OpenRaiser/Intern-Atlas huggingface dataset:
An Evaluation of Intra-Transaction Parallelism in Actor-Relational Database Systems
Venue: arXiv cs.DB 2022
Year: 2022

Over the past decade, we have witnessed a dramatic evolution in main-memory capacity and multi-core parallelism of server hardware. To leverage this hardware potential, multi-core in-memory OLTP database systems have been extensively re-designed. The core objective of this re-design has been scaling up sequential execution of OLTP transactions, wherein alternative database architectures have been explored to eliminate system bottlenecks and overheads impeding inter-transaction parallelism to fully manifest. However, intra-transaction parallelism has been largely ignored by this previous work. We conjecture this situation to have developed because OLTP workloads are sometimes deemed to have far too little intra-transactional parallelism and, even when this kind of parallelism is available, program analyses to recognize it in arbitrary stored procedures are considered too brittle to be used as a general tool. Recently, however, a new concept of actor-relational database systems has been proposed, raising hopes that application developers can specify intra-transaction parallelism by modeling database applications as communicating actors. In this scenario, a natural question is whether an actor-relational database system with an asynchronous programming model can adequately expose the intra-transaction parallelism available in application logic to modern multi-core hardware. Towards that aim, we conduct in this paper an experimental evaluation of the factors that affect intra-transaction parallelism in the prototype actor-relational database system REACTDB, with an OLTP application designed with task-level parallelism in mind, and with the system running on a multi-core machine.
PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling
Venue: arXiv cs.CV 2022
Year: 2022

High-quality point clouds have practical significance for point-based rendering, semantic understanding, and surface reconstruction. Upsampling sparse, noisy and nonuniform point clouds for a denser and more regular approximation of target objects is a desirable but challenging task. Most existing methods duplicate point features for upsampling, constraining the upsampling scales at a fixed rate. In this work, the flexible upsampling rates are achieved via edge vector based affine combinations, and a novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed. The edge vector based approximation encodes the neighboring connectivity via affine combinations based on edge vectors, and restricts the approximation error within the second-order term of Taylor's Expansion. The EVA upsampling decouples the upsampling scales with network architecture, achieving the flexible upsampling rates in one-time training. Qualitative and quantitative evaluations demonstrate that the proposed PU-EVA outperforms the state-of-the-art in terms of proximity-to-surface, distribution uniformity, and geometric details preservation.
Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review
Venue: arXiv cs.CL 2022
Year: 2022

Peer review is a key component of the publishing process in most fields of science. The increasing submission rates put a strain on reviewing quality and efficiency, motivating the development of applications to support the reviewing and editorial work. While existing NLP studies focus on the analysis of individual texts, editorial assistance often requires modeling interactions between pairs of texts -- yet general frameworks and datasets to support this scenario are missing. Relationships between texts are the core object of the intertextuality theory -- a family of approaches in literary studies not yet operationalized in NLP. Inspired by prior theoretical work, we propose the first intertextual model of text-based collaboration, which encompasses three major phenomena that make up a full iteration of the review-revise-and-resubmit cycle: pragmatic tagging, linking and long-document version alignment. While peer review is used across the fields of science and publication formats, existing datasets solely focus on conference-style review in computer science. Addressing this, we instantiate our proposed model in the first annotated multi-domain corpus in journal-style post-publication open peer review, and provide detailed insights into the practical aspects of intertextual annotation. Our resource is a major step towards multi-domain, fine-grained applications of NLP in editorial support for peer review, and our intertextual framework paves the path for general-purpose modeling of text-based collaboration. Our corpus and accompanying code are publicly available.
Convergence of the Riemannian Langevin Algorithm
Venue: arXiv cs.LG 2022
Year: 2022

We study the Riemannian Langevin Algorithm for the problem of sampling from a distribution with density $ν$ with respect to the natural measure on a manifold with metric $g$. We assume that the target density satisfies a log-Sobolev inequality with respect to the metric and prove that the manifold generalization of the Unadjusted Langevin Algorithm converges rapidly to $ν$ for Hessian manifolds. This allows us to reduce the problem of sampling non-smooth (constrained) densities in ${\bf R}^n$ to sampling smooth densities over appropriate manifolds, while needing access only to the gradient of the log-density, and this, in turn, to sampling from the natural Brownian motion on the manifold. Our main analytic tools are (1) an extension of self-concordance to manifolds, and (2) a stochastic approach to bounding smoothness on manifolds. A special case of our approach is sampling isoperimetric densities restricted to polytopes by using the metric defined by the logarithmic barrier.
Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging
Venue: arXiv cs.CV 2022
Year: 2022

We propose a novel self-supervised Video Object Segmentation (VOS) approach that strives to achieve better object-background discriminability for accurate object segmentation. Distinct from previous self-supervised VOS methods, our approach is based on a discriminative learning loss formulation that takes into account both object and background information to ensure object-background discriminability, rather than using only object appearance. The discriminative learning loss comprises cutout-based reconstruction (cutout region represents part of a frame, whose pixels are replaced with some constant values) and tag prediction loss terms. The cutout-based reconstruction term utilizes a simple cutout scheme to learn the pixel-wise correspondence between the current and previous frames in order to reconstruct the original current frame with added cutout region in it. The introduced cutout patch guides the model to focus as much on the significant features of the object of interest as the less significant ones, thereby implicitly equipping the model to address occlusion-based scenarios. Next, the tag prediction term encourages object-background separability by grouping tags of all pixels in the cutout region that are similar, while separating them from the tags of the rest of the reconstructed frame pixels. Additionally, we introduce a zoom-in scheme that addresses the problem of small object segmentation by capturing fine structural information at multiple scales. Our proposed approach, termed CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS. A detailed ablation showcases the importance of the proposed loss formulation to effectively capture object-background discriminability and the impact of our zoom-in scheme to accurately segment small-sized objects.
Quantum Fault Trees
Venue: arXiv cs.OH 2022
Year: 2022

Fault tree analysis is a technique widely used in risk and reliability analysis of complex engineering systems given its deductive nature and relatively simple interpretation. In a fault tree, events are usually represented by a binary variable that indicates whether an event occurs or not, traditionally associated with the values 1 and 0, respectively. Different events are linked together using logical gates, modelling the dependencies that a subsystem or system may have over its basic components. In this study, quantum computing is leveraged to propose a novel approach to encode a traditional fault tree into a quantum algorithm. This quantum fault tree method uses quantum bits to represent basic events, effectively encoding the original fault tree into a quantum circuit. The execution of the resulting quantum circuit represents a full simulation of the fault tree, and multiple executions can be utilized to compute the failure probability of the whole system. The proposed approach is tested on a case study portraying a dynamic positioning system. Results verify that the quantum-based proposed approach is able to effectively obtain the dynamic positioning failure probability through simulation, opening promising opportunities for future investigations in the area.
Die Einflüsse von Arbeitsbelastung auf die Arbeitsqualität agiler Software-Entwicklungsteams
Venue: arXiv cs.SE 2022
Year: 2022

Due to the Covid 19 pandemic and the associated effects on the world of work, the burden on employees has been brought into focus. This fact also applies to agile software development teams in many companies due to the extensive switch to remote work. Too high a workload can lead to various negative effects, such as increased sick leave, the well-being of employees, or reduced productivity. It is also known that the workload in knowledge work impacts the quality of the work results. This research article identifies potential factors of the workload of the agile software development team members at Otto GmbH & Co KG. Based on the factors, we present measures to reduce workload and explain our findings, which we have validated in an experiment. Our results show that even small-scale actions, such as the introduction of rest work phases during the working day, lead to positive effects, for example, increased ability to concentrate and how these affect the quality of the work results.
Latency Analysis of Vehicle-to-Pedestrian C-V2X Communications at Urban Street Intersections
Venue: arXiv cs.NI 2022
Year: 2022

Cellular Vehicle-to-Everything (C-V2X) technology promises to provide ultra-reliable low latency communication (URLLC) framework for connected vehicles. Connected vehicles can help us improve traffic safety, congestion and reduce fatal accidents. C-V2X promises new levels of connectivity and intelligence providing numerous features like infotainment, always-on telematics, real-time navigation, etc. with heterogeneous network architecture. C-V2X leverages existing cellular architecture and is on the way to replace DSRC/WAVE for vehicular communication. There are still some challenges which needs to be tackled before C-V2X can be deployed for efficient on-road V2X. In this paper, we look at end-to-end latency for Vehicle-to-Pedestrian (V2P) which demands ultra low latencies with reliable packet delivery ratio. To evaluate the LTE-V2P performance, we used network simulator for an urban intersection scenario where vulnerable road-side users (VRUs) are trying to cross the street and they communicate with moving vehicles. We evaluated the end-to-end latency and throughput for the given scenario and concluded that with the existing network architecture the latency is high. By utilizing multi-edge access computing (MEC) servers latency can be reduce drastically and hence can be made feasible for cellular V2P communication.
The Dark Souls of Archaeology: Recording Elden Ring
Venue: arXiv cs.HC 2022
Year: 2022

Archaeology can be broadly defined as the study and interpretation of the past through material remains. Videogame worlds, though immaterial in nature, can also afford opportunities to study the people who existed within them based on what they leave behind. In this paper we present the first formal archaeological survey of a predominantly single-player game, by examining the player-generated content that is asynchronously distributed to players in the videogame Elden Ring.
The Lack of Convexity of the Relevance-Compression Function
Venue: arXiv cs.IT 2022
Year: 2022

In this paper we investigate the convexity of the relevance-compression function for the Information Bottleneck and the Information Distortion problems. This curve is an analog of the rate-distortion curve, which is convex. In the problems we discuss in this paper, the distortion function is not a linear function of the quantizer, and the relevance-compression function is not necessarily convex (concave), but can change its convexity. We relate this phenomena with existence of first order phase transitions in the corresponding Lagrangian as a function of the annealing parameter.
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles
Venue: arXiv cs.CR 2022
Year: 2022

Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertainty and diversity regarding attack methods make this task challenging. However, the existing works have the limitation of only considering local features or the weak feature mapping of multi-features. To address these limitations, we present a novel model for automotive intrusion detection by spatial-temporal correlation features of in-vehicle communication traffic (STC-IDS). Specifically, the proposed model exploits an encoding-detection architecture. In the encoder part, spatial and temporal relations are encoded simultaneously. To strengthen the relationship between features, the attention-based convolutional network still captures spatial and channel features to increase the receptive field, while attention-LSTM builds meaningful relationships from previous time series or crucial bytes. The encoded information is then passed to detector for generating forceful spatial-temporal attention features and enabling anomaly classification. In particular, single-frame and multi-frame models are constructed to present different advantages respectively. Under automatic hyper-parameter selection based on Bayesian optimization, the model is trained to attain the best performance. Extensive empirical studies based on a real-world vehicle attack dataset demonstrate that STC-IDS has outperformed baseline methods and obtains fewer false-alarm rates while maintaining efficiency.
LAB: A Leader-Advocate-Believer Based Optimization Algorithm
Venue: arXiv cs.NE 2022
Year: 2022

This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behaviour exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (Leader, Advocate, Believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. Besides benchmark problems, the LAB algorithm was applied for solving challenging engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes, and process parameter optimization for turning titanium alloy in a minimum quantity lubrication environment. The results were superior to the other algorithms compared such as Firefly Algorithm, Variations of Co-hort Intelligence, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimisation, and Multi-Cohort Intelligence. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features of the LAB algorithm along with its limitations are also discussed.
$π$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Venue: arXiv cs.LG 2022
Year: 2022

Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. Thus, BO disregards a valuable source of information, reducing its appeal to ML practitioners. To address this issue, we propose $π$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user. In contrast to previous approaches, $π$BO is conceptually simple and can easily be integrated with existing libraries and many acquisition functions. We provide regret bounds when $π$BO is applied to the common Expected Improvement acquisition function and prove convergence at regular rates independently of the prior. Further, our experiments show that $π$BO outperforms competing approaches across a wide suite of benchmarks and prior characteristics. We also demonstrate that $π$BO improves on the state-of-the-art performance for a popular deep learning task, with a 12.5 $\times$ time-to-accuracy speedup over prominent BO approaches.
Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition
Venue: arXiv cs.CV 2022
Year: 2022

High-quality annotated images are significant to deep facial expression recognition (FER) methods. However, uncertain labels, mostly existing in large-scale public datasets, often mislead the training process. In this paper, we achieve uncertain label correction of facial expressions using auxiliary action unit (AU) graphs, called ULC-AG. Specifically, a weighted regularization module is introduced to highlight valid samples and suppress category imbalance in every batch. Based on the latent dependency between emotions and AUs, an auxiliary branch using graph convolutional layers is added to extract the semantic information from graph topologies. Finally, a re-labeling strategy corrects the ambiguous annotations by comparing their feature similarities with semantic templates. Experiments show that our ULC-AG achieves 89.31% and 61.57% accuracy on RAF-DB and AffectNet datasets, respectively, outperforming the baseline and state-of-the-art methods.
Dimension Reduction for time series with Variational AutoEncoders
Venue: arXiv cs.LG 2022
Year: 2022

In this work, we explore dimensionality reduction techniques for univariate and multivariate time series data. We especially conduct a comparison between wavelet decomposition and convolutional variational autoencoders for dimension reduction. We show that variational autoencoders are a good option for reducing the dimension of high dimensional data like ECG. We make these comparisons on a real world, publicly available, ECG dataset that has lots of variability and use the reconstruction error as the metric. We then explore the robustness of these models with noisy data whether for training or inference. These tests are intended to reflect the problems that exist in real-world time series data and the VAE was robust to both tests.
Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI
Venue: arXiv cs.CY 2022
Year: 2022

The threat hunting lifecycle is a complex atmosphere that requires special attention from professionals to maintain security. This paper is a collection of recent work that gives a holistic view of the threat hunting ecosystem, identifies challenges, and discusses the future with the integration of artificial intelligence (AI). We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting.
Time Series Forecasting (TSF) Using Various Deep Learning Models
Venue: arXiv cs.LG 2022
Year: 2022

Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based Transformer models, which has had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.
Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models
Venue: arXiv cs.LG 2022
Year: 2022

Data assimilation presents computational challenges because many high-fidelity models must be simulated. Various deep-learning-based surrogate modeling techniques have been developed to reduce the simulation costs associated with these applications. However, to construct data-driven surrogate models, several thousand high-fidelity simulation runs may be required to provide training samples, and these computations can make training prohibitively expensive. To address this issue, in this work we present a framework where most of the training simulations are performed on coarsened geomodels. These models are constructed using a flow-based upscaling method. The framework entails the use of a transfer-learning procedure, incorporated within an existing recurrent residual U-Net architecture, in which network training is accomplished in three steps. In the first step. where the bulk of the training is performed, only low-fidelity simulation results are used. The second and third steps, in which the output layer is trained and the overall network is fine-tuned, require a relatively small number of high-fidelity simulations. Here we use 2500 low-fidelity runs and 200 high-fidelity runs, which leads to about a 90% reduction in training simulation costs. The method is applied for two-phase subsurface flow in 3D channelized systems, with flow driven by wells. The surrogate model trained with multifidelity data is shown to be nearly as accurate as a reference surrogate trained with only high-fidelity data in predicting dynamic pressure and saturation fields in new geomodels. Importantly, the network provides results that are significantly more accurate than the low-fidelity simulations used for most of the training. The multifidelity surrogate is also applied for history matching using an ensemble-based procedure, where accuracy relative to reference results is again demonstrated.