Matrix factorization for co-training algorithm to classify human rights abuses

In the human rights domain, there is need to filter, efficiently classify and prioritize the types of violation endured by victims in order to provide the necessary rehabilitation and support. However, the domain is dominated by unstructured data either from victims' accounts, doctors'/professionals' reports or available on line. Manual classification still prevails in this domain which is extremely time consuming and slow. This is a problem for non-government operated charities. To this end we have explored the application of the co-training algorithm in order to improve the performance of a semi-supervised learning algorithm by incorporating large amounts of unlabeled data into the training data set. However, it remains challenging to apply co-training on the data without two independent and self sufficient views. This paper puts forth a method of randomly dividing the available features to apply matrix factorization so as to discover latent features underlying the interactions between different kinds of entities present in a single view dataset. These labeled views balance the biased information in the dataset, but still satisfy the co-training assumptions. Alongside, the views are constrained such that pairs of labeled views create weak classifiers which in turn increase the prediction accuracy when combined. In the majority of cases, any classification tries to connect a single class to each sample or object. However, in the human rights domain, a victim can be subjected to more than one type of violation or abuse. This is multi-label classification where a sample can be assigned to more than one class. This paper aims to address all these aspects by bringing together a semi supervised classification model that relies on the effectiveness of matrix collaborative filtering in order to classify stories narrated by victims into one or more types of human rights abuses. Experimental results demonstrate the efficiency of this approach when applied on real-world stories from different victims

Using attention methods to predict judicial outcomes

Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict specific judicial characteristics, such as judicial outcome, a judicial class, predict an specific case. In this research, we have used AI classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. These texts formed a dataset of second-degree murder and active corruption cases. We applied different classifiers, such as Support Vector Machines and Neural Networks, to predict judicial outcomes by analyzing textual features from the dataset. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets. As a final goal, we explored the weights of one of the algorithms, the Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants

Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201

A systematic review of ethical challenges and opportunities of addressing domestic violence with AI-technologies and online tools

Domestic violence remains a pressing complex social problem of people of any gender, age, socio-economic status, and ethno-cultural background, an issue that worsened worldwide during the COVID-19 pandemic. Digital, online, or artificial intelligence-based smart technological services, applications, and tools provide novel approaches in addressing domestic violence, including intimate partner violence. This systematic literature review analyses the ethical challenges and opportunities these (protective) digital and smart technologies provide to the stakeholders involved. Our results highlight that the public health and societal issue are the leading narratives of domestic violence, which is predominantly interpreted as gender-based violence. The review highlights an emerging trend of the role of machine learning- and artificial intelligence-based approaches in identifying and preventing domestic violence. However, we argue that little recommendation is available to professionals about how to use these approaches in a responsible way, and that the smartness of high-tech technologies is often challenged by basic-level technologies from perpetrators, creating an imbalance that also limits an impactful development of a comprehensive socio-technical regime that serves the safety and resilience of families in their communal setting

Biometric Identification Systems: Feature Level Clustering of Large Biometric Data and DWT Based Hash Coded Bar Biometric System

Biometric authentication systems are fast replacing conventional identification schemes such as passwords and PIN numbers. This paper introduces a novel matching scheme that uses a image hash scheme. It uses Discrete Wavelet Transformation (DWT) of biometric images and randomized processing strategies for hashing. In this scheme the input image is decomposed into approximation, vertical, horizontal and diagonal coefficients using the discrete wavelet transform. The algorithm converts images into binary strings and is robust against compression, distortion and other transformations. As a case study the system is tested on ear database and is outperforming with an accuracy of 96.37% with considerably low FAR of 0.17%. The performance shows that the system can be deployed for high level security applications

Measuring bias in international news: a large-scale analysis of news agency coverage of the Ukraine crisis

I present a new methodological approach to measuring news bias, aiming to settle the disagreement on how to define and measure bias in media and communication studies in this thesis. Unlike earlier research on TV news or newspapers, I choose international news agencies’ coverage of the Ukraine crisis in this study as a case to highlight the strength of the new approach. Utilizing newly-developed geographical news classification and sentiment analysis techniques, I analyse news coverage of the Ukraine crisis by Russia’s official news agency, ITAR-TASS, along with the independent news agency, Interfax, over two years to estimate partisan news bias resulting from stateownership. In this longitudinal content analysis, I focus on the change in sentiment of ITAR-TASS’s news coverage of Ukraine relative to Interfax’s coverage during periods following six key events in the crisis. The analysis shows that the sentiment of ITAR-TASS’s news on Ukraine’s democracy and sovereignty changed significantly after key events, reflecting the desirability of these events to the Russian government. ITAR-TASS’s news coverage became the most negative when the new Ukraine government launched military operations against pro-Russian separatists in east Ukraine, claiming that the revolution was instigated by Ukrainian fascists, who threatened the safety of ethnic Russians. This result indicates that the Russian government utilized the news agency for international propaganda to justify its actions. Further, an additional content analysis including western news agencies revealed that Reuters’s news coverage of the Ukraine crisis during this period was strongly correlated with ITAR-TASS, being influenced by the Russian government’s false statements on Ukraine. Reuters news stories were circulated internationally, and published in the most popular news sites in the United States without context. I argue that the publication of the Russian government’s false narratives by American online news sites through Reuters indicates the vulnerability of today’s international news gathering and distribution system, and the rapidly changing relationship between states and corporations in the global news industry. This suggests that western news agencies’ use of temporary correspondents in covering rapidly developing international crises increases the risk of spreading false information globally. In this case, western news agencies are, in effect, supporting international propaganda by non-western states