Miracle-dz/UniFAR-FTUs huggingface dataset:
A Brief Survey on Oracle-based Test Adequacy Metrics

Code coverage is a widespread and popular test adequacy metric that measures the percentage of program codes executed by a test suite. Despite its popularity, code coverage has several limitations. One of the major limitations is that it does not provide any insights into the quality of the test oracles, one of the core components of testing activity. Furthermore, studies showed that code coverage is a poor test adequacy metric, and it is not a good indicator of the fault detection eﬀectiveness of a test suite. To address this limitation, researchers proposedextensions totraditional structuralcode coverage metrics to explicitly consider test oracles when computing coverage. We name these metrics as oracle-based code coverage . This survey paper has discussed all oracle-based techniques published so far, starting from 2007. We have discussed seven oracle-based code coverage metrics, their formal deﬁnitions, methodologies, evaluation, and ﬁndings. To the best of our knowledge, this is the ﬁrst paper that surveys oracle-based coverage metrics that are shown to be more eﬀective than traditional coverage; however, they received little attention in the software testing community.
FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification

Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords.Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed.
Mutimodal Ranking Optimization for Heterogeneous Face Re-identification

Heterogeneous face re-identification, namely matching heterogeneous faces across disjoint visible light (VIS) and near-infrared (NIR) cameras, has become an important problem in video surveillance application. However, the large domain discrepancy between heterogeneous NIR-VIS faces makes the performance of face re-identification degraded dramatically. To solve this problem, a multimodal fusion ranking optimization algorithm for heterogeneous face re-identification is proposed in this paper. Firstly, we design a heterogeneous face translation network to obtain multimodal face pairs, including NIR-VIS/NIR-NIR/VIS-VIS face pairs, through mutual transformation between NIR-VIS faces. Secondly, we propose linear and non-linear fusion strategies to aggregate initial ranking lists of multimodal face pairs and acquire the optimized re-ranked list based on modal complementarity. The experimental results show that the proposed multimodal fusion ranking optimization algorithm can effectively utilize the complementarity and outperforms some relative methods on the SCface dataset.
Robust Modelling Framework for Short-Term Forecasting of Global Horizontal Irradiance

The increasing demand for electricity and the need for clean energy sources have increased solar energy use. Accurate forecasts of solar energy are required for easy management of the grid. This paper compares the accuracy of two Gaussian Process Regression (GPR) models combined with Additive Quantile Regression (AQR) and Bayesian Structural Time Series (BSTS) models in the 2-day ahead forecasting of global horizontal irradiance using data from the University of Pretoria from July 2020 to August 2021. Four methods were adopted for variable selection, Lasso, ElasticNet, Boruta, and GBR (Gradient Boosting Regression). The variables selected using GBR were used because they produced the lowest MAE (Minimum Absolute Errors) value. A comparison of seven models GPR (Gaussian Process Regression), Two-layer DGPR (Two-layer Deep Gaussian Process Regression), bstslong (Bayesian Structural Time Series long), AQRA (Additive Quantile Regression Averaging), QRNN(Quantile Regression Neural Network), PLAQR(Partial Linear additive Quantile Regression), and Opera(Online Prediction by ExpRt Aggregation) was made. The evaluation metrics used to select the best model were the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). Further evaluations were done using proper scoring rules and Murphy diagrams. The best individual model was found to be the GPR. The best forecast combination was AQRA ((AQR Averaging) based on MAE. However, based on RMSE, GPNN was the best forecast combination method. Companies such as Eskom could use the methods adopted in this study to control and manage the power grid. The results will promote economic development and sustainability of energy resources.
RGBD2: Generative Scene Synthesis via Incremental View Inpainting Using RGBD Diffusion Models

We address the challenge of recovering an underlying scene geometry and colors from a sparse set of RGBD view observations. In this work, we present a new solution termed RGBD2 that sequentially generates novel RGBD views along a camera trajectory, and the scene geometry is simply the fusion result of these views. More specifically, we maintain an intermediate surface mesh used for rendering new RGBD views, which subsequently becomes complete by an inpainting network; each rendered RGBD view is later back-projected as a partial surface and is supplemented into the intermediate mesh. The use of intermediate mesh and camera projection helps solve the tough problem of multi-view inconsistency. We practically implement the RGBD inpainting network as a versatile RGBD diffusion model, which is previously used for 2D generative modeling; we make a modification to its reverse diffusion process to enable our use. We evaluate our approach on the task of 3D scene synthesis from sparse RGBD inputs; extensive experiments on the ScanNet dataset demonstrate the superiority of our approach over existing ones. Project page: https://jblei.site/proj/rgbd-diffusion.
Isomorphism between the Białynicki-Birula and the Landau-Peierls Fock space quantization of the electromagnetic field in position representation

We first present a summary of the quantization of the electromagnetic field in position space representation, using two main approaches: the Landau-Peierls approach in the Coulomb gauge and the Białynicki-Birula (BB) approach, based on the Riemann–Silberstein vector. We describe both in a framework that starts with a classical Hamiltonian structure and builds the quantum model in a bosonic Fock space by a precisely defined principle of correspondence. We show that the two approaches are completely equivalent. This is formulated by showing that there is a unitary map between the Fock spaces that makes them isomorphic. Since all the physically measurable quantities can be expressed in terms of scalar products, this implies that the two quantizations lead to exactly the same physical properties. We show furthemore that the isomorphism is preserved in the time evolutions. To show the equivalence, we use the concepts of helicity and frequency operators. The combination of these two operators provides a formulation that allows one to make the link between these two methods of quantization in a precise way. We also show that the construction in the BB quantization that avoids the presence of negative eigenvalues in the Hamiltonian, in analogy with the one for the Dirac equation for electrons and positrons, can be performed through an alternative choice of the canonical variables for Maxwell’s equations.
Evolutionary Multitasking With Solution Space Cutting for Point Cloud Registration

Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of its robustness to the initial pose and flexibility in objective function design. However, most evolving registration methods cannot tackle the local optimum well and they have rarely investigated the success ratio, which implies the probability of not falling into local optima and is closely related to the practicality of the algorithm. Evolutionary multi-task optimization (EMTO) is a widely used paradigm, which can boost exploration capability through knowledge transfer among related tasks. Inspired by this concept, this study proposes a novel evolving registration algorithm via EMTO, where the multi-task configuration is based on the idea of solution space cutting. Concretely, one task searching in cut space assists another task with complex function landscape in escaping from local optima and enhancing successful registration ratio. To reduce unnecessary computational cost, a sparse-to-dense strategy is proposed. In addition, a novel fitness function robust to various overlap rates as well as a problem-specific metric of computational cost is introduced. Compared with 8 evolving approaches, 4 traditional approaches and 3 deep learning approaches on the object-scale and scene-scale registration datasets, experimental results demonstrate that the proposed method has superior performances in terms of precision and tackling local optima.
Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery

Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.
Noise in single injection diodes. II. Applications

The noise of single injection diodes of varying degrees of complexity is studied: the trap−free insulator, the trap−free semiconductor, structures with traps, and three−dimensional insulators. Where possible, results for hot as well as thermal carriers are derived. The one−dimensional results are consistently obtained with the transfer impedance method and the emphasis is on results valid in the entire characteristic [Ohmic, space−charge−limited (SCL), and mixed conduction]. Agreement with other work in limiting current regimes is generally observed. For devices with traps explicit new formulas for the trapping noise are obtained.
Wikipedia's Balancing Act: A Tool for Collective Intelligence or Mass Surveillance?

Wikipedia has evolved beyond its original function as an online encyclopedia in an increasingly complex data-driven society. The social platform is met with a balancing act between collective intelligence and mass surveillance; processes need to be developed to protect individuals and the community from government mass surveillance without sacriﬁcing the important contributions made through prohibited anonymous communication software. Case studies are provided from NSA government surveillance practices, the anti-SOPA legislation movement, and research that covers Wikipedia’s involvement with participatory journalism, disinformation, self-censorship, and the use of Tor. This paper proposes that a common ground can be developed between individuals, public and private institutions through future research in socio-cultural anthropology and policy frameworks around data retention and government accountability. Wikipedia is used as an example within the US intelligence community as a complex organisation that can adapt to changes through its iterative nature, which draws insight into how policy frameworks can be future-proofed. Finally, this paper is a wake-up call to individuals, private institutions, and governments to remain vigilant about the storage and use of personal information as a result of contributing to online communities.
MAPS-KB: A Million-scale Probabilistic Simile Knowledge Base

The ability to understand and generate similes is an imperative step to realize human-level AI. However, there is still a considerable gap between machine intelligence and human cognition in similes, since deep models based on statistical distribution tend to favour high-frequency similes. Hence, a large-scale symbolic knowledge base of similes is required, as it contributes to the modeling of diverse yet unpopular similes while facilitating additional evaluation and reasoning. To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. Overall, we construct MAPS-KB, a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. We conduct sufficient experiments to justify the effectiveness and necessity of the methods of our framework. We also apply MAPS-KB on three downstream tasks to achieve state-of-the-art performance, further demonstrating the value of MAPS-KB. Resources of MAPS-KB are publicly available at https://github.com/Abbey4799/MAPS-KB.
Momentum Contrastive Pre-training for Question Answering

Existing pre-training methods for extractive Question Answering (QA) generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. In order to address this problem, we propose a novel Momentum Contrastive pRe-training fOr queStion anSwering (MCROSS) method for extractive QA. Specifically, MCROSS introduces a momentum contrastive learning framework to align the answer probability between cloze-like and natural query-passage sample pairs. Hence, the pre-trained models can better transfer the knowledge learned in cloze-like samples to answering natural questions. Experimental results on three benchmarking QA datasets show that our method achieves noticeable improvement compared with all baselines in both supervised and zero-shot scenarios.
Parameter-Efficient Finetuning of Transformers for Source Code

Pretrained Transformers achieve state-of-the-art performance in various code-processing tasks but may be too large to be deployed. As software development tools often incorporate modules for various purposes which may potentially use a single instance of the pretrained model, it appears relevant to utilize parameter-efficient fine-tuning for the pretrained models of code. In this work, we test two widely used approaches, adapters and LoRA, which were initially tested on NLP tasks, on four code-processing tasks. We find that though the efficient fine-tuning approaches may achieve comparable or higher performance than the standard, full, fine-tuning in code understanding tasks, they underperform full fine-tuning in code-generative tasks. These results underline the importance of testing efficient fine-tuning approaches on other domains than NLP and motivate future research in efficient fine-tuning for source code.
Clustered Formation of Massive Stars within an Ionized Rotating Disk

We present Atacama Large Millimeter/submillimeter Array observations with a 800 au resolution and radiative-transfer modeling of the inner part (r ≈ 6000 au) of the ionized accretion flow around a compact star cluster in formation at the center of the luminous ultracompact H ii region G10.6-0.4. We modeled the flow with an ionized Keplerian disk with and without radial motions in its outer part, or with an external Ulrich envelope. The Markov Chain Monte Carlo fits to the data give total stellar masses M ⋆ from 120 to 200 M ⊙, with much smaller ionized-gas masses M ion-gas = 0.2–0.25 M ⊙. The stellar mass is distributed within the gravitational radius R g ≈ 1000 to 1500 au, where the ionized gas is bound. The viewing inclination angle from the face-on orientation is i = 49°–56°. Radial motions at radii r > R g converge to v r,0 ≈ 8.7 km s−1, or about the speed of sound of ionized gas, indicating that this gas is marginally unbound at most. From additional constraints on the ionizing-photon rate and far-IR luminosity of the region, we conclude that the stellar cluster consists of a few massive stars with M star = 32–60 M ⊙, or one star in this range of masses accompanied by a population of lower-mass stars. Any active accretion of ionized gas onto the massive (proto)stars is residual. The inferred cluster density is very large, comparable to that reported at similar scales in the Galactic center. Stellar interactions are likely to occur within the next million years.
A Survey on Natural Language Processing for Programming

Natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct from natural language, a programming language is highly structured and functional. Constructing a structure-based representation and a functionality-oriented algorithm is at the heart of program understanding and generation. In this paper, we conduct a systematic review covering tasks, datasets, evaluation methods, techniques, and models from the perspective of the structure-based and functionality-oriented property, aiming to understand the role of the two properties in each component. Based on the analysis, we illustrate unexplored areas and suggest potential directions for future work.
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning

Audio-visual speech recognition (AVSR) has gained remarkable success for ameliorating the noise-robustness of speech recognition. Mainstream methods focus on fusing audio and visual inputs to obtain modality-invariant representations. However, such representations are prone to over-reliance on audio modality as it is much easier to recognize than video modality in clean conditions. As a result, the AVSR model underestimates the importance of visual stream in face of noise corruption. To this end, we leverage visual modality-specific representations to provide stable complementary information for the AVSR task. Specifically, we propose a reinforcement learning (RL) based framework called MSRL, where the agent dynamically harmonizes modality-invariant and modality-specific representations in the auto-regressive decoding process. We customize a reward function directly related to task-specific metrics (i.e., word error rate), which encourages the MSRL to effectively explore the optimal integration strategy. Experimental results on the LRS3 dataset show that the proposed method achieves state-of-the-art in both clean and various noisy conditions. Furthermore, we demonstrate the better generality of MSRL system than other baselines when test set contains unseen noises.
Evaluation of RGB-D SLAM in Large Indoor Environments

Simultaneous localization and mapping (SLAM) is one of the key components of a control system that aims to ensure autonomous navigation of a mobile robot in unknown environments. In a variety of practical cases a robot might need to travel long distances in order to accomplish its mission. This requires long-term work of SLAM methods and building large maps. Consequently the computational burden (including high memory consumption for map storage) becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific techniques and optimizations to tackle this challenge, still their performance in long-term scenarios needs proper assessment. To this end, we perform an empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods, suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them in a large simulated indoor environment, consisting of corridors and halls, while varying the odometer noise for a more realistic setup. We provide both qualitative and quantitative analysis of both methods uncovering their strengths and weaknesses. We find that both methods build a high-quality map with low odometry noise but tend to fail with high odometry noise. Voxgraph has lower relative trajectory estimation error and memory consumption than RTAB-Map, while its absolute error is higher.
Sub-millijansky 1.4 GHz source counts and multicolor studies of weak radio galaxy populations

On presente un catalogue de 124 sources observees a 21 cm avec la VLA; 93 d'entre elles constituent un echantillon complet au niveau 5σ. On presente les comptages des sources a 1462 MHz. On verifie si une modification significative se produit dans la pente des comptages au-dessous de 100 mJy en combinant tous les comptages anterieurs a 1,4 GHz. On utilise les diagrammes couleur-magnitude et couleur-couleur pour etudier la nature des identifications dans le domaine sub-millijansky
Introducing Hermes: Executing Clinical Quality Language (CQL) at over 66 Million Resources per Second (inexpensively)

Clinical Quality Language (CQL) has emerged as a standard for rule representation in Clinical Decision Support (CDS) and Electronic Clinical Quality Measurement (eCQM) in healthcare. While open-source reference implementations and a few commercial engines exist, there is still a market need for high-performance engines that can execute CQL queries on the scales of millions of patients. We introduce the \Hermes{} engine as the world's fastest commercial CQL execution engine.
Error-aware Quantization through Noise Tempering

Quantization has become a predominant approach for model compression, enabling deployment of large models trained on GPUs onto smaller form-factor devices for inference. Quantization-aware training (QAT) optimizes model parameters with respect to the end task while simulating quantization error, leading to better performance than post-training quantization. Approximation of gradients through the non-differentiable quantization operator is typically achieved using the straight-through estimator (STE) or additive noise. However, STE-based methods suffer from instability due to biased gradients, whereas existing noise-based methods cannot reduce the resulting variance. In this work, we incorporate exponentially decaying quantization-error-aware noise together with a learnable scale of task loss gradient to approximate the effect of a quantization operator. We show this method combines gradient scale and quantization noise in a better optimized way, providing ﬁner-grained estimation of gradients at each weight and activation layer’s quantizer bin size. Our controlled noise also contains an implicit curvature term that could encourage ﬂatter minima, which we show is indeed the case in our experiments. Experiments training ResNet architectures on the CIFAR-10, CIFAR-100 and ImageNet benchmarks show that our method obtains state-of-the-art top-1 classiﬁcation accuracy for uniform (non mixed-precision) quantization, out-performing previous methods by 0.5-1.2% absolute.
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking

Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decreasing the reward, but also in modifying specific temporal logic properties of the policy. This paper presents a metric that measures the exact impact of adversarial attacks against such properties. We use this metric to craft optimal adversarial attacks. Furthermore, we introduce a model checking method that allows us to verify the robustness of RL policies against adversarial attacks. Our empirical analysis confirms (1) the quality of our metric to craft adversarial attacks against temporal logic properties, and (2) that we are able to concisely assess a system's robustness against attacks.