Reviewerly/exHarmony huggingface dataset:
Remembering the City: Stumbling Stones, Memory Sites and Augmented Reality

This paper describes the development of Remembering the City, a digital and public humanities project on an urban scale, which takes advantage of augmented reality techniques in order to bring memories of the Holocaust victims to the surface of the urban fabric. The project has been built in conjunction with the Stolpersteine (Stumbling Stones), the largest decentralized monument in Europe dedicated to the Holocaust, allowing for the first time a contextual access to information related to the stones. The paper describes the conceptual design of the project, the technical challenge of tracking bi-dimensional objects in the outdoors and the ethical concerns. The iterative development led to prototypical implementations that were tested with two evaluation studies, performed respectively with 10 volunteers aged between 30 and 50 and 22 students from a local high school. Both studies provided valuable feedback and directions for future development.
Hierarchical planning with state abstractions for temporal task specifications

We often specify tasks for a robot using temporal language that can include different levels of abstraction. For example, the command "go to the kitchen before going to the second floor" contains spatial abstraction, given that "floor" consists of individual rooms that can also be referred to in isolation ("kitchen", for example). There is also a temporal ordering of events, defined by the word "before". Previous works have used syntactically co-safe Linear Temporal Logic (sc-LTL) to interpret temporal language (such as "before"), and Abstract Markov Decision Processes (AMDPs) to interpret hierarchical abstractions (such as "kitchen" and "second floor"), separately. To handle both types of commands at once, we introduce the Abstract Product Markov Decision Process (AP-MDP), a novel approach capable of representing non-Markovian reward functions at different levels of abstractions. The AP-MDP framework translates LTL into its corresponding automata, creates a product Markov Decision Process (MDP) of the LTL specification and the environment MDP, and decomposes the problem into subproblems to enable efficient planning with abstractions. AP-MDP performs faster than a non-hierarchical method of solving LTL problems in over 95% of path planning tasks, and this number only increases as the size of the environment domain increases. In a cleanup world domain, AP-MDP performs faster in over 98% of tasks. We also present a neural sequence-to-sequence model trained to translate language commands into LTL expression, and a new corpus of non-Markovian language commands spanning different levels of abstraction. We test our framework with the collected language commands on two drones, demonstrating that our approach enables robots to efficiently solve temporal commands at different levels of abstraction in both indoor and outdoor environments.
Processing the structure of documents: Logical Layout Analysis of historical newspapers in French

Background. In recent years, libraries and archives led important digitisation campaigns that opened the access to vast collections of historical documents. While such documents are often available as XML ALTO documents, they lack information about their logical structure. In this paper, we address the problem of Logical Layout Analysis applied to historical documents in French. We propose a rule-based method, that we evaluate and compare with two Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set contains French newspapers, periodicals and magazines, published in the first half of the twentieth century in the Franche-Comt\'e Region. Results. Our rule-based system outperforms the two other models in nearly all evaluations. It has especially better Recall results, indicating that our system covers more types of every logical label than the other two models. When comparing RIPPER with Gradient Boosting, we can observe that Gradient Boosting has better Precision scores but RIPPER has better Recall scores. Conclusions. The evaluation shows that our system outperforms the two Machine Learning models, and provides significantly higher Recall. It also confirms that our system can be used to produce annotated data sets that are large enough to envisage Machine Learning or Deep Learning approaches for the task of Logical Layout Analysis. Combining rules and Machine Learning models into hybrid systems could potentially provide even better performances. Furthermore, as the layout in historical documents evolves rapidly, one possible solution to overcome this problem would be to apply Rule Learning algorithms to bootstrap rule sets adapted to different publication periods.
Runs on Algorithmic Stablecoins: Evidence from Iron, Titan, and Steel

Stablecoins---digital currencies pegged to an external reference (e.g., the US dollar)---play an increasingly important role in transacting digital currencies. However, with a peg to an external reference comes the risk that the peg breaks and, akin to runs on other financial instruments, the risk of a stablecoin run.
NLP Cross-Domain Recognition of Retail Products

Self‐checkout systems aim to provide a seamless and high-quality shopping experience and increase the profitability of stores. These advantages come with some challenges such as shrinkage loss. To overcome these challenges, automatic recognition of the purchased products is a potential solution. In this context, one of the big issues that emerge is the data shifting, which is caused by the difference between the environment in which the recognition model is trained and the environment in which the model is deployed. In this paper, we use transfer learning to handle the shift caused by the change of camera and lens or their position as well as critical factors, mainly lighting, reflection, and occlusion. We motivate the use of Natural Language Processing (NLP) techniques on textual data extracted from images instead of using image recognition to study the efficiency of transfer learning techniques. The results show that cross-domain NLP retail recognition using the BERT language model only results in a small reduction in performance between the source and target domain. Furthermore, a small number of additional training samples from the target domain improves the model to perform comparable as a model trained on the source domain.
Enhanced Teaching-Learning-Based Optimization for 3D Path Planning of Multicopter UAVs

This paper introduces a new path planning algorithm for unmanned aerial vehicles (UAVs) based on the teaching-learning-based optimization (TLBO) technique. We first define an objective function that incorporates requirements on the path length and constraints on the movement and safe operation of UAVs to convert the path planning into an optimization problem. The optimization algorithm named Multi-subject TLBO is then proposed to minimize the formulated objective function. The algorithm is developed based on TLBO but enhanced with new operations including mutation, elite selection and multi-subject training to improve the solution quality and speed up the convergence rate. Comparison with state-of-the-art algorithms and experiments with real UAVs have been conducted to evaluate the performance of the proposed algorithm. The results confirm its validity and effectiveness in generating optimal, collision-free and flyable paths for UAVs in complex operating environments.
A Data-Driven Platform for the Coordination of Independent Visual Analytics Tools

Visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools. • We provide an in-depth investigation on different aspects for the coordination of independent Visual Analytics (VA) tools that lead to the development of the the Analytical Process Constructor (AnyProc). • We address issues from related work regarding data exchange, UI integration and analytic process support and propose first solution ideas based on our conceptual preliminary work. • We discuss the idea of pairwise data exchange mechanisms for VA tool coordination in domain workflows and present design principles to deal with persisting challenges. • Our main contribution is the introduction of our platform for the coordination of independent VA tools. We describe its features in detail especially regarding different solutions to the implementation of data exchange mechanisms. • We demonstrate the use of our platform for an example scenario in the healthcare sector and link its video presentation from our original submission at the EuroVA 2021. • We discuss our recent advances regarding UI layouts and conclude by summarizing our findings and suggesting entry points for future research.
Development of a Testing Protocol to Assess the Washability of E-Textiles

Most e-textiles are developed for wearable use and thus need to be washable to guarantee a textile typical usability. Yet, there are no e-textile specific wash testing standards and as a result, employed testing protocols vary greatly, resulting in a lack of comparability. To address this issue, an e-textile wash testing protocol modelled after testing methods provided by the standard ISO 6330 (the standard currently most often used as a basis for e-textile wash testing) as well as gentle household washing methods was developed and verified regarding its cleaning capability.
MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information

There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton , that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a Domain- Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data anal- ysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems. The results confirm that MODELFY is useful for defining such a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain experts.
Elcare: Elderly Care With Fall Detection

Abstract Continuous growth in the aged population over the years around the globe suggests advancements in medical facilities to ensure good medical health of people. The elder population is vulnerable to many medical problems as the immune system as well as the musculoskeletal system weakens with time. Falls are the major cause leading to serious medical conditions such as paralysis or even death for elders. Early fall detection can help in reducing the severity of these accidents and provide immediate medical assistance. To detect falls this paper discusses the use of pose estimation and sensor-based mobile devices. The system makes use of MediaPipe’s Pose Detection method to form a skeletal structure of the person. Exploiting the features such as drastic change in coordinate axes, angle of inclination while falling, and the sudden change in velocity during a fall helps to verify the authenticity of the fall. The sensor-based method makes use of mobile sensors and records the change in their values to decide whether the activity is a fall or not. The combination of both these methods allows for an accurate fall detection mechanism where the system finally notifies the concerned authorities via real-time feedback techniques such as text, message, or push notification.
Designing Patterns for DIC with Poisson Image Editing

The pattern used in DIC directly influences the quality of the measurement obtained with this technique, so it is of prime importance to optimize the geometry of the features characterizing the pattern to achieve the best metrological performance. The primary objective of this study is to quantify the influence of the collinearity between the actual displacement and the pattern image gradient on the systematic and random errors affecting displacement fields measured by DIC. Poisson Image Editing (PIE), a technique borrowed from the image processing community where it was introduced to perform seamless cloning of images, has been employed here to render various patterns whose gradient is globally collinear to the displacement field. Directly integrating the displacement field considered as the image gradient does not lead to a sufficiently contrasted pattern. A texture is therefore superposed to the displacement field before integration. Different patterns obtained or not by PIE are compared, and it is shown with suitable simulations performed with synthetic images that rendering a pattern where the image gradient is globally collinear to the displacement significantly reduces the total error affecting the measurements. The value of this improvement depends on the texture added to the displacement field before applying PIE. This study demonstrates that the collinearity between the speckle image gradient and the displacement field reduces the error affecting the displacement fields measured by DIC. It also proposes a route to account for this displacement in the design of an optimized pattern when a priori knowledge on the direction of the displacement is available. If not, it is shown that a small checkerboard inlayed in a larger one leads to a metrological performance much better than that obtained with a classic random pattern, and close to that achieved with an optimized pattern for which the displacement field is accounted for.
Probabilistic Circuit Implementation Based on P-Bits Using the Intrinsic Random Property of RRAM and P-Bit Multiplexing Strategy

Probabilistic computing is an emerging computational paradigm that uses probabilistic circuits to efficiently solve optimization problems such as invertible logic, where traditional digital computations are difficult to solve. This paper proposes a true random number generator (TRNG) based on resistive random-access memory (RRAM), which is combined with an activation function implemented by a piecewise linear function to form a standard p-bit cell, one of the most important parts of a p-circuit. A p-bit multiplexing strategy is also applied to reduce the number of p-bits and improve resource utilization. To verify the superiority of the proposed probabilistic circuit, we implement the invertible p-circuit on a field-programmable gate array (FPGA), including AND gates, full adders, multi-bit adders, and multipliers. The results of the FPGA implementation show that our approach can significantly save the consumption of hardware resources.
Explainable machine learning pipeline for Twitter bot detection during the 2020 US Presidential Elections

This study introduces a novel, reproducible and reusable Twitter bot identification system. The system uses a machine learning (ML) pipeline, fed with hundreds of features extracted from a Twitter corpus. The main objective of the proposed ML pipeline is to train and validate different state-of-the-art machine learning models, where the eXtreme Gradient Boosting (XGBoost) model is selected since it achieves the highest detection performance. The Twitter dataset was collected during the 2020 US Presidential Elections, and additional experimental evaluation on distinct Twitter datasets demonstrates the superiority of our approach, in terms of high bot detection accuracy.
Equi-explanation Maps: Concise and Informative Global Summary Explanations

We attempt to summarize the model logic of a black-box classification model in order to generate concise and informative global explanations. We propose equi-explanation maps, a new explanation data-structure that presents the region of interest as a union of equi-explanation subspaces along with their explanation vectors. We then propose E-Map, a method to generate equi-explanation maps. We demonstrate the broad utility of our approach by generating equi-explanation maps for various binary classification models (Logistic Regression, SVM, MLP, and XGBoost) on the UCI Heart disease dataset and the Pima Indians diabetes dataset. Each subspace in our generated map is the union of d-dimensional hyper-cuboids which can be compactly represented for the sake of interpretability. For each of these subspaces, we present linear explanations assigning a weight to each explanation feature. We justify the use of equi-explanation maps in comparison to other global explanation methods by evaluating in terms of interpretability, fidelity, and informativeness. A user study further corroborates the use of equi-explanation maps to generate compact and informative global explanations.
Robustness analysis of denoising neural networks for bone scintigraphy

This paper describes and compares two neural network (NN) based noise filters developed for planar bone scintigraphy. Images taken with a gamma camera typically have a low signal-to-noise ratio and are subject to significant Poisson noise. In our work, we have designed a neural network based noise filter that can be used with planar bone scintigraphy recordings at multiple noise levels, instead of developing a separate network for each noise level. The proposed denoising solution is a convolutional neural network (CNN) inspired by U-NET architecture. A total of 1215 pairs of anterior and posterior patient images were available for training and evaluation during the analysis. The noise-filtering network was trained using bone scintigraphy recordings with real statistics according to the standard protocol, without noise-free recordings. The resulting solution proved to be robust to the noise level of the images within the examined limits. During the evaluation, we compared the performance of the networks to Gaussian and median filters and to the Block-matching and 3D filtering (BM3D) filter. Our presented evaluation method in this article does not require noiseless images and we measured the performance and robustness of our solution on specialized validation sets. We showed that particularly high signal-to-noise ratios can be achieved using noise-filtering neural networks (NNs), which are more robust than the traditional methods and can help diagnosis, especially for images with high noise content.
A MaxEnt learner for super-additive counting cumulativity

Whereas most previous studies on (super-)gang effects examined cases where two weaker constraints jointly beat another stronger constraint (Albright 2012; Shih 2017; Breiss &amp;amp; Albright 2022), this paper addresses gang effects that arise from multiple violations of a single constraint, which Jäger &amp;amp; Rosenbach (2006) referred to as counting cumulativity. The super-additive version of counting cumulativity is the focus of this paper; cases where multiple violations of a weaker constraint not only overpower a single violation of a stronger constraint, but also surpass the mere multiplication of the severity of its single violation. I report two natural language examples where a morphophonological alternation in a compound is suppressed by the existence of marked segments in a super-additive manner: laryngeally marked consonants in Korean compound tensification and nasals in Japanese Rendaku. Using these two test cases, this paper argues that these types of super-additivity cannot be entirely captured by the traditional MaxEnt grammar; instead, a modified MaxEnt model is proposed, in which the degree of penalty is scaled up by the number of violations, through a power function. This paper also provides a computational implementation of the proposed MaxEnt model which learns necessary parameters given quantitative language data. A series of learning simulations on Korean and Japanese show that the MaxEnt learner is able to detect super-additive constraints and find the appropriate exponent values for those, correctly capturing the probability distributions in the input data.
SAC-based Computation Offloading and Resource Allocation in Vehicular Edge Computing

The Vehicular Edge Computing (VEC) provides powerful computing resources for intelligent terminals. However, the diversity of computing resources at edge nodes (i.e., edge servers and idle vehicles) and the mobility of vehicles impose great challenges on computation offloading. In this paper, we investigate the joint optimization problem of computation offloading and resource allocation in a cooperative vehicular network by exploiting idle vehicles and Road Side Units (RSUs) equipped with edge servers. In order to minimize the task completion time under latency constraint, a Soft Actor-Critic (SAC)-based algorithm is proposed to solve the problem. The simulation results show that the proposed SAC-based algorithm can effectively reduce the total latency of the system, and its performance is significantly better than other benchmark methods.
Organizational and Managerial Problems of the Functioning of Academic Journals of the RAS

The article raises the questions about the use of scientific periodicals in modern conditions in academic journals of a socio-humanitarian profile. It is also outlines the main production problems associated with holding competitions for the publication of RAS journals and their financing, special personnel, editorial phenomena, institutions for reviewing and participation of the editorial board, reviews and authors, as well as general issues of the development of scientific periodicals are identified. It is shown that an indicator of the importanceof the role of the editorial board is in maintaining a high level of scientific periodicals.