OpenRaiser/Intern-Atlas huggingface dataset:
Crossing the Tepper Line: An Emerging Ontology for Describing the Dynamic Sociality of Embodied AI
Venue: arXiv cs.HC 2021
Year: 2021

Artificial intelligences (AI) are increasingly being embodied and embedded in the world to carry out tasks and support decision-making with and for people. Robots, recommender systems, voice assistants, virtual humans - do these disparate types of embodied AI have something in common? Here we show how they can manifest as "socially embodied AI." We define this as the state that embodied AI "circumstantially" take on within interactive contexts when perceived as both social and agentic by people. We offer a working ontology that describes how embodied AI can dynamically transition into socially embodied AI. We propose an ontological heuristic for describing the threshold: the Tepper line. We reinforce our theoretical work with expert insights from a card sort workshop. We end with two case studies to illustrate the dynamic and contextual nature of this heuristic.
A Program Logic to Verify Signal Temporal Logic Specifications of Hybrid Systems: Extended Technical Report
Venue: arXiv cs.LO 2021
Year: 2021

Signal temporal logic (STL) was introduced for monitoring temporal properties of continuous-time signals for continuous and hybrid systems. Differential dynamic logic (dL) was introduced to reason about the end states of a hybrid program. Over the past decade, STL and its variants have significantly gained in popularity in the industry for monitoring purposes, while dL has gained in popularity for verification of hybrid systems. In this paper, we bridge the gap between the two different logics by introducing signal temporal dynamic logic (STdL) -- a dynamic logic that reasons about a subset of STL specifications over executions of hybrid systems. Our work demonstrates that STL can be used for deductive verification of hybrid systems. STdL significantly augments the expressiveness of dL by allowing reasoning about temporal properties in given time intervals. We provide a semantics and a proof calculus for STdL, along with a proof of soundness and relative completeness.
Regional economic integration via detection of circular flow in international value-added network
Venue: arXiv cs.SI 2021
Year: 2021

Global value chains (GVCs) are formed through value-added trade, and some regions promote economic integration by concluding regional trade agreements to promote these chains. However, there is no way to quantitatively assess the scope and extent of economic integration involving various sectors in multiple countries. In this study, we used the World Input--Output Database to create a cross-border sector-wise trade in value-added network (international value-added network (IVAN)) covering the period of 2000--2014 and evaluated them using network science methods. By applying Infomap to the IVAN, we confirmed for the first time the existence of two regional communities: Europe and the Pacific Rim. Helmholtz--Hodge decomposition was used to decompose the value flows within the region into potential and circular flows, and the annual evolution of the potential and circular relationships between countries and sectors was clarified. The circular flow component of the decomposition was used to define an economic integration index, and findings confirmed that the degree of economic integration in Europe declined sharply after the economic crisis in 2009 to a level lower than that in the Pacific Rim. The European economic integration index recovered in 2011 but again fell below that of the Pacific Rim in 2013. Moreover, sectoral analysis showed that the economic integration index captured the effect of Russian mineral resources, free movement of labor in Europe, and international division of labor in the Pacific Rim, especially in GVCs for the manufacture of motor vehicles and high-tech products.
Computational timbre and tonal system similarity analysis of the music of Northern Myanmar-based Kachin compared to Xinjiang-based Uyghur ethnic groups
Venue: arXiv cs.SD 2021
Year: 2021

The music of Northern Myanmar Kachin ethnic group is compared to the music of western China, Xijiang based Uyghur music, using timbre and pitch feature extraction and machine learning. Although separated by Tibet, the muqam tradition of Xinjiang might be found in Kachin music due to myths of Kachin origin, as well as linguistic similarities, e.g., the Kachin term 'makan' for a musical piece. Extractions were performed using the apollon and COMSAR (Computational Music and Sound Archiving) frameworks, on which the Ethnographic Sound Recordings Archive (ESRA) is based, using ethnographic recordings from ESRA next to additional pieces. In terms of pitch, tonal systems were compared using Kohonen self-organizing map (SOM), which clearly clusters Kachin and Uyghur musical pieces. This is mainly caused by the Xinjiang muqam music showing just fifth and fourth, while Kachin pieces tend to have a higher fifth and fourth, next to other dissimilarities. Also, the timbre features of spectral centroid and spectral sharpness standard deviation clearly tells Uyghur from Kachin pieces, where Uyghur music shows much larger deviations. Although more features will be compared in the future, like rhythm or melody, these already strong findings might introduce an alternative comparison methodology of ethnic groups beyond traditional linguistic definitions.
Boosting ship detection in SAR images with complementary pretraining techniques
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning methods have made significant progress in ship detection in synthetic aperture radar (SAR) images. The pretraining technique is usually adopted to support deep neural networks-based SAR ship detectors due to the scarce labeled SAR images. However, directly leveraging ImageNet pretraining is hardly to obtain a good ship detector because of different imaging perspective and geometry. In this paper, to resolve the problem of inconsistent imaging perspective between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique, which transfers the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset. On the other hand, to handle the problem of different imaging geometry between optical and SAR images, we propose an optical-SAR matching (OSM) pretraining technique, which transfers plentiful texture features from optical images to SAR images by common representation learning on the optical-SAR matching task. Finally, observing that the OSD pretraining based SAR ship detector has a better recall on sea area while the OSM pretraining based SAR ship detector can reduce false alarms on land area, we combine the predictions of the two detectors through weighted boxes fusion to further improve detection results. Extensive experiments on four SAR ship detection datasets and two representative CNN-based detection benchmarks are conducted to show the effectiveness and complementarity of the two proposed detectors, and the state-of-the-art performance of the combination of the two detectors. The proposed method won the sixth place of ship detection in SAR images in 2020 Gaofen challenge.
An FE-DMN method for the multiscale analysis of fiber reinforced plastic components
Venue: arXiv cs.CE 2021
Year: 2021

In this work, we propose a fully coupled multiscale strategy for components made from short fiber reinforced composites, where each Gauss point of the macroscopic finite element model is equipped with a deep material network (DMN) which covers the different fiber orientation states varying within the component. These DMNs need to be identified by linear elastic precomputations on representative volume elements, and serve as high-fidelity surrogates for full-field simulations on microstructures with inelastic constituents. We discuss how to extend direct DMNs to account for varying fiber orientation, and propose a simplified sampling strategy which significantly speeds up the training process. To enable concurrent multiscale simulations, evaluating the DMNs efficiently is crucial. We discuss dedicated techniques for exploiting sparsity and high-performance linear algebra modules, and demonstrate the power of the proposed approach on an industrial-scale three-dimensional component. Indeed, the DMN is capable of accelerating two-scale simulations significantly, providing possible speed-ups of several magnitudes.
Scaling a Blockchain-based Railway Control System Prototype for Mainline Railways: a Progress Report
Venue: arXiv cs.CR 2021
Year: 2021

Railway operations require control systems to ensure safety and efficiency, and to coordinate infrastructure elements such as switches, signals and train protection. To compete with the traditional approaches to these systems, a blockchain-based approach has been proposed, with the intent to build a more resilient, integrated and cost-efficient system. Additionally, the developed blockchain-based architecture enables to run safety-relevant and security-focused business logic on off-the-shelf platforms such as cloud, rather than on specialized (and expensive) secure hardware. After implementing a prototype of the blockchain-based railway control system, scaling the approach to real-world mainline and branch operations required a thorough validation of the design choices. In this technical report, we show how performance calculations, long-term technology perspectives and law-mandated norms have impacted the architecture, the technology choices, and the make-buy-reuse decisions.
Boosted Genetic Algorithm using Machine Learning for traffic control optimization
Venue: arXiv cs.LG 2021
Year: 2021

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods under normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper aims at tackling this problem and presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. As a benchmark, we first start with deploying a typical GA algorithm by considering the phase duration as the decision variable and the objective function to minimize the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train various machine learning regression models to predict the total travel time of the studied traffic network, and select the best performing regressor which we further hyper-tune to find the optimal training parameters. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework. Comparison and results show that the new BGA-ML is much faster than the original GA algorithm and can be successfully applied under non-recurrent incident conditions.
Maximum Number of Steps of Topswops on 18 and 19 Cards
Venue: arXiv cs.DM 2021
Year: 2021

Let $f(n)$ be the maximum number of steps of Topswops on $n$ cards. In this note, we report our computational experiments to determine the values of $f(18)$ and $f(19)$. By applying an algorithm developed by Knuth in a parallel fashion, we conclude that $f(18)=191$ and $f(19)=221$.
Probabilistic Grammatical Evolution
Venue: arXiv cs.NE 2021
Year: 2021

Grammatical Evolution (GE) is one of the most popular Genetic Programming (GP) variants, and it has been used with success in several problem domains. Since the original proposal, many enhancements have been proposed to GE in order to address some of its main issues and improve its performance. In this paper we propose Probabilistic Grammatical Evolution (PGE), which introduces a new genotypic representation and new mapping mechanism for GE. Specifically, we resort to a Probabilistic Context-Free Grammar (PCFG) where its probabilities are adapted during the evolutionary process, taking into account the productions chosen to construct the fittest individual. The genotype is a list of real values, where each value represents the likelihood of selecting a derivation rule. We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE). The results show that PGE has a a better performance than GE, with statistically significant differences, and achieved similar performance when comparing with SGE.
Flexible FOND Planning with Explicit Fairness Assumptions
Venue: arXiv cs.AI 2021
Year: 2021

We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A new planner is implemented by reducing FOND+ planning to answer set programs, and the performance of the planner is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools.
Metric Learning for Anti-Compression Facial Forgery Detection
Venue: arXiv cs.CV 2021
Year: 2021

Detecting facial forgery images and videos is an increasingly important topic in multimedia forensics. As forgery images and videos are usually compressed into different formats such as JPEG and H264 when circulating on the Internet, existing forgery-detection methods trained on uncompressed data often suffer from significant performance degradation in identifying them. To solve this problem, we propose a novel anti-compression facial forgery detection framework, which learns a compression-insensitive embedding feature space utilizing both original and compressed forgeries. Specifically, our approach consists of three ideas: (i) extracting compression-insensitive features from both uncompressed and compressed forgeries using an adversarial learning strategy; (ii) learning a robust partition by constructing a metric loss that can reduce the distance of the paired original and compressed images in the embedding space; (iii) improving the accuracy of tampered localization with an attention-transfer module. Experimental results demonstrate that, the proposed method is highly effective in handling both compressed and uncompressed facial forgery images.
A novel approach for the efficient modeling of material dissolution in electrochemical machining
Venue: arXiv cs.CE 2021
Year: 2021

This work presents a novel approach to efficiently model anodic dissolution in electrochemical machining. Earlier modeling approaches employ a strict space discretization of the anodic surface that is associated with a remeshing procedure at every time step. Besides that, the presented model is formulated by means of effective material parameters. Thereby, it allows to use a constant mesh for the entire simulation and, thus, decreases the computational costs. Based on Faraday's law of electrolysis, an effective dissolution level is introduced, which describes the ratio of a dissolved volume and its corresponding reference volume. This inner variable allows the modeling of the complex dissolution process without the necessity of computationally expensive remeshing by controlling the effective material parameters. Additionally, full coupling of the thermoelectric problem is considered and its linearization and numerical implementation are presented. The model shows good agreement with analytical and experimental validation examples by yielding realistic results. Furthermore, simulations of a pulsed electrochemical machining process yield a process signature of the surface roughness related to the specific accumulated electric charge. The numerical examples confirm the simulation's computational efficiency and accurate modeling qualities.
Visualizing Data Velocity using DSNE
Venue: arXiv cs.LG 2021
Year: 2021

We present a new technique called "DSNE" which learns the velocity embeddings of low dimensional map points when given the high-dimensional data points with its velocities. The technique is a variation of Stochastic Neighbor Embedding, which uses the Euclidean distance on the unit sphere between the unit-length velocity of the point and the unit-length direction from the point to its near neighbors to define similarities, and try to match the two kinds of similarities in the high dimension space and low dimension space to find the velocity embeddings on the low dimension space. DSNE can help to visualize how the data points move in the high dimension space by presenting the movements in two or three dimensions space. It is helpful for understanding the mechanism of cell differentiation and embryo development.
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset
Venue: arXiv cs.CV 2021
Year: 2021

Cardiac Magnetic Resonance (CMR) is the most effective tool for the assessment and diagnosis of a heart condition, which malfunction is the world's leading cause of death. Software tools leveraging Artificial Intelligence already enhance radiologists and cardiologists in heart condition assessment but their lack of transparency is a problem. This project investigates if it is possible to discover concepts representative for different cardiac conditions from the deep network trained to segment crdiac structures: Left Ventricle (LV), Right Ventricle (RV) and Myocardium (MYO), using explainability methods that enhances classification system by providing the score-based values of qualitative concepts, along with the key performance metrics. With introduction of a need of explanations in GDPR explainability of AI systems is necessary. This study applies Discovering and Testing with Concept Activation Vectors (D-TCAV), an interpretaibilty method to extract underlying features important for cardiac disease diagnosis from MRI data. The method provides a quantitative notion of concept importance for disease classified. In previous studies, the base method is applied to the classification of cardiac disease and provides clinically meaningful explanations for the predictions of a black-box deep learning classifier. This study applies a method extending TCAV with a Discovering phase (D-TCAV) to cardiac MRI analysis. The advantage of the D-TCAV method over the base method is that it is user-independent. The contribution of this study is a novel application of the explainability method D-TCAV for cardiac MRI anlysis. D-TCAV provides a shorter pre-processing time for clinicians than the base method.
Time-Indexed Parallel Spatial Modulation for Large-scale MIMO Systems with Antenna Grouping
Venue: arXiv cs.IT 2021
Year: 2021

A novel space-time parallel index modulation scheme is proposed in this paper for conveying extra digital information with the aid of space and time indices. In this proposed scheme, time-indexing is cleverly performed in parallel spatial modulation (PSM) schemes using transmit antenna grouping, and referred to as time-indexed parallel spatial modulation (TI-PSM). In this scheme, transmit antennas are divided into groups to adopt TI-SM scheme in large-scale multiple input multiple output (MIMO) systems with inter-channel interference (ICI) avoidance. This scheme is attractive due to both the high data rate and good performance improvement that can be achieved. The bit error rate (BER) performance of the TI-PSM scheme is evaluated and compared to that of the conventional schemes. Simulation results demonstrated that a significant improvement is achieved by the TI-PSM scheme as compared to the TI-SM and PSM schemes for the same achieved rate. It is also shown that TI-SM and TI-PSM schemes provide higher performance drop with channel estimation errors (CEEs) than the conventional SM and PSM systems. Therefore, due to the high performance improvements achieved in TI-PSM scheme, it can be effectively regarded as a promising solution for various 5G and beyond wireless networks.
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models
Venue: arXiv cs.LG 2021
Year: 2021

Bayesian experimental design (BED) aims at designing an experiment to maximize the information gathering from the collected data. The optimal design is usually achieved by maximizing the mutual information (MI) between the data and the model parameters. When the analytical expression of the MI is unavailable, e.g., having implicit models with intractable data distributions, a neural network-based lower bound of the MI was recently proposed and a gradient ascent method was used to maximize the lower bound. However, the approach in Kleinegesse et al., 2020 requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models. In this work, we propose a hybrid gradient approach that leverages recent advances in variational MI estimator and evolution strategies (ES) combined with black-box stochastic gradient ascent (SGA) to maximize the MI lower bound. This allows the design process to be achieved through a unified scalable procedure for implicit models without sampling path gradients. Several experiments demonstrate that our approach significantly improves the scalability of BED for implicit models in high-dimensional design space.
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition
Venue: arXiv cs.CV 2021
Year: 2021

As Facial Expression Recognition (FER) systems become integrated into our daily lives, these systems need to prioritise making fair decisions instead of aiming at higher individual accuracy scores. Ranging from surveillance systems to diagnosing mental and emotional health conditions of individuals, these systems need to balance the accuracy vs fairness trade-off to make decisions that do not unjustly discriminate against specific under-represented demographic groups. Identifying bias as a critical problem in facial analysis systems, different methods have been proposed that aim to mitigate bias both at data and algorithmic levels. In this work, we propose the novel usage of Continual Learning (CL), in particular, using Domain-Incremental Learning (Domain-IL) settings, as a potent bias mitigation method to enhance the fairness of FER systems while guarding against biases arising from skewed data distributions. We compare different non-CL-based and CL-based methods for their classification accuracy and fairness scores on expression recognition and Action Unit (AU) detection tasks using two popular benchmarks, the RAF-DB and BP4D datasets, respectively. Our experimental results show that CL-based methods, on average, outperform other popular bias mitigation techniques on both accuracy and fairness metrics.