Miracle-dz/UniFAR-FTUs huggingface dataset:
AGR2, a unique tumor-associated antigen, is a promising candidate for antibody targeting

Anterior gradient 2 (AGR2), a protein disulfide isomerase, shows two subcellular localizations: intracellular (iAGR2) and extracellular (eAGR2). In healthy cells that express AGR2, the predominant form is iAGR2, which resides in the endoplasmic reticulum. In contrast, cancer cells secrete and express eAGR2 on the cell surface. We wanted to test if AGR2 is a cancer-specific tumor-associated antigen. We utilized two AGR2 antibodies, P3A5 and P1G4, for in vivo tumor localization and tumor growth inhibition. The monoclonal antibodies recognized both human AGR2 and mouse Agr2. Biodistribution experiments using a syngeneic mouse model showed high uptake of P3A5 AGR2 antibody in xenografted eAgr2+ pancreatic tumors, with limited uptake in normal tissues. In implanted human patient-derived eAGR2+ pancreatic cancer xenografts, tumor growth inhibition was evaluated with antibodies and Gemcitabine (Gem). Inhibition was more potent by P1G4 + Gem combination than Gem alone or P3A5 + Gem. We converted these two antibodies to human:mouse chimeric forms: the constructed P3A5 and P1G4 chimeric mVLhCκ and mVHhCγ (γ1, γ2, γ4) genes were inserted in a single mammalian expression plasmid vector, and transfected into human 293F cells. Expressed human:mouse chimeric IgG1, IgG2 and IgG4 antibodies retained AGR2 binding. Increase in IgG yield by transfected cells could be obtained with serial transfection of vectors with different drug resistance. These chimeric antibodies, when incubated with human blood, effectively lysed eAGR2+ PC3 prostate cancer cells. We have, thus, produced humanized anti-AGR2 antibodies that, after further testing, might be suitable for treatment against a variety of eAGR2+ solid tumors.
Grade Prediction Based on Cumulative Knowledge and Co-taken Courses

Over the past decade, low graduation and retention rates have plagued higher education institutions. To help students graduate on time and achieve optimal learning outcomes, many institutions provide advising services supported by educational technologies. Accurate grade prediction is an integral part of these services such as degree planning software, personalized advising systems and early warning systems that can identify students at-risk of dropping from their ﬁeld of study. In this work, we present next-term grade prediction models based on students’ cumulative knowledge and co-taken courses. The proposed models are based on a matrix factorization framework and incorporate a co-taken course interaction function to learn the inﬂuence from the co-taken courses on the target course. The co-taken course interaction function is formed by a neural network, which takes the knowledge difference between the co-taken courses and the target course as input, and outputs an inﬂuence value that will be used to predict students’ grades on the target course. The experimental results on various datasets from a U.S. University demonstrate that the proposed models signiﬁcantly outperform competitive baselines across different test sets. Furthermore, we analyze the proposed models’ performance with different numbers of co-taken courses as well as different numbers of co-taken course subjects, and highlight with an application case study how a student might make decisions related to selection of courses. The codes are available at https://github.com/Zhiyun0411/EDM .
Mining constraints for grammar fuzzing

Grammar-based fuzzing has been shown to significantly improve bug detection in programs with highly structured inputs. However, since grammars are largely handwritten, it is rarely used as a standalone technique in large-spectrum fuzzers as it requires human expertise. To fill this gap, promising techniques begin to emerge to automate the extraction of context-free grammars directly from the program under test. Unfortunately, the resulting grammars are usually not expressive enough and generate too many wrong inputs to provide results capable of competing with other fuzzing techniques. In this paper we propose a technique to automate the creation of attribute grammars from context-free grammars, thus significantly lowering the barrier of entry for efficient and effective large-scale grammar-based fuzzing.
A large-scale study of application incompatibilities in Android

The rapid expansion of the Android ecosystem is accompanied by continuing diversification of platforms and devices, resulting in increasing incompatibility issues which damage user experiences and impede app development productivity. In this paper, we conducted a large-scale, longitudinal study of compatibility issues in 62,894 benign apps developed in the past eight years, to understand the symptoms and causes of these issues. We further investigated the incompatibilities that are actually exercised at runtime through the system logs and execution traces of 15,045 apps. Our study revealed that, among others, (1) compatibility issues were prevalent and persistent at both installation and run time, with greater prevalence of run-time incompatibilities, (2) there were no certain Android versions that consistently saw more or less app incompatibilities than others, (3) installation-time incompatibilities were strongly correlated with the minSdkVersion specified in apps, while run-time incompatibilities were most significantly correlated with the underlying platform’s API level, and (4) installation-time incompatibilities were mostly due to apps’ use of architecture-incompatible native libraries, while run-time incompatibilities were mostly due to API changes during SDK evolution. We offered further insights into app incompatibilities, as well as recommendations on dealing with the issues for bother developers and end users of Android apps.
Continuous software performance assessment: detecting performance problems of software libraries on every build

Degradation of software performance can become costly for companies and developers, yet it is hardly assessed continuously. A strategy that would allow continuous performance assessment of software libraries is software microbenchmarking, which faces problems such as excessive execution times and unreliable results that hinder wide-spread adoption in continuous integration. In my research, I want to develop techniques that allow including software microbenchmarks into continuous integration by utilizing cloud infrastructure and execution time reduction techniques. These will allow assessing performance on every build and therefore catching performance problems before they are released into the wild.
Exploring Neural Network Models for the Classification of Students in Highly Interactive Environments

Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and effective exploration. Guidance in the form of targeted interventions or feedback therefore has the potential to improve educational outcomes. A promising approach for adaptation in OELEs is the design of interventions based on the detection of characteristic learning behaviors through offline clustering, followed by a real-time classification of new students. In this paper, we explore the possibility of using recurrent neural network (RNN) models for this online classification task. We extensively evaluate the predictive performance of different variants of RNNs, namely long-short term memory models and gated recurrent units, and different architectures on a data set collected from an exploration-based educational game. We also compare the prediction accuracy of the different RNN models to the performance of traditional classifiers on the same data set. Our results demonstrate that RNNs perform similar or better than traditional methods regarding early classification and therefore constitute a promising alternative for the online classification of new students.
Scalable Entity-Based Modeling of Population-Based Systems, Final LDRD Report

The goal of this project has been to develop tools, capabilities and expertise in the modeling of complex population-based systems via scalable entity-based modeling (EBM). Our initial focal application domain has been the dynamics of large populations exposed to disease-causing agents, a topic of interest to the Department of Homeland Security in the context of bioterrorism. In the academic community, discrete simulation technology based on individual entities has shown initial success, but the technology has not been scaled to the problem sizes or computational resources of LLNL. Our developmental emphasis has been on the extension of this technology to parallel computers and maturation of the technology from an academic to a lab setting.
Comparison of experts and novices in problem-based learning for engineering education

Ill-structured problems, problems that do not have simple structures and one finite correct solution, are the most common form of problems that engineers meet in everyday situations. However, the curriculum of engineering education mostly focuses on well-structured problems, leading to the possibility that students might not apply the knowledge they have learned from school to the workplace after they graduate. Problem-based learning using ill-structured problems is more effective in teaching students to approach a solution. After an introduction to these three areas, I propose a research study in which novice participants who are majoring in Engineering and expert participants who are in the Civil or Mechanical Engineering fields are asked to solve ill-structured problems. The focus of analysis will be on the different types of analogies they use. Self-efficacy will also be measured using a survey to observe if different levels of self-efficacy affect problem solving differently in the two groups, and if there is any
Deep Learning in Personalized Medicine: Applications in Patient Similarity, prognosis, and Optimal treatment Selection

Author(s): Norgeot, Beau | Advisor(s): Butte, Atul J | Abstract: Two information technology revolutions are colliding in medicine. The first revolution has been the digitalization of health data, specifically Electronic Health Records (EHR). These records contain the details of who we are as patients, our ailments, treatments, and outcomes. Tragically, despite billions of dollars in investment from the US government, hardly any of this data is being utilized to better understand medicine or improve healthcare. This is largely because the data is voluminous, sparse, complex, and poorly formatted; making it unsuitable for traditional analytics methods. However the second revolution, modern Artificial Intelligence, specifically deep learning, provides tools, in the form of algorithms, to address exactly these problems. The primary difference between these modern algorithms and older ones is that the former are able to learn, more or less on their own, how to transform large complex data into a format that makes it easier to use and learn from. In this dissertation, I have developed methods to apply deep learning to digital health data. Doing so, I have shown that we can predict the future health of individual patients with highly complex diseases, produced approaches to understand and leverage what these complex models are learning, and provided a framework for how healthcare systems of the near future could automatically learn to improve care daily. For the first time in history, we are in a position to learn from the combined knowledge of tens of thousands of physicians and their experiences caring for hundreds of millions of patients. The potential transformations to healthcare are difficult to fully fathom, but certainly include safer, more powerful and efficient medicine, and a rapid speed up in new medical discoveries and treatments. Despite the promise, we must proceed carefully, balancing the great need to collectively use our data for better medicine with the individual right to privacy.
Toward Near Zero-Parameter Prediction Using a Computational Model of Student Learning

Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners’ performance in instructional technologies given only the technology itself without fitting any parameters to existing learners’ data. While these so call “zero-parameter” models have been successful in modeling student learning in intelligent tutoring systems they still show systematic deviation from human learning performance. One deviation stems from the computational models’ lack of prior knowledge—all models start off as a blank slate—leading to substantial differences in performance at the first practice opportunity. In this paper, we explore three different strategies for accounting for prior knowledge within computational models of learning and the effect of these strategies on the predictive accuracy of these models.
Content-based Course Recommender System for Liberal Arts Education

Liberal Arts programs are often characterized by their open curriculum. Yet, the abundance of courses available and the highly personalized curriculum are often overwhelming for students who must select courses relevant to their academic interests and suitable to their academic background. This paper presents the course recommender system that we have developed for the Liberal Arts bachelor of the University College Maastricht, the Netherlands. It aims to complement academic advising and help students make better-informed course selections. The system recommends courses whose content best matches the student’s academic interests, issues warnings for courses that are too advanced given the student’s academic background and, in the latter case, suggests suitable preparatory courses. We base the course recommendations on a topic model fitted on course descriptions, and the warnings on a sparse predictive model for grade based on students’ past academic performance and level of academic expertise. Preparatory courses consist of courses whose content has the best preparatory value according to the predictive model. We find that course recommendations are relevant for a wide range of academic interests present in the student population and that students found recommendations for courses at other departments especially helpful. The preparatory courses often lack coherence with the target course and need to be improved.
TestMig: migrating GUI test cases from iOS to Android

Nowadays, Apple iOS and Android are two most popular platforms for mobile applications. To attract more users, many software companies and organizations are migrating their applications from one platform to the other, and besides source files, they also need to migrate their GUI tests. The migration of GUI tests is tedious and difficult to be automated, since two platforms have subtle differences and there are often few or even no migrated GUI tests for learning. To address the problem, in this paper, we propose a novel approach, TestMig, that migrates GUI tests from iOS to Android, without any migrated code samples. Specifically, TestMig first executes the GUI tests of the iOS version, and records their GUI event sequences. Guided by the iOS GUI events, TestMig explores the Android version of the application to generate the corresponding Android event sequences. We conducted an evaluation on five well known mobile applications: 2048, SimpleNote, Wire, Wikipedia, and WordPress. The results show that, on average, TestMig correctly converts 80.2% of recorded iOS UI events to Android UI events and have them successfully executed, and our migrated Android test cases achieve similar statement coverage compared with the original iOS test cases (59.7% vs 60.4%).
Exploring Secrecy Outage Probability of AF-NOMA and AF-OMA Networks

—A new design of non-orthogonal multiple access (NOMA) is explored in this study under secrecy outage probability performance considerations. In particular, the Amplify-and-Forward (AF) relaying scheme is applied in this system model. In case of existence of an external eavesdropper, a transmitter sends confidential messages to far users in concerned NOMA system. To highlight performance of NOMA system model, orthogonal multiple access (OMA) is compared with respect to tradeoff between secure performances and transmit SNR. As important observation, the secrecy outage probability is evaluated as the secrecy metric to limit impacts of the practical passive eavesdropper in real scenario. It is confirmed that the secrecy outage is significant lower than OMA due to related parameters characterization in NOMA, and it should be controlled by varying related coefficients. Finally, we show the both of NOMA and OMA against to impact of eavesdropper in the studied problems in terms of analytically result and numerically result.
Automatically testing self-driving cars with search-based procedural content generation

Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving car software in real traffic is not only expensive but also dangerous, and has already caused fatalities. Virtual tests, in which self-driving car software is tested in computer simulations, offer a more efficient and safer alternative compared to naturalistic field operational tests. However, creating suitable test scenarios is laborious and difficult. In this paper we combine procedural content generation, a technique commonly employed in modern video games, and search-based testing, a testing technique proven to be effective in many domains, in order to automatically create challenging virtual scenarios for testing self-driving car soft- ware. Our AsFault prototype implements this approach to generate virtual roads for testing lane keeping, one of the defining features of autonomous driving. Evaluation on two different self-driving car software systems demonstrates that AsFault can generate effective virtual road networks that succeed in revealing software failures, which manifest as cars departing their lane. Compared to random testing AsFault was not only more efficient, but also caused up to twice as many lane departures.
Learning Opinions by Observing Actions: Simulation of Opinion Dynamics Using an Action-Opinion Inference Model

Opinion dynamics models are based on the implicit assumption that people can observe the opinions of others directly, and update their own opinions based on the observation. This assumption significantly reduces the complexity of the process of learning opinions, but seems to be rather unrealistic. Instead, we argue that the opinion itself is unobservable, and that people attempt to infer the opinions of others by observing and interpreting their actions. Building on the notion of Bayesian learning, we introduce an action-opinion inference model (AOI model); this model describes and predicts opinion dynamics where actions are governed by underlying opinions, and each agent changes her opinion according to her inference of othersâ€™ opinions from their actions. We study different action-opinion relations in the framework of the AOI model, and show how opinion dynamics are determined by the relations between opinions and actions. We also show that the well-known voter model can be formulated as being a special case of the AOI model when adopting a bijective action-opinion relation. Furthermore, we show that a so-called inclusive opinion, which is congruent with more than one action (in contrast with an exclusive opinion which is only congruent with one action), plays a special role in the dynamic process of opinion spreading. Specifically, the system containing an inclusive opinion always ends up with a full consensus of an exclusive opinion that is incompatible with the inclusive opinion, or with a mixed state of other opinions, including the inclusive opinion itself. A mathematical solution is given for some simple action-opinion relations to help better understand and interpret the simulation results. Finally, the AOI model is compared with the constrained voter model and the language competition model; several avenues for further research are discussed at the end of the paper.
Codebase-adaptive detection of security-relevant methods

More and more companies use static analysis to perform regular code reviews to detect security vulnerabilities in their code, configuring them to detect various types of bugs and vulnerabilities such as the SANS top 25 or the OWASP top 10. For such analyses to be as precise as possible, they must be adapted to the code base they scan. The particular challenge we address in this paper is to provide analyses with the correct security-relevant methods (Srm): sources, sinks, etc. We present SWAN, a fully-automated machine-learning approach to detect sources, sinks, validators, and authentication methods for Java programs. SWAN further classifies the Srm into specific vulnerability classes of the SANS top 25. To further adapt the lists detected by SWAN to the code base and to improve its precision, we also introduce SWANAssist, an extension to SWAN that allows analysis users to refine the classifications. On twelve popular Java frameworks, SWAN achieves an average precision of 0.826, which is better or comparable to existing approaches. Our experiments show that SWANAssist requires a relatively low effort from the developer to significantly improve its precision.
Computational perception for multi-modal document understanding

Multimodal documents occur in a variety of forms, as graphs in technical reports, diagrams in textbooks, and graphic designs in bulletins. Humans can efficiently process the visual and textual information contained within to make decisions on topics including business, healthcare, and science. Building the computational tools to understand multimodal documents can have important applications for web search, information retrieval, captioning and summarization, and automated design. This thesis makes contributions on two fronts: (i) to the development of data collection methods for measuring how humans perceive multimodal documents (i.e., where they look, what they find important), and (ii) to the development of computer vision tools for automatically parsing and making predictions about multimodal documents (i.e., the subject matter they are about). Specifically, the crowdsourced attention data captured from our novel user interfaces is used to train neural network models to predict where people look in graphic designs and information visualizations, with demonstrated applications to thumbnailing, design retargeting, and interactive feedback within graphic design tools. Separately, our models for detecting visual elements and parsing text elements in infographics (information graphics) are used for topic prediction and to present a system for automatic summarization. This thesis makes contributions at the interface of human and computer vision, with applications to human-computer interfaces and design.