Miracle-dz/UniFAR-FTUs huggingface dataset:
Less training, more repairing please: revisiting automated program repair via zero-shot learning

Due to the promising future of Automated Program Repair (APR), researchers have proposed various APR techniques, including heuristic-based, template-based, and constraint-based techniques. Among such classic APR techniques, template-based techniques have been widely recognized as state of the art. However, such template-based techniques require predefined templates to perform repair, and their effectiveness is thus limited. To this end, researchers have leveraged the recent advances in Deep Learning to further improve APR. Such learning-based techniques typically view APR as a Neural Machine Translation problem, using the buggy/fixed code snippets as the source/target languages for translation. In this way, such techniques heavily rely on large numbers of high-quality bug-fixing commits, which can be extremely costly/challenging to construct and may limit their edit variety and context representation. In this paper, we aim to revisit the learning-based APR problem, and propose AlphaRepair, the first cloze-style (or infilling-style) APR approach to directly leveraging large pre-trained code models for APR without any fine-tuning/retraining on historical bug fixes. Our main insight is instead of modeling what a repair edit should look like (i.e., a NMT task), we can directly predict what the correct code is based on the context information (i.e., a cloze or text infilling task). Although our approach is general and can be built on various pre-trained code models, we have implemented AlphaRepair as a practical multilingual APR tool based on the recent CodeBERT model. Our evaluation of AlphaRepair on the widely used Defects4J benchmark shows for the first time that learning-based APR without any history bug fixes can already outperform state-of-the-art APR techniques. We also studied the impact of different design choices and show that AlphaRepair performs even better on a newer version of Defects4J (2.0) with 3.3X more fixes than best performing baseline, indicating that AlphaRepair can potentially avoid the dataset-overfitting issue of existing techniques. Additionally, we demonstrate the multilingual repair ability of AlphaRepair by evaluating on the QuixBugs dataset where AlphaRepair achieved the state-of-the-art results on both Java and Python versions.
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes

We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D shapes of indoor objects with limited geometry quality because of the heavy occlusion of indoor scenes. To solve this, we propose an instance-aligned implicit function (InstPIFu) for detailed object reconstruction. Combining with instance-aligned attention module, our method is empowered to decouple mixed local features toward the occluded instances. Additionally, unlike previous methods that simply represents the room background as a 3D bounding box, depth map or a set of planes, we recover the fine geometry of the background via implicit representation. Extensive experiments on the SUN RGB-D, Pix3D, 3D-FUTURE, and 3D-FRONT datasets demonstrate that our method outperforms existing approaches in both background and foreground object reconstruction. Our code and model will be made publicly available.
Scene Graph for Embodied Exploration in Cluttered Scenario

The ability to handle objects in cluttered environment has been long anticipated by robotic community. However, most of works merely focus on manipulation instead of rendering hidden semantic information in cluttered objects. In this work, we introduce the scene graph for embodied exploration in cluttered scenarios to solve this problem. To validate our method in cluttered scenario, we adopt the Manipulation Question Answering (MQA) tasks as our test benchmark, which requires an embodied robot to have the active exploration ability and semantic understanding ability of vision and language.As a general solution framework to the task, we propose an imitation learning method to generate manipulations for exploration. Meanwhile, a VQA model based on dynamic scene graph is adopted to comprehend a series of RGB frames from wrist camera of manipulator along with every step of manipulation is conducted to answer questions in our framework.The experiments on of MQA dataset with different interaction requirements demonstrate that our proposed framework is effective for MQA task a representative of tasks in cluttered scenario.
Non-Gaussian Bayesian Filtering by Density Parametrization Using Power Moments

Non-Gaussian Bayesian filtering is a core problem in stochastic filtering. The difficulty of the problem lies in parameterizing the state estimates. However the existing methods are not able to treat it well. We propose to use power moments to obtain a parameterization. Unlike the existing parametric estimation methods, our proposed algorithm does not require prior knowledge about the state to be estimated, e.g. the number of modes and the feasible classes of function. Moreover, the proposed algorithm is not required to store massive parameters during filtering as the existing nonparametric Bayesian filters, e.g. the particle filter. The parameters of the proposed parametrization can also be determined by a convex optimization scheme with moments constraints, to which the solution is proved to exist and be unique. A necessary and sufficient condition for all the power moments of the density estimate to exist and be finite is provided. The errors of power moments are analyzed for the density estimate being either light-tailed or heavy-tailed. Error upper bounds of the density estimate for the one-step prediction are proposed. Simulation results on different types of density functions of the state are given, including the heavy-tailed densities, to validate the proposed algorithm.
Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations

Numerically solving ordinary differential equations (ODEs) is a naturally serial process and as a result the vast majority of ODE solver software are serial. In this manuscript we developed a set of parallelized ODE solvers using extrapolation methods which exploit “parallelism within the method” so that arbitrary user ODEs can be parallelized. We describe the specific choices made in the implementation of the explicit and implicit extrapolation methods which allow for generating low overhead static schedules to then exploit with optimized multi-threaded implementations. We demonstrate that while the multi-threading gives a noticeable acceleration on both explicit and implicit problems, the explicit parallel extrapolation methods gave no significant improvement over state-of-the-art even with a multi-threading advantage against current optimized high order Runge- Kutta tableaus. However, we demonstrate that the implicit parallel extrapolation methods are able to achieve state-of-the-art performance (2x-4x) on standard multicore x86 CPUs for systems of < 200 stiff ODEs solved at low tolerance, a typical setup for a vast majority of users of high level language equation solver suites. The resulting method is distributed as the first widely available open source software for within-method parallel acceleration targeting typical modest compute architectures.
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors

As we move away from the data, the predictive uncertainty should increase, since a great variety of explanations are consistent with the little available information. We introduce Distance-Aware Prior (DAP) calibration, a method to correct overconfidence of Bayesian deep learning models outside of the training domain. We define DAPs as prior distributions over the model parameters that depend on the inputs through a measure of their distance from the training set. DAP calibration is agnostic to the posterior inference method, and it can be performed as a post-processing step. We demonstrate its effectiveness against several baselines in a variety of classification and regression problems, including benchmarks designed to test the quality of predictive distributions away from the data.
Personalized PCA: Decoupling Shared and Unique Features

In this paper, we tackle a significant challenge in PCA: heterogeneity. When data are collected from different sources with heterogeneous trends while still sharing some congruency, it is critical to extract shared knowledge while retaining the unique features of each source. To this end, we propose personalized PCA (PerPCA), which uses mutually orthogonal global and local principal components to encode both unique and shared features. We show that, under mild conditions, both unique and shared features can be identified and recovered by a constrained optimization problem, even if the covariance matrices are immensely different. Also, we design a fully federated algorithm inspired by distributed Stiefel gradient descent to solve the problem. The algorithm introduces a new group of operations called generalized retractions to handle orthogonality constraints, and only requires global PCs to be shared across sources. We prove the linear convergence of the algorithm under suitable assumptions. Comprehensive numerical experiments highlight PerPCA's superior performance in feature extraction and prediction from heterogeneous datasets. As a systematic approach to decouple shared and unique features from heterogeneous datasets, PerPCA finds applications in several tasks, including video segmentation, topic extraction, and feature clustering.
NeFSAC: Neurally Filtered Minimal Samples

Since RANSAC, a great deal of research has been devoted to improving both its accuracy and run-time. Still, only a few methods aim at recognizing invalid minimal samples early, before the often expensive model estimation and quality calculation are done. To this end, we propose NeFSAC, an efficient algorithm for neural filtering of motion-inconsistent and poorly-conditioned minimal samples. We train NeFSAC to predict the probability of a minimal sample leading to an accurate relative pose, only based on the pixel coordinates of the image correspondences. Our neural filtering model learns typical motion patterns of samples which lead to unstable poses, and regularities in the possible motions to favour well-conditioned and likely-correct samples. The novel lightweight architecture implements the main invariants of minimal samples for pose estimation, and a novel training scheme addresses the problem of extreme class imbalance. NeFSAC can be plugged into any existing RANSAC-based pipeline. We integrate it into USAC and show that it consistently provides strong speed-ups even under extreme train-test domain gaps – for example, the model trained for the autonomous driving scenario works on PhotoTourism too. We tested NeFSAC on more than 100k image pairs from three publicly available real-world datasets and found that it leads to one order of magnitude speed-up, while often finding more accurate results than USAC alone. The source code is available at https://github.com/cavalli1234/NeFSAC .
Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks

Estimating the political leanings of social media users is a challenging and ever more pressing problem given the increase in social media consumption. We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Retweet-BERT leverages the retweet network structure and the language used in users' profile descriptions. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies. Retweet-BERT demonstrates competitive performance against other state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter datasets (a COVID-19 dataset and a 2020 United States presidential elections dataset). We also perform manual validation to validate the performance of Retweet-BERT on users not in the training data. Finally, in a case study of COVID-19, we illustrate the presence of political echo chambers on Twitter and show that it exists primarily among right-leaning users. Our code is open-sourced and our data is publicly available.
The Variable Projected Augmented Lagrangian Method

Inference by means of mathematical modeling from a collection of observations remains a crucial tool for scientific discovery and is ubiquitous in application areas such as signal compression, imaging restoration, and supervised machine learning. The inference problems may be solved using variational formulations that provide theoretically proven methods and algorithms. With ever-increasing model complexities and growing data size, new specially designed methods are urgently needed to recover meaningful quantifies of interest. We consider the broad spectrum of linear inverse problems where the aim is to reconstruct quantities with a sparse representation on some vector space; often solved using the (generalized) least absolute shrinkage and selection operator (lasso). The associated optimization problems have received significant attention, in particular in the early 2000's, because of their connection to compressed sensing and the reconstruction of solutions with favorable sparsity properties using augmented Lagrangians, alternating directions and splitting methods. We provide a new perspective on the underlying l1 regularized inverse problem by exploring the generalized lasso problem through variable projection methods. We arrive at our proposed variable projected augmented Lagrangian (vpal) method. We analyze this method and provide an approach for automatic regularization parameter selection based on a degrees of freedom argument. Further, we provide numerical examples demonstrating the computational efficiency for various imaging problems.
Charity Donation System using Blockchain

----------------------------------------------------------------------------------------------------------------------------------- ABSTRACT: It is very common for organizations to receive anonymous donations. Centralized financial services are prone to corruption. There is a need to expand the distribution of resources. This can be achieved through blockchain. It is a transparent, distributed and distributed technology that operates without a central control element. The main purpose of the system is to eradicate corruption in organizations that legitimize people's black money or regulate resources. The system will be separate, not owned or used by an individual or organization but rather shared between users. We have created a blockchain-based Donation System that can streamline the resources used by a particular organization. The user can create any new campaigns and others will be able to donate money to the organization in the form of ether.Proposed system makes people real owners of resources and brings transparency to the entire Charity Donation System. Thus, making wrong and illegal use of money donated to organizations will be minimized to a great extent and thereby, corruption will be reduced with the use of this blockchain system, solving problems in many nations.
Device and materials requirements for neuromorphic computing

Energy efficient hardware implementation of artificial neural network is challenging due the ‘memory-wall’ bottleneck. Neuromorphic computing promises to address this challenge by eliminating data movement to and from off-chip memory devices. Emerging non-volatile memory (NVM) devices that exhibit gradual changes in resistivity are a key enabler of in-memory computing—a type of neuromorphic computing. In this paper, we present a review of some of the NVM devices (RRAM, CBRAM, PCM) commonly used in neuromorphic application. The review focuses on the trade-off between device parameters such as retention, endurance, device-to-device variation, speed and resistance levels, and the interplay with target applications. This work aims at providing guidance for finding the optimized resistive memory devices material stack suitable for neuromorphic application.
Victim’s negative emotion processes in cybersecurity breach situations: a testimony of anger and fear related emotion processes

The numerous benefits of increased internet connection are accompanied by a growing number of cy-bersecurity breaches. The successful protection of users’ wellbeing and their security, premised on pre-ventative training and effective care of victims, requires an understanding of their experiences. To map this landscape, we explored a spectrum of emotional experiences in cyber security breach situations from the victim’s perspective. Participants (N=130) were asked to describe a cybersecurity breach on an Internet of Things device, computer, smartphone, email, or social network account they experienced themselves or heard about from friends or the media. They were asked to report their personal or anticipated emotional experiences in that situation, based on Scherer’s Components Process Model (Scherer, 2001) of emotions and emotion regulation. Answers were qualitatively analyzed by coding them into categories of emotion processes components within the GRID framework (Fontaine et al., 2013) and for emotion regulation within the Conceptual Integration Model (Connor-Smith & Flachsbart, 2007). The most frequently reported emotion processes in cybersecurity breach situations include: (i) the appraisals privacy intrusion, unknown and no control, (ii) the action tendencies intervention, defensive and attack, (iii) the expressions high vocal energy, abrupt movement, eyes closed/tears, (iv) the bodily responses high autonomic arousal, distress, high temperature, (v) the subjective feelings fear, anger and anxiety and (vi) the emotion regulations instrumental support, relaxation and suppression. This profile of reactions is similar to the emotional experiences of victims of physical security breaches (e.g. burglary), but with specific characteristics of uncertainty, unknown consequences and absence of punishment.
A Large‐Area Smart Window with Tunable Shading and Solar‐Thermal Harvesting Ability Based on Remote Switching of a Magneto‐Active Liquid

Switchable windows provide intriguing opportunities for addressing the challenges of modern building skins. In particular, indoor comfort and the control of radiative heat transfer into and out of the building require adaptive and tunable solutions for shading and emissivity. Here, a switchable, ultrathin suspended particle device (SPD) for large‐area integration with smart facades is presented. The system is based on a fluidic window, manufactured at low cost from a laminate of structured, rolled glass and a thin cover with high surface strength. Loading the circulating fluid with magnetic nanoparticles enables active shading and solar‐thermal energy harvesting, whereby the loading state and, hence, the optical properties of the liquid can be controlled through remote switching in a particle collector‐suspender device. In the fully shaded state, a typical harvesting efficiency of 45% of the incoming solar power is obtained. For an average solar irradiance of 1000 W m−2 during 800 h a−1, this corresponds to a solar thermal harvesting capacity in the range of 360 kWh a−1 m−2. In comparison to alternative SPD concepts, this enables high flexibility and compatibility with established production lines. In addition, there is no need for further electrical contact, transparent conductive layers, or electrolytes.
STATISTICAL ANOMALY DETECTION IN ETHEREUM TRANSACTION GRAPHS

The set of transactions that occurs on the public ledger of an Ethereum network in a specific time frame can be represented as a directed graph, with vertices representing addresses and an edge indicating the interaction between two addresses. While there exists preliminary research on analyzing an Ethereum network by the means of graph analysis, most existing work is focused on either the public Ethereum Mainnet or on analyzing the different semantic transaction layers using static graph analysis in order to carve out the different network properties (such as interconnectivity, degrees of centrality, etc.) needed to characterize a blockchain network. By analyzing the consortium-run bloxberg Proof-of-Authority (PoA) Ethereum network, we show that we can identify suspicious and potentially malicious behaviour of network participants by employing statistical graph analysis. We thereby show that it is possible to identify the potentially malicious exploitation of an unmetered and weakly secured blockchain network resource. In addition, we show that Temporal Network Analysis is a promising technique to identify the occurrence of anomalies in a PoA Ethereum network.
Scaling Of Medical Disease Data Classification Based On A Hybrid Model Using Feature Optimization

The classification of medical disease data leverages the healthcare informatics system. The variant of medical disease data is very complex such as document, numerical and constant. Therefore, the classification algorithms face a problem of data transformation and are compromised with the accuracy and sensitivity of classification algorithms. The increased classification rate of medical disease data promotes telemedicine's automatic diagnosis of critical illness. This paper proposed a feature optimization-based hybrid model for medical disease data classification. The feature optimization of disease data applies particle swarm optimization, and optimal features constraints increase the classification rate of proposed algorithms. The hybrid model of the classification algorithm is the process of clustering and classification. The proposed algorithm uses K-means and support vector machines (KSVM). The KSVM algorithm boosts the process of classification of medical disease data. The hybrid algorithm has been simulated in MATLAB environments and tested with a series of reputed datasets such as diabetics, cancer, hepatitis, lymphography and other three datasets. The performance of the proposed algorithm compares SVM and CNN. The analysis of evaluation results suggests that the proposed algorithm increase 2-3% of accuracy and sensitivity of data classification.
Optimal Prediction of Tessarine Signals from Multi-sensor Uncertain Observations under Tk-Properness Conditions

: In this paper, the optimal one-stage prediction problem of tessarine signals from multi-sensor uncertain observations is approached. At each instant of time, there exists a non-null probability that the observation tessarine component coming from each sensor, contains the corresponding signal component, or only noise. To model the uncertainty, multiplicative noises modeled by Bernoulli random variables are included in the observation equations. Under correlation hypotheses between the signal and observation additive noises, a recursive algorithm to calculate the optimal least-squares linear predictor of the signal and its mean-squared error is proposed, derived by using an innovation approach. The theoretical results are illustrated by means of a numerical simulation example, in which the performance of the proposed estimator is evaluated under different uncertainty probabilities.