Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers

While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases.Comment: Published at the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 9 pages, 6 figure

TSFool: Crafting Highly-imperceptible Adversarial Time Series through Multi-objective Black-box Attack to Fool RNN Classifiers

Neural network (NN) classifiers are vulnerable to adversarial attacks. Although the existing gradient-based attacks achieve state-of-the-art performance in feed-forward NNs and image recognition tasks, they do not perform as well on time series classification with recurrent neural network (RNN) models. This is because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity of time series data to perturbations challenges the traditional local optimization objective of the adversarial attack. In this paper, a black-box method called TSFool is proposed to efficiently craft highly-imperceptible adversarial time series for RNN classifiers. We propose a novel global optimization objective named Camouflage Coefficient to consider the imperceptibility of adversarial samples from the perspective of class distribution, and accordingly refine the adversarial attack as a multi-objective optimization problem to enhance the perturbation quality. To get rid of the dependence on gradient information, we also propose a new idea that introduces a representation model for RNN to capture deeply embedded vulnerable samples having otherness between their features and latent manifold, based on which the optimization solution can be heuristically approximated. Experiments on 10 UCR datasets are conducted to confirm that TSFool averagely outperforms existing methods with a 46.3% higher attack success rate, 87.4% smaller perturbation and 25.6% better Camouflage Coefficient at a similar time cost.Comment: 9 pages, 7 figure

MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.Comment: 9 pages, 6 figure

MARTE/pCCSL: Modeling and Refining Stochastic Behaviors of CPSs with Probabilistic Logical Clocks

Best Paper AwardInternational audienceCyber-Physical Systems (CPSs) are networks of heterogeneous embedded systems immersed within a physical environment. Several ad-hoc frameworks and mathematical models have been studied to deal with challenging issues raised by CPSs. In this paper, we explore a more standard-based approach that relies on SysML/MARTE to capture different aspects of CPSs, including structure, behaviors, clock constraints, and non-functional properties. The novelty of our work lies in the use of logical clocks and MARTE/CCSL to drive and coordinate different models. Meanwhile, to capture stochastic behaviors of CPSs, we propose an extension of CCSL, called pCCSL, where logical clocks are adorned with stochastic properties. Possible variants are explored using Statistical Model Checking (SMC) via a transformation from the MARTE/pCCSL models into Stochastic Hybrid Automata. The whole process is illustrated through a case study of energy-aware building, in which the system is modeled by SysML/MARTE/pCCSL and different variants are explored through SMC to help expose the best alternative solutions

Statistical Model Checking for Stochastic Hybrid Systems

This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.Comment: In Proceedings HSB 2012, arXiv:1208.315

Minor clone of del(17p) provides a reservoir for relapse in multiple myeloma

The deletion of chromosome 17p (del(17p)) is considered a crucial prognostic factor at the time of diagnosis in patients with multiple myeloma (MM). However, the impact of del(17p) on survival at different clonal sizes at relapse, as well as the patterns of clonal evolution between diagnosis and relapse and their prognostic value, has not been well described. To address these issues, we analyzed the interphase fluorescence in situ hybridization (iFISH) results of 995 newly diagnosed MM (NDMM) patients and 293 patients with MM at their first relapse. Among these patients, 197 had paired iFISH data at diagnosis and first relapse. Our analysis of paired iFISH revealed that a minor clone of del(17p) at relapse but not at diagnosis was associated with poor prognosis in MM (hazard ratio for median overall survival 1.64 vs. 1.44). Fifty-six and 12 patients developed one or more new cytogenetic abnormalities at relapse, mainly del(17p) and gain/amp(1q), respectively. We classified the patients into six groups based on the change patterns in the clonal size of del(17p) between the two time points. Patients who did not have del(17p) during follow-up showed the best outcomes, whereas those who acquired del(17p) during their disease course, experienced compromised survival (median overall survival: 61.3 vs. 49.4 months; hazard ratio =1.64; 95% confidence interval: 1.06-2.56; P<0.05). In conclusion, our data confirmed the adverse impact of a minor clone of del(17p) at relapse and highlighted the importance of designing optimal therapeutic strategies to eliminate high-risk cytogenetic abnormalities (clinicaltrials gov. identifier: NCT04645199)

xSHS: An Executable Domain-Specific Modeling Language for Modeling Stochastic and Hybrid Behaviors of Cyber-Physical Systems

International audienceCyber-Physical Systems (CPS) integrate discrete computational processes and continuous physical ones in a feedback loop. Design and analysis of CPS become difficult since their dynamic behaviors rely on heterogeneous descriptions from many fields. Domain-Specific Modeling Language (DSML) offers an effective and tailor-made solution for focusing on a specific field. However, to address CPS we need to bring together several DSMLs in a coordinated sensible way. The GEMOC Studio is meant to be an integration platform for putting together several DSMLs. This paper relies on it and brings a new DSML, called xSHS (for Executable Stochastic Hybrid Statechart), into the focus. It aims at modeling the stochastic and hybrid behaviors of CPS. We discuss here the abstract syntax, a proposed concrete syntax and an operational semantics that makes the language executable. We exploit both the language and modeling workbenches of the GEMOC Studio and we provide a simulation engine that implements the operational semantics. A temperature control system is used as a case study

Semantic segmentation of large-scale point cloud scenes via dual neighborhood feature and global spatial-aware

As a core task in 3D scene information extraction, point cloud semantic segmentation is crucial for understanding 3D scenes and environmental perception. While extracting local geometric structural features from point clouds, existing research often overlooks the long-range dependencies present in the scene, making it challenging to fully uncover the long-range contextual features hidden within point clouds. On this basis, we propose a segmentation algorithm (DG-Net) that integrates dual neighborhood features with global spatial-aware. Initially, the local structure information encoding module is designed to learn about local geometric shapes by encoding spatial position and directional features, thus supplementing structural information. Subsequently, a dual neighborhood features complementary module is introduced to merge the geometric structural and semantic features within local neighborhoods, learning local dependencies and capturing distinguishable local contextual features. Finally, these features are relayed to a global spatial-aware module equipped with a gated unit, which dynamically adjusts the weights of features at different stages, effectively modeling long-range dependencies between local structures and finely extracting long-range contextual features. We conducted experiments on benchmark datasets of point cloud scenes, and both quantitative and qualitative results demonstrate that our algorithm can accurately identify small-scale objects with complex geometric structures within scenes, surpassing other mainstream networks in segmentation performance. The mIoU on the S3DIS, Toronto3D, and SensatUrban datasets are 71.9 %, 82.1 %, and 59.8 %, respectively