Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of Test Coverage Criteria for Test Case Generation from Finite State Machines Modelled as Directed Graphs

Test Coverage criteria are an essential concept for test engineers when generating the test cases from a System Under Test model. They are routinely used in test case generation for user interfaces, middleware, and back-end system parts for software, electronics, or Internet of Things (IoT) systems. Test Coverage criteria define the number of actions or combinations by which a system is tested, informally determining a potential "strength" of a test set. As no previous study summarized all commonly used test coverage criteria for Finite State Machines and comprehensively discussed them regarding their subsumption, equivalence, or non-comparability, this paper provides this overview. In this study, 14 most common test coverage criteria and seven of their synonyms for Finite State Machines defined via a directed graph are summarized and compared. The results give researchers and industry testing engineers a helpful overview when setting a software-based or IoT system test strategy.
Trackers Bounce Back: Measuring Evasion of Partitioned Storage in the Wild

This work presents a systematic study of navigational tracking, the latest development in the cat-and-mouse game between browsers and online trackers. Navigational tracking allows trackers to aggregate users’ activities and behaviors across sites by modifying their navigation requests. This technique is particularly important because it circumvents the increasing efforts by browsers to partition or block third-party storage, which was previously necessary for most cross-website tracking. While previous work has studied specific navigational tracking techniques (i.e. “bounce tracking”), our work is the first effort to systematically study and measure the entire category of navigational tracking techniques. We describe and measure the frequency of two different navigational tracking techniques on the Web, and find that navigational tracking is present on slightly more than ten percent of all navigations that we made. Our contributions include identifying 214 domains belonging to at least 104 organizations tracking users across sites through link decoration techniques using direct or indirect navigation flows. We identify a further 23 domains belonging to at least 16 organizations tracking users through bounce tracking (i.e. bouncing users through unrelated third parties to generate user profiles). We also improve on prior techniques for differenting user identifiers from non-sensitive information, which is necessary to detect one class of navigational tracking. We discuss how our findings can used to protect users from navigational tracking, and commit to releasing both our complete dataset and our measurement pipeline. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Conference’17, July 2017, Washington, DC, USA © 2022 Association for Computing Machinery. ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn ACM Reference Format: Audrey Randall, Peter Snyder, Alisha Ukani, Alex Snoeren, Geoff Voelker, Stefan Savage, andAaron Schulman. 2022. Trackers Bounce Back:Measuring Evasion of Partitioned Storage in theWild. In Proceedings of ACMConference (Conference’17). ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks

Machine learning models often fail to generalize well under distributional shifts. Understanding and overcoming these failures have led to a research field of Out-of-Distribution (OOD) generalization. Despite being extensively studied for static computer vision tasks, OOD generalization has been underexplored for time series tasks. To shine light on this gap, we present WOODS: eight challenging open-source time series benchmarks covering a diverse range of data modalities, such as videos, brain recordings, and sensor signals. We revise the existing OOD generalization algorithms for time series tasks and evaluate them using our systematic framework. Our experiments show a large room for improvement for empirical risk minimization and OOD generalization algorithms on our datasets, thus underscoring the new challenges posed by time series tasks. Code and documentation are available at https://woods-benchmarks.github. io .
GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface Registration Using Gaussian Process Regression

In this paper, we unify popular non-rigid registration methods for point sets and surfaces under our general framework, GiNGR. GiNGR builds upon Gaussian Process Morphable Models (GPMM) and hence separates modeling the deformation prior from model adaptation for registration. In addition, it provides explainable hyperparameters, multi-resolution registration, trivial inclusion of expert annotation, and the ability to use and combine analytical and statistical deformation priors. But more importantly, the reformulation allows for a direct comparison of registration methods. Instead of using a general solver in the optimization step, we show how Gaussian process regression (GPR) iteratively can warp a reference onto a target, leading to smooth deformations following the prior for any dense, sparse, or partial estimated correspondences in a principled way. We show how the popular CPD and ICP algorithms can be directly explained with GiNGR. Furthermore, we show how existing algorithms in the GiNGR framework can perform probabilistic registration to obtain a distribution of different registrations instead of a single best registration. This can be used to analyze the uncertainty e.g. when registering partial observations. GiNGR is publicly available and fully modular to allow for domain-specific prior construction.
Adversarial Attacks on Deep Learning-based Video Compression and Classification Systems

Video compression plays a crucial role in enabling video streaming and classiﬁcation systems and maximizing the end-user quality of experience (QoE) at a given bandwidth budget. In this paper, we conduct the ﬁrst systematic study for adversarial attacks on deep learning based video compression and downstream classiﬁcation systems. We propose an adaptive adversarial attack that can manipulate the Rate-Distortion ( R - D ) relationship of a video compression model to achieve two adversarial goals: (1) increasing the network bandwidth or (2) degrading the video quality for end-users. We further devise novel objectives for targeted and untargeted attacks to a downstream video classiﬁcation service. Finally, we de-sign an input-invariant perturbation that universally disrupts video compression and classiﬁcation systems in real time. Unlike previously proposed attacks on video classiﬁcation, our adversarial perturbations are the ﬁrst to withstand compression. We empirically show the resilience of our attacks against various defenses, i.e., adversarial training, video de-noising, and JPEG compression. Our extensive experimental results on various video datasets demonstrate the effectiveness of our attacks. Our video quality and bandwidth attacks deteriorate peak signal-to-noise ratio by up to 5 . 4dB and the bit-rate by up to ∼ 2 . 4 × on the standard video compression datasets while achieving over 90% attack success rate on a downstream classiﬁer.
Prioritized Variable-length Test Cases Generation for Finite State Machines

Model-based Testing (MBT) is an effective approach for testing when parts of a system-under-test have the characteristics of a finite state machine (FSM). Despite various strategies in the literature on this topic, little work exists to handle special testing situations. More specifically, when concurrently: (1) the test paths can start and end only in defined states of the FSM, (2) a prioritization mechanism that requires only defined states and transitions of the FSM to be visited by test cases is required, and (3) the test paths must be in a given length range, not necessarily of explicit uniform length. This paper presents a test generation strategy that satisfies all these requirements. A concurrent combination of these requirements is highly practical for real industrial testing. Six variants of possible algorithms to implement this strategy are described. Using a mixture of 180 problem instances from real automotive and defense projects and artificially generated FSMs, all variants are compared with a baseline strategy based on an established N-switch coverage concept modification. Various properties of the generated test paths and their potential to activate fictional defects defined in FSMs are evaluated. The presented strategy outperforms the baseline in most problem configurations. Out of the six analyzed variants, three give the best results even though a universal best performer is hard to identify. Depending on the application of the FSM, the strategy and evaluation presented in this paper are applicable both in testing functional and non-functional software requirements.
Why we need biased AI - How including cognitive and ethical machine biases can enhance AI systems

This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards. First, in order to foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the structurewise implementation of human cognitive biases in learning algorithms. Secondly, we argue that in order to achieve ethical machine behavior, filter mechanisms have to be applied for selecting biased training stimuli that represent social or behavioral traits that are ethically desirable. We use insights from cognitive science as well as ethics and apply them to the AI field, combining theoretical considerations with seven case studies depicting tangible bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly pursue the idea of a re-evaluation of the ethical significance of machine biases, as well as putting the idea forth to implement cognitive biases into machines.
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information

Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i.e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We propose a novel approach to formulate, extract, encode and inject hierarchical structure information explicitly into an extractive summarization model based on a pre-trained, encoder-only Transformer language model (HiStruct+ model), which improves SOTA ROUGEs for extractive summarization on PubMed and arXiv substantially. Using various experimental settings on three datasets (i.e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected. It is also observed that the more conspicuous hierarchical structure the dataset has, the larger improvements our method gains. The ablation study demonstrates that the hierarchical position information is the main contributor to our model’s SOTA performance.
Keeping Up with “The Joneses”: Reference-Dependent Choice with Social Comparisons

Keeping up with “The Joneses” matters. This paper examines a model of reference-dependent choice where reference points are determined by social comparisons. An increase in the strength of social comparisons, even by only a few agents, increases consumption and decreases welfare for everyone. Strikingly, a higher marginal cost of consumption can increase welfare. In a labor market, social comparisons with coworkers create a big fish in a small pond effect, inducing incomplete labor market sorting. Further, it is the skilled workers with the weakest social networks who are induced to give up income to become the big fish. (JEL D85, J22, J24, J61)
A Walking Space Robot for On-Orbit Satellite Servicing: The ReCoBot

A key factor in the economic efficiency of satellites is their availability in orbit. Replacing standardized building blocks, such as empty fuel tanks or outdated electronic modules, could greatly extend the satellites’ lifetime. This, however, requires flexible robots that can locomote on the surface of these satellites for optimal accessibility and manipulation. This paper introduces ReCoBot, a 7-axis walking space manipulator for locomotion and manipulation. The robot can connect to compatible structures with its symmetric ends and provides interfaces for manual teleoperation and motion planning with a constantly changing base and tip. We build on open-source robotics software and easily available components to evaluate the overall concept with an early stage demonstrator. The proposed manipulator has a length of 1.20 m and a weight of 10.4 kg and successfully locomotes over a satellite mockup in our lab environment.
Inferring Articulated Rigid Body Dynamics from RGBD Video

Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain. Despite recent progress, significant human effort is needed to configure simulators to accurately reproduce real-world behavior. We introduce a pipeline that combines inverse rendering with differentiable simulation to create digital twins of real-world articulated mechanisms from depth or RGB videos. Our approach automatically discovers joint types and estimates their kinematic parameters, while the dynamic properties of the overall mechanism are tuned to attain physically accurate simulations. Control policies optimized in our derived simulation transfer successfully back to the original system, as we demonstrate on a simulated system. Further, our approach accurately reconstructs the kinematic tree of an articulated mechanism being manipulated by a robot, and highly nonlinear dynamics of a real-world coupled pendulum mechanism. Website: https://eric-heiden.github. io/video2sim
Has Your FaaS Application Been Decommissioned Yet? - A Case Study on the Idle Timeout in Function as a Service Infrastructure

Function as a Service (FaaS) is a new cloud technology with automated resource management. Different from traditional cloud computing, each FaaS cloud function can only run a fixed period of time before being decommissioned. Furthermore, FaaS cloud providers often update their platforms (e.g., idle timeout). These changes and their associated impact are not transparent and could potentially impact the execution of the cloud functions. Hence, in this paper, we develop a methodology to characterize the cloud function idle timeout which is the duration a FaaS cloud provider keeps a cloud function instance alive without serving active traffic. Our study was conducted on three popular FaaS platforms, namely AWS Lambda, IBM and Azure Cloud Function. Moreover, we also report how long a cloud function instance can be kept alive when a user regularly polls the instance. Experimental results show that the idle timeout period has evolved from 01/2020 till 01/2022.
DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes

In classical cosmological analysis of large scale structure surveys with 2-pt functions, the parameter measurement precision is limited by several key degeneracies within the cosmology and astrophysics sectors. For cosmic shear, clustering amplitude $\sigma_8$ and matter density $\Omega_m$ roughly follow the $S_8=\sigma_8(\Omega_m/0.3)^{0.5}$ relation. In turn, $S_8$ is highly correlated with the intrinsic galaxy alignment amplitude $A_{\rm{IA}}$. For galaxy clustering, the bias $b_g$ is degenerate with both $\sigma_8$ and $\Omega_m$, as well as the stochasticity $r_g$. Moreover, the redshift evolution of IA and bias can cause further parameter confusion. A tomographic 2-pt probe combination can partially lift these degeneracies. In this work we demonstrate that a deep learning analysis of combined probes of weak gravitational lensing and galaxy clustering, which we call DeepLSS, can effectively break these degeneracies and yield significantly more precise constraints on $\sigma_8$, $\Omega_m$, $A_{\rm{IA}}$, $b_g$, $r_g$, and IA redshift evolution parameter $\eta_{\rm{IA}}$. The most significant gains are in the IA sector: the precision of $A_{\rm{IA}}$ is increased by approximately 8x and is almost perfectly decorrelated from $S_8$. Galaxy bias $b_g$ is improved by 1.5x, stochasticity $r_g$ by 3x, and the redshift evolution $\eta_{\rm{IA}}$ and $\eta_b$ by 1.6x. Breaking these degeneracies leads to a significant gain in constraining power for $\sigma_8$ and $\Omega_m$, with the figure of merit improved by 15x. We give an intuitive explanation for the origin of this information gain using sensitivity maps. These results indicate that the fully numerical, map-based forward modeling approach to cosmological inference with machine learning may play an important role in upcoming LSS surveys. We discuss perspectives and challenges in its practical deployment for a full survey analysis.
Multi-view Multi-behavior Contrastive Learning in Recommendation

Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user. In this work, we propose a novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR) framework, including three new CL tasks to solve the above challenges, respectively. The multi-behavior CL aims to make different user single-behavior representations of the same user in each view to be similar. The multi-view CL attempts to bridge the gap between a user's sequence-view and graph-view representations. The behavior distinction CL focuses on modeling fine-grained differences of different behaviors. In experiments, we conduct extensive evaluations and ablation tests to verify the effectiveness of MMCLR and various CL tasks on two real-world datasets, achieving SOTA performance over existing baselines. Our code will be available on \url{https://github.com/wyqing20/MMCLR}
End-to-End Video Text Spotting with Transformer

Recent video text spotting methods usually require the three-staged pipeline, i.e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results. These methods typically follow the tracking-by-match paradigm and develop sophisticated pipelines. In this paper, rooted in Transformer sequence modeling, we propose a simple, but effective end-to-end video text DEtection, Tracking, and Recognition framework (TransDETR). TransDETR mainly includes two advantages: 1) Different from the explicit match paradigm in the adjacent frame, TransDETR tracks and recognizes each text implicitly by the different query termed text query over long-range temporal sequence (more than 7 frames). 2) TransDETR is the first end-to-end trainable video text spotting framework, which simultaneously addresses the three sub-tasks (e.g., text detection, tracking, recognition). Extensive experiments in four video text datasets (i.e.,ICDAR2013 Video, ICDAR2015 Video, Minetto, and YouTube Video Text) are conducted to demonstrate that TransDETR achieves state-of-the-art performance with up to around 8.0% improvements on video text spotting tasks. The code of TransDETR can be found at https://github.com/weijiawu/TransDETR.
Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning

In this paper we address the challenging problem of domain adaptation in LiDAR semantic segmentation. We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples. We propose a domain adaption framework that mitigates the issue of domain shift and produces appealing performance on the target domain. To this end, we develop a GAN-based image-to-image translation engine that has generators with alternating connections, and couple it with a state-of-the-art LiDAR semantic segmentation network. Our framework is hybrid in nature in the sense that our model learning is composed of self-supervision, semi-supervision and unsupervised learning. Extensive experiments on benchmark LiDAR semantic segmentation data sets demonstrate that our method achieves superior performance in comparison to strong baselines and prior arts.
Preferential Trade Agreements as Dynamic Farsighted Networks

In the presence of multilateral negotiations, are preferential trade agreements (PTAs) necessary for, or will they necessarily lead away from, global free trade? This question is explored using a novel dynamic network theoretic model where countries are farsighted and asymmetric in terms of market size. When two countries have a PTA, one member's formation of an additional PTA may create incentives for its original partner to form a PTA with its new partner. When considering forming an additional PTA, the current member therefore recognizes the potential for erosion of its preferential access in both partner markets. This fear of preference erosion undermines its willingness to form the additional PTA. Global free trade is attained when countries fear of preference erosion is su ciently small. Even when countries are symmetric in terms of market size, the discount factor determines the importance of preference erosion. Since greater asymmetry increases rents protected by members, preference erosion becomes more costly and the scope for global free trade falls. However, under high enough asymmetry, some future PTAs do not form. This eliminates the fear of preference erosion for certain countries and dramatically increases the extent to which PTAs are necessary for global free trade. The model provides insights into the dynamics of recent trade negotiations involving the US. It also suggests that the ambiguities inherent in GATT Article XXIV may actually promote global free trade.