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@DaoudiNadia
DaoudiNadia committed Nov 6, 2023
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8 changes: 4 additions & 4 deletions css/style.css
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Expand Up @@ -618,22 +618,22 @@ a:focus {

.presenter-details {
display: flex;
width: 20%;
flex-direction: column;
align-items: center;
margin-right: 20px;
margin-right: 15px;
}

.presenter-details h5 {
align-items: center;
margin-right: 20px;
color: black; /* Change the color of the presenter's name */
font-weight: bold;
margin-top: 30px;
}

.presenter-details p {
align-items: center;
margin-right: 20px;
margin-right: 10px;
color: black; /* Change the color of the presenter's name */
margin-top: -5px;
}
Expand All @@ -643,7 +643,6 @@ a:focus {
max-width: 150px; /* Set a maximum width if needed */
height: auto; /* Maintain the aspect ratio */
border-radius: 50%;
margin-right: 20px;
}


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.presenter-details {
margin-right: 0; /* Remove margin for speaker details */
margin-bottom: 10px; /* Add a little space between speaker details and event info */
width: 100%;
}
}

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74 changes: 63 additions & 11 deletions index.html
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Expand Up @@ -173,19 +173,33 @@ <h2>Upcoming Seminars<h2>
</div>

<div class="event-frame">
<div class="event">


<div class="event">
<div class="presenter-details">
<img src="img/ye_he.png">
<h5> He Ye</h5>
<p> Carnegie Mellon University</p>
<img src="img/gohar.jpg">
<h5> Usman Gohar </h5>
<p> Iowa State University </p>
</div>
<div class="event-info">
<h3>ITER: Iterative Neural Repair for Multi-Location Patches</h3>
<p> Automated program repair (APR) has achieved promising results, especially using neural networks. Yet, the overwhelming majority of patches produced by APR tools are confined to one single location. When looking at the patches produced with neural repair, most of them fail to compile, while a few uncompilable ones go in the right direction. In both cases, the fundamental problem is to ignore the potential of partial patches. In this paper, we propose an iterative program repair paradigm called ITER founded on the concept of improving partial patches until they become plausible and correct. First, ITER iteratively improves partial single-location patches by fixing compilation errors and further refining the previously generated code. Second, ITER iteratively improves partial patches to construct multi-location patches, with fault localization re-execution. ITER is implemented for Java based on battle-proven deep neural networks and code representation. ITER is evaluated on 476 bugs from 10 open-source projects in Defects4J 2.0. ITER succeeds in repairing 76 of them, including 15 multi-location bugs which is a new frontier in the field.</p>
<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black">Monday, November 6th, 2023 at 3:00 PM CET </span></p>
<h3>Towards Understanding Fairness and its Composition in Ensemble Machine Learning</h3>
<p> Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc.
Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models.
However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way.
How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble? Can fair learners result in an unfair ensemble?
Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in
different categories of ensembles. In this paper, we comprehensively study popular real-world ensembles: Bagging, Boosting, Stacking, and Voting. We have developed a benchmark
of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that
ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair
ensemble design.</p>

<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"><strong>Monday, November 20, 2023 at 4:00 PM CET</strong></span></p>

</div>

</div>


</div>


Expand All @@ -206,9 +220,34 @@ <h2>Past Seminars<h2>

<div class="event-frame">
<ul class="speech-list">

<li>
<div class="speech-header">
<b>ITER: Iterative Neural Repair for Multi-Location Patches</b>, Monday, November 6, 2023, by <b>He Ye</b> from <b>CMU</b>

</div>

<div class="details">
<div class="event">
<div class="presenter-details">
<img src="img/ye_he.png">
<h5> He Ye</h5>
<p> Carnegie Mellon University</p>
</div>
<div class="event-info">
<h3>ITER: Iterative Neural Repair for Multi-Location Patches</h3>
<p>Automated program repair (APR) has achieved promising results, especially using neural networks. Yet, the overwhelming majority of patches produced by APR tools are confined to one single location. When looking at the patches produced with neural repair, most of them fail to compile, while a few uncompilable ones go in the right direction. In both cases, the fundamental problem is to ignore the potential of partial patches. In this paper, we propose an iterative program repair paradigm called ITER founded on the concept of improving partial patches until they become plausible and correct. First, ITER iteratively improves partial single-location patches by fixing compilation errors and further refining the previously generated code. Second, ITER iteratively improves partial patches to construct multi-location patches, with fault localization re-execution. ITER is implemented for Java based on battle-proven deep neural networks and code representation. ITER is evaluated on 476 bugs from 10 open-source projects in Defects4J 2.0. ITER succeeds in repairing 76 of them, including 15 multi-location bugs which is a new frontier in the field.</p>
<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"> Monday, November 6, 2023 at 3:00 PM CET </span></p>
</div>
</div>
</div>
</li>



<li>
<div class="speech-header">
<b>Guided Malware Sample Analysis Based on Graph Neural Networks</b>, Monday, October 30th, 2023, by <b>Yi-Hsien Chen</b> from <b>NTU</b>
<b>Guided Malware Sample Analysis Based on Graph Neural Networks</b>, Monday, October 30, 2023, by <b>Yi-Hsien Chen</b> from <b>NTU</b>

</div>

Expand All @@ -223,7 +262,7 @@ <h5> Yi-Hsien Chen </h5>
<h3>Guided Malware Sample Analysis Based on Graph Neural Networks</h3>
<p> Malicious binaries have caused data and monetary loss to people, and these binaries keep evolving rapidly nowadays. While manual analysis is slow and ineffective, automated malware report generation is a long-term goal for malware analysts and researchers. This study moves one step toward the goal by identifying essential functions in malicious binaries to accelerate and even automate the analyzing process. We design and implement an expert system based on our proposed graph neural network called MalwareExpert. The system pinpoints the essential functions of an analyzed sample and visualizes the relationships between involved parts. The evaluation results show that our approach has a competitive detection performance (97.3% accuracy and 96.5% recall rate) compared to existing malware detection models. Moreover, it gives an intuitive and
easy-to-understand explanation of the model predictions by visualizing and correlating essential functions. We compare the identified essential functions reported by our system against several expert-made malware analysis reports from multiple sources. Our qualitative and quantitative analyses show that the pinpointed functions indicate accurate directions. In the best case, the top 2% of functions reported from the system can cover all expert-annotated functions in three steps. We believe that the MalwareExpert system has shed light on automated program behavior analysis.</p>
<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"> Monday, October 30th, 2023 at 10:30 AM CET </span></p>
<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"> Monday, October 30, 2023 at 10:30 AM CET </span></p>
</div>
</div>
</div>
Expand All @@ -233,7 +272,7 @@ <h3>Guided Malware Sample Analysis Based on Graph Neural Networks</h3>

<li>
<div class="speech-header">
<b>Rete: Learning Namespace Representation for Program Repair</b>, Monday, October 9th, 2023, by <b>Nikhil Parasaram</b> from <b>UCL</b>
<b>Rete: Learning Namespace Representation for Program Repair</b>, Monday, October 9, 2023, by <b>Nikhil Parasaram</b> from <b>UCL</b>

</div>

Expand All @@ -249,12 +288,19 @@ <h3>Rete: Learning Namespace Representation for Program Repair</h3>
<p>A key challenge of automated program repair is finding correct patches in the vast search space of candidate patches. Real-world programs define large namespaces of variables that considerably contributes to the search space explosion. Existing program repair approaches neglect information about the program namespace, which makes them inefficient and increases the chance of test-overfitting.
We propose Rete, a new program repair technique, that learns project-independent information about program namespace and uses it to navigate the search space of patches. Rete uses a neural network to extract project-independent information about variable CDU chains, def-use chains augmented with control flow. Then, it ranks patches by jointly ranking variables and the patch templates into which the variables are inserted.
We evaluated Rete on 142 bugs extracted from two datasets, ManyBugs and BugsInPy. Our experiments demonstrate that Rete generates six new correct patches that fix bugs that previous tools did not repair, an improvement of 31% and 59% over the existing state of the art.</p>
<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"> Monday, October 9th, 2023 at 10:30 AM CEST </span></p>
<p><b><span class="black-underligned">Presentation Date:</span></b> <span class="black"> Monday, October 9, 2023 at 10:30 AM CEST </span></p>
</div>
</div>
</div>
</li>









<!-- Add more speech items as needed -->
</ul>
Expand All @@ -264,6 +310,12 @@ <h3>Rete: Learning Namespace Representation for Program Repair</h3>
</div>
</div>
<!-- Past Events End -->







<script src="js/script.js"></script>

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