Miracle-dz/UniFAR-FTUs huggingface dataset:
MINER: A Hybrid Data-Driven Approach for REST API Fuzzing

In recent years, REST API fuzzing has emerged to explore errors on a cloud service. Its performance highly depends on the sequence construction and request generation. However, existing REST API fuzzers have trouble generating long sequences with well-constructed requests to trigger hard-to-reach states in a cloud service, which limits their performance of finding deep errors and security bugs. Further, they cannot find the specific errors caused by using undefined parameters during request generation. Therefore, in this paper, we propose a novel hybrid data-driven solution, named MINER, with three new designs working together to address the above limitations. First, MINER collects the valid sequences whose requests pass the cloud service's checking as the templates, and assigns more executions to long sequence templates. Second, to improve the generation quality of requests in a sequence template, MINER creatively leverages the state-of-the-art neural network model to predict key request parameters and provide them with appropriate parameter values. Third, MINER implements a new data-driven security rule checker to capture the new kind of errors caused by undefined parameters. We evaluate MINER against the state-of-the-art fuzzer RESTler on GitLab, Bugzilla, and WordPress via 11 REST APIs. The results demonstrate that the average pass rate of MINER is 23.42% higher than RESTler. MINER finds 97.54% more unique errors than RESTler on average and 142.86% more reproducible errors after manual analysis. We have reported all the newly found errors, and 7 of them have been confirmed as logic bugs by the corresponding vendors.
Bounding the Optimal Value Function in Compositional Reinforcement Learning

In the field of reinforcement learning (RL), agents are often tasked with solving a variety of problems differing only in their reward functions. In order to quickly obtain solutions to unseen problems with new reward functions, a popular approach involves functional composition of previously solved tasks. However, previous work using such functional composition has primarily focused on specific instances of composition functions whose limiting assumptions allow for exact zero-shot composition. Our work unifies these examples and provides a more general framework for compositionality in both standard and entropy-regularized RL. We find that, for a broad class of functions, the optimal solution for the composite task of interest can be related to the known primitive task solutions. Specifically, we present double-sided inequalities relating the optimal composite value function to the value functions for the primitive tasks. We also show that the regret of using a zero-shot policy can be bounded for this class of functions. The derived bounds can be used to develop clipping approaches for reducing uncertainty during training, allowing agents to quickly adapt to new tasks.
An Analysis of Physics-Informed Neural Networks

Whilst the partial differential equations that govern the dynamics of our world have been studied in great depth for centuries, solving them for complex, high-dimensional conditions and domains still presents an incredibly large mathematical and computational challenge. Analytical methods can be cumbersome to utilise, and numerical methods can lead to errors and inaccuracies. On top of this, sometimes we lack the information or knowledge to pose the problem well enough to apply these kinds of methods. Here, we present a new approach to approximating the solution to physical systems - physics-informed neural networks. The concept of artificial neural networks is introduced, the objective function is defined, and optimisation strategies are discussed. The partial differential equation is then included as a constraint in the loss function for the optimisation problem, giving the network access to knowledge of the dynamics of the physical system it is modelling. Some intuitive examples are displayed, and more complex applications are considered to showcase the power of physics informed neural networks, such as in seismic imaging. Solution error is analysed, and suggestions are made to improve convergence and/or solution precision. Problems and limitations are also touched upon in the conclusions, as well as some thoughts as to where physics informed neural networks are most useful, and where they could go next.
Stochastic networks theory to model single-cell genomic count data

We propose a novel way of representing and analysing single-cell genomic count data, by modelling the observed data count matrix as a network adjacency matrix, noting that similar levels of sparsity are observed in both these types of matrices. As the adjacency matrix is equivalent to the network it represents, this perspective enables theory from stochastic networks modelling to be applied in a principled way to single-cell genomic data, providing new ways to view and analyse data of this type, and giving first-principles theoretical justification to established, successful methods. From this perspective, we show how understanding the Laplacian spectral embedding is key to both visualisation of and unsupervised learning from single-cell genomic count data. We show the success of this approach for visualisation and unsupervised learning of cellular identities in three cell-biological contexts from the epiblast/epithelial/neural lineage. New technology has made it possible to gather genomic data from single cells at unprecedented scale, and this brings with it new challenges to deal with much higher levels of heterogeneity than expected between individual cells. Novel, tailored, computational-statistical methodology, as proposed in this paper, is crucial to deriving meaningful information from these new types of data, involving collaboration between mathematical and biomedical scientists.
Texture-Guided Saliency Distilling for Unsupervised Salient Object Detection

Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundaries. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance. Code is available at www.github. com/moothes/A2S-v2.
Cross-Hospital Sepsis Early Detection via Semi-Supervised Optimal Transport With Self-Paced Ensemble

Leveraging machine learning techniques for Sepsis early detection and diagnosis has attracted increasing interest in recent years. However, most existing methods require a large amount of labeled training data, which may not be available for a target hospital that deploys a new Sepsis detection system. More seriously, as treated patients are diversified between hospitals, directly applying a model trained on other hospitals may not achieve good performance for the target hospital. To address this issue, we propose a novel semi-supervised transfer learning framework based on optimal transport theory and self-paced ensemble for Sepsis early detection, called SPSSOT, which can efficiently transfer knowledge from the source hospital (with rich labeled data) to the target hospital (with scarce labeled data). Specifically, SPSSOT incorporates a new optimal transport-based semi-supervised domain adaptation component that can effectively exploit all the unlabeled data in the target hospital. Moreover, self-paced ensemble is adapted in SPSSOT to alleviate the class imbalance issue during transfer learning. In a nutshell, SPSSOT is an end-to-end transfer learning method that automatically selects suitable samples from two domains (hospitals) respectively and aligns their feature spaces. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SPSSOT outperforms state-of-the-art transfer learning methods by improving 1–3% of AUC.
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning

In the last 10 years, various automated machine learning (AutoML) systems have been proposed to build end-to-end machine learning (ML) pipelines with minimal human interaction. Even though such automatically synthesized ML pipelines are able to achieve competitive performance, recent studies have shown that users do not trust models constructed by AutoML due to missing transparency of AutoML systems and missing explanations for the constructed ML pipelines. In a requirements analysis study with 36 domain experts, data scientists, and AutoML researchers from different professions with vastly different expertise in ML, we collect detailed informational needs for AutoML. We propose XAutoML, an interactive visual analytics tool for explaining arbitrary AutoML optimization procedures and ML pipelines constructed by AutoML. XAutoML combines interactive visualizations with established techniques from explainable artificial intelligence (XAI) to make the complete AutoML procedure transparent and explainable. By integrating XAutoML with JupyterLab, experienced users can extend the visual analytics with ad-hoc visualizations based on information extracted from XAutoML. We validate our approach in a user study with the same diverse user group from the requirements analysis. All participants were able to extract useful information from XAutoML, leading to a significantly increased understanding of ML pipelines produced by AutoML and the AutoML optimization itself.
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method

We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM) method in the single-objective context, we generalize ADMM to derive a first-order method for cVIs, that we refer to as ADMM-based interior-point method for constrained VIs (ACVI). We provide convergence guarantees for ACVI in two general classes of problems: (i) when the operator is $\xi$-monotone, and (ii) when it is monotone, some constraints are active and the game is not purely rotational. When the operator is, in addition, L-Lipschitz for the latter case, we match known lower bounds on rates for the gap function of $\mathcal{O}(1/\sqrt{K})$ and $\mathcal{O}(1/K)$ for the last and average iterate, respectively. To the best of our knowledge, this is the first presentation of a first-order interior-point method for the general cVI problem that has a global convergence guarantee. Moreover, unlike previous work in this setting, ACVI provides a means to solve cVIs when the constraints are nontrivial. Empirical analyses demonstrate clear advantages of ACVI over common first-order methods. In particular, (i) cyclical behavior is notably reduced as our methods approach the solution from the analytic center, and (ii) unlike projection-based methods that zigzag when near a constraint, ACVI efficiently handles the constraints.
Efficiency of 76-GHz post-wall waveguide-fed parallel-plate slot arrays

We design and fabricate various sizes of 76-GHz post-wall waveguide-fed parallel-plate slot arrays. The post-wall waveguide is an array of metalized via-holes with a narrow spacing in a grounded dielectric substrate. The antenna can be easily made at low cost by conventional PCB (printed circuit board) fabrication techniques such as via-holing, metal plating, and etching. We have obtained 40-50% efficiency for various sizes from 26 mm/spl times/24 mm to 104 mm/spl times/100 mm. We have measured 35.3 dBi gain with 39.3% efficiency at 77.0 GHz in an antenna sized 104 mm/spl times/100 mm.
A Survey of Computational Infrastructure to Help Preserve and Revitalize Bodwéwadmimwen

This paper surveys the previous language initiatives that Potawatomi language communities have undertaken with technology to help them preserve and revitalize Bodw´ewadmimwen (the Potawatomi language) and provides a menu of newer computational tools that could supplement previous initiatives or create new ones. The most immediate steps that could be taken are to establish unicode numbers for several characters, compile a usable corpus of language materials, and build a morphological parser that could be implemented on top of an online dictionary. The creation of automatic speech recognition and language models would have to wait for a usable language corpus to be created. Additionally, the utility of automatic speech recognition is largely contingent on whether language community workers perceive it as a crutch or something more. While the focus of this paper is on archival materials, a necessary step to improve any of these computational tools is to continue to learn and document Bodw ´ ewadmimwen.
Studying the impact of language model size for low-resource ASR

This paper investigates the impact of language model (LM) size on low-resource ASR, using data from five widely-spoken low-resource languages and one endangered Native American language. Our findings demonstrate that having larger LMs does not necessarily result in lower WER; this is most evident for the endangered language, where larger LMs actually led to significantly worse performance than that observed in the widely-spoken low-resource languages. We conjecture that one of the potential driving forces behind this discrepancy is the domain mismatch between the transcripts of the audio data and the supplementary texts used to train the LM. We discuss the implications of our results in the context of creating ASR corpora for low-resource languages.
Exploiting structure to efficiently solve large scale partially observable Markov decision processes

Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in real-world problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finite-horizon discrete POMDP is PSPACE-complete. In practice, two important sources of intractability plague most solution algorithms: Large policy spaces and large state spaces. On the other hand, for many real-world POMDPs it is possible to define effective policies with simple rules of thumb. This suggests that we may be able to find small policies that are near optimal. This thesis first presents a Bounded Policy Iteration (BPI) algorithm to robustly find a good policy represented by a small finite state controller. Real-world POMDPs also tend to exhibit structural properties that can be exploited to mitigate the effect of large state spaces. To that effect, a value-directed compression (VDC) technique is also presented to reduce POMDP models to lower dimensional representations. In practice, it is critical to simultaneously mitigate the impact of complex policy representations and large state spaces. Hence, this thesis describes three approaches that combine techniques capable of dealing with each source of intractability: VDC with BPI, VDC with Perseus (a randomized point-based value iteration algorithm by Spaan and Vlassis [136]), and state abstraction with Perseus. The scalability of those approaches is demonstrated on two problems with more than 33 million states: synthetic network management and a real-world system designed to assist elderly persons with cognitive deficiencies to carry out simple daily tasks such as hand-washing. This represents an important step towards the deployment of POMDP techniques in ever larger, real-world, sequential decision making problems.
A preliminary study to identify existing drugs for potential repurposing in breast cancer based on side effect profile

Abstract Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer-related death in women after lung cancer. The present study aims to identify potential drug candidates using the PROMISCUOUS database for breast cancer based on side effect profile and then proceed with in silico and in vitro studies. PROMISCUOUS database was used to construct a group of drugs that share maximum side effects with letrozole. Based on the existing literature, ropinirole, risperidone, pregabalin, and gabapentin were selected for in silico and in vitro studies. The molecular docking was carried out using AUTODOCK 4.2. 6. MCF-7 cell line was used to evaluate the anti-cancer activity of the selected drugs. PROMISCUOUS database revealed that as many as 23 existing drugs shared between 62 and 79 side-effects with letrozole. From docking result, we found that, ropinirole showed a good binding affinity (−7.7 kcal/mol) against aromatase compared to letrozole (−7.1 kcal/mol) which was followed by gabapentin (−6.4 kcal/mol), pregabalin (−5.7 kcal/mol) and risperidone (−5.1 kcal/mol). From the in vitro results, ropinirole and risperidone showed good anti-cancer activity of IC 50 with 40.85±11.02 μg/ml and 43.10±9.58 μg/ml cell viability. Based on this study results and existing literature we conclude that risperidone, pregabalin, and gabapentin are not ideal candidates for repurposing in breast cancer but ropinirole could be an excellent choice for repurposing in breast cancer after further studies.
Spatially resolved surface acoustic wave studies for image processing

The interaction between a surface acoustic wave (SAW) and photogenerated charge carriers in a piezoelectric semiconductor substrate leads to pronounced changes in the transmission properties of the SAW. Depending on the charge carrier density one observes an attenuation and a change in sound velocity in a SAW delay line comprising a light sensitive semiconductor film. Using specially designed surface acoustic wave transducers, we are able to spatially resolve this interaction. The transducers used for this purpose consist of fan shaped interdigitated metal fingers, which show a broadband frequency transmission. It turns out, that for such transducers the excited SAW propagates on a narrow path, whose position can be controlled by the driving frequency. We axe able to reconstruct a 2D charge distribution by applying a tomographic technique or by nonlinear interaction between a probe and a perpendicular pump SAW crossing each other in the area where charge carriers axe generated.
Time dependent canonical transformations and the symmetry‐equals‐invariant theorem

Expressions for the remainder function of a time dependent infinitesimally generated canonical transformation have recently been found by Dewar, who considered the action of the transformation operators on Liouville’s equation. Here an alternate proof of the remainder function expression is given, based on the transformations of particle trajectories. Then, using this expression, a proof of the symmetry‐equals‐invariant theorem is given.
Optimization of Filter Bandwidth in Spectral Analysis of Wavetrains

Summary An equation relating the duration of a dispersed impulse to the bandwidth, and to the dispersiveness of the dispersing system, is used to obtain a filter bandwidth variation with frequency which will maximize the temporal resolution of the multiple filter technique used for the analysis of seismograms. Computational results based on a synthetic seismogram are given.
The FE-BIE technique applied to some 2-D problems relevant to electromagnetic compatibility: optimal choice of mechanisms to take into account periodicity

A study of mechanisms to account for periodicity when modeling two-dimensional (2-D) structures with a hybrid finite-element boundary integral equation (FE-BIE) method is presented. These techniques are either based on the use of Green's functions or on the application of the Floquet-Bloch theorem as a periodic boundary condition. The described formalism can be used to model very diverse problem geometries as is demonstrated by means of some typical examples. For these configurations, it is shown how an optimal choice can be made between the mechanisms that impose periodicity.
Fostering Dispositions and Engaging Computing Educators

Dispositions are cultivable behaviors desirable in the workplace. Examples of dispositions are being adaptable, meticulous, and self-directed. The eleven dispositions described in the CC2020 report should not be confused with the professional knowledge of computing topics, or with skills, including technical skills, along with cross-disciplinary skills such as critical thinking, problem-solving, teamwork, or communication. Dispositions, more inherent to human characteristics, identify personal qualities and behavioral patterns important for successful professional careers. The leaders of this special session collaborate on a multi-institutional project funded by the National Science Foundation. Using their experiences at four higher education institutions, they will demonstrate how to foster dispositions among computing students through two hands-on activities. The audience will get first-hand experience using reflection exercises and vignettes, and will participate in debating their design, merits, and limitations. The resulting interaction will provide the audience ample time to discuss the benefits and challenges of incorporating and fostering dispositions in computing programs. It is hoped that participants will leave with concrete ideas on how to extend the current work to their own courses, programs, and institutions.
Perception Method of Abnormal Events in Rail Transit based on Multi-source Point Cloud Information

With the development of the rail transportation business, the perception of abnormal events in rail transportation has become particularly important. It is necessary to explore and innovate the technology by acquiring point cloud information through a new perception method - LiDAR(Light Detection and Ranging). In this paper, we propose a method of rail transit abnormal event sensing based on multi-source point cloud information. It can sense rail transit abnormal events in different areas of rail transit station lobby scenes. We use Matlab-based statistical filtering, plane fitting, and point cloud rotation to complete the pre-processing, and extract the valid point cloud data contained in the original point cloud. Then we use the voxel-centered difference method to perceive the dynamic targets in the rail transit environment. And we use the model of human characteristics and the deep learning method based on PointNet to complete the perception of human point cloud data in the dynamic targets. Finally, the human behavior patterns are discriminated based on the enclosing frame and skeleton extraction methods. The experimental results show that the proposed method can effectively sense the dynamic human targets in the station lobby scenes and provide data support for subsequent abnormal event detection.