reasonable, since many ad hoc networking algorithms already provide it. Even if it is not available, an approximation of the topological properties is often sufficient. So, what this provides is a model for predicting how long it will take for one’s message to eventually reach someone with a watch and, subsequently, how long it will take for the response to be returned. What I discovered was that the variance is often small enough such that this estimate can be used to correctly adjust for the delay in returning the time. Furthermore, this model provides a probability distribution for how likely it is that one would have received at least one response as a function of waiting time. Therefore, if n seconds have passed and the model says that with probability 0.99 one should have received a response to the time query, yet no response has been received, one can conclude with 99% certainty that there is no one in the network with a watch!

The last challenge question, “What if not everyone speaks and/or reads English?” will have to wait for another post…

Positive Train Control

or, Jet Fuel Can’t Melt Train Tracks

It’s been a week since the tragic Amtrak derailment in my home town of Philadelphia. Being an avid train passenger—commuting to and from DC several times per week—and having taken the ill fated Northeast Regional Train No. 188 on multiple occasions, this has struck close to home. I am posting this blog entry from the café car of Train No. 111, the first Southbound train to commence full Amtrak service since the disaster.

I realize that it is very early, and the National Transportation Safety Board investigation is still ongoing. Speculation—especially by someone like me who is not a transportation expert—would be unproductive at best, and offensive to the victims at worst. Perhaps it’s my job as a security professional—in which I am paid to find vulnerabilities in systems … or perhaps it’s the recent spate of news that both cars’ and even commercial airplanes’ heavily computerized control systems can be commandeered, wirelessly, by a remote attacker … or perhaps it is the fact that the crash occurred immediately after national rail safety week and on the eve of a legislative debate on cutting Amtrak funding … or perhaps I’ve just been reading too much Pynchon… but ever since I heard that the train was speeding and that there is no direct evidence incriminating the train operators of negligence (other than the speed), the first thing that popped into my mind was: Software. I haven’t heard anyone (other than well-known security expert Simson Garfinkel) discuss it, so that’s what I’m going to do in the remainder of this post.

One topic that the media has latched onto is Positive Train Control (PTC): a technology that, if only it had been implemented, is purported to have been able to avert the crash. What the media doesn’t say is that the ACS-64 locomotive that was pulling the fateful train was already equipped with PTC. You see, the Advanced Civil Speed Enforcement System (ACSES)—Amtrak’s version of PTC for the Northeast Corridor—requires components both on-board the locomotive and wayside (on the tracks). In other words, PTC will only be fully functional if both the locomotive and tracks are upgraded. Portions of track South of Philadelphia and North of New York already have support. In the case of the Philadelphia crash, the locomotive was a new model that had support, but the tracks on which it derailed did not.

I contend that a software bug in the ACSES system should not be ruled out as a potential cause of or contributing factor to the derailment. Let me be clear: I am not claiming that software was a likely cause of the crash. I am neither a transportation expert nor do I have any detailed knowledge of the ACSES implementation. The purpose of this article is to provide enough evidence that software errors are a plausible enough explanation that the possibility should at least become a part of the public discussion.

There is a reasonable precedent of software bugs causing physical catastrophes. For example, a software bug in Toyota’s electronic throttle control system recently caused the massively publicized “unintended acceleration” problem in many of their vehicles, killing at least 89 people as a result. In 2007, a group of six F-22 Raptor fighter jets experienced multiple computer crashes coincident with crossing the international dateline caused by a software bug that did not anticipate that corner case. The planes lost all navigation and communication, and were only able to make it back to land by following their tankers. Vehicles and transportation systems in general are so complex, automated, and computerized these days that a single software bug can wreak havoc.

But how could a system that is intended to provide a safeguard against crashing actually cause a crash?

A relatively recent report to congress by the Federal Railroad Commission on the implementation of PTC states that ACSES has control over the

…event recorder, train line data sensors, horn circuit, brake systems, cab signal system (if equipped), and the Communication Segment.
So, presumably, the system has no control over acceleration, just deceleration.

I am perhaps about to delve too far into the sea of speculation, but as James Burke so eloquently demonstrated, a failure in one system can cascade to cause failures in seemingly independent and unconnected others.

A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable.
There is a display in the cab of the locomotive with a speedometer, looking something like this:

CC BY-SA 3.0, from here

When the track is PTC-enabled, there is a second speed readout on the bottom, showing the maximum speed allowed on the track. When the track is not PTC-enabled, the readout looks as pictured here. If the conductor or engineer is relying on that display to gauge the train’s current speed, he or she is thereby relying on the output of ACSES’s algorithms, programming, hardware, and sensors. A failure in any of those pieces could result in an incorrect speed readout, e.g., causing one to believe that the speed of the train were actually slower than in reality. This is similar to how Air France Flight 447 was doomed by an engineering design flaw in its airspeed sensors, which caused a failure in the autopilot software, which reported inaccurate instrumentation to the pilots, who relied upon the incorrect information, making manual piloting errors that caused the plane to crash.

I do not wish the tragedy of Amtrak Train No. 188 on anyone; it could have very easily been me sitting in that café car a week ago. While history has proven that human error is the most frequent cause of these types of accidents, we increasingly need to also look at the software for possible fault.

Tracking Trains

Reverse Engineering and Hacking Text Messages for Great Good

I’ve been regularly commuting between Philly and DC on Amtrak for over six months now. Being in the Northeast corridor—the only profitable region in Amtrak’s network—and specifically being in the NYC↔Philly↔Washington trifecta that accounts for about a third of Amtrak’s overall nationwide ridership, the service is generally excellent. Not Shinkansen excellent, but good enough to take me where I need to go in relative comfort, in a third less time than would be necessary to drive. And in English, too, so I can die with a smile on my face, without feelin’ like the good Lord gypped me.

For a pseudo-public entity, Amtrak does a surprisingly good job keeping up with the technological times. It’s had free WiFi on its trains for a number of years, the conductors scan tickets using ruggedized iPhones, and its iOS app lets me store and organize my myriad tickets in Passbook. One can even present tickets via an Apple Watch.

The one gripe I have about the system is that, despite Amtrak’s excellent online tools for tracking the exact location of trains, there is no good way to get useful train status alerts. I often work at a location ~45 minutes outside of DC, so I want to know whether my train is delayed before I depart for the station.

The Southbound trains I take from Philadelphia originate in either New York or Boston. Therefore, I want to get an alert texted to my phone when the train has departed New York. If it departed New York on time, then I can proceed as normal. If it was delayed, then I can hit the snooze button. If it didn’t even leave yet, then I know that it will be at least an hour before it arrives in Philly.

The first thing I had to do was figure out a way to send text messages for free. Every major cellular provider has some sort of gateway service where an E-mail sent to the proper address will be forwarded to the associated phone number. Therefore, I created a simple Python library (in pure Python; no dependencies) for sending free (to the sender) text messages to phones from every major international carrier:

Next, I had to reverse engineerdevise a way to get accurate train status updates. This was relatively straightforward, but I hesitate to go into details because it might implicate me in several terms-of-use violations.

I pieced this all together into a new service that I have been using and allowing several fellow commuters to beta-test for the past couple months. I’m excited to release it to the public now:


This website has no affiliation with any railways or train operators. If you are a railway or train operator, please keep in mind that this is a toy website created by a single guy with altruistic motives. Please don’t sue me. Instead, let’s talk about how I can give you what I’ve created here so that you might host it and provide it as a service for your customers.

No profit is made off of the services on this website. In fact, I lose money by running it. Therefore, there are certain concessions that have been made due to lack of fuding. Namely, there is the possibility that a malicious party could illegally intercept the messages sent between a user’s web browser and this server, potentially altering his or her account and alerts. I have implemented as many security features as possible to protect against this, but in order to be almost foolproof I would need to host the site on SSL, which requires money. If you desire additional security and features, please consider donating.

Enjoy, and please drop me a line if you find the service useful!

Musical Uncanny Valley (Continued)

In which I annoyingly and gratuitously riff on a theme.

Musical Uncanny Valley

In which I compare annoying music to artisanal ketchup.

Recently, a friend of mine shared a link to Max Richter‘s solo debut album Memoryhouse on social media:

While I appreciate Richter’s work and think it’s good, it’s very hard for me to enjoy it. The problem is that I find much of it—and particularly Memoryhouse—too reminiscent of Philip Glass’. The latter’s minimalism and distinctive brand of ostinato, harmonic chord progressions, variations in half-time, &c., is clearly being referenced in Memoryhouse, albeit with perhaps “post-minimalist” orchestration. For example, when I first listened to November, the first track of Memoryhouse (q.v. above), it immediately reminded me of Glass’s String Quartet No. 3, which predates Memoryhouse by about 20 years:

It’s a bit like when a TV show wants to parody Jeopardy but doesn’t have the budget to license rights to the Jeopardy Think! music, so it creates a slightly different version which ends up sounding wrong, despite the fact that if the original Jeopardy theme had never existed this new version would be just as popular. What is called a musical pastiche.

That just sounds wrong to me, to the extent that my subconscious is offended by it. It’s like going to a Michelin 3-starred restaurant and being served artisanal, house-made ketchup, from locally sourced organic tomatoes and garlic harvested from the chef’s own private garden during the last full moon in spring: No matter how good that ketchup tastes, it’s not going to taste as right as Heinz, because that’s what you grew up eating. And heaven forbid you’re served Hunt’s. Did we lose a war?

In my mind, a pastiche is distinct from something like an homage or inspiration since its similarity to the source material is much more noticeable. For example, Glass’s predilection for pairing low-pitch ostinato with higher-pitch simple melodies is technically very similar to Mozart’s modus operandi, yet we rarely ever hear Glass being directly compared to Mozart.

A number of years ago I had a subscription to the Philadelphia Orchestra and attended a concert debuting a new symphony by a relatively unknown composer (whose name escapes me). The theme was “The United States.” I’m guessing the concert was held around the time of Independence Day, but I neither remember nor care to look it up. The whole thing was very frustrating for me to sit through, because it was clear to me that every single movement was simply a pastiche of the work of a much more famous American composer. I would have much rather heard a performance of “the real thing.”

Music from completely different genres can also either purposefully or accidentally trick our brains into finding similarity. From almost exactly five years ago today:

Been bugging me for years: Does Aesop Rock‘s “9-5er’s Anthem” quote Gershwin‘s “Rhapsody in Blue”?
Evan Sultanik

The field of æsthetics has produced a hypothesis of what is know as the uncanny valley: When an object moves and/or looks very similar to (but not exactly like) a human being, it causes revulsion to the observer. Here is a video with some examples, if you want to be creeped out a bit. As objects become increasingly similar to the likeness of a human, human observers become increasingly empathetic toward the object. However, once the object passes a certain threshold of human likeness, the human starts to be repulsed, until another likeness threshold at which point the object is almost indistinguishable from a real human.

CC BY-SA 3.0, from here

I posit that there is a similar phenomenon in music, and that is what I am experiencing when I listen to Memoryhouse. One of the theories explaining the uncanny valley is that conflicting perceptual cues cause a sort of cognitive dissonance. It is well-known that the brain behaves almost identically when imagining a familiar piece of music as it does when listening to it. This suggests that the brain is internally “playing” the music in synchrony with what it is hearing, anticipating each note. In a sense, one’s brain is subconsciously humming along to the tune. My theory is that when a song (or, particularly, a pastiche) is similar enough to another song that is much more familiar, this evokes the same synchronous imagining. However, once the pastiche deviates from anticipated pattern, this ruins the synchrony and causes cognitive dissonance.

Excuse the pun, but on that note I’ll leave you with something that will hopefully not be in your musical uncanny valley: This wonderful, recent recomposition of Glass’s String Quartet No. 3 for guitar:

Defending Cyberspace

or: No, I thought you were doing it.

Yesterday I attended the Balancing Act symposium on big data, cybersecurity, and privacy. During the panel discussion, an excerpt from Clarke’s oft-quoted and debatably hyperbolic book on cyber war was invoked:

At the beginning of the era of strategic nuclear war capability, the U.S. deployed thousands of air defense fighter aircraft and ground-based missiles to defend the population and the industrial base, not just to protect military facilities. Every major city was ringed with Nike missile bases to shoot down Soviet bombers. At the beginning of the age of cyber war, the U.S. government is telling the population and industry to defend themselves. As one friend of mine asked, “Can you imagine if in 1958 the Pentagon told U.S. Steel and General Motors to go buy their own Nike missiles to protect themselves? That’s in effect what the Obama Administration is saying to industry today.”

That passage has always struck me as proffering a false equivalence. The US Government has near absolute control over the country’s airspace, and is thereby responsible to defend against any foreign aggression therein. Cyber attacks, on the other hand, are much less overt—with the possible exception of relatively unsophisticated and ephemeral denial of service (DOS) attacks. A typical attacker targeting a private company is most likely interested stealing intellectual property. That’s certainly the trend we’ve seen since the publication of Clarke’s book back in 2012, vi&., Sony, Target, Home Depot, &c. The way those types of attacks are perpetrated is more similar to a single, covert operative sabotaging or exfiltrating data from the companies rather than an overt aerial bombardment.

The actual crime happens within the private company’s infrastructure, not on the “public” Internet. The Internet is just used as a means of communication between the endpoints. More often than not the intermediate communication is encrypted, so even if a third party snooping on the “conversation” were somehow able to detect that a crime were being committed, the conversation would only be intelligible if the eavesdropper were “listening” at the endpoints.

A man has a completely clean criminal record. He drives a legally registered vehicle, obeying all traffic regulations, on a federal interstate highway. A police officer observing the vehicle would have no probable cause to stop him. The man drives to a bank which he robs using an illegal, unregistered gun. Had a police officer stopped the bank robber while in the car, the gun would have been found and the bank robber arrested. Does that give the police the right to stop cars without probable cause? Should the bank have expected the government to protect it from this bank robber?

The only way I can see for the government to provide such protections to the bank, without eroding civil liberties (i.e., the fourth amendment), would be to provide additional security within the bank. Now, that may be well and good in the bank analogy, but jumping back to the case of cyber warfare, would a huge, multi-national company like Sony be willing to allow the US Government to install security hardware/software/analysts into its private network? I think not.

Social Signals Part 2

The (gratuitous) math behind the magic.


In the last post, I presented a new phenomenon called Social Signals. If you have not yet read that post, please do so before reading this continuation. Herein I shall describe the nitty-gritty details of the approach. If you don’t care for all the formalism, the salient point is that we present an unsupervised approach to efficiently detect social signals. It is the approach that achieved the results I presented in the previous post.

The Formal Model (i.e., Gratuitous Math)

We call a measurement of this overall Twitter traffic a baseline. We posit that the signal associated with a bag of words is anomalous for a given time window if the frequency of the words’ usage in that window is uncorrelated with the baseline frequency of Twitter traffic. The aberrance of an anomalous signal is a function of both its frequency and the extent to which it is uncorrelated from the baseline. The significance of an anomalous signal is a function of both its frequency and also the significance of the other abnormal signals to which it is temporally correlated.

Let $\Sigma^*$ be the set of all possible bags of words in a Twitter stream. Let $T \subset \mathbb{N}$ be a consecutive set of points in time that will be used as the index set for the baseline time series, $B = \{B_t : t \in T\}$. Given a bag of words $W \in \Sigma^*$, the associated time series for the signal over the window $T$ is given by $S_W = \{S_{W,t} : t \in T\}$.

The correlation, $\rho : \mathbb{N} \times \mathbb{N} \rightarrow [-1,1] \subset \mathbb{R}$, between two time series, $B$ and $S_W$, is calculated using Pearson’s product-moment correlation coefficient: \begin{multline*} \rho(B, S_W) \mapsto \frac{\mathrm{Cov}\left(B, S_W\right)}{\sigma_B \sigma_{S_W}}\\= \frac{1}{|T|-1}\sum_{i=1}^{|T|} \left(\frac{B_i - \mu_{B}}{\sqrt{\sigma_B}}\right)\left(\frac{S_{W,i} - \mu_{S_W}}{\sqrt{\sigma_{S_W}}}\right), \end{multline*} where $\mathrm{Cov}$ is covariance, $\mu$ is the mean, and $\sigma$ is the standard deviation of the time series. Values of $\rho$ closer to zero indicate a lack of correlation, while values closer to positive and negative one respectively indicate positive and negative correlation. We define the aberrance of a signal with respect to the baseline, $\delta : \mathbb{N} \times \mathbb{N} \rightarrow \mathbb{R}^+$, as the ratio of the signal’s frequency to the magnitude of its correlation to the baseline: $$\delta\left(B, S_W\right) \mapsto \frac{\sum_{t \in T} S_{W,t}}{|\rho(B, S_W)|}.$$

We therefore want to find the set of $n$ signals of maximum aberrance, $\mathcal{S} = \{S_{W_i} : W_i \in \Sigma^*\}$ where $\delta(B,S_{W_1}) \geq \delta(B,S_{W_2}) \geq \ldots \geq \delta(B,S_{W_n})$. Let $\phi : \mathcal{S} \rightarrow \mathcal{S}$ be a convenience function mapping abnormal signals to other abnormal signals to which they are temporally correlated: $$\Big(S_{W_2} \in \phi(S_{W_1})\Big) \implies \left(|\rho(S_{W_1}, S_{W_2})| \geq \frac{1}{2}\right).$$

The significance, $\alpha : \mathbb{N} \times \mathbb{N} \rightarrow \mathbb{R}$, of an abnormal signal $S_W \in \mathcal{S}$ with respect to the baseline is defined recursively: The significance of an abnormal signal is equal to its frequency times the weighted significance of the other abnormal signals to which it is highly temporally correlated. Formally, \begin{multline} \alpha(B, S_W) \mapsto \left(\sum_{t \in T} S_{W,t}\right)\times\\\left(\sum_{S_{W^\prime} \in \phi(S_W)} 2\left(|\rho(S_W, S_{W^\prime})| - \frac{1}{2}\right)\alpha(B, S_{W^\prime}) \right). \label{equ:alpha} \end{multline} Therefore, given the maximally aberrant signals $\mathcal{S}$, we can use the significance function $\alpha$ to provide a total ordering over the signals.

Finally, some signals might be so highly temporally correlated that their bags of words can be merged to create a new, aggregate signal. This can be accomplished by performing clustering on a graph in which each vertex is a signal and edges are weighted according to $\phi$. We have found that this graph is often extremely sparse; simply creating one cluster per connected component provides good results.

Automatically Finding Maximally Aberrant Signals (i.e., The Algorithm)

The model described in the previous section provides a framework in which to find the maximally aberrant signals (i.e., what I was hoping would turn out to be social signals). Creating an algorithm to solve that optimization problem is non-trivial, however. For instance, the recursive nature of the definition of the significance function $\alpha$ requires solving a system of $n$ equations. Fortunately, I was able to identify some relaxations and approximations that make an algorithmic solution feasible. In the remainder of this section, I will describe how I did it.

Given a stream of tweets, a desired time window $T$, and a desired time resolution, the baseline, $B$, and set of signals, $\Sigma^*$, are collected by constructing histograms of term frequency in the tweets for the given time window with bins sized to the desired time resolution. This process is given in Algorithm 1. Since the maximum number of tokens in a tweet is bounded (given Twitter’s 140 character limit), this algorithm will run in $|E|$ time.

Algorithm 1 Time series construction from a set of tweets.
1:   procedure Collect-Signals($E$, $T$, $\gamma$)
Require: $E = \{e_1, e_2, \ldots\}$ is a set of tweets, each being a tuple $\langle \tau_i, \kappa_i\rangle$ containing the timestamp of the tweet, $\tau_i$ and the content of the tweet, $\kappa_i$. $T$ is the desired time window and $\gamma$ is the desired time resolution.
Ensure: $B$ is the baseline measurement and $\Sigma^*$ is the set of possible signals. For all $W \in \Sigma^*$, $S_W$ is the time series for signal $W$.
2:       $k \gets \frac{|T|}{\gamma}$    /* this is the total number of buckets */
3:       $B \gets$ an array of $k$ integers, all initialized to zero
4:       $\Sigma^* \gets \emptyset$
5:       for all $\langle \tau_i, \kappa_i\rangle \in E$ do
6:           if $\tau_i \in T$ then
7:               for all $t \in $ Tokenize$(\kappa_i)$ do
8:                   $b \gets \left\lfloor\frac{\tau_i - \inf(T)}{\sup(T) - \inf(T)} k + \frac{1}{2}\right\rfloor$    /* the bucket into which tweet $i$ falls */
9:                   $B[b] \gets B[b] + 1$
10:                   if $\{t\} \notin \Sigma^*$ then
11:                       $\Sigma^* \gets \Sigma^* \cup \{\{t\}\}$
12:                       $S_{\{t\}} \gets$ an array of $k$ integers, all initialized to zero
13:                   end if
14:                   $S_{\{t\}}[b] \gets S_{\{t\}}[b] + 1$
15:               end for
16:           end if
17:       end for
18:   end procedure

Next, we need to find $\mathcal{S}$: the set of $n$ signals of maximum aberrance. This can be efficiently calculated in $O\left(|\Sigma^*|\frac{|T|}{\gamma}\right)$ time (for $n$ independent of $|\Sigma^*|$ or $n \ll |\Sigma^*|$), e.g., using the median of medians linear selection algorithm or by using data structures like Fibonacci heaps or skip lists.

The next step is to sort the $n$ maximally aberrant signals in $\mathcal{S}$ by significance. The recursive nature of the definition of the significance function $\alpha$ requires solving a system of $n$ equations. It turns out, however, that the mapping of (\ref{equ:alpha}) is equivalent to the stationary distribution of the distance matrix defined by $\phi$. To see this, first construct a $1 \times n$ vector ${\bf F}$ consisting of the frequencies of the signals: $${\bf F}_i = \sum_{t \in T} S_{W_i, t}.$$ Next, construct an $n \times n$ matrix ${\bf R}$ such that $${\bf R}_{ij} = \begin{cases} 2\left(|\rho(S_{W_i},S_{W_j})| - \frac{1}{2}\right) & \text{if}\ S_{W_j} \in \phi(S_{W_i}),\\ 0 & \text{otherwise.} \end{cases}$$ Let $\hat{\bf R}$ be a normalization of matrix ${\bf R}$ such that the entries of each row sum to one.

The limiting (or stationary) distribution of the Markov transition matrix $\hat{\bf R}$, notated $\boldsymbol{\pi}$, is equivalent to the principal eigenvector (in this case the eigenvector associated with the eigenvalue 1) of $\hat{\bf R}$: $$\boldsymbol{\pi} = \hat{\bf R}\boldsymbol{\pi}.$$ It is a folklore result that $\boldsymbol{\pi}$ can be numerically calculated as follows: \begin{equation} \label{equ:principal} \boldsymbol{\pi} = [\underbrace{1, 0, 0, 0, 0, \ldots}_{n}]\left(\left(\hat{\bf R} - {\bf I}_n\right)_1\right)^{-1}, \end{equation} where ${\bf I}$ is the identity matrix and the notation $({\bf X})_1$ represents the matrix resulting from the first column of ${\bf X}$ being replaced by all ones. Let $C_i$ be the set of states in the closed communicating class (i.e., the set of signals in the connected component of $S_{W_i}$ as defined by $\phi$) of state $i$ in $\hat{\bf R}$. Then, by definition of $\alpha$ in (\ref{equ:alpha}) and by construction of ${\bf F}$ and $\hat{\bf R}$, $$\alpha(B, S_{W_i}) = \pi_i\sum_{j \in C_i}{\bf F}_j.$$

$\boldsymbol{\pi}$ can also be calculated by iterating the Markov process, much like Google’s PageRank algorithm: $$\lim_{k\rightarrow\infty} \hat{\bf R}^k = {\bf 1}\boldsymbol{\pi}.$$ For large values of $n$, this approach will often converge in fewer iterations than what would have been necessary to compute (\ref{equ:principal}). This approach is given in Algorithm 2. If convergence is in a constant number of iterations, the algorithm will run in $O\left({n \choose 2}\right)$ time.

Algorithm 2 Calculation of the significance function $\alpha$.
1:   procedure Calculate-Significance(${\bf F}, \hat{\bf R}$)
Require: $d \in (0,1)$ is a constant damping factor (set to a value of $0.85$).
Ensure: ${\bf A}$ is the resulting significance vector: $\alpha(B, S_{W_i}) = {\bf A}_i$.
2:       ${\bf A} \gets {\bf F}$
3:       while ${\bf A}$ has not converged do
4:           ${\bf A} \gets (1-d){\bf A} + d{\bf A}\hat{\bf R}$
5:       end while
6:   end procedure

Finally, we want to merge signals that are highly temporally correlated. This is accomplished by clustering the states of the transition matrix $\hat{\bf R}$. We have found that this graph is often extremely sparse; simply creating one cluster per connected component provides good results. An $O(n)$ method for accomplishing this is given in Algorithm 3. Therefore, the entire process of automatically discovering social signals will run in $O\left(|\Sigma^*|\frac{|T|}{\gamma} + n^2\right)$ time.

Algorithm 3 Clustering of the significant signals by connected component.
1:   procedure Merge-Signals($\mathcal{S}, \hat{\bf R}$)
Ensure: $\mathcal{S}^\prime$ is the set of merged signals.
2:       $\mathcal{S}^\prime \gets \emptyset$    /* the set of merged signals */
3:       $s \gets 0$    /* the current aggregate signal index */
4:       $H \gets \emptyset$    /* a history set to prevent loops when performing a traversal of the graph */
5:       for $i \leftarrow 1$ to $n$ do
6:           if $i \notin H$ then
            /* for each signal/vertex that has not yet been traversed */
7:               $H \gets H \cup \{i\}$
8:               $X \gets \{i\}$
9:               $s \gets s + 1$
10:               $S^\prime_s \gets \emptyset$
11:               $\mathcal{S}^\prime \gets \mathcal{S}^\prime \cup \{S^\prime_s\}$
12:               while $X \neq \emptyset$ do
13:                   $j \gets $ Stack-Pop$(X)$
14:                   $S^\prime_s \gets S^\prime_s \cup \{S_{W_j}\}$    /* add signal $S_{W_j}$ to the new aggregate signal $S^\prime_s$ */
15:                   for $k \leftarrow 1$ to $n$ do
16:                       if $\hat{\bf R}_{jk} > 0 \wedge k \notin H$ then
                        /* add all of $S_{W_j}$’s temporally correlated neighbors to the traversal */
17:                           $H \gets H \cup \{k\}$
18:                           Stack-Push$(X, k)$
19:                       end if
20:                   end for
21:               end while
22:           end if
23:       end for
24:   end procedure

So What?

Social networking sites such as Twitter and Facebook are becoming an important channel of communication during natural disasters. People affected by an event are using these tools to share information about conditions on the ground and relief efforts, as well as an outlet for expressions of grief and sympathy. Such responses to an event in these public channels can be leveraged to develop methods of detecting events as they are happening, possibly giving emergency management personnel timely notifications that a crisis event is under way.

Much of the recent research on addressing this need for event detection has focused on Twitter. Twitter has certain advantages that make it a good model for such research. Among these is the ability to query tweets from a particular location, making it possible to know the geographic location of an emerging event.

Ideally, analysts and first responders would have some system by which they could mine Twitter for anomalous signals, filtering out irrelevant traffic. Twitter does have a proprietary algorithm for detecting “Trending Topics,” however, Twitter’s “Local Trends” feature for identifying trends by geographic region is currently limited to countries and major metropolitan areas (exclusive of cities like Tuscaloosa, Alabama). Furthermore, there is little or no transparency on the specifics of Twitter’s algorithm. It is known that the algorithm relies heavily on rapid spikes in the frequency of usage of a term, therefore biasing toward signals that have broad appeal. An analyst, on the other hand, would be interested in any signal that is abnormal; Twitter’s Trending Topics heuristic might overlook such anomalous signals that have a more gradual increase in frequency.

This work deviates from existing approaches in a number of ways. First and foremost, our approach is based upon a model of a phenomena that was discovered by human analysts that has proven successful for identifying signals specifically related to general events of interest, as opposed to ephemeral topics and memes that happen to have momentarily captured the attention of the collective consciousness of Twitter. Secondly, the proposed approach does not rely on detecting spikes in the frequency or popularity of a signal. Instead, emphasis is placed on detecting signals that deviate from the baseline pattern of Twitter traffic, regardless of the signal’s burstiness. Finally, the proposed approach does not rely on term- or document-frequency weighting of signals (e.g.tf-idf), which can be troublesome for short documents (e.g., tweets) because signals’ weights will be uniformly low and therefore have very high entropy, i.e., the probability of co-occurrence of two terms in the same tweet is extremely low, especially if the number of documents is low. Since we are not characterizing signal vectors for individual tweets, we can examine the frequencies of individual signals independently. In a sense, we treat the entire stream of tweets as a single document.

Once again, many thanks to my collaborators, Clay Fink and Jaime Montemayor, without whom this work would not have been possible.

Social Signals

or, the basis for an article that was nominated for best paper and subsequently rejected.


Looking back on my previous job at The Johns Hopkins University, I am struck by and grateful for the breadth of different topics I was able to research: distrubted phased array radar resource scheduling, Linux kernel integrity monitoring, TLS, hypervisors, probabilistic databases, streaming algorithms, and even some DNA sequencing, just to name a few.

Toward the end of my—to abuse an overloaded term—tenure at JHU/APL, I became involved in their social media analysis work. For example, I had some success creating novel algorithms to rapidly geolocate the context of social media posts, based solely upon their textual content. But one of the most interesting things I encountered along these lines was a phenomena that we came to call Social Signals.

Discovery of Social Signals

As will become evident from the following chronology, I was not party to this line of research until after many of the initial discoveries were made, so my account of the earlier details may be inaccurate. To my knowledge, however, this is the first time any of these discoveries have been made publicly available in writing.

In 2011, my colleague Clay Fink had been mining Twitter for tweets for quite some time. He had gigabytes of them, spanning months and covering various worldwide events like the London riots, the Mumbai bombings, the Tuscaloosa—Birmingham tornado, the Christchurch earthquake, the Nigerian bombing, &c. These provided a very interesting and, as it turned out, fruitful source of data to research. They shared the common theme of being crises, yet they were each of a very different nature, e.g., some were man-made and others natural disasters.

Clay had created some algorithms to process the mass of textual data, but there was no good, usable way for a human to sift through it to do manual analysis. Step in another colleague, Jaime Montemayor, who created a data exploration tool called TweetViewer.

TweetViewer is very simple in concept: It plots the relative term frequencies used in a text corpus over time. Yet this simple concept—coupled with the algorithms and HCI workflow accelerators (as Jaime calls them) behind-the-scenes that allow real-time interaction with and instantaneous feedback from the data—enabled some interesting disoveries. Take the large spike in Twitter traffic around May 2nd as an example: it corresponds to the killing of Osama bin Laden—the event associated with the highest sustained rate of Twitter traffic up to that point. Selecting the term “bin Laden” instantly generates its time series in the lower graph, which correlates perfectly with the spike in overall traffic.

This example perfectly illustrates one of the challenges of social media analytics, for there were three other major events being concurrently discussed on Twitter that were masked by the popularity of the bin Laden killing: the British royal wedding, a $100k fine levvied on Kobe Bryant, the NBA finals, and a tornado that devastated several cities in the Southern United States. Monitoring “trending topics” based upon spikes in the frequency or popularity of terms would not detect these less popular topics of discussion.

By exploring Clay’s corpora in TweetViewer, he and Jaime quickly noticed a pattern: there were certain terms that always seemed to spike in frequency during a crisis event. These included “help,” “please,” and “blood,” as well as various profanities. In the Nigerian bombing corpora, for example, there was a large spike in Twitter traffic temporally correlated with the bomb explosion, and a subsequent spike minutes later correlated with a bomb scare (but no actual blast). The frequency timeseries for the term “blood” pefectly correllates to the actual bomb blast, but does not spike again during the bomb scare. Clay and Jaime dubbed these terms Social Signals.

Automating the Process

When I was first briefed on this phenomenon I was immediately intrigued, primarily around the potential of publishing an academic paper peppered with legitimate use of profanity.

Nam castum esse decet pium poetam
ipsum, versiculos nihil necessest;

The challenge was that these social signals were discovered by humans. Were there others we had missed? Would this phenomenon extend to languages other than English? Could an algorithm be designed to classify and track social signals?

I played around with the data in TweetViewer for a while, and I eventually developed a theory as to what made a term a social signal. My underlying hypothesis was that the human-identified social signals are a subset of a larger class of abnormal terms whose frequency of usage does not correlate with the overall frequency of Twitter traffic. For each geographic region, Twitter traffic ebbs and flows in a pattern much like a circadian rhythm. In the Twitter posts captured from the Tuscaloosa tornado event, for example, traffic peaks around 23:00 every night and then rapidly falls—as people go to sleep—and reaches a nadir at about 09:00—when people are arriving at work. It is actually very regular, except for when people are discussing an unexpected event.

As an example, consider the terms “please” and “blood,” which we have observed to temporally co-occur with certain types of disaster events. Under normal circumstances these terms are not temporally correlated to each other; however, during a disaster scenario they are. Furthermore, the term “blood” does not have a very high frequency, so methods based strictly on the frequency domain or burstiness of the terms might ignore that signal. Therefore, we first identify the set of maximally abnormal term signals, vi&., those that are temporally uncorrelated with the overall Twitter traffic. Next, these abnormal signals are sorted according to a significance metric which is based on how temporally correlated they are to each other. Finally, an unsupervised graph clustering algorithm is used to merge similar signals.

For the sake of brevity, I am going to save the technical details for a later post. I will likely also be publishing a related manuscript on the arXiv.


We evaluated our approach on a number of Twitter datasets captured during various disaster scenarios. The first corpus we examined includes tweets from the April 2011 tornados in Tuscaloosa, Alabama. This is the interesting corpus mentioned above in which the tornados co-occurred with the British Royal wedding, a much discussed $100,000 fine of NBA player Kobe Bryant, and the killing of Osama bin Laden—the event associated with the highest sustained rate of tweets per second to date. The second corpus includes tweets from the February 2011 earthquake in Christchurch, New Zealand. The final corpus includes tweets leading up to and including the July 2011 bombings in Mumbai, India. This corpus is interesting because it includes tweets from the prior thirteen days leading up to the bombings.

The time bucket size was set to 20 minutes. The number of maximally aberrant signals discovered was 300. For each corpus, the resulting automatically detected signals included the human-identified social signals “help” and “please.” The Mumbai corpus also included the “blood” social signal. The clustered signals of highest significance are given in the following table, in which green signals are ones that are clearly related to an important event:

Dataset Start Date End Date # Tweets # Terms
Tuscaloosa April 28, 2011 15:46:35 CDT May 8, 2011 21:43:48 CDT 97409 58232

Top Detected Signals (sorted by decreasing significance)

tuscaloosanews greek relief need disaster followers text tell redcross like needs just back alabama tornado laden osama news dead obama know tuscaloosa really come thanks will help youtube helpttown right right love lakers think supplies mothers mother happy first volunteers give spann time donation make donate please courtside tickets
Christchurch February 17, 2011 23:10:03 NZDT February 24, 2011 11:51:11 NZDT 12973 15223

Top Detected Signals (sorted by decreasing significance)

need safe #eqnz earthquake still people know help please chch family anyone christchurch will quake #chch jobs #job canstaff victims darkest donate zealand towards donation make redcross just today power thanks everyone news home thank water christchurchcc open yeah tumblr photo another bexielady haha dairy nzne wellington nzpa #christchurch want
Mumbai July 1, 2011 21:39:06 IST July 15, 2011 03:29:30 IST 164820 123516

Top Detected Signals (sorted by decreasing significance)

need govt terror good every people varsha love twitter thanks hear time help please police anybody says serious right work control mumbai karia going think blood like #mumbai injured kasab will even make hospital room blasts stop home call parents blast today well looking life india dadar want just safe #mumbaiblasts stay news rahul come indian #here back know take anyone spirit also #bse #stocks #tips turn following sexy #instantfollow bitch #images #india #free

The Aftermath

Excited that we had developed an automated, completely unsupervised, language-agnostic technique for detecting social signals—which also happened to detect the same signals discovered by human analysis—we quickly wrote up a paper and submitted it to a conference. A few months later the reviews came back:

Reviewer 1

I read this in detail and it is amazing!
Accept Nominate for Best Paper

Reviewer 2

My graduate advisor pawned this review off on me, and I’d rather not be doing it, but this paper looks interesting and I find no technical faults in it.

Reviewer 3

I don’t see any real technical faults, but why didn’t you cite [a tangentially relevant paper that was likely written by Reviewer 3]?

As you might have guessed, the paper was ultimately rejected.

Shortly after, I left JHU and didn’t continue this line of research, so it’s just been bit-rotting for the past few years.

In summary, our paper introduced the discovery of social signals: elements of text in social media that convey information about peoples’ response to significant events, independent of specific terms describing or naming the event. The second contribution of the paper was a model of the mechanisms by which such signals can be identified, based on the patterns of human-identified signals. Finally, a novel unsupervised method for the detection of social signals in streams of tweets was presented and demonstrated. For a set of corpora containing both natural and human-caused disasters, in each instance our model and algorithms were able to identify a class of signals containing the manually classified social signals.

Future work includes optimizing the algorithms for efficiency in processing streaming Twitter traffic. Incorporating estimates of provenance and trustworthiness from the Twitter social network, as well as accounting for the propagation of signals through re-tweets, might improve the relevance of the detected signals. Finally, we hoped to perform a sensitivity analysis of the algorithms by detecting signals across domains, e.g., using a baseline from one disaster-related event to detect signals during a similar event at a different time.

Success in OS X

10 easy steps (and lots of unnecessary prose) on how to set up a new PowerBook in 48 hours or more.

Five years ago I wrote about my travails solving some networking issues on my Linux laptop. I equated the experience to a classic XKCD comic in which a simple software fix escalates to a life-and-death situation. The same thing happened to me again, this time on Mac OS X. Read on to find out.

Hashing Pointers

or: How I Learned to Stop Worrying and Love C++11

For as long as I’ve understood object-oriented programming, I’ve had an ambivalent relationship with C++. On the one hand, it promised the low-level control and performance of C, but, on the other, it also had many pitfalls that did not exist in other high-level managed languages like Java. For example, I would often find myself in a position where I wouldn’t be quite sure exactly what the compiler would be doing under-the-hood, particularly when passing around objects by value. Would the compiler be “smart” enough to know that it can rip out the guts of an object instead of making an expensive copy? Granted, much of my uncomfortableness could have been remedied by a better understanding of the language specification. But why should I have to read an ~800 page specification just to understand what the compiler is allowed to do? The template engine and the STL are incredibly powerful, but it can make code just as verbose as Java, one of Java’s primary criticisms. Therefore, I found myself gravitating toward more “purely” object-oriented languages, like Java, when a project fit with that type of abstraction, and falling back to C when I needed absolute control and speed.

A couple years ago, around the time when compilers started having full support for C++11, I started a project that was tightly coupled to the LLVM codebase, which was written in C++. Therefore, I slowly started to learn about C++11’s features. I now completely agree with Stroustrup: It’s best to think of C++11 like a completely new language. Features like move semantics give the programmer complete control over when the compiler is able to move the guts of objects. The new auto type deduction keyword gets rid of a significant amount of verbosity, and makes work with complex templates much easier. Coupled with the new decltype keyword, refactoring object member variable types becomes a breeze. STL threads now make porting concurrent code much easier. That’s not to mention syntactic sugar like ranged for statements, constructor inheritance, and casting keywords. And C++ finally has lambdas! C++11 seems to be a bigger leap forward from C++03 than even Java 1.5 (with its addition of generics) was to its predecessor.

As an example, I recently needed an unordered hash map where the keys were all pointers. For example,

std::unordered_map<char*,bool> foo;
I wanted the keys to be hashed based upon the memory addresses of the character pointers, not the actual strings. This is similar to Java’s concept of an IdentityHashMap. Unfortunately, the STL does not have a built-in hash function for pointers. So I created one thusly:
/* for SIZE_MAX and UINTPTR_MAX: */
#include <cstdint>
namespace hashutils {
  /* hash any pointer */
  template<typename T>
  struct PointerHash {
    inline size_t operator()(const T* pointer) const {
      auto addr = reinterpret_cast<uintptr_t>(pointer);
      /* size_t is not large enough to hold the pointer’s memory address */
      addr %= SIZE_MAX; /* truncate the address so it is small enough to fit in a size_t */
      return addr;
Note that I am using auto here to reduce verbosity, since it is evident that addr is a uintptr_t from the righthand side of the assignment. The hashutils::PointerHash object allows me to do this:
std::unordered_map<char*,bool,hashutils::PointerHash<char>> foo;
The neat part is that C++11 has a new using keyword that essentially lets me generically alias that definition:
template<typename K,typename V>
using unordered_pointer_map = std::unordered_map<K,V,hashutils::PointerHash<typename std::remove_pointer<K>::type>>;

unordered_pointer_map<char*,bool> foo;
Note the use of std::remove_pointer<_>, a great new STL template that gets the base type of a pointer.

In another instance, I wanted to have a hash map where the keys were pointers, but the hash was based off of the dereferenced version of the keys. This can be useful, e.g., if you need to hash a bunch of objects that are stored on the heap, or whose memory is managed outside of the current scope. This, too, was easy to implement:

namespace hashutils {
  template<typename T>
  inline size_t hash(const T& v) {
    return std::hash<T>()(v);

  /* hash based off of a pointer dereference */
  template<typename T>
  struct PointerDereferenceHash {
    inline size_t operator()(const T& pointer) const {
      return hash(*pointer);

  /* equality based off of pointer dereference */
  template<typename T>
  struct PointerDereferenceEqualTo {
    inline bool operator()(const T& lhs, const T& rhs) const {
      return *lhs == *rhs;

  template<typename K,typename V>
  using unordered_pointer_dereference_map = std::unordered_map<K,V,PointerDereferenceHash<K>,PointerDereferenceEqualTo<K>>;
Note that, through the magic of the C++ template engine, this code supports keys that are pure pointers as well as C++11’s new smart pointers.

As another example of the afforementioned auto keyword and ranged for statements, this is how easy it is in C++11 to hash an entire collection (e.g., a std::vector or std::set):

namespace hashutils {
  class HashCombiner {
    size_t h;
    HashCombiner() : h(0) {}
    template <class T>
    inline HashCombiner& operator<<(const T& obj) {
      /* adapted from boost::hash_combine */
      h ^= hash(obj) + 0x9e3779b9 + (h << 6) + (h >> 2);
      return *this;
    operator size_t() const { return h; }

  /* hash any container */
  template<typename T>
  struct ContainerHash {
    size_t operator()(const T& v) const {
      HashCombiner h;
      for(const auto& e : v) {
        h << e;
      return h;
Then, to make all sets hashable (and thereby valid to be used as keys in a map), simply add this:
namespace std {
  template<typename... T>
  struct hash<set<T...>> : hashutils::ContainerHash<set<T...>> {};

I realize that this post is a collection of rather mundane code snippets that are nowhere near a comprehensive representation of the new language features. Nevertheless, I hope that they will give you as much hope and excitement as they have given me, and perhaps inspire you to (re)visit this “new” language called C++11.