OpenRaiser/Intern-Atlas huggingface dataset:
Tai-e: A Static Analysis Framework for Java by Harnessing the Best Designs of Classics
Venue: arXiv cs.PL 2022
Year: 2022

Static analysis is a mature field with applications to bug detection, security analysis, and code optimization, etc. To facilitate these applications, static analysis frameworks play an essential role by providing a series of fundamental services such as program abstraction, control flow graph construction, and points-to/alias information computation, etc. However, despite impressive progress of static analysis, and this field has seen several popular frameworks in the last decades, it is still not clear how a static analysis framework should be designed in a way that analysis developers could benefit more: for example, what a good IR (for analysis) ought to look like? What functionalities should the module of fundamental analyses provide to ease client analyses? How to develop and integrate new analysis conveniently? How to manage multiple analyses? To answer these questions, in this work, we discuss the design trade-offs for the crucial components of a static analysis framework, and argue for the most appropriate design by following the HBDC (Harnessing the Best Designs of Classics) principle: for each crucial component, we compare the design choices made for it (possibly) by different classic frameworks such as Soot, WALA, SpotBugs and Doop, and choose arguably the best one, but if none is good enough, we then propose a better design. These selected or newly proposed designs finally constitute Tai-e, a new static analysis framework for Java. Specifically, Tai-e is novel in the designs of several aspects like IR, pointer analysis and development of new analyses, etc., leading to an easy-to-learn, easy-to-use and efficient system. To our knowledge, this is the first work that systematically explores the designs and implementations of various static analysis frameworks, and we believe it provides useful materials and viewpoints for building better static analysis infrastructures.
Age of View: A New Metric for Evaluating Heterogeneous Information Fusion in Vehicular Cyber-Physical Systems
Venue: arXiv cs.NI 2022
Year: 2022

Heterogeneous information fusion is one of the most critical issues for realizing vehicular cyber-physical systems (VCPSs). This work makes the first attempt at quantitatively measuring the quality of heterogeneous information fusion in VCPS by designing a new metric called Age of View (AoV). Specifically, we derive a sensing model based on a multi-class M/G/1 priority queue and a transmission model based on Shannon theory. On this basis, we formally define AoV by modeling the timeliness, completeness, and consistency of the heterogeneous information fusion in VCPS and formulate the problem aiming to minimize the system's average AoV. Further, we propose a new solution called Multi-agent Difference-Reward-based deep reinforcement learning with a Greedy Bandwidth Allocation (MDR-GBA) to solve the problem. In particular, each vehicle acts as an independent agent and decides the sensing frequencies and uploading priorities of heterogeneous information. Meanwhile, the roadside unit (RSU) decides the Vehicle-to-Infrastructure (V2I) bandwidth allocation for each vehicle based on a greedy scheme. Finally, we build the simulation model and compare the performance of the proposed solution with state-of-the-art algorithms. The experimental results conclusively demonstrate the significance of the new metric and the superiority of the proposed solution.
One for All: One-stage Referring Expression Comprehension with Dynamic Reasoning
Venue: arXiv cs.CV 2022
Year: 2022

Referring Expression Comprehension (REC) is one of the most important tasks in visual reasoning that requires a model to detect the target object referred by a natural language expression. Among the proposed pipelines, the one-stage Referring Expression Comprehension (OSREC) has become the dominant trend since it merges the region proposal and selection stages. Many state-of-the-art OSREC models adopt a multi-hop reasoning strategy because a sequence of objects is frequently mentioned in a single expression which needs multi-hop reasoning to analyze the semantic relation. However, one unsolved issue of these models is that the number of reasoning steps needs to be pre-defined and fixed before inference, ignoring the varying complexity of expressions. In this paper, we propose a Dynamic Multi-step Reasoning Network, which allows the reasoning steps to be dynamically adjusted based on the reasoning state and expression complexity. Specifically, we adopt a Transformer module to memorize & process the reasoning state and a Reinforcement Learning strategy to dynamically infer the reasoning steps. The work achieves the state-of-the-art performance or significant improvements on several REC datasets, ranging from RefCOCO (+, g) with short expressions, to Ref-Reasoning, a dataset with long and complex compositional expressions.
Nested Cover-Free Families for Unbounded Fault-Tolerant Aggregate Signatures
Venue: arXiv cs.CR 2022
Year: 2022

Aggregate signatures are used to create one short proof of authenticity and integrity from a set of digital signatures. However, one invalid signature in the set invalidates the entire aggregate, giving no information on which signatures are valid. Hartung et al. (2016) propose a fault-tolerant aggregate signature scheme based on combinatorial group testing. Given a bound $d$ on the number of invalid signatures among $n$ signatures to be aggregated, this scheme uses $d$-cover-free families to determine which signatures are invalid. These combinatorial structures guarantee a moderate increase on the size of the aggregate signature that can reach the best possible compression ratio of $O(\frac{n}{\log n})$, for fixed $d$, coming from an information theoretical bound. The case where the total number of signatures grows dynamically (unbounded scheme) was not satisfactorily solved in their original paper, since explicit constructions had constant compression ratios. In the present paper, we propose efficient solutions for the unbounded scheme, relying on sequences of $d$-cover-free families that we call {\em nested families}. Some of our constructions yield high compression ratio close to \rmv{the information theoretical bound}\todo{the best known upper bound}. We also propose the use of $(d,λ)$-cover-free families to support the loss of up to $λ-1$ parts of the aggregate.
PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Introduction: Blood vessels can be non-invasively visualized from a digital fundus image (DFI). Several studies have shown an association between cardiovascular risk and vascular features obtained from DFI. Recent advances in computer vision and image segmentation enable automatising DFI blood vessel segmentation. There is a need for a resource that can automatically compute digital vasculature biomarkers (VBM) from these segmented DFI. Methods: In this paper, we introduce a Python Vasculature BioMarker toolbox, denoted PVBM. A total of 11 VBMs were implemented. In particular, we introduce new algorithmic methods to estimate tortuosity and branching angles. Using PVBM, and as a proof of usability, we analyze geometric vascular differences between glaucomatous patients and healthy controls. Results: We built a fully automated vasculature biomarker toolbox based on DFI segmentations and provided a proof of usability to characterize the vascular changes in glaucoma. For arterioles and venules, all biomarkers were significant and lower in glaucoma patients compared to healthy controls except for tortuosity, venular singularity length and venular branching angles. Conclusion: We have automated the computation of 11 VBMs from retinal blood vessel segmentation. The PVBM toolbox is made open source under a GNU GPL 3 license and is available on physiozoo.com (following publication).
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Supervised deep learning methods for semantic medical image segmentation are getting increasingly popular in the past few years.However, in resource constrained settings, getting large number of annotated images is very difficult as it mostly requires experts, is expensive and time-consuming.Semi-supervised segmentation can be an attractive solution where a very few labeled images are used along with a large number of unlabeled ones. While the gap between supervised and semi-supervised methods have been dramatically reduced for classification problems in the past couple of years, there still remains a larger gap in segmentation methods. In this work, we adapt a state-of-the-art semi-supervised classification method FixMatch to semantic segmentation task, introducing FixMatchSeg. FixMatchSeg is evaluated in four different publicly available datasets of different anatomy and different modality: cardiac ultrasound, chest X-ray, retinal fundus image, and skin images. When there are few labels, we show that FixMatchSeg performs on par with strong supervised baselines.
COCOA: Cross Modality Contrastive Learning for Sensor Data
Venue: arXiv cs.CV 2022
Year: 2022

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labelled data and has reached comparable or even state-of-the-art results in comparison to supervised counterparts. Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data. CL methods have been mostly developed for applications in computer vision and natural language processing where only a single sensor modality is used. A majority of pervasive computing applications, however, exploit data from a range of different sensor modalities. While existing CL methods are limited to learning from one or two data sources, we propose COCOA (Cross mOdality COntrastive leArning), a self-supervised model that employs a novel objective function to learn quality representations from multisensor data by computing the cross-correlation between different data modalities and minimizing the similarity between irrelevant instances. We evaluate the effectiveness of COCOA against eight recently introduced state-of-the-art self-supervised models, and two supervised baselines across five public datasets. We show that COCOA achieves superior classification performance to all other approaches. Also, COCOA is far more label-efficient than the other baselines including the fully supervised model using only one-tenth of available labelled data.
Building an Efficiency Pipeline: Commutativity and Cumulativeness of Efficiency Operators for Transformers
Venue: arXiv cs.CL 2022
Year: 2022

There exists a wide variety of efficiency methods for natural language processing (NLP) tasks, such as pruning, distillation, dynamic inference, quantization, etc. We can consider an efficiency method as an operator applied on a model. Naturally, we may construct a pipeline of multiple efficiency methods, i.e., to apply multiple operators on the model sequentially. In this paper, we study the plausibility of this idea, and more importantly, the commutativity and cumulativeness of efficiency operators. We make two interesting observations: (1) Efficiency operators are commutative -- the order of efficiency methods within the pipeline has little impact on the final results; (2) Efficiency operators are also cumulative -- the final results of combining several efficiency methods can be estimated by combining the results of individual methods. These observations deepen our understanding of efficiency operators and provide useful guidelines for their real-world applications.
Safer Traffic Recovery from the Pandemic in London -- Spatiotemporal Data Mining of Car Crashes
Venue: arXiv cs.SI 2022
Year: 2022

In the aim to support London's safer recovery from the pandemic by improving road safety intelligently, this study investigated the spatiotemporal patterns of age-involved car crashes and affecting factors, upon answering two main research questions: (1)"What are the spatial and temporal patterns of car crashes as well as their changes in two typical years, 2019 and 2020, in London, and how the influential factors work?"; (2)"What are the spatiotemporal patterns of casualty by age groups, and how people's daily activities affect the patterns pre- and para- the pandemic"? Three approaches, i.e., spatial analysis (network Kernel Density Estimation, NetKDE), factor analysis, and spatiotemporal data mining (tensor decomposition), had been implemented to identify the temporal patterns of car crashes on weekly and daily basis respectively, detect the crashes' hot spots, and to gain better understanding the effect from citizens' daily activity on crashes' patterns pre- and para- the pandemic. It had been found from the study that car crashes mainly clustered in the central part of London, especially busier areas around denser hubs of point-of-interest (POIs); the POIs, as a reflector for citizens' daily activities and travel behaviours, can be of help to gain a better understanding of the crashes' patterns, upon further assessment on interactions through the geographical detector; the crashes' casualty patterns varied by age group, with distinctive relationships between POIs and crashes' pattern for corresponding age group categorised. In all, the paper provided an in-depth exploratory analysis of car crashes and their casualty patterns in London to facilitate deployment policies towards post-pandemic safer recovery upon COVID-19.
AvatarGen: a 3D Generative Model for Animatable Human Avatars
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not generative and thus unable to synthesize high-quality virtual humans and animate them. In this work, we propose AvatarGen, the first method that enables not only non-rigid human generation with diverse appearance but also full control over poses and viewpoints, while only requiring 2D images for training. Specifically, it extends the recent 3D GANs to clothed human generation by utilizing a coarse human body model as a proxy to warp the observation space into a standard avatar under a canonical space. To model non-rigid dynamics, it introduces a deformation network to learn pose-dependent deformations in the canonical space. To improve geometry quality of the generated human avatars, it leverages signed distance field as geometric representation, which allows more direct regularization from the body model on the geometry learning. Benefiting from these designs, our method can generate animatable human avatars with high-quality appearance and geometry modeling, significantly outperforming previous 3D GANs. Furthermore, it is competent for many applications, e.g., single-view reconstruction, reanimation, and text-guided synthesis. Code and pre-trained model will be available.
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization
Venue: arXiv cs.LG 2022
Year: 2022

Transformers have achieved remarkable success in sequence modeling and beyond but suffer from quadratic computational and memory complexities with respect to the length of the input sequence. Leveraging techniques include sparse and linear attention and hashing tricks; efficient transformers have been proposed to reduce the quadratic complexity of transformers but significantly degrade the accuracy. In response, we first interpret the linear attention and residual connections in computing the attention map as gradient descent steps. We then introduce momentum into these components and propose the \emph{momentum transformer}, which utilizes momentum to improve the accuracy of linear transformers while maintaining linear memory and computational complexities. Furthermore, we develop an adaptive strategy to compute the momentum value for our model based on the optimal momentum for quadratic optimization. This adaptive momentum eliminates the need to search for the optimal momentum value and further enhances the performance of the momentum transformer. A range of experiments on both autoregressive and non-autoregressive tasks, including image generation and machine translation, demonstrate that the momentum transformer outperforms popular linear transformers in training efficiency and accuracy.
HBMax: Optimizing Memory Efficiency for Parallel Influence Maximization on Multicore Architectures
Venue: arXiv cs.DC 2022
Year: 2022

Influence maximization aims to select k most-influential vertices or seeds in a network, where influence is defined by a given diffusion process. Although computing optimal seed set is NP-Hard, efficient approximation algorithms exist. However, even state-of-the-art parallel implementations are limited by a sampling step that incurs large memory footprints. This in turn limits the problem size reach and approximation quality. In this work, we study the memory footprint of the sampling process collecting reverse reachability information in the IMM (Influence Maximization via Martingales) algorithm over large real-world social networks. We present a memory-efficient optimization approach (called HBMax) based on Ripples, a state-of-the-art multi-threaded parallel influence maximization solution. Our approach, HBMax, uses a portion of the reverse reachable (RR) sets collected by the algorithm to learn the characteristics of the graph. Then, it compresses the intermediate reverse reachability information with Huffman coding or bitmap coding, and queries on the partially decoded data, or directly on the compressed data to preserve the memory savings obtained through compression. Considering a NUMA architecture, we scale up our solution on 64 CPU cores and reduce the memory footprint by up to 82.1% with average 6.3% speedup (encoding overhead is offset by performance gain from memory reduction) without loss of accuracy. For the largest tested graph Twitter7 (with 1.4 billion edges), HBMax achieves 5.9X compression ratio and 2.2X speedup.
RISeer: Inspecting the Status and Dynamics of Regional Industrial Structure via Visual Analytics
Venue: arXiv cs.HC 2022
Year: 2022

Restructuring the regional industrial structure (RIS) has the potential to halt economic recession and achieve revitalization. Understanding the current status and dynamics of RIS will greatly assist in studying and evaluating the current industrial structure. Previous studies have focused on qualitative and quantitative research to rationalize RIS from a macroscopic perspective. Although recent studies have traced information at the industrial enterprise level to complement existing research from a micro perspective, the ambiguity of the underlying variables contributing to the industrial sector and its composition, the dynamic nature, and the large number of multivariant features of RIS records have obscured a deep and fine-grained understanding of RIS. To this end, we propose an interactive visualization system, RISeer, which is based on interpretable machine learning models and enhanced visualizations designed to identify the evolutionary patterns of the RIS and facilitate inter-regional inspection and comparison. Two case studies confirm the effectiveness of our approach, and feedback from experts indicates that RISeer helps them to gain a fine-grained understanding of the dynamics and evolution of the RIS.
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
Venue: arXiv cs.LG 2022
Year: 2022

We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real line. The model interprets the distances of the input vector to prototypes as pieces of evidence represented by Gaussian random fuzzy numbers (GRFN's) and combined by the generalized product intersection rule, an operator that extends Dempster's rule to random fuzzy sets. The network output is a GRFN that can be summarized by three numbers characterizing the most plausible predicted value, variability around this value, and epistemic uncertainty. Experiments with real datasets demonstrate the very good performance of the method as compared to state-of-the-art evidential and statistical learning algorithms. \keywords{Evidence theory, Dempster-Shafer theory, belief functions, machine learning, random fuzzy sets.
Similarity-based web element localization for robust test automation
Venue: arXiv cs.SE 2022
Year: 2022

Non-robust (fragile) test execution is a commonly reported challenge in GUI-based test automation, despite much research and several proposed solutions. A test script needs to be resilient to (minor) changes in the tested application but, at the same time, fail when detecting potential issues that require investigation. Test script fragility is a multi-faceted problem, but one crucial challenge is reliably identifying and locating the correct target web elements when the website evolves between releases or otherwise fails and reports an issue. This paper proposes and evaluates a novel approach called similarity-based web element localization (Similo), which leverages information from multiple web element locator parameters to identify a target element using a weighted similarity score. The experimental study compares Similo to a baseline approach for web element localization. To get an extensive empirical basis, we target 40 of the most popular websites on the Internet in our evaluation. Robustness is considered by counting the number of web elements found in a recent website version compared to how many of these existed in an older version. Results of the experiment show that Similo outperforms the baseline representing the current state-of-the-art; it failed to locate the correct target web element in 72 out of 598 considered cases compared to 146 failed cases for the baseline approach. This study presents evidence that quantifying the similarity between multiple attributes of web elements when trying to locate them, as in our proposed Similo approach, is beneficial. With acceptable efficiency, Similo gives significantly higher effectiveness (i.e., robustness) than the baseline web element localization approach.
e-Genia3 An AgentSpeak extension for empathic agents
Venue: arXiv cs.MA 2022
Year: 2022

In this paper, we present e-Genia3 an extension of AgentSpeak to provide support to the development of empathic agents. The new extension modifies the agent's reasoning processes to select plans according to the analyzed event and the affective state and personality of the agent. In addition, our proposal allows a software agent to simulate the distinction between self and other agents through two different event appraisal processes: the empathic appraisal process, for eliciting emotions as a response to other agents emotions, and the regular affective appraisal process for other non-empathic affective events. The empathic regulation process adapts the elicited empathic emotion based on intrapersonal factors (e.g., the agent's personality and affective memory) and interpersonal characteristics of the agent (e.g., the affective link between the agents). The use of a memory of past events and their corresponding elicited emotions allows the maintaining of an affective link to support long-term empathic interaction between agents.