OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Algorithms for Rotation Averaging Problems
Venue: arXiv cs.CV 2021
Year: 2021

The rotation averaging problem is a fundamental task in computer vision applications. It is generally very difficult to solve due to the nonconvex rotation constraints. While a sufficient optimality condition is available in the literature, there is a lack of \yhedit{a} fast convergent algorithm to achieve stationary points. In this paper, by exploring the problem structure, we first propose a block coordinate descent (BCD)-based rotation averaging algorithm with guaranteed convergence to stationary points. Afterwards, we further propose an alternative rotation averaging algorithm by applying successive upper-bound minimization (SUM) method. The SUM-based rotation averaging algorithm can be implemented in parallel and thus is more suitable for addressing large-scale rotation averaging problems. Numerical examples verify that the proposed rotation averaging algorithms have superior convergence performance as compared to the state-of-the-art algorithm. Moreover, by checking the sufficient optimality condition, we find from extensive numerical experiments that the proposed two algorithms can achieve globally optimal solutions.
Constructive and Toxic Speech Detection for Open-domain Social Media Comments in Vietnamese
Venue: arXiv cs.CL 2021
Year: 2021

The rise of social media has led to the increasing of comments on online forums. However, there still exists invalid comments which are not informative for users. Moreover, those comments are also quite toxic and harmful to people. In this paper, we create a dataset for constructive and toxic speech detection, named UIT-ViCTSD (Vietnamese Constructive and Toxic Speech Detection dataset) with 10,000 human-annotated comments. For these tasks, we propose a system for constructive and toxic speech detection with the state-of-the-art transfer learning model in Vietnamese NLP as PhoBERT. With this system, we obtain F1-scores of 78.59% and 59.40% for classifying constructive and toxic comments, respectively. Besides, we implement various baseline models as traditional Machine Learning and Deep Neural Network-Based models to evaluate the dataset. With the results, we can solve several tasks on the online discussions and develop the framework for identifying constructiveness and toxicity of Vietnamese social media comments automatically.
Blockchain Testing: Challenges, Techniques, and Research Directions
Venue: arXiv cs.SE 2021
Year: 2021

Specific testing solutions targeting blockchain-based software are gaining huge attention as blockchain technologies are being increasingly incorporated into enterprise systems. As blockchain-based software enters production systems, it is paramount to follow proper engineering practices, ensure the required level of testing, and assess the readiness of the developed system. The existing research aims at addressing the testing-related issues and challenges of engineering blockchain-based software by providing suitable techniques and tools. However, like any emerging discipline, the best practices and tools for testing blockchain-based systems are not yet sufficiently developed. In this paper, we provide a comprehensive survey on the testing of Blockchain-based Applications (BC-Apps). First, we provide a discussion on identified challenges that are associated with BCApp testing. Second, we use a layered approach to discuss the state-of-the-art testing efforts in the area of BC technologies. In particular, we present an overview of the existing testing tools and techniques that provide testing solutions either for different components at various layers of the BC-App stack or across the whole stack. Third, we provide a set of future research directions based on the identified BC testing challenges and gaps in the literature review of existing testing solutions for BC-Apps. Moreover, we reflect on the specificity of BC-based software development procedure, which makes some of the existing tools or techniques inadequate, and call for the definition of standardised testing procedures and techniques for BC-Apps. The aim of our study is to highlight the importance of BC-based software testing and to pave the way for disciplined, testable, and verifiable BC software development.
TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification
Venue: arXiv cs.CV 2021
Year: 2021

Hyperspectral Image(HSI) classification is the most vibrant field of research in the hyperspectral community, which aims to assign each pixel in the image to one certain category based on its spectral-spatial characteristics. Recently, some spectral-spatial-feature based DCNNs have been proposed and demonstrated remarkable classification performance. When facing a real HSI, however, these Networks have to deal with the pixels in the image one by one. The pixel-wise processing strategy is inefficient since there are numerous repeated calculations between adjacent pixels. In this paper, firstly, a brand new Network design mechanism TPPI (training based on pixel and prediction based on image) is proposed for HSI classification, which makes it possible to provide efficient and practical HSI classification with the restrictive conditions attached to the hyperspectral dataset. And then, according to the TPPI mechanism, TPPI-Net is derived based on the state of the art networks for HSI classification. Experimental results show that the proposed TPPI-Net can not only obtain high classification accuracy equivalent to the state of the art networks for HSI classification, but also greatly reduce the computational complexity of hyperspectral image prediction.
Towards Shaping the Software Lifecycle with Methods and Practices
Venue: arXiv cs.SE 2021
Year: 2021

As software projects are very diverse, each software development process must be adjusted to the needs of the project and the corresponding development team. Frequently, we find different methods and practices combined in a so-called hybrid development method. Research has shown that these hybrid methods evolve over time and are devised based on experience. However, when devising a hybrid method, the methods and practices used should cover the whole software project with its different phases including, among others, project management, requirements analysis, quality management, risk management, and implementation. In this paper, we analyze which methods and practices are used in which phase of a software project. Based on an initial survey with 27 practitioners, we provide a mapping of methods and practices to different project phases and vice versa. Despite the preliminary nature of our study and the small sample size, we observe three remarkable aspects: (1) there are discrepancies between the intended use of methods and practices according to literature and the real use in practice, (2) practices are used more consistently than methods, and (3) parts of the software lifecycle such as maintenance and evolution are hardly covered by widely distributed methods and practices. Consequently, when devising a development process, it is worth a thought whether all phases of the software lifecycle are addressed or not.
Exploring the Drivers and Barriers to Uptake for Digital Contact Tracing
Venue: arXiv cs.CY 2021
Year: 2021

Digital contact tracing has been deployed as a public health intervention to help suppress the spread of COVID-19 in many jurisdictions. However, most governments have struggled with low uptake and participation rates, limiting the effectiveness of the tool. This paper characterises a number of systems developed around the world, comparing the uptake rates for systems with different technology, data architectures, and mandates. The paper then introduces the MAST framework (motivation, access, skills, and trust), adapted from the digital inclusion literature, to explore the drivers and barriers that influence people's decisions to participate or not in digital contact tracing systems. Finally, the paper discusses some suggestions for policymakers on how to influence those drivers and barriers in order to improve uptake rates. Examples from existing digital contact tracing systems are presented throughout, although more empirical experimentation is required to support more concrete conclusions on what works.
Learning Multimodal Affinities for Textual Editing in Images
Venue: arXiv cs.CV 2021
Year: 2021

Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent. Unlike natural images that capture physical objects, document-images contain a significant amount of text with critical semantics and complicated layouts. In this work, we devise a generic unsupervised technique to learn multimodal affinities between textual entities in a document-image, considering their visual style, the content of their underlying text and their geometric context within the image. We then use these learned affinities to automatically cluster the textual entities in the image into different semantic groups. The core of our approach is a deep optimization scheme dedicated for an image provided by the user that detects and leverages reliable pairwise connections in the multimodal representation of the textual elements in order to properly learn the affinities. We show that our technique can operate on highly varying images spanning a wide range of documents and demonstrate its applicability for various editing operations manipulating the content, appearance and geometry of the image.
Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser
Venue: arXiv cs.CV 2021
Year: 2021

The success of deep denoisers on real-world color photographs usually relies on the modeling of sensor noise and in-camera signal processing (ISP) pipeline. Performance drop will inevitably happen when the sensor and ISP pipeline of test images are different from those for training the deep denoisers (i.e., noise discrepancy). In this paper, we present an unpaired learning scheme to adapt a color image denoiser for handling test images with noise discrepancy. We consider a practical training setting, i.e., a pre-trained denoiser, a set of test noisy images, and an unpaired set of clean images. To begin with, the pre-trained denoiser is used to generate the pseudo clean images for the test images. Pseudo-ISP is then suggested to jointly learn the pseudo ISP pipeline and signal-dependent rawRGB noise model using the pairs of test and pseudo clean images. We further apply the learned pseudo ISP and rawRGB noise model to clean color images to synthesize realistic noisy images for denoiser adaption. Pseudo-ISP is effective in synthesizing realistic noisy sRGB images, and improved denoising performance can be achieved by alternating between Pseudo-ISP training and denoiser adaption. Experiments show that our Pseudo-ISP not only can boost simple Gaussian blurring-based denoiser to achieve competitive performance against CBDNet, but also is effective in improving state-of-the-art deep denoisers, e.g., CBDNet and RIDNet.
Comparative Design-Choice Analysis of Color Refinement Algorithms Beyond the Worst Case
Venue: arXiv cs.DS 2021
Year: 2021

Color refinement is a crucial subroutine in symmetry detection in theory as well as practice. It has further applications in machine learning and in computational problems from linear algebra. While tight lower bounds for the worst case complexity are known [Berkholz, Bonsma, Grohe, ESA2013] no comparative analysis of design choices for color refinement algorithms is available. We devise two models within which we can compare color refinement algorithms using formal methods, an online model and an approximation model. We use these to show that no online algorithm is competitive beyond a logarithmic factor and no algorithm can approximate the optimal color refinement splitting scheme beyond a logarithmic factor. We also directly compare strategies used in practice showing that, on some graphs, queue based strategies outperform stack based ones by a logarithmic factor and vice versa. Similar results hold for strategies based on priority queues.
Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services
Venue: arXiv cs.NI 2021
Year: 2021

The combination of cloud computing capabilities at the network edge and artificial intelligence promise to turn future mobile networks into service- and radio-aware entities, able to address the requirements of upcoming latency-sensitive applications. In this context, a challenging research goal is to exploit edge intelligence to dynamically and optimally manage the Radio Access Network Slicing (that is a less mature and more complex technology than fifth-generation Network Slicing) and Radio Resource Management, which is a very complex task due to the mostly unpredictably nature of the wireless channel. This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management optimization supporting latency-sensitive applications. The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.
Preventing Technical Debt by Technical Debt Aware Project Management
Venue: arXiv cs.SE 2021
Year: 2021

Technical Debts (TD) are problems of the internal software quality. They are often contracted due to tight project deadlines, for example quick fixes and workarounds, and can make future changes more costly or impossible. TD prevention should be more important than TD repayment, because subsequent refactoring and reengineering is usually more expensive than building the right solution from the beginning. While there are numerous works on TD repayment, solutions for TD prevention are understudied. This paper evaluates a framework that focuses on both TD prevention and TD repayment. It was developed by and applied in an IT unit of a publishing house. The unique contribution of this framework is the integration of TD management into project management. The evaluation was carried out by a study based on ticket statistics and a structured survey with participants from the observed IT unit and a comparison unit. The evaluation shows that the adoption of this framework leads to a raised awareness for the contraction of TD. This results in benefits like more rational discussions and decisions, TD prevention and timelier repayment of TD tickets.
Optimal soil sampling design based on the maxvol algorithm
Venue: arXiv cs.CY 2021
Year: 2021

Spatial soil sampling is an integral part of a soil survey aimed at creating a soil map. We propose considering the soil sampling procedure as a task of optimal design. In practical terms, optimal experiments can reduce experimentation costs, as they allow the researcher to obtain one optimal set of points. We present a sampling design, based on the fundamental idea of selecting sample locations by performing an optimal design method called the maxvol algorithm. It is shown that the maxvol-base algorithm has a high potential for practical usage. Our method outperforms popular sampling methods in soil taxa prediction based on topographical features of the site and deals with massive agricultural datasets in a reasonable time.
Decomposing and Recomposing Event Structure
Venue: arXiv cs.CL 2021
Year: 2021

We present an event structure classification empirically derived from inferential properties annotated on sentence- and document-level Universal Decompositional Semantics (UDS) graphs. We induce this classification jointly with semantic role, entity, and event-event relation classifications using a document-level generative model structured by these graphs. To support this induction, we augment existing annotations found in the UDS1.0 dataset, which covers the entirety of the English Web Treebank, with an array of inferential properties capturing fine-grained aspects of the temporal and aspectual structure of events. The resulting dataset (available at decomp.io) is the largest annotation of event structure and (partial) event coreference to date.
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics. These merits, however, stand at great odds with the low availability of diverse image data which are needed to train these neural networks. We propose to overcome this bottleneck via a deep reinforcement learning (DRL) approach that is integrated with a style-transfer (ST) methodology, where the DRL generates the anatomical shapes and the ST synthesizes the texture detail. We show that our method bears high promise for generating novel and anatomically accurate high resolution CT images at large and diverse quantities. Our approach is specifically designed to work with even small image datasets which is desirable given the often low amount of image data many researchers have available to them.
GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction
Venue: arXiv cs.SI 2021
Year: 2021

Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem \textit{anchor link prediction} is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on learning matching function with explicit or implicit features on observed user data. However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice. In this paper, we explore local structure consistency and then construct a matching graph in order to circumvent matching collisions. Furthermore, we propose graph convolution networks with mini-batch strategy, efficiently solving anchor link prediction on matching graph. The experimental results on three real application scenarios show the great potentials of our proposed method in both prediction accuracy and efficiency. In addition, the visualization of learned embeddings provides us a qualitative way to understand the inference of anchor links on the matching graph.
Boosting Adversarial Transferability through Enhanced Momentum
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack methods have achieved great white-box attack performance, but exhibit low transferability when attacking other models. Various momentum iterative gradient-based methods are shown to be effective to improve the adversarial transferability. In what follows, we propose an enhanced momentum iterative gradient-based method to further enhance the adversarial transferability. Specifically, instead of only accumulating the gradient during the iterative process, we additionally accumulate the average gradient of the data points sampled in the gradient direction of the previous iteration so as to stabilize the update direction and escape from poor local maxima. Extensive experiments on the standard ImageNet dataset demonstrate that our method could improve the adversarial transferability of momentum-based methods by a large margin of 11.1% on average. Moreover, by incorporating with various input transformation methods, the adversarial transferability could be further improved significantly. We also attack several extra advanced defense models under the ensemble-model setting, and the enhancements are remarkable with at least 7.8% on average.
An Ontological Analysis of a Proposed Theory for Software Development
Venue: arXiv cs.SE 2021
Year: 2021

There is growing acknowledgement within the software engineering community that a theory of software development is needed to integrate the myriad methodologies that are currently popular, some of which are based on opposing perspectives. We have been developing such a theory for a number of years. In this paper, we overview our theory and report on a recent ontological analysis of the theory constructs. We suggest that, once fully developed, this theory, or one similar to it, may be applied to support situated software development, by providing an overarching model within which software initiatives might be categorised and understood. Such understanding would inevitably lead to greater predictability with respect to outcomes.
CE-FPN: Enhancing Channel Information for Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

Feature pyramid network (FPN) has been an effective framework to extract multi-scale features in object detection. However, current FPN-based methods mostly suffer from the intrinsic flaw of channel reduction, which brings about the loss of semantical information. And the miscellaneous fused feature maps may cause serious aliasing effects. In this paper, we present a novel channel enhancement feature pyramid network (CE-FPN) with three simple yet effective modules to alleviate these problems. Specifically, inspired by sub-pixel convolution, we propose a sub-pixel skip fusion method to perform both channel enhancement and upsampling. Instead of the original 1x1 convolution and linear upsampling, it mitigates the information loss due to channel reduction. Then we propose a sub-pixel context enhancement module for extracting more feature representations, which is superior to other context methods due to the utilization of rich channel information by sub-pixel convolution. Furthermore, a channel attention guided module is introduced to optimize the final integrated features on each level, which alleviates the aliasing effect only with a few computational burdens. Our experiments show that CE-FPN achieves competitive performance compared to state-of-the-art FPN-based detectors on MS COCO benchmark.
Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear Manifold Clustering Algorithms
Venue: arXiv cs.LG 2021
Year: 2021

Subspace clustering is an important unsupervised clustering approach. It is based on the assumption that the high-dimensional data points are approximately distributed around several low-dimensional linear subspaces. The majority of the prominent subspace clustering algorithms rely on the representation of the data points as linear combinations of other data points, which is known as a self-expressive representation. To overcome the restrictive linearity assumption, numerous nonlinear approaches were proposed to extend successful subspace clustering approaches to data on a union of nonlinear manifolds. In this comparative study, we provide a comprehensive overview of nonlinear subspace clustering approaches proposed in the last decade. We introduce a new taxonomy to classify the state-of-the-art approaches into three categories, namely locality preserving, kernel based, and neural network based. The major representative algorithms within each category are extensively compared on carefully designed synthetic and real-world data sets. The detailed analysis of these approaches unfolds potential research directions and unsolved challenges in this field.