Automatic Detection of Egg Shell Cracks

The challenge was to find a reliable, non-intrusive means of detecting cracks in eggs. Intensity data from eggs were collected by VisionSmart for the group to analyse. Given the short time period three main questions were addressed. 1) Is there a feature of the intensity data which detects, and discriminates between pinholes, cage marks and cracks? 2) Are there ways to improve the current data collection process? 3) Are there other data collection methods which should be tried? A partial positive response to 1) is presented and describes the many problems that arose. Some answers to 2) and 3) are also presented

Automatic Detection of Seizures with Applications

There are an estimated two million people with epilepsy in the United States. Many of these people do not respond to anti-epileptic drug therapy. Two devices can be developed to assist in the treatment of epilepsy. The first is a microcomputer-based system designed to process massive amounts of electroencephalogram (EEG) data collected during long-term monitoring of patients for the purpose of diagnosing seizures, assessing the effectiveness of medical therapy, or selecting patients for epilepsy surgery. Such a device would select and display important EEG events. Currently many such events are missed. A second device could be implanted and would detect seizures and initiate therapy. Both of these devices require a reliable seizure detection algorithm. A new algorithm is described. It is believed to represent an improvement over existing seizure detection algorithms because better signal features were selected and better standardization methods were used

Cognitive Biases and Gaze Direction: An Experimental Study

This paper investigates the validity of the model of dual processing by means of eyetracking methods. In this theoretical framework, gaze direction may be a revealing signal of how automatic detection is modified or sustained by controlled search. We performed an experiment by using a stylized decisional framework, i.e. informational cascade, proposed by economists to investigate the rationality of imitative behavior. Our main result is that automatic detection as revealed by gaze direction is driven by mechanisms that are dependent on cognitive biases. In particular, we find significant statistical correlation between subjects’ first fixation and their revealed patterns of choice. Our findings support the hypothesis that the process of automatic detection is not independent on cognitive processes.informational cascades, overconfidence, eye-tracking, information processing, cognitive biases

Automatic Detection of Online Jihadist Hate Speech

We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.Comment: 31 page

Automatic Detection and Categorization of Election-Related Tweets

With the rise in popularity of public social media and micro-blogging services, most notably Twitter, the people have found a venue to hear and be heard by their peers without an intermediary. As a consequence, and aided by the public nature of Twitter, political scientists now potentially have the means to analyse and understand the narratives that organically form, spread and decline among the public in a political campaign. However, the volume and diversity of the conversation on Twitter, combined with its noisy and idiosyncratic nature, make this a hard task. Thus, advanced data mining and language processing techniques are required to process and analyse the data. In this paper, we present and evaluate a technical framework, based on recent advances in deep neural networks, for identifying and analysing election-related conversation on Twitter on a continuous, longitudinal basis. Our models can detect election-related tweets with an F-score of 0.92 and can categorize these tweets into 22 topics with an F-score of 0.90.Comment: ICWSM'16, May 17-20, 2016, Cologne, Germany. In Proceedings of the 10th AAAI Conference on Weblogs and Social Media (ICWSM 2016). Cologne, German