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

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

An artificial intelligence approach to predicting personality types in dogs

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles

Unobtrusive Assessment Of Student Engagement Levels In Online Classroom Environment Using Emotion Analysis

Measuring student engagement has emerged as a significant factor in the process of learning and a good indicator of the knowledge retention capacity of the student. As synchronous online classes have become more prevalent in recent years, gauging a student\u27s attention level is more critical in validating the progress of every student in an online classroom environment. This paper details the study on profiling the student attentiveness to different gradients of engagement level using multiple machine learning models. Results from the high accuracy model and the confidence score obtained from the cloud-based computer vision platform - Amazon Rekognition were then used to statistically validate any correlation between student attentiveness and emotions. This statistical analysis helps to identify the significant emotions that are essential in gauging various engagement levels. This study identified emotions like calm, happy, surprise, and fear are critical in gauging the student\u27s attention level. These findings help in the earlier detection of students with lower attention levels, consequently helping the instructors focus their support and guidance on the students in need, leading to a better online learning environment

Altered expression of glutamate signaling, growth factor, and glia genes in the locus coeruleus of patients with major depression.

Several studies have proposed that brain glutamate signaling abnormalities and glial pathology have a role in the etiology of major depressive disorder (MDD). These conclusions were primarily drawn from post-mortem studies in which forebrain brain regions were examined. The locus coeruleus (LC) is the primary source of extensive noradrenergic innervation of the forebrain and as such exerts a powerful regulatory role over cognitive and affective functions, which are dysregulated in MDD. Furthermore, altered noradrenergic neurotransmission is associated with depressive symptoms and is thought to have a role in the pathophysiology of MDD. In the present study we used laser-capture microdissection (LCM) to selectively harvest LC tissue from post-mortem brains of MDD patients, patients with bipolar disorder (BPD) and from psychiatrically normal subjects. Using microarray technology we examined global patterns of gene expression. Differential mRNA expression of select candidate genes was then interrogated using quantitative real-time PCR (qPCR) and in situ hybridization (ISH). Our findings reveal multiple signaling pathway alterations in the LC of MDD but not BPD subjects. These include glutamate signaling genes, SLC1A2, SLC1A3 and GLUL, growth factor genes FGFR3 and TrkB, and several genes exclusively expressed in astroglia. Our data extend previous findings of altered glutamate, astroglial and growth factor functions in MDD for the first time to the brainstem. These findings indicate that such alterations: (1) are unique to MDD and distinguishable from BPD, and (2) affect multiple brain regions, suggesting a whole-brain dysregulation of such functions