OpenRaiser/Intern-Atlas huggingface dataset:
Edge-Aided Sensor Data Sharing in Vehicular Communication Networks
Venue: arXiv cs.MA 2022
Year: 2022

Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors (also referred as measurement noise) is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80 % with only 12 kbps uplink and 28 kbps downlink bandwidth.
niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text
Venue: arXiv cs.CL 2022
Year: 2022

This paper describes the system description for the HinglishEval challenge at INLG 2022. The goal of this task was to investigate the factors influencing the quality of the code-mixed text generation system. The task was divided into two subtasks, quality rating prediction and annotators disagreement prediction of the synthetic Hinglish dataset. We attempted to solve these tasks using sentence-level embeddings, which are obtained from mean pooling the contextualized word embeddings for all input tokens in our text. We experimented with various classifiers on top of the embeddings produced for respective tasks. Our best-performing system ranked 1st on subtask B and 3rd on subtask A.
Wearable Haptic Device for Individuals with Congenital Absence of Proprioception
Venue: arXiv cs.HC 2022
Year: 2022

A rare genetic condition, PIEZO2 loss of function (LOF) is characterized by absence of proprioception and light touch, which makes functional tasks (e.g., walking, manipulation) difficult. There are no pharmacological treatments or assistive technologies available for individuals with PIEZO2-LOF. We propose a sensory substitution device that communicates proprioceptive feedback via detectable haptic stimuli. We created a wearable prototype that maps measurements of elbow movement to deep pressure applied to the forearm. The prototype applies up to 18 N, includes an embedded force sensor, and is programmable to allow for various angle-to-pressure mappings. Future work includes comparing proprioceptive acuity and movement ability with and without the device in healthy and PIEZO2-LOF individuals, developing low-profile devices using soft robotics, providing sensory substitution for multiple joints simultaneously, and encoding additional aspects of joint dynamics.
Programs and algorithms for the shell decomposition of oscillating functions in space
Venue: arXiv cs.CE 2022
Year: 2022

Real-space refinement of atomic models in macromolecular crystallography or in cryo electron microscopy fits a model to a map obtained experimentally. This requires generating model maps of a limited resolution which moreover may vary from one molecular region to another. Calculating such map as a sum of atomic contributions requires that these contributions reflect the local resolution of the experimental map. A possibility to refine the parameters of these contribution means to express it as a function of atomic coordinates, displacement factor and eventually of resolution. Recently, Urzhumtsev & Lunin (BioRxiv, 10.1101/2022.03.28.486044) suggested to decompose finite-resolution atomic images, and more generally spherically symmetric oscillating functions in space, into a sum of specially designed terms analytically dependent on all atomic parameters. Each term is a spherically symmetric function concentrated in a spherical shell. Here we describe the software and respective algorithms to carry out such shell decomposition of oscillating functions.
Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification
Venue: arXiv cs.LG 2022
Year: 2022

The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks because PEF measurement is sensitive to elements with high atomic number. Furthermore, the ratio of rock minerals could be determined by combining PEF log with other well logs. However, PEF log could be missing in some cases such as in old well logs and wells drilled with barite-based mud. Therefore, developing models for estimating missing PEF log is essential in those circumstances. In this work, we developed various machine learning models to predict PEF values using the following well logs as inputs: bulk density (RHOB), neutron porosity (NPHI), gamma ray (GR), compressional and shear velocity. The predictions of PEF values using adaptive-network-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have errors of about 16% and 14% average absolute percentage error (AAPE) in the testing dataset, respectively. Thus, a different approach was proposed that is based on the concept of automated machine learning. It works by automatically searching for the optimal model type and optimizes its hyperparameters for the dataset under investigation. This approach selected a Gaussian process regression (GPR) model for accurate estimation of PEF values. The developed GPR model decreases the AAPE of the predicted PEF values in the testing dataset to about 10% AAPE. This error could be further decreased to about 2% by modeling the potential noise in the measurements using the GPR model.
Designing MacPherson Suspension Architectures using Bayesian Optimization
Venue: arXiv cs.LG 2022
Year: 2022

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by a finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months, and is a significant cost in practice. We have developed a Bayesian optimization system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing a generalized inverse of a high-dimensional non-linear function that does not require e.g. gradient information, which is often unavailable from discipline models. We furthermore develop a two-tier convergence criterion based on (i) convergence to a solution optimally satisfying all specified design criteria, or (ii) convergence to a minimum-norm solution. We demonstrate the proposed approach on a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient, and that the novel convergence criteria can be implemented straightforwardly based on existing concepts and subroutines in popular Bayesian optimization software packages.
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
Venue: arXiv cs.CG 2022
Year: 2022

This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological Representation Learning". The competition asked participants to provide implementations of machine learning algorithms on manifolds that would respect the API of the open-source software Geomstats (manifold part) and Scikit-Learn (machine learning part) or PyTorch. The challenge attracted seven teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings.
Analysis & Computational Complexity Reduction of Monocular and Stereo Depth Estimation Techniques
Venue: arXiv cs.CV 2022
Year: 2022

Accurate depth estimation with lowest compute and energy cost is a crucial requirement for unmanned and battery operated autonomous systems. Robotic applications require real time depth estimation for navigation and decision making under rapidly changing 3D surroundings. A high accuracy algorithm may provide the best depth estimation but may consume tremendous compute and energy resources. A general trade-off is to choose less accurate methods for initial depth estimate and a more accurate yet compute intensive method when needed. Previous work has shown this trade-off can be improved by developing a state-of-the-art method (AnyNet) to improve stereo depth estimation. We studied both the monocular and stereo vision depth estimation methods and investigated methods to reduce computational complexity of these methods. This was our baseline. Consequently, our experiments show reduction of monocular depth estimation model size by ~75% reduces accuracy by less than 2% (SSIM metric). Our experiments with the novel stereo vision method (AnyNet) show that accuracy of depth estimation does not degrade more than 3% (three pixel error metric) in spite of reduction in model size by ~20%. We have shown that smaller models can indeed perform competitively.
Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact
Venue: arXiv cs.LG 2022
Year: 2022

A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact. Previous studies have utilized supervised ML techniques that require substantial amounts of hand-labeled data. However, manually harvesting such data can be costly, time-consuming, and mundane, and is a key factor limiting the widespread adoption of ML in healthcare (HC). Instead, we explore the use of multiple, individually imperfect heuristics to automatically assign probabilistic labels to unlabeled training data using weak supervision. Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.
Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion
Venue: arXiv cs.SD 2022
Year: 2022

Recent years have witnessed the extraordinary development of automatic speaker verification (ASV). However, previous works show that state-of-the-art ASV models are seriously vulnerable to voice spoofing attacks, and the recently proposed high-performance spoofing countermeasure (CM) models only focus solely on the standalone anti-spoofing tasks, and ignore the subsequent speaker verification process. How to integrate the CM and ASV together remains an open question. A spoofing aware speaker verification (SASV) challenge has recently taken place with the argument that better performance can be delivered when both CM and ASV subsystems are optimized jointly. Under the challenge's scenario, the integrated systems proposed by the participants are required to reject both impostor speakers and spoofing attacks from target speakers, which intuitively and effectively matches the expectation of a reliable, spoofing-robust ASV system. This work focuses on fusion-based SASV solutions and proposes a multi-model fusion framework to leverage the power of multiple state-of-the-art ASV and CM models. The proposed framework vastly improves the SASV-EER from 8.75% to 1.17\%, which is 86% relative improvement compared to the best baseline system in the SASV challenge.
Efficacy of Asynchronous GPS Spoofing Against High Volume Consumer GNSS Receivers
Venue: arXiv cs.CR 2022
Year: 2022

The vulnerability of the Global Positioning System (GPS) against spoofing is known for quite some time. Also, the positioning and navigation of most semi-autonomous and autonomous drones are dependent on Global Navigation Satellite System (GNSS) signals. In prior work, simplistic or asynchronous GPS spoofing was found to be a simple, efficient, and effective cyber attack against L1 GPS or GNSS dependent commercial drones. In this paper, first we make some important observations on asynchronous GPS spoofing attacks on drones presented in prior research literature. Then, we design an asynchronous GPS spoofing attack plan. Next, we test the effectiveness of this attack against GNSS receivers (high volume consumer devices based on Android mobile phones) of different capabilities and a commercial drone (DJI Mavic 2 Pro) under various conditions. Finally, we present several novel insights based on the results of the tests.
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Venue: arXiv cs.GT 2022
Year: 2022

\emph{Ex ante} correlation is becoming the mainstream approach for \emph{sequential adversarial team games}, where a team of players faces another team in a zero-sum game. It is known that team members' asymmetric information makes both equilibrium computation \textsf{APX}-hard and team's strategies not directly representable on the game tree. This latter issue prevents the adoption of successful tools for huge 2-player zero-sum games such as, \emph{e.g.}, abstractions, no-regret learning, and subgame solving. This work shows that we can recover from this weakness by bridging the gap between sequential adversarial team games and 2-player games. In particular, we propose a new, suitable game representation that we call \emph{team-public-information}, in which a team is represented as a single coordinator who only knows information common to the whole team and prescribes to each member an action for any possible private state. The resulting representation is highly \emph{explainable}, being a 2-player tree in which the team's strategies are behavioral with a direct interpretation and more expressive than the original extensive form when designing abstractions. Furthermore, we prove payoff equivalence of our representation, and we provide techniques that, starting directly from the extensive form, generate dramatically more compact representations without information loss. Finally, we experimentally evaluate our techniques when applied to a standard testbed, comparing their performance with the current state of the art.
Structured Light with Redundancy Codes
Venue: arXiv cs.CV 2022
Year: 2022

Structured light (SL) systems acquire high-fidelity 3D geometry with active illumination projection. Conventional systems exhibit challenges when working in environments with strong ambient illumination, global illumination and cross-device interference. This paper proposes a general-purposed technique to improve the robustness of SL by projecting redundant optical signals in addition to the native SL patterns. In this way, projected signals become more distinguishable from errors. Thus the geometry information can be more easily recovered using simple signal processing and the ``coding gain" in performance is obtained. We propose three applications using our redundancy codes: (1) Self error-correction for SL imaging under strong ambient light, (2) Error detection for adaptive reconstruction under global illumination, and (3) Interference filtering with device-specific projection sequence encoding, especially for event camera-based SL and light curtain devices. We systematically analyze the design rules and signal processing algorithms in these applications. Corresponding hardware prototypes are built for evaluations on real-world complex scenes. Experimental results on the synthetic and real data demonstrate the significant performance improvements in SL systems with our redundancy codes.
RuArg-2022: Argument Mining Evaluation
Venue: arXiv cs.CL 2022
Year: 2022

Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argumentation structure of texts. This paper is a report of the organizers on the first competition of argumentation analysis systems dealing with Russian language texts within the framework of the Dialogue conference. During the competition, the participants were offered two tasks: stance detection and argument classification. A corpus containing 9,550 sentences (comments on social media posts) on three topics related to the COVID-19 pandemic (vaccination, quarantine, and wearing masks) was prepared, annotated, and used for training and testing. The system that won the first place in both tasks used the NLI (Natural Language Inference) variant of the BERT architecture, automatic translation into English to apply a specialized BERT model, retrained on Twitter posts discussing COVID-19, as well as additional masking of target entities. This system showed the following results: for the stance detection task an F1-score of 0.6968, for the argument classification task an F1-score of 0.7404. We hope that the prepared dataset and baselines will help to foster further research on argument mining for the Russian language.
Argumentative Text Generation in Economic Domain
Venue: arXiv cs.CL 2022
Year: 2022

The development of large and super-large language models, such as GPT-3, T5, Switch Transformer, ERNIE, etc., has significantly improved the performance of text generation. One of the important research directions in this area is the generation of texts with arguments. The solution of this problem can be used in business meetings, political debates, dialogue systems, for preparation of student essays. One of the main domains for these applications is the economic sphere. The key problem of the argument text generation for the Russian language is the lack of annotated argumentation corpora. In this paper, we use translated versions of the Argumentative Microtext, Persuasive Essays and UKP Sentential corpora to fine-tune RuBERT model. Further, this model is used to annotate the corpus of economic news by argumentation. Then the annotated corpus is employed to fine-tune the ruGPT-3 model, which generates argument texts. The results show that this approach improves the accuracy of the argument generation by more than 20 percentage points (63.2\% vs. 42.5\%) compared to the original ruGPT-3 model.
The Framework For The Discipline Of Software Engineering in Connection to Information Technology Discipline
Venue: arXiv cs.SE 2022
Year: 2022

This paper represents preliminary work in identifying the foundation for the discipline of Software Engineering and discovering the links between the domains of Software Engineering and Information Technology (IT). Our research utilized IEEE Transactions on Software Engineering (IEEE-TSE), ACM Transactions on Software Engineering and Methodology (ACM-TOSEM), Automated Software Engineering (ASE), the International Conference on Software Engineering(ICSE), and other related journal publication in the software engineering domain to address our research questions. We explored existing frameworks and described the need for software engineering as an academic discipline. We went further to clarify the distinction difference between Software Engineering and Computer Science. Through this efforts we contribute to an understanding of how evidence from IT research can be used to improve Software Engineering as a discipline.
Improving Robotic Grasping Ability Through Deep Shape Generation
Venue: arXiv cs.RO 2022
Year: 2022

Data-driven approaches have become a dominant paradigm for robotic grasp planning. However, the performance of these approaches is enormously influenced by the quality of the available training data. In this paper, we propose a framework to generate object shapes to improve the grasping dataset quality, thus enhancing the grasping ability of a pre-designed learning-based grasp planning network. In this framework, the object shapes are embedded into a low-dimensional feature space using an AutoEncoder (encoder-decoder) based structure network. The rarity and graspness scores are defined for each object shape using outlier detection and grasp-quality criteria. Subsequently, new object shapes are generated in feature space that leverages the original high rarity and graspness score objects' features, which can be employed to augment the grasping dataset. Finally, the results obtained from the simulation and real-world experiments demonstrate that the grasping ability of the learning-based grasp planning network can be effectively improved with the generated object shapes.
Coverage Analysis of LEO Satellite Downlink Networks: Orbit Geometry Dependent Approach
Venue: arXiv cs.IT 2022
Year: 2022

The low-earth-orbit (LEO) satellite network with mega-constellations can provide global coverage while supporting the high-data rates. The coverage performance of such a network is highly dependent on orbit geometry parameters, including satellite altitude and inclination angle. Traditionally, simulation-based coverage analysis dominates because of the lack of analytical approaches. This paper presents a novel systematic analysis framework for the LEO satellite network by highlighting orbit geometric parameters. Specifically, we assume that satellite locations are placed on a circular orbit according to a one-dimensional Poisson point process. Then, we derive the distribution of the nearest distance between the satellite and a fixed user's location on the Earth in terms of the orbit-geometry parameters. Leveraging this distribution, we characterize the coverage probability of the single-orbit LEO network as a function of the network geometric parameters in conjunction with small and large-scale fading effects. Finally, we extend our coverage analysis to multi-orbit networks and verify the synergistic gain of harnessing multi-orbit satellite networks in terms of the coverage probability. Simulation results are provided to validate the mathematical derivations and the accuracy of the proposed model.
Two Results on Separation Logic With Theory Reasoning
Venue: arXiv cs.LO 2022
Year: 2022

Two results are presented concerning the entailment problem in Separation Logic with inductively defined predicate symbols and theory reasoning. First, we show that the entailment problem is undecidable for rules with bounded tree-width, if theory reasoning is considered. The result holds for a wide class of theories, even with a very low expressive power. For instance it applies to the natural numbers with the successor function, or with the usual order. Second, we show that every entailment problem can be reduced to an entailment problem containing no equality (neither in the formulas nor in the recursive rules defining the semantics of the predicate symbols).