Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

While favouring communications and easing information sharing, Social Network Sites are also used to launch harmful campaigns against specific groups and individuals. Cyberbullism, incitement to self-harm practices, sexual predation are just some of the severe effects of massive online offensives. Moreover, attacks can be carried out against groups of victims and can degenerate in physical violence. In this work, we aim at containing and preventing the alarming diffusion of such hate campaigns. Using Facebook as a benchmark, we consider the textual content of comments appeared on a set of public Italian pages. We first propose a variety of hate categories to distinguish the kind of hate. Crawled comments are then annotated by up to five distinct human annotators, according to the defined taxonomy. Leveraging morpho-syntactical features, sentiment polarity and word embedding lexicons, we design and implement two classifiers for the Italian language, based on different learning algorithms: the first based on Support Vector Machines (SVM) and the second on a particular Recurrent Neural Network named Long Short Term Memory (LSTM). We test these two learning algorithms in order to verify their classification performances on the task of hate speech recognition. The results show the effectiveness of the two classification approaches tested over the first manually annotated Italian Hate Speech Corpus of social media text.

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Hate Me, Hate Me Not: Hate Speech Detection on Facebook.

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a cyberbullying corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for the task. Experiments on a hold-out test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1 score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems.

/pdf/automatic-detection-of-cyberbullying-in-social-media-text-2l5z18yzoc.pdf

Automatic detection of cyberbullying in social media text

/pdf/national-innovation-system-scientific-concept-or-political-w9gv7zthrq.pdf

National Innovation System - Scientific Concept or Political Rhetoric

The use of new technologies along with the popularity of social networks has given the power of anonymity to the users. The ability to create an alter-ego with no relation to the actual user, creates a situation in which no one can certify the match between a profile and a real person. This problem generates situations, repeated daily, in which users with fake accounts, or at least not related to their real identity, publish news, reviews or multimedia material trying to discredit or attack other people who may or may not be aware of the attack. These acts can have great impact on the affected victims’ environment generating situations in which virtual attacks escalate into fatal consequences in real life. In this paper, we present a methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analysing the content of comments generated by both profiles. Accompanying this approach we also present a successful real life use case in which this methodology was applied to detect and stop a cyberbullying situation in a real elementary school.

Supervised Machine Learning for the Detection of Troll Profiles in Twitter Social Network: Application to a Real Case of Cyberbullying.

Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying

Over the last few years, computers and smartphones have become essential tools in our ways of communicating with each-other. Nowadays, the amount of applications in the Google store has grown exponentially, therefore, malware developers have introduced malicious applications in that market. The Android system uses the Dalvik virtual machine. Through reverse engineering, we may be able to get the different opcodes for each application. For this reason, in this paper an approach to detect malware on Android is presented, by using the techniques of reverse engineering and putting an emphasis on operational codes used for these applications. After obtaining these opcodes, machine learning techniques are used to classify apps.

Using Dalvik Opcodes for Malware Detection on Android

Industrial agglomerations are key in explaining the development paths followed by territories, particularly at sub-national levels This field of research has received increasing attention in the last decades, what has led to the emergence of a variety of models intended to characterize innovation at the regional level Moulaert and Sekia (Reg Stud 37:289–302, 2003) introduced the concept of ‘Territorial Innovation Models’ (TIMs) as a generic name that embraced these conceptual models of regional innovation in the literature However, the literature does not help to assess the extent to which convergence or divergence is found across TIMs In this paper we aim to clarify if there are clear boundaries across TIMs, so each TIM has particular characteristics that make it conceptually different from others, and hence, justify its introduction in the literature Based on natural language processing methodologies, we extract the key terms of a large volume of academic papers published in peer review journals indexed in the Web of Science for the following TIMS: industrial districts, innovative milieu, learning regions, clusters, regional innovation systems, local production systems and new industrial spaces We resort to Rapid Automatic Keyword Extraction to identify the associations between the topics extracted from the previous corpus Finally, a configuration to visualise the results of the methodology followed is also proposed Our results evidence that the previous models do not have a unique flavour but are rather similar in their taste We evidence that there is quite little that is truly new in the different TIMs in terms of theory-building and the concepts being used in each model

Territorial innovation models: to be or not to be, that’s the question

Over the last few years, computers and smartphones have become essential tools in our ways of communicating with each-other. Nowadays, the amount of applications in the Google store has grown exponentially, therefore, malware developers have introduced malicious applications in that market. The Android system uses the Dalvik virtual machine. Through reverse engineering, we may be able to get the di erent opcodes for each application. For this reason, in this paper an approach to detect malware on Android is presented, by using the techniques of reverse engineering and putting an emphasis on operational codes used for these applications. After obtaining these opcodes, machine learning techniques are used to classify apps.

José Gaviria de la Puerta

Papers

Supervised Machine Learning for the Detection of Troll Profiles in Twitter Social Network: Application to a Real Case of Cyberbullying.

Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying

Using Dalvik Opcodes for Malware Detection on Android

Territorial innovation models: to be or not to be, that’s the question

Using Dalvik opcodes for malware detection on android