Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Combining type checking with model checking for system verification

Type checking is widely used in mainstream programming languages to detect programming errors at compile time. Model checking is gaining popularity as an automated technique for systematically analyzing behaviors of systems. My research focuses on combining these two software verification techniques synergically into one platform for the creation of correct models for software designs. This thesis describes two modeling languages ATS/PML and ATS/Veri that inherit the advanced type system from an existing programming language ATS, in which both dependent types of Dependent ML style and linear types are supported. A detailed discussion is given for the usage of advanced types to detect modeling errors at the stage of model construction. Going further, various modeling primitives with well-designed types are introduced into my modeling languages to facilitate a synergic combination of type checking with model checking. The semantics of ATS/PML is designed to be directly rooted in a well-known modeling language PROMELA. Rules for translation from ATS/PML to PROMELA are designed and a compiler is developed accordingly so that the SPIN model checker can be readily employed to perform checking on models constructed in ATS/PML. ATS/Veri is designed to be a modeling language, which allows a programmer to construct models for real-world multi-threaded software applications in the same way as writing a functional program with support for synchronization, communication, and scheduling among threads. Semantics of ATS/Veri is formally defined for the development of corresponding model checkers and a compiler is built to translate ATS/Veri into CSP# and exploit the state-of-the-art verification platform PAT for model checking ATS/Veri models. The correctness of such a transformational approach is illustrated based on the semantics of ATS/Veri and CSP#. In summary, the primary contribution of this thesis lies in the creation of a family of modeling languages with highly expressive types for modeling concurrent software systems as well as the related platform supporting verification via model checking. As such, we can combine type checking and model checking synergically to ensure software correctness with high confidence

Disinformation and Fact-Checking in Contemporary Society

Funded by the European Media and Information Fund and research project PID2022-142755OB-I00

Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge

The proliferation of fake news on social media sites is a serious problem with documented negative impacts on individuals and organizations. This makes detection of fake news an extremely important challenge. A fake news item is usually created by manipulating photos, text or videos that indicate the need for multimodal detection. Researchers are building detection algorithms with the aim of high accuracy as this will have a massive impact on the prevailing social and political issues. A shortcoming of existing strategies for identifying fake news is their inability to learn a feature representation of multimodal (textual+visual) information. In this thesis research, we present a novel approach using a Cultural Algorithm with situational and normative knowledge to detect fake news using both text and images. The proposed model’s principal innovation is to use the power of natural language processing like sentiment analysis, segmentation process for feature extraction, and optimizing it with a Cultural algorithm. Then the representations from both modalities are fused, which is finally used for classification. An extensive set of experiments is carried out on real-world multimedia datasets collected from Weibo and Twitter. The proposed method outperforms the state-of-the-art methods for identifying fake new

Blame Tracking and Type Error Debugging

In this work, we present an unexpected connection between gradual typing and type error debugging. Namely, we illustrate that gradual typing provides a natural way to defer type errors in statically ill-typed programs, providing more feedback than traditional approaches to deferring type errors. When evaluating expressions that lead to runtime type errors, the usefulness of the feedback depends on blame tracking, the defacto approach to locating the cause of such runtime type errors. Unfortunately, blame tracking suffers from the bias problem for type error localization in languages with type inference. We illustrate and formalize the bias problem for blame tracking, present ideas for adapting existing type error debugging techniques to combat this bias, and outline further challenges

Elephants Never Forget: Partisan Schemas and the Continued Influence of Misinformation

In an age where information is plentiful and access to it is practically unlimited, the veracity of information is frequently an afterthought. Previous research has demonstrated that individuals may often be reluctant to alter their beliefs and attitudes even after false information is corrected. This phenomenon is known as the continued-influence effect or the continued influence of misinformation (CIM). Misinformation and “fake news” have grown more common, and their effectiveness may be explained by CIM. Research also shows that schemas can have significant effects on how information is processed, and preexisting beliefs, values and attitudes can affect what information is readily absorbed, ignored, forgotten or invented. Individuals with more extreme partisan schemas, particularly conservatives, may be more vulnerable to misinformation. The current study was an examination of CIM in college students and the general population who were exposed to fake news, corrections of fake news, or both. The hypotheses that attitudes about initial misinformation and degree of belief change upon correction would vary by partisan schema strength were partially supported