OpenRaiser/Intern-Atlas huggingface dataset:
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy
Venue: arXiv cs.LG 2022
Year: 2022

Approaches based on generative adversarial networks for imitation learning are promising because they are sample efficient in terms of expert demonstrations. However, training a generator requires many interactions with the actual environment because model-free reinforcement learning is adopted to update a policy. To improve the sample efficiency using model-based reinforcement learning, we propose model-based Entropy-Regularized Imitation Learning (MB-ERIL) under the entropy-regularized Markov decision process to reduce the number of interactions with the actual environment. MB-ERIL uses two discriminators. A policy discriminator distinguishes the actions generated by a robot from expert ones, and a model discriminator distinguishes the counterfactual state transitions generated by the model from the actual ones. We derive structured discriminators so that the learning of the policy and the model is efficient. Computer simulations and real robot experiments show that MB-ERIL achieves a competitive performance and significantly improves the sample efficiency compared to baseline methods.
General Framework for Reversible Data Hiding in Texts Based on Masked Language Modeling
Venue: arXiv cs.CR 2022
Year: 2022

With the fast development of natural language processing, recent advances in information hiding focus on covertly embedding secret information into texts. These algorithms either modify a given cover text or directly generate a text containing secret information, which, however, are not reversible, meaning that the original text not carrying secret information cannot be perfectly recovered unless much side information are shared in advance. To tackle with this problem, in this paper, we propose a general framework to embed secret information into a given cover text, for which the embedded information and the original cover text can be perfectly retrieved from the marked text. The main idea of the proposed method is to use a masked language model to generate such a marked text that the cover text can be reconstructed by collecting the words of some positions and the words of the other positions can be processed to extract the secret information. Our results show that the original cover text and the secret information can be successfully embedded and extracted. Meanwhile, the marked text carrying secret information has good fluency and semantic quality, indicating that the proposed method has satisfactory security, which has been verified by experimental results. Furthermore, there is no need for the data hider and data receiver to share the language model, which significantly reduces the side information and thus has good potential in applications.
Codebook Mismatch Can Be Fully Compensated by Mismatched Decoding
Venue: arXiv cs.IT 2022
Year: 2022

We consider an ensemble of constant composition codes that are subsets of linear codes: while the encoder uses only the constant-composition subcode, the decoder operates as if the full linear code was used, with the motivation of simultaneously benefiting both from the probabilistic shaping of the channel input and from the linear structure of the code. We prove that the codebook mismatch can be fully compensated by using a mismatched additive decoding metric that achieves the random coding error exponent of (non-linear) constant composition codes. As the coding rate tends to the mutual information, the optimal mismatched metric approaches the maximum a posteriori probability (MAP) metric, showing that codebook mismatch with mismatched MAP metric is capacity-achieving for the optimal input assignment.
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks
Venue: arXiv cs.LG 2022
Year: 2022

Node classification on attributed networks is a semi-supervised task that is crucial for network analysis. By decoupling two critical operations in Graph Convolutional Networks (GCNs), namely feature transformation and neighborhood aggregation, some recent works of decoupled GCNs could support the information to propagate deeper and achieve advanced performance. However, they follow the traditional structure-aware propagation strategy of GCNs, making it hard to capture the attribute correlation of nodes and sensitive to the structure noise described by edges whose two endpoints belong to different categories. To address these issues, we propose a new method called the itshape Propagation with Adaptive Mask then Training (PAMT). The key idea is to integrate the attribute similarity mask into the structure-aware propagation process. In this way, PAMT could preserve the attribute correlation of adjacent nodes during the propagation and effectively reduce the influence of structure noise. Moreover, we develop an iterative refinement mechanism to update the similarity mask during the training process for improving the training performance. Extensive experiments on four real-world datasets demonstrate the superior performance and robustness of PAMT.
Resource Allocation and Computation Offloading in a Millimeter-Wave Train-Ground Network
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we consider an mmWave-based trainground communication system in the high-speed railway (HSR) scenario, where the computation tasks of users can be partially offloaded to the rail-side base station (BS) or the mobile relays (MRs) deployed on the roof of the train. The MRs operate in the full-duplex (FD) mode to achieve high spectrum utilization. We formulate the problem of minimizing the average task execution latency of all users, under local device and MRs energy consumption constraints. We propose a joint resource allocation and computation offloading scheme (JRACO) to solve the problem. It consists of a resource allocation and computation offloading (RACO) algorithm and an MR Energy constraint algorithm. RACO utilizes the matching game theory to iterate between two subproblems, i.e., data segmentation and user association and sub-channel allocation. With the RACO results, the MR energy constraint algorithm ensures that the MR energy consumption constraint is satisfied. Extensive simulations validate that JRACO can effectively reduce the average latency and increase the number of served users compared with three baseline schemes.
Effective Reductions of Mealy Machines
Venue: arXiv cs.FL 2022
Year: 2022

We revisit the problem of reducing incompletely specified Mealy machines with reactive synthesis in mind. We propose two techniques: the former is inspired by the tool MeMin and solves the minimization problem, the latter is a novel approach derived from simulationbased reductions but may not guarantee a minimized machine. However, we argue that it offers a good enough compromise between the size of the resulting Mealy machine and performance. The proposed methods are benchmarked against MeMin on a large collection of test cases made of well-known instances as well as new ones.
Deep Active Latent Surfaces for Medical Geometries
Venue: arXiv cs.CV 2022
Year: 2022

Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often involves learning a latent representation, which can be either in the form of a single global vector or of multiple local ones. The latter allows more flexibility but is prone to overfitting. In this paper, we advocate a hybrid approach representing shapes in terms of 3D meshes with a separate latent vector at each vertex. During training the latent vectors are constrained to have the same value, which avoids overfitting. For inference, the latent vectors are updated independently while imposing spatial regularization constraints. We show that this gives us both flexibility and generalization capabilities, which we demonstrate on several medical image processing tasks.
Towards Optimizing OCR for Accessibility
Venue: arXiv cs.CV 2022
Year: 2022

Visual cues such as structure, emphasis, and icons play an important role in efficient information foraging by sighted individuals and make for a pleasurable reading experience. Blind, low-vision and other print-disabled individuals miss out on these cues since current OCR and text-to-speech software ignore them, resulting in a tedious reading experience. We identify four semantic goals for an enjoyable listening experience, and identify syntactic visual cues that help make progress towards these goals. Empirically, we find that preserving even one or two visual cues in aural form significantly enhances the experience for listening to print content.
Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed
Venue: arXiv cs.RO 2022
Year: 2022

Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Therefore imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers dynamics to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful action data obtained during autonomous operations. By fine-tuning the successful data, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded the task more than 75% for other objects.
Global Contentious Politics Database (GLOCON) Annotation Manuals
Venue: arXiv cs.CL 2022
Year: 2022

The database creation utilized automated text processing tools that detect if a news article contains a protest event, locate protest information within the article, and extract pieces of information regarding the detected protest events. The basis of training and testing the automated tools is the GLOCON Gold Standard Corpus (GSC), which contains news articles from multiple sources from each focus country. The articles in the GSC were manually coded by skilled annotators in both classification and extraction tasks with the utmost accuracy and consistency that automated tool development demands. In order to assure these, the annotation manuals in this document lay out the rules according to which annotators code the news articles. Annotators refer to the manuals at all times for all annotation tasks and apply the rules that they contain. The content of the annotation manual is built on the general principles and standards of linguistic annotation laid out in other prominent annotation manuals such as ACE, CAMEO, and TimeML. These principles, however, have been adapted or rather modified heavily to accommodate the social scientific concepts and variables employed in the EMW project. The manual has been molded throughout a long trial and error process that accompanied the annotation of the GSC. It owes much of its current shape to the meticulous work and invaluable feedback provided by highly specialized teams of annotators, whose diligence and expertise greatly increased the quality of the corpus.
Prediction of Maneuvering Status for Aerial Vehicles using Supervised Learning Methods
Venue: arXiv cs.RO 2022
Year: 2022

Aerial Vehicles follow a guided approach based on Latitude, Longitude and Altitude. This information can be used for calculating the status of maneuvering for the aerial vehicles along the line of trajectory. This is a binary classification problem and Machine Learning can be leveraged for solving such problem. In this paper we present a methodology for deriving maneuvering status and its prediction using Linear, Distance Metric, Discriminant Analysis and Boosting Ensemble supervised learning methods. We provide various metrics along the line in the results section that give condensed comparison of the appropriate algorithm for prediction of the maneuvering status.
Sustainably Grown: The Underdog Robots of the Future
Venue: arXiv cs.RO 2022
Year: 2022

It is hard to imagine with the progress in robotics that current approaches are lacking somewhere, yet they will not be applicable to the majority of robots in the near future. We are on the verge of two new transitions that will transform robotics. One is already under way -- the miniaturization of robots, to the point where invisible, microscopic robots could be around us and inside us, performing monitoring or even life-saving functions. We have seen systematic bio-inspired efforts to create microbe-like, microscopic robots. The trend has parallels with miniaturization in the electronics industry, where exponentially smaller and more energy efficient units have been produced each generation. To put this statement in context, examples already include magnetic microswimmer robots, employing bacterial modes of locomotion, which are biocompatible, potentially ready for integration within our bodies. They require lithography to create clever microscopic screw-type structures, enough to produce the cork-screw swimming movement. Such micro-robots have encapsulated, picked, and delivered cells, protecting them from shear forces in fluids, while others have captured non-motile sperm, propelled them, and ultimately fertilized an egg. We explore how such developments in micro-robots will change our world in the relatively near future. The second trend is bottom-up robotics, growing robots from a solution medium, as if they were bacteria. This field is emerging at the intersection of a number of disciplines, discussed below. An overarching common theme is the creation of artificial life from a non-biological starting point.
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source
Venue: arXiv cs.CL 2022
Year: 2022

As the demands for large-scale information processing have grown, knowledge graph-based approaches have gained prominence for representing general and domain knowledge. The development of such general representations is essential, particularly in domains such as manufacturing which intelligent processes and adaptive education can enhance. Despite the continuous accumulation of text in these domains, the lack of structured data has created information extraction and knowledge transfer barriers. In this paper, we report on work towards developing robust knowledge graphs based upon entity and relation data for both commercial and educational uses. To create the FabKG (Manufacturing knowledge graph), we have utilized textbook index words, research paper keywords, FabNER (manufacturing NER), to extract a sub knowledge base contained within Wikidata. Moreover, we propose a novel crowdsourcing method for KG creation by leveraging student notes, which contain invaluable information but are not captured as meaningful information, excluding their use in personal preparation for learning and written exams. We have created a knowledge graph containing 65000+ triples using all data sources. We have also shown the use case of domain-specific question answering and expression/formula-based question answering for educational purposes.
Vehicle Teleoperation: Successive Reference-Pose Tracking
Venue: arXiv cs.RO 2022
Year: 2022

Vehicle teleoperation is an interesting feature in many fields. A typical problem of teleoperation is communication time delay which, together with actuator saturation and environmental disturbance, can cause a vehicle deviation from the target trajectory imposed by the human operator who imposes to the vehicle a steering wheel angle reference and a speed/acceleration reference. With predictive techniques, time-delay can be accounted at sufficient extent. But, in presence of disturbances, due to the absence of instantaneous haptic and visual feedback, human-operator steering command transmitted to the the vehicle is unaccounted with disturbances observed by the vehicle. To improve reference tracking without losing promptness in driving control, reference trajectory in the form of successive reference poses can be transmitted instead of steering commands to the vehicle. We introduce this new concept, namely, the 'successive reference-pose tracking (SRPT)' to improve path tracking in vehicle teleoperation. This paper discusses feasibility and advantages of this new method, compare to the smith predictor control approach. Simulations are performed in SIMULINK environment, where a 14-dof vehicle model is being controlled with Smith and SRPT controllers in presence of variable network delay. Scenarios for performance comparison are low adhesion ground, strong lateral wind and steer-rate demanding maneuvers. Simulation result shows significant improvement in reference tracking with SRPT approach.
Static Analysis of Infrastructure as Code: a Survey
Venue: arXiv cs.SE 2022
Year: 2022

The increasing use of Infrastructure as Code (IaC) in DevOps leads to benefits in speed and reliability of deployment operation, but extends to infrastructure challenges typical of software systems. IaC scripts can contain defects that result in security and reliability issues in the deployed infrastructure: techniques for detecting and preventing them are needed. We analyze and survey the current state of research in this respect by conducting a literature review on static analysis techniques for IaC. We describe analysis techniques, defect categories and platforms targeted by tools in the literature.
Can process mining help in anomaly-based intrusion detection?
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, we consider the naive applications of process mining in network traffic comprehension, traffic anomaly detection, and intrusion detection. We standardise the procedure of transforming packet data into an event log. We mine multiple process models and analyse the process models mined with the inductive miner using ProM and the fuzzy miner using Disco. We compare the two types of process models extracted from event logs of differing sizes. We contrast the process models with the RFC TCP state transition diagram and the diagram by Bishop et al. We analyse the issues and challenges associated with process mining in intrusion detection and explain why naive process mining with network data is ineffective.
The Complexity of the Co-Occurrence Problem
Venue: arXiv cs.DS 2022
Year: 2022

Let $S$ be a string of length $n$ over an alphabet $Σ$ and let $Q$ be a subset of $Σ$ of size $q \geq 2$. The 'co-occurrence problem' is to construct a compact data structure that supports the following query: given an integer $w$ return the number of length-$w$ substrings of $S$ that contain each character of $Q$ at least once. This is a natural string problem with applications to, e.g., data mining, natural language processing, and DNA analysis. The state of the art is an $O(\sqrt{nq})$ space data structure that -- with some minor additions -- supports queries in $O(\log\log n)$ time [CPM 2021]. Our contributions are as follows. Firstly, we analyze the problem in terms of a new, natural parameter $d$, giving a simple data structure that uses $O(d)$ space and supports queries in $O(\log\log n)$ time. The preprocessing algorithm does a single pass over $S$, runs in expected $O(n)$ time, and uses $O(d)$ space in addition to the input. Furthermore, we show that $O(d)$ space is optimal and that $O(\log\log n)$-time queries are optimal given optimal space. Secondly, we bound $d = O(\sqrt{nq})$, giving clean bounds in terms of $n$ and $q$ that match the state of the art. Furthermore, we prove that $Ω(\sqrt{nq})$ bits of space is necessary in the worst case, meaning that the $O(\sqrt{nq})$ upper bound is tight to within polylogarithmic factors. All of our results are based on simple and intuitive combinatorial ideas that simplify the state of the art.
Driving Digital Engineering Integration and Interoperability Through Semantic Integration of Models with Ontologies
Venue: arXiv cs.AI 2022
Year: 2022

Engineered solutions are becoming more complex and multi-disciplinary in nature. This evolution requires new techniques to enhance design and analysis tasks that incorporate data integration and interoperability across various engineering tool suites spanning multiple domains at different abstraction levels. Semantic Web Technologies (SWT) offer data integration and interoperability benefits as well as other opportunities to enhance reasoning across knowledge represented in multiple disparate models. This paper introduces the Digital Engineering Framework for Integration and Interoperability (DEFII) for incorporating SWT into engineering design and analysis tasks. The framework includes three notional interfaces for interacting with ontology-aligned data. It also introduces a novel Model Interface Specification Diagram (MISD) that provides a tool-agnostic model representation enabled by SWT that exposes data stored for use by external users through standards-based interfaces. Use of the framework results in a tool-agnostic authoritative source of truth spanning the entire project, system, or mission.
Device for measuring the plant physiology and electrophysiology
Venue: arXiv cs.OH 2022
Year: 2022

This paper briefly describes the device - the phytosensor - for measuring physiological and electrophysiological parameters of plants. This system is developed as a bio-physiological sensor in precise agriculture, as a tool in plant research and environmental biology, and for plant enthusiasts in smart home or entertainment applications. The phytosentor measures main physiological parameters such as the leaf transpiration rate, sap flow, tissue conductivity and frequency response, biopotentials (action potentials and variation potentials), and can conduct electrochemical impedance spectroscopy with organic tissues. Soil moisture and temperature, air quality (CO2, NO2, O3 and other sensors on I2C bus), and general environmental parameters (light, temperature, humidity, air pressure, electromagnetic and magnetic fields) are also recorded in real time. In addition to phytosensing, the device can also perform phytoactuation, i.e. execute electrical or light stimulation of plants, control irrigation and lighting modes, conduct fully autonomous experiments with complex feedback-based and adaptive scenarios in robotic or biohybrid systems. This article represents the revised and extended version of original paper and includes some descriptions and images from the FloraRobotica and BioHybrids projects.