Miracle-dz/UniFAR-FTUs huggingface dataset:
Go or No Go: Differential Fuzzing of Native and C Libraries

In little more than a decade, Go has become one of the most popular programming languages in use today. It is a statically-typed, compiled language with spatial and temporal memory safety achieved by way of strong typing, automatically inserted bounds checks, and a mark-and-sweep garbage collector. Go developers can make immediate use of a large set of native libraries, whether shipped as part of the runtime or available to be imported from community code. Alternatively Go developers can directly link to C/C++ libraries which can be called from Go sources thanks to cgo functionality. Factors that go into this decision are stability, performance, and availability. As a result developers have a choice between Go native libraries or non-native code. However, today there is little understanding how to consider security implications in this decision. Our work is the first to investigate security implications of choosing between native and non-native libraries for Go programs. We first investigate to what extent popular GitHub projects make use of cgo, revealing that this choice is in fact quite popular. We then design and build a differential fuzzer that can compare native and C/C++ implementations of the same functionality. We implement the fuzzer and test its effectiveness on four popular packages (libcrypto, libpng, libssl, and libz), describing the results and highlighting their security impact. Finally, we present two real-world case studies (anti-virus evasion including the anti-virus scanner included in Gmail plus Certificate Transparency case study) and discuss how our differential fuzzer discovered implementation differences with security impact. Our work has led to changes in Golang zlib which have since shipped.
Applying Natural Language Processing in Answering Multiple-Choice Questions for Assessing Child/Youth and Adults Needs and Strengths (CANS © /ANSA)

In this paper, we propose an NLP-based Multi-Choice Machine Reading Compre-hension (MRC) solution which can fill out the CANS © assessment form by reading vignettes of patients with a semi-supervised approach. The solution comprises (1) a self-supervised encoder based Pre-trained Language Model (PrLM), such as bert-base-uncased (Devlin et al., 2018) and its other flavors, along with a fine-tuning process using CANS © dataset, and (2) a matching network, such as DCMN (Dual co-matching network) which was implemented by Zhang et al. (2020) and supposedly capture the relationships among the triplets of passage (P), question (Q), and answering options (O). In our baseline model we implemented and tested part (1) of the solution, achieving 62% during training, and 59% during testing. We also implemented the complete solution (1) + (2), with DCMN and DUMA (Zhu et al., 2022) matching networks. We achieved 73.0% / 60.0% and 74.0% / 72.0% for training/testing sets, respectively. Nevertheless, our results demonstrated that the best results were achieved with the PrLM Albert (Lan et al., 2019) albert-xxlarge , without a matching network: 78.0% / 72.0% for training/testing. Based on these results, we performed a qualitative analysis to understand some reasons for such a performance, and other alternatives that can improve our training results.
Predicting Associated Comorbidities of Obesity from MIMIC IV Clinical Notes

Our goal is to create a successful NLP deep-learning model to predict diseases associated with obesity from clinical notes, namely diabetes and hypertension. This is important for the field of automated machine learning from a biomedical perspective and can improve health outcomes while cutting healthcare costs (Waring et al., 2020); since it reduces the amount of human labor if we could predict common health conditions just from clinical notes. For our clinical notes dataset, we use MIMIC-IV as it is a large and free database comprising recent de-identified health-related data. We compare and analyze multiple models’ performance and optimizations for predicting diabetes and hypertension. These model variants include Logistic Regression with BoW, BERT, BioDischargeSummaryBERT (trained on ICU discharge summaries) and BioDischargeSummaryBERT with class weights to combat class imbalance. Our baseline, data pre-processing and graph generation code were written from scratch, while the other models were modified and tuned versions of pretrained models.
On Product Distance of Lattice Code over a Biquadratic Number Field

Lattice code is a type of error correcting codes composed of the image of embedding map of an integral ideal in a certain algebraic number field with a bilinear form. In a mobile communication, transmitted signals are attenuated by phenomenon called fading, and the Rayleigh fading of multi-path radio wave is one of important and essential type of fading. Some lattice codes are applicable in order to mitigate the influence of the fading and to extract the most feasible source information from the received signal. The minimum product distance is one of key indicators of lattice code along with the modulation diversity. In this paper, we show some experimental results on the minimum product distance for some lattices of prime ideal in a composition fields of two real quadratic fields using a free formula manipulation system, PARI/GP.
Saliency Driven Gaze Control for Autonomous Pedestrians

How and why an agent looks at its environment can inform its navigation, behaviour and interaction with the environment. A human agent’s visual-motor system is complex and requires both an understanding of visual stimulus as well as adaptive methods to control and aim its gaze in accordance with goal-driven behaviour or intent. Drawing from observations and techniques in psychology, computer vision and human physiology, we present techniques to procedurally generate various types of gaze movements (head movements, saccades, microsaccades, and smooth pursuits) driven entirely by visual input in the form of saliency maps which represent pre-attentive processing of visual stimuli in order to replicate human gaze behaviour. Each method is designed to be agnostic to attention and cognitive processing, able to cover the nuances for each type of gaze movement, and desired intentional or passive behaviours. In combination with parametric saliency map generation, they serve as a foundation for modelling completely visually driven, procedural gaze in simulated human agents.
BERT Fine-Tuning with Contrastive Loss and Smoothness-Inducing Regularization

Semantically rich sentence embeddings can prove extremely useful for NLP tasks. Thus, The goal of this project is to develop a baseline BERT model and fine-tune it for three downstream tasks simultaneously. We implement three techniques – cosine similarity finetuning, contrastive loss for additional pretraining, and smoothness-inducing regularization – to improve upon the pre-trained BERT embeddings. Results have shown that all three techniques boost overall model performance.
More Informative Relative Position Encoding for Table-to-Text Generation

Table-to-text generation is an important task because a lot of valuable information is stored as tables. Recently, Kale and Rastogi (2020) showed that the T5 model can achieve SoTA results on table-to-text generation. Wang et al. (2022) introduced the Lattice model which further improved upon the standard T5 model by tailoring the T5 architecture (i.e. encoder self-attention and relative position encoding) to tabular input. The Lattice model encodes very limited information within the relative position encoding: whether the query token and key token are both in the metadata or same table cell, and if so, their relative position. We believe that encoding additional information within the relative position encoding (e.g. whether the query and token are in the same row or column) could further improve the model’s understanding of tabular input and table-to-text generation quality. In this project, we showed that our approach ("Additional Positional Info") achieves slightly better results than the baseline Lattice model on the ToTTo dev set (Parikh et al., 2020). Furthermore, we showed that, with Additional Positional Info, the Structural Attention in the Lattice model is no longer necessary.
URUGUAY NOTES ON HYPERBOLIC 3-MANIFOLDS

Notes for a 6-hour minicourse in Uruguay on hyperbolic 3-manifolds. The goal is to complete a sketch of Thurston’s original proof that 3-manifolds which fiber over the circle are geometrizable. 1. Review of hyperbolic space and Teichmüller space This section consists of background material on hyperbolic space and Teichmüller space. This material will be covered in Matilda Martinez’s lectures, so will not be covered in my lectures. Most of this section is lifted and lightly rewritten from my Informal Lecture Notes on Anosov Representations which are available on my webpage. 1.1. The hyperbolic plane Recall that the upper half-plane model for the hyperbolic plane is given by H = {z ∈ C | Im(z) > 0} with Riemannian metric dshyp = 1 y2 dxdy. Prosaically, if ~v ∈ T(x,y)H, then its hyperbolic length ||~v||hyp = |~v| y where |~v| is the Eucliden length of ~v One may easily check that the y-axis L is a geodesic in this metric, since if p : H2 → L is Euclidean perpendicular projection, then ||Dp(~v)||hyp ≤ ||~v||hyp with equality if and only if ~v is vertical. Moreover, segments of L are the only geodesic joining points on L. One may check that Möbius transformations with real co-efficients act as orientation-preserving isometries of H2, by a simple calculation. Or you can notice that all such Mobius transformations are generated by maps of the form z → a, z → 1 z̄ and z → λz and checking that each of these maps is an isometry. It follows that all lines and semi-circles perpendicular to the x-axis are geodesics and that these are the only geodesics. Therefore, an orientation-preserving isometry is determined by its action on a single unit tangent vector. Since PSL(2,R) acts transitively on T 1H2, we see that Isom+(H) = PSL(2,R). An ideal triangle is determined by the three geodesics joining any three points in ∂H2. Since PSL(2,R) acts transitively on triples of distinct points in ∂H2, any two ideal triangles are isometric. We say that the angle at an ideal vertex is 0. One may move the end points to 1, −1 and ∞ and compute that the triangle has area π. We say a geodesic triangle is 1/3-ideal if it has two endpoints in ∂H2 and the other in H2. We may move the two ideal vertices to Date: March 2, 2023. 1
Unsupervised Code-switched Text Generation from Parallel Text

There has been great interest in developing automatic speech recognition (ASR) systems that can handle code-switched (CS) speech to meet the needs of a growing bilingual population. However, existing datasets are limited in size. It is expensive and difficult to collect real transcribed spoken CS data due to the challenges of finding and identifying CS data in the wild. As a result, many attempts have been made to generate synthetic CS data. Existing methods either require the existence of CS data during training, or are driven by linguistic knowledge. We introduce a novel approach of forcing a multilingual MT system that was trained on non-CS data to generate CS translations. Comparing against two prior methods, we show that simply leveraging the shared representations of two languages (Mandarin and English) yields better CS text generation and, ultimately, better CS ASR.
Assessing Anonymity Techniques Employed in German Court Decisions: A De-Anonymization Experiment

Democracy requires transparency. Consequently, courts of law must publish their decisions. At the same time, the interests of the persons involved in these court decisions must be protected. For this reason, court decisions in Europe are anonymized using a variety of techniques. To understand how well these techniques protect the persons involved, we conducted an empirical experiment with 54 law students, whom we asked to de-anonymize 50 German court decisions. We found that all anonymization techniques used in these court decisions were vulnerable, most notably the use of ini-tials. Since even supposedly secure anonymization techniques proved vulnerable, our work empirically reveals the complexity involved in the anonymization of court decisions, and thus calls for further research to increase anonymity while preserving comprehensibility. Toward that end, we provide recommendations for improving anonymization quality. Finally, we provide an empirical notion of “reasonable effort,” to flesh out the definition of anonymity in the legal context. In doing so, we bridge the gap between the technical and the legal understandings of anonymity.
Computational anisotropic Willmore flow

We study a geometrically consistent formulation of anisotropic Willmore ﬂow for parametric hypersurfaces in R n . After giving a mixed formulation that is suitable for discretization by piecewise linear ﬁnite elements, we provide a stable semi-discrete scheme. An experimentally stable fully discrete semi-implicit scheme is then discussed and several tests for the evolution of curves and surfaces are presented.
EPF: Evil Packet Filter

The OS kernel is at the forefront of a system’s security. Therefore, its own security is crucial for the correctness and integrity of user applications. With a plethora of bugs continuously discovered in OS kernel code, defenses and mitiga-tions are essential for practical kernel security. One important defense strategy is to isolate user-controlled memory from kernel-accessible memory, in order to mitigate attacks like ret2usr and ret2dir. We present EPF (Evil Packet Filter): a new method for bypassing various (both deployed and proposed) kernel isolation techniques by abusing the BPF infrastructure of the Linux kernel: i.e., by leveraging BPF code, provided by unprivileged users/programs, as attack payloads. We demonstrate two different EPF instances, namely BPF-Reuse and BPF-ROP, which utilize malicious BPF payloads to mount privilege escalation attacks in both 32-and 64-bit x86 platforms. We also present the design, implementation, and evaluation of a set of defenses to enforce the isolation between BPF instructions and benign kernel data, and the integrity of BPF program execution, effectively providing protection against EPF-based attacks. Our implemented defenses show minimal overhead ( < 3%) in BPF-heavy tasks.
Construction of disease risk scoring systems using logistic group lasso: application to porcine reproductive and respiratory syndrome survey data

We propose to utilize the group lasso algorithm for logistic regression to construct a risk scoring system for predicting disease in swine. This work is motivated by the need to develop a risk scoring system from survey data on risk factor for porcine reproductive and respiratory syndrome (PRRS), which is a major health, production and financial problem for swine producers in nearly every country. Group lasso provides an attractive solution to this research question because of its ability to achieve group variable selection and stabilize parameter estimates at the same time. We propose to choose the penalty parameter for group lasso through leave-one-out cross-validation, using the criterion of the area under the receiver operating characteristic curve. Survey data for 896 swine breeding herd sites in the USA and Canada completed between March 2005 and March 2009 are used to construct the risk scoring system for predicting PRRS outbreaks in swine. We show that our scoring system for PRRS significantly improves the current scoring system that is based on an expert opinion. We also show that our proposed scoring system is superior in terms of area under the curve to that developed using multiple logistic regression model selected based on variable significance.
AI Can Look Up StackOverflow too: Retrieval-Augmented Code Generation

This research paper investigates the effectiveness of retrieval-augmented generation for generating code from natural language queries for specific domains, without the need for fine-tuning a model on a domain-specific corpus. Code generation from natural language is a challenging task with numerous applications in software engineering and data science. Retraining or fine-tuning Large Language Models (LLMs) is an expensive undertaking, and LLMs have been shown to often effectively use in-context examples to arrive at the correct answer. The paper specifically explores using retrieval from a bimodal corpus with snippets consisting of description and code, to enhance prompting for code generation for data science problems in the Python programming language. Retrieval is performed against a corpus of StackOverflow posts using a weighted ensemble of 2 models, one for ranking query-code similarity and the other for query-description similarity. We compare our approach to 3 baselines using 2 widely used metrics on the DS-1000 data set. Experimental results show that (1) Our method is able to generate more correct programs, improving the pass @5 score by up to 10% compared to the baseline(sans retrieval) score of generative models such as codex-davinci-002 from OpenAI, achieving the new state-of-the-art. (2) Our method enhances robustness to semantic perturbation of the query text.
Generating Molecules from Natural Language with Multimodal Contrastive Pre-Training

Deep molecular neural representations have demonstrated significant potential in accelerating scientific research in the natural sciences. However, existing AI models typically focus on pure graph-based representations, or knowledge extraction from natural language, leaving a wide gap between these two modalities. In this paper, we explore what a foundation model for chemistry could look like, by introducing and evaluating improvements on a recently proposed molecular multimodal (MoMu) model for contrastive joint text-graph representation learning (Su et al. [2022]). By implementing neural relevance scoring strategies for retrieving molecule text descriptions, and augmenting molecular graphs via more chemically relevant transformations, we surpass MoMu’s performance on most MoleculeNet molecular property predictions tasks. We explore zero-shot molecular generation based on the improved graph encoders we trained, propose avenues for future work, and highlight the ethics and safety implications of generative AI for molecules.
An empirical comparison between PLS, Lasso, Elasticnet and other models for highly correlated data.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. An empirical comparison between PLS, LASSO, Elasticnet and other models for highly correlated data Genane Youness, Wassim Nassar
Less reliable media drive interest in anti-vaccine information

As progress on vaccine rollout in the United States slowed down in Spring 2021, it became clear that anti-vaccine information posed a public health threat. Using text data from 5,613 distinct COVID misinformation stories and 70 anti-vaccination Facebook groups, we tracked highly salient keywords regarding anti-vaccine discourse across Twitter, thousands of news websites, and the Google and Bing search engines from May through June 2021, a key period when progress on vaccinations very clearly stalled. Granger causality tests showed that searches for anti-vaccination terms on Google as well as the appearance of these terms on Twitter followed spikes in their appearance in less reliable media sites, but not discussion in the mainstream press.
Explainable and Efficient Randomized Voting Rules

With a rapid growth in the deployment of AI tools for making critical decisions (or aiding humans in doing so), there is a growing demand to be able to explain to the stakeholders how these tools arrive at a decision. Consequently, voting is frequently used to make such decisions due to its inherent explainability. Recent work suggests that using randomized (as opposed to deterministic) voting rules can lead to significant efficiency gains measured via the distortion framework. However, rules that use intricate randomization can often become too complex to explain to the stakeholders; losing explainability can eliminate the key advantage of voting over black-box AI tools, which may outweigh the efficiency gains. We study the efficiency gains which can be unlocked by using voting rules that add a simple randomization step to a deterministic rule, thereby retaining explainability. We focus on two such families of rules, randomized positional scoring rules and random committee member rules, and show, theoretically and empirically, that they indeed achieve explainability and efficiency simultaneously to some extent.
Model-based language-instructed reinforcement learning

We explore how we can build accurate world 001 models which are partially specified by lan-002 guage and how we can plan with them in the 003 face of novelty and uncertainty. We propose the 004 first Model-Based Reinforcement Learning ap-005 proach to tackle the environment Read To Fight 006 Monsters (Zhong et al., 2019), a grounded 007 policy learning problem. In RTFM an agent 008 has to reason over a set of rules and a goal, 009 both described in a language manual, and the 010 observations, while taking into account the 011 uncertainty arising from the stochasticity of 012 the environment, in order to generalize suc-013 cessfully its policy to test episodes. We pro-014 vide a sample-efficient proof-of-concept of the 015 model-based approach for the basic dynamic 016 task of RTFM. Furthermore, we show that the 017 main open challenge of RTFM is learning the 018 language-dependent reward function and sug-019 gest that future research should focus primarily 020 on that task. 021
Artificial Intelligence Application in Nuclear Medicine

: In the field of medication, specifically, clinical imaging, the publicity of late years about man-made consciousness (AI) has had a critical effect. Despite the fact that news in the day to day press and clinical distributions about new capacities and accomplishments of AI is practically overpowering, for some mediators the term and the working of AI stay a ''discovery,'' prompting overstated assumptions from one perspective and unwarranted feelings of dread on the other. Individuals as of now connect with AI in an assortment of routes in day to day existence - for instance, on cell phones, in the vehicle, or while riding the web - however frequently without really acknowledging it. Artificial intelligence likewise can possibly take on an assortment of basic or dreary undertakings in the medical services area sooner rather than later. Be that as it may, AI unquestionably won't make radiologists or atomic medication experts outdated as clinical specialists within a reasonable time-frame. As opposed to the disturbance evoked in certain media, a consistent change can be anticipated; this change doubtlessly will start or has started in the demonstrative disciplines, specifically, clinical imaging. According to the point of view of the radiologist or atomic medication trained professional, this turn of events, rather than being seen as a danger, should be visible as a valuable chance to play a spearheading job inside the medical care area and to effectively shape this change interaction. In this article, we endeavor to give a calculated order of AI, a short rundown of what we view as the main specialized basics, a conversation of potential applications in atomic medication and, at last, a concise thought of the conceivable effect of these innovations on the calling of the doctor.