OpenRaiser/Intern-Atlas huggingface dataset:
A repository of automatic GUI test patterns in Android applications: Specification and Analysis using Alloy modeling language
Venue: arXiv cs.SE 2022
Year: 2022

The software industry aims to provide customers with quality software. Testing software is a critical and sensitive stage in ensuring software quality. Due to the increasing popularity of mobile devices, the use of Android applications has increased. Almost all are equipped with Graphical User Interface to interact with users or systems. GUI is the most common tool to communicate with modern software. Therefore, the perfect GUI is a GUI that ensures the safety, strength, and usability of the whole software system. The GUI testing is a vital stage in ensuring the product quality because the GUI is the user s first impression and the final view of the final product. This paper has proposed a new technique to promote the model based test efficiency using Alloy modeling language. The findings showed that this approach needs less configuration and modeling time than previous methods. Moreover, using GUI patterns may decrease errors and violations.
Removing membrane locking in quadratic NURBS-based discretizations of linear plane Kirchhoff rods: CAS elements
Venue: arXiv cs.CE 2022
Year: 2022

NURBS-based discretizations suffer from membrane locking when applied to primal formulations of curved thin-walled structures. We consider linear plane curved Kirchhoff rods as a model problem to study how to remove membrane locking from NURBS-based discretizations. In this work, we propose continuous-assumed-strain (CAS) elements, an assumed strain treatment that removes membrane locking from quadratic NURBS for an ample range of slenderness ratios. CAS elements take advantage of the C1 inter-element continuity of the displacement vector given by quadratic NURBS to interpolate the membrane strain using linear Lagrange polynomials while preserving the C0 inter-element continuity of the membrane strain. CAS elements are the first NURBS-based element type able to remove membrane locking for a broad range of slenderness ratios that combines the following characteristics: (1) No additional degrees of freedom are added, (2) No additional systems of algebraic equations need to be solved, and (3) The nonzero pattern of the stiffness matrix is preserved. Since the only additional computations required by the proposed element type are to evaluate the derivatives of the basis functions and the unit tangent vector at the knots, the proposed scheme barely increases the computational cost with respect to the locking-prone NURBS-based discretization of the primal formulation. The benchmark problems show that the convergence of CAS elements is independent of the slenderness ratio while the convergence of quadratic NURBS elements, local Bbar elements, and local ANS elements depends heavily on the slenderness ratio. The numerical examples also show how CAS elements remove the spurious oscillations in stress resultants caused by membrane locking while quadratic NURBS elements, local Bbar elements, and local ANS elements suffer from large-amplitude spurious oscillations in stress resultants.
Predict-and-Update Network: Audio-Visual Speech Recognition Inspired by Human Speech Perception
Venue: arXiv cs.MM 2022
Year: 2022

Audio and visual signals complement each other in human speech perception, so do they in speech recognition. The visual hint is less evident than the acoustic hint, but more robust in a complex acoustic environment, as far as speech perception is concerned. It remains a challenge how we effectively exploit the interaction between audio and visual signals for automatic speech recognition. There have been studies to exploit visual signals as redundant or complementary information to audio input in a synchronous manner. Human studies suggest that visual signal primes the listener in advance as to when and on which frequency to attend to. We propose a Predict-and-Update Network (P&U net), to simulate such a visual cueing mechanism for Audio-Visual Speech Recognition (AVSR). In particular, we first predict the character posteriors of the spoken words, i.e. the visual embedding, based on the visual signals. The audio signal is then conditioned on the visual embedding via a novel cross-modal Conformer, that updates the character posteriors. We validate the effectiveness of the visual cueing mechanism through extensive experiments. The proposed P&U net outperforms the state-of-the-art AVSR methods on both LRS2-BBC and LRS3-BBC datasets, with the relative reduced Word Error Rate (WER)s exceeding 10% and 40% under clean and noisy conditions, respectively.
Forensicability Assessment of Questioned Images in Recapturing Detection
Venue: arXiv cs.CV 2022
Year: 2022

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency.
BiRank vs PageRank: Using SNA on Company Register Data for Fiscal Risk Prediction
Venue: arXiv cs.SI 2022
Year: 2022

Efficient financial administrations need to ensure compliant behavior of all tax subjects without excessive personnel costs or obstruction of compliant companies. To do so, accurate prediction of non-compliance or fraud is crucial. Social Network Analysis (SNA) provides powerful tools for fraud prediction as fraudulence is often clustered in certain areas of real world social networks. In this paper we present our results of comparing PageRank and the more recent BiRank to infer risk-ranks based on network structure and prior fraud information. Specifically, we model our social network from company register data. We find that in this case study BiRank outperforms PageRank in both quality of the resulting ranks for fraud prediction and run time. The results show that this class of algorithms is generally useful for fraud and risk prediction and more specifically also illustrate the potential of BiRank in comparison, as it opens up new modeling opportunities. Our results show that selecting companies for tax audits based on BiRank yields a precision of 16.38% for the top 20.000 subjects selecting 83.4% of all fraud cases (recall).
Authentication of Underwater Assets
Venue: arXiv cs.CR 2022
Year: 2022

Secure digital wireless communication underwater has become a key issue as maritime operations shift towards employing a heterogeneous mix of robotic assets and as the security of digital systems becomes challenged across all domains. At the same time, a proliferation of underwater signal coding and physical layer options are delivering greater bandwidth and flexibility, but mostly without the standards necessary for interoperability. We address here an essential requirement for security, namely a confirmation of asset identities also known as authentication. We propose, implement, verify and validate an authentication protocol based on the first digital underwater communications standard. Our scheme is applicable primarily to AUVs operating around offshore oil and gas facilities, but also to other underwater devices that may in the future have acoustic modems. It makes communication including command and control significantly more secure and provides a foundation for the development of more sophisticated security mechanisms.
HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

The construction of spatiotemporal networks using graph convolution networks (GCNs) has become one of the most popular methods for predicting traffic signals. However, when using a GCN for traffic speed prediction, the conventional approach generally assumes the relationship between the sensors as a homogeneous graph and learns an adjacency matrix using the data accumulated by the sensors. However, the spatial correlation between sensors is not specified as one but defined differently from various viewpoints. To this end, we aim to study the heterogeneous characteristics inherent in traffic signal data to learn the hidden relationships between sensors in various ways. Specifically, we designed a method to construct a heterogeneous graph for each module by dividing the spatial relationship between sensors into static and dynamic modules. We propose a network decentralization attention based heterogeneity-aware graph convolution network (HAGCN) method that aggregates the hidden states of adjacent nodes by considering the importance of each channel in a heterogeneous graph. Experimental results on real traffic datasets verified the effectiveness of the proposed method, achieving a 6.35% improvement over the existing model and realizing state-of-the-art prediction performance.
Volumetric video streaming: Current approaches and implementations
Venue: arXiv cs.MM 2022
Year: 2022

The rise of capturing systems for objects and scenes in 3D with increased fidelity and immersion has led to the popularity of volumetric video contents that can be seen from any position and angle in 6 degrees of freedom navigation. Such contents need large volumes of data to accurately represent the real world. Thus, novel optimization solutions and delivery systems are needed to enable volumetric video streaming over bandwidth-limited networks. In this chapter, we discuss theoretical approaches to volumetric video streaming optimization, through compression solutions, as well as network and user adaptation, for high-end and low-powered devices. Moreover, we present an overview of existing end-to-end systems, and we point to the future of volumetric video streaming.
How Much User Context Do We Need? Privacy by Design in Mental Health NLP Application
Venue: arXiv cs.CL 2022
Year: 2022

Clinical NLP tasks such as mental health assessment from text, must take social constraints into account - the performance maximization must be constrained by the utmost importance of guaranteeing privacy of user data. Consumer protection regulations, such as GDPR, generally handle privacy by restricting data availability, such as requiring to limit user data to 'what is necessary' for a given purpose. In this work, we reason that providing stricter formal privacy guarantees, while increasing the volume of user data in the model, in most cases increases benefit for all parties involved, especially for the user. We demonstrate our arguments on two existing suicide risk assessment datasets of Twitter and Reddit posts. We present the first analysis juxtaposing user history length and differential privacy budgets and elaborate how modeling additional user context enables utility preservation while maintaining acceptable user privacy guarantees.
A Study on Representation Transfer for Few-Shot Learning
Venue: arXiv cs.CV 2022
Year: 2022

Few-shot classification aims to learn to classify new object categories well using only a few labeled examples. Transferring feature representations from other models is a popular approach for solving few-shot classification problems. In this work we perform a systematic study of various feature representations for few-shot classification, including representations learned from MAML, supervised classification, and several common self-supervised tasks. We find that learning from more complex tasks tend to give better representations for few-shot classification, and thus we propose the use of representations learned from multiple tasks for few-shot classification. Coupled with new tricks on feature selection and voting to handle the issue of small sample size, our direct transfer learning method offers performance comparable to state-of-art on several benchmark datasets.
A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis
Venue: arXiv cs.CV 2022
Year: 2022

Aerial robots such as drones have been leveraged to perform bridge inspections. Inspection images with both recognizable structural elements and apparent surface defects can be collected by onboard cameras to provide valuable information for the condition assessment. This article aims to determine a suitable deep neural network (DNN) for parsing multiclass bridge elements in inspection images. An extensive set of quantitative evaluations along with qualitative examples show that High-Resolution Net (HRNet) possesses the desired ability. With data augmentation and a training sample of 130 images, a pre-trained HRNet is efficiently transferred to the task of structural element parsing and has achieved a 92.67% mean F1-score and 86.33% mean IoU.
Spatial Parquet: A Column File Format for Geospatial Data Lakes [Extended Version]
Venue: arXiv cs.DB 2022
Year: 2022

Modern data analytics applications prefer to use column-storage formats due to their improved storage efficiency through encoding and compression. Parquet is the most popular file format for column data storage that provides several of these benefits out of the box. However, geospatial data is not readily supported by Parquet. This paper introduces Spatial Parquet, a Parquet extension that efficiently supports geospatial data. Spatial Parquet inherits all the advantages of Parquet for non-spatial data, such as rich data types, compression, and column/row filtering. Additionally, it adds three new features to accommodate geospatial data. First, it introduces a geospatial data type that can encode all standard spatial data types in a column format compatible with Parquet. Second, it adds a new lossless and efficient encoding method, termed FP-delta, that is customized to efficiently store geospatial coordinates stored in floating-point format. Third, it adds a light-weight spatial index that allows the reader to skip non-relevant parts of the file for increased read efficiency. Experiments on large-scale real data showed that SpatialParquet can reduce the data size by a factor of three, even without compression. Compression can further reduce the storage size. Additionally, Spatial Parquet can reduce the reading time by two orders of magnitude when the light-weight index is applied. This initial prototype can open new research directions to further improve geospatial data storage in column format.
Improving Assistive Robotics with Deep Reinforcement Learning
Venue: arXiv cs.RO 2022
Year: 2022

Assistive Robotics is a class of robotics concerned with aiding humans in daily care tasks that they may be inhibited from doing due to disabilities or age. While research has demonstrated that classical control methods can be used to design policies to complete these tasks, these methods can be difficult to generalize to a variety of instantiations of a task. Reinforcement learning can provide a solution to this issue, wherein robots are trained in simulation and their policies are transferred to real-world machines. In this work, we replicate a published baseline for training robots on three tasks in the Assistive Gym environment, and we explore the usage of a Recurrent Neural Network and Phasic Policy Gradient learning to augment the original work. Our baseline implementation meets or exceeds the baseline of the original work, however, we found that our explorations into the new methods was not as effective as we anticipated. We discuss the results of our baseline and some thoughts on why our new methods were not as successful.
CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal
Venue: arXiv cs.CV 2022
Year: 2022

The key to shadow removal is recovering the contents of the shadow regions with the guidance of the non-shadow regions. Due to the inadequate long-range modeling, the CNN-based approaches cannot thoroughly investigate the information from the non-shadow regions. To solve this problem, we propose a novel cleanness-navigated-shadow network (CNSNet), with a shadow-oriented adaptive normalization (SOAN) module and a shadow-aware aggregation with transformer (SAAT) module based on the shadow mask. Under the guidance of the shadow mask, the SOAN module formulates the statistics from the non-shadow region and adaptively applies them to the shadow region for region-wise restoration. The SAAT module utilizes the shadow mask to precisely guide the restoration of each shadowed pixel by considering the highly relevant pixels from the shadow-free regions for global pixel-wise restoration. Extensive experiments on three benchmark datasets (ISTD, ISTD+, and SRD) show that our method achieves superior de-shadowing performance.
Carbon-Neutralized Task Scheduling for Green Computing Networks
Venue: arXiv cs.DC 2022
Year: 2022

Climate change due to increasing carbon emissions by human activities has been identified as one of the most critical threat to Earth. Carbon neutralization, as a key approach to reverse climate change, has triggered the development of new regulations to enforce the economic activities toward low carbon solutions. Computing networks that enable users to process computation-intensive tasks contribute huge amount of carbon emissions due to rising energy consumption. To analyze the achievable reduction of carbon emissions by a scheduling policy, we first propose a novel virtual queueing network model that captures communication and computing procedures in networks. To adapt to highly variable and unpredictable nature of renewable energy utilized by computing networks (i.e., carbon intensity of grid varies by time and location), we propose a novel carbon-intensity based scheduling policy that dynamically schedules computation tasks over clouds via the drift-plus-penalty methodology in Lyapunov optimization. Our numerical analysis using real-world data shows that the proposed policy achieves 54% reduction on the cumulative carbon emissions for AI model training tasks compared to the queue-length based policy.
Reference Resolution and Context Change in Multimodal Situated Dialogue for Exploring Data Visualizations
Venue: arXiv cs.CL 2022
Year: 2022

Reference resolution, which aims to identify entities being referred to by a speaker, is more complex in real world settings: new referents may be created by processes the agents engage in and/or be salient only because they belong to the shared physical setting. Our focus is on resolving references to visualizations on a large screen display in multimodal dialogue; crucially, reference resolution is directly involved in the process of creating new visualizations. We describe our annotations for user references to visualizations appearing on a large screen via language and hand gesture and also new entity establishment, which results from executing the user request to create a new visualization. We also describe our reference resolution pipeline which relies on an information-state architecture to maintain dialogue context. We report results on detecting and resolving references, effectiveness of contextual information on the model, and under-specified requests for creating visualizations. We also experiment with conventional CRF and deep learning / transformer models (BiLSTM-CRF and BERT-CRF) for tagging references in user utterance text. Our results show that transfer learning significantly boost performance of the deep learning methods, although CRF still out-performs them, suggesting that conventional methods may generalize better for low resource data.
Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition
Venue: arXiv cs.CL 2022
Year: 2022

As aspect-level sentiment labels are expensive and labor-intensive to acquire, zero-shot aspect-level sentiment classification is proposed to learn classifiers applicable to new domains without using any annotated aspect-level data. In contrast, document-level sentiment data with ratings are more easily accessible. In this work, we achieve zero-shot aspect-level sentiment classification by only using document-level reviews. Our key intuition is that the sentiment representation of a document is composed of the sentiment representations of all the aspects of that document. Based on this, we propose the AF-DSC method to explicitly model such sentiment composition in reviews. AF-DSC first learns sentiment representations for all potential aspects and then aggregates aspect-level sentiments into a document-level one to perform document-level sentiment classification. In this way, we obtain the aspect-level sentiment classifier as the by-product of the document-level sentiment classifier. Experimental results on aspect-level sentiment classification benchmarks demonstrate the effectiveness of explicit utilization of sentiment composition in document-level sentiment classification. Our model with only 30k training data outperforms previous work utilizing millions of data.
A Generic Type System for Higher-Order $Ψ$-calculi
Venue: arXiv cs.LO 2022
Year: 2022

The Higher-Order $Ψ$-calculus framework (HO$Ψ$) is a generalisation of many first- and higher-order extensions of the $π$-calculus. It was proposed by Parrow et al. who showed that higher-order calculi such as HO$π$ and CHOCS can be expressed as HO$Ψ$-calculi. In this paper we present a generic type system for HO$Ψ$-calculi which extends previous work by Hüttel on a generic type system for first-order $Ψ$-calculi. Our generic type system satisfies the usual property of subject reduction and can be instantiated to yield type systems for variants of HOπ, including the type system for termination due to Demangeon et al. Moreover, we derive a type system for the $ρ$-calculus, a reflective higher-order calculus proposed by Meredith and Radestock. This establishes that our generic type system is richer than its predecessor, as the $ρ$-calculus cannot be encoded in the $π$-calculus in a way that satisfies standard criteria of encodability.