Journal of Parallel and Distributed Computing 104, 19-35.

The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing research, we take a "signal" view of heterogeneous information and adopt the Kalman filter to fuse multiple signals into daily vote predictions for the candidates. We also consider events that influenced the election in a quantitative manner based on the so-called event study model that originated in the field of financial research. We obtained the following interesting findings. First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness. But offline polls can still function on alleviating the sample bias of online opinions. Second, although online signals converge as election day approaches, the simple Facebook "Like" is consistently the strongest indicator of the election result. Third, most influential events have a strong connection to cross-strait relations, and the Chou Tzu-yu flag incident followed by the apology video one day before the election increased the vote share of Tsai Ing-Wen by 3.66%. This research justifies the predictive power of online media in politics and the advantages of information fusion. The combined use of the Kalman filter and the event study method contributes to the data-driven political analytics paradigm for both prediction and attribution purposes.

The discovery of time series motifs has emerged as one of the most useful primitives in time series data mining. Researchers have shown its utility for exploratory data mining, summarization, visualization, segmentation, classification, clustering, and rule discovery. Although there has been more than a decade of extensive research, there is still no technique to allow the discovery of time series motifs in the presence of missing data, despite the welldocumented ubiquity of missing data in scientific, industrial, and medical datasets. In this work, we introduce a technique for motif discovery in the presence of missing data. We formally prove that our method is admissible, producing no false negatives. We also show that our method can "piggyback" off the fastest known motif discovery method with a small constant factor time/space overhead. We will demonstrate our approach on diverse datasets with varying amounts of missing data.

For our lab, we began digging into the application of machine learning beginning in 2014, exploring its application in everything from supply chain optimization to factory automation and retail, including predicting terrorist attacks. Where we can apply knowledge for a given domain and weave it into a learning algorithm for the sake of doing non-deterministic pattern recognition, machine learning grounded in only statistics (not symbology, logic, or evolutionary) can readily improve upon guessing. Learning from a productive data set, and where overfitting is sufficiently avoided or mitigated, a learning algorithm can recognize patterns and generalize to cases not yet encountered. Such explorations for us started more than two years ago with SAP NS2 and ConvergentAI (formerly AxxonAI) where we find the project team's proof-of-concept (POC) results remain relevant today, but applicable to problem-solving the same way in other domains. While conceptually different, a strong relationship exists between machine learning and analytics where machine learning uses data and learning algorithms (supervised and unsupervised) to optimize a model based on performance and prior experience.

Imagine a situation where a group of adversaries is preparing an attack on the United States or U.S. interests. An intelligence analyst has observed some signals, but the situation is rapidly changing. The analyst faces the decision to alert a principal decision maker that an attack is imminent, or to wait until more is known about the situation. This warning decision is based on the analyst's observation and evaluation of signals, independent or correlated, and on her updating of the prior probabilities of possible scenarios and their outcomes. The warning decision also depends on the analyst's assessment of the crisis' dynamics and perception of the preferences of the principal decision maker, as well as the lead time needed for an appropriate response. This article presents a model to support this analyst's dynamic warning decision. As with most problems involving warning, the key is to manage the tradeoffs between false positives and false negatives given the probabilities and the consequences of intelligence failures of both types. The model is illustrated by revisiting the case of the attack on Pearl Harbor in December 1941. It shows that the radio silence of the Japanese fleet carried considerable information (Sir Arthur Conan Doyle's "dog in the night" problem), which was misinterpreted at the time. Even though the probabilities of different attacks were relatively low, their consequences were such that the Bayesian dynamic reasoning described here may have provided valuable information to key decision makers.

This working note discusses the topic of story generation, with a view to identifying the knowledge required to understand aviation incident narratives (which have structural similarities to stories), following the premise that to understand aviation incidents, one should at least be able to generate examples of them. We give a brief overview of aviation incidents and their relation to stories, and then describe two of our earlier attempts (using `scripts' and `story grammars') at incident generation which did not evolve promisingly. Following this, we describe a simple incident generator which did work (at a `toy' level), using a `world simulation' approach. This generator is based on Meehan's TALE-SPIN story generator (1977). We conclude with a critique of the approach.

One day after an American drone strike killed a leader of the militant Haqqani network in northwestern Pakistan, United States officials on Thursday rejected a claim by Pakistan that the strike had targeted an Afghan refugee camp. There were also conflicting accounts of the location of the drone strike and the number of people killed. A statement by Pakistan's Ministry of Foreign Affairs on Wednesday condemned the strike and maintained that it had "targeted an Afghan refugee camp in Kurram Agency" -- an assertion that the United States rejected on Thursday. "The claim in an M.F.A. statement yesterday that U.S. forces struck an Afghan refugee camp in Kurram Agency yesterday is false," said Richard W. Snelsire, the United States Embassy spokesman in Islamabad, Pakistan's capital. American officials said that there were no Afghan refugee camps in Kurram, a remote tribal region straddling the border with Afghanistan, where they said Wednesday's drone strike had taken place.

Fri 19 Jan 2018 09.09 EST Last modified on Sat 20 Jan 2018 01.44 EST Russia responded on 5 January to an attack by a swarm of drones targeting a Russian airbase in north-western Syria and a naval station on the Mediterranean Sea. The multi-drone attack, which is suspected to have been launched by militants, is the first of its kind, representing a new threat from terrorist groups. The use of a swarm attack demonstrates a militant capability, which was previously limited to states, to simultaneously control and coordinate several commercial drones at one time using a GPS unit. This development may send viewers of the science-fiction series Black Mirror into hiding, but it should prompt professional militaries to double down on countermeasures, specifically the creation of electronic jamming tech. Swashbuckling drones operated by rebels and militants have been shoring up the frontlines of conflict internationally, in some cases braving the choppy waters off the coast of Yemen, and in others crowding the skies over Syria and Iraq.

A pilot has flown a drone into a crane, according to an air-accident report. The pilot had planned the drone flight in Kent with four reference points, all at 400ft above ground level - higher than three existing cranes on the site. But another crane was erected after his site safety visit, and on take-off the drone crashed into the jib of the new structure, damaging the unmanned craft. The crash, in June last year, is listed in the Air Accidents Investigation Branch (AAIB) update this month. The incident report was picked up by The Register.

In this paper we present a broad overview of the last 40 years of research on cognitive architectures. Although the number of existing architectures is nearing several hundred, most of the existing surveys do not reflect this growth and focus on a handful of well-established architectures. Thus, in this survey we wanted to shift the focus towards a more inclusive and high-level overview of the research on cognitive architectures. Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning and reasoning. In order to assess the breadth of practical applications of cognitive architectures we gathered information on over 900 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight overall trends in the development of the field. In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress.