Reviewerly/exHarmony huggingface dataset:
Mobile Visual Servoing Based Control of a Complex Autonomous System Assisting a Manufacturing Technology on a Mechatronics Line

The main contribution of this paper is the modeling and control for a complex autonomous system (CAS). It is equipped with a visual sensor to operate precision positioning in a technology executed on a laboratory mechatronics line. The technology allows the retrieval of workpieces which do not completely pass the quality test. Another objective of this paper is the implementation of an assisting technology for a laboratory processing/reprocessing mechatronics line (P/RML) containing four workstations, assisted by the following components: a complex autonomous system that consists of an autonomous robotic system (ARS), a wheeled mobile robot (WMR) PeopleBot, a robotic manipulator (RM) Cyton 1500 with seven degrees of freedom (7 DOF), and a mobile visual servoing system (MVS) with a Logitech camera as visual sensor used in the process of picking, transporting and placing the workpieces. The purpose of the MVS is to increase the precision of the RM by utilizing the look and move principle, since the initial and final positions of the CAS can slightly deviate from their trajectory, thus increasing the possibility of errors to appear during the process of catching and releasing the pieces. If the processed piece did not pass the quality test and has been rendered as defective, it is retrieved from the last station of the P/RML and transported to the first station for reprocessing. The control of the WMR is done using the trajectory-tracking sliding-mode control (TTSMC). The RM control is based on inverse kinematics model, and the MVS control is implemented with the image moments method.
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning

Many robotic tasks are composed of a lot of temporally correlated sub-tasks in a highly complex environment. It is important to discover situational intentions and proper actions by deliberating on temporal abstractions to solve problems effectively. To understand the intention separated from changing task dynamics, we extend an empowerment-based regularization technique to situations with multiple tasks based on the framework of a generative adversarial network. Under the multitask environments with unknown dynamics, we focus on learning a reward and policy from the unlabeled expert examples. In this study, we define situational empowerment as the maximum of mutual information representing how an action conditioned on both a certain state and sub-task affects the future. Our proposed method derives the variational lower bound of the situational mutual information to optimize it. We simultaneously learn the transferable multi-task reward function and policy by adding an induced term to the objective function. By doing so, the multi-task reward function helps to learn a robust policy for environmental change. We validate the advantages of our approach on multi-task learning and multi-task transfer learning. We demonstrate our proposed method has the robustness of both randomness and changing task dynamics. Finally, we prove that our method has significantly better performance and data efficiency than existing imitation learning methods on various benchmarks.
A comparison of local explanation methods for high-dimensional industrial data: A simulation study

Prediction methods can be augmented by local explanation methods (LEMs) to perform root cause analysis for individual observations. But while most recent research on LEMs focus on low-dimensional problems, real-world datasets commonly have hundreds or thousands of variables. Here, we investigate how LEMs perform for high-dimensional industrial applications. Seven prediction methods (penalized logistic regression, LASSO, gradient boosting, random forest and support vector machines) and three LEMs (TreeExplainer, Kernel SHAP, and the conditional normal sampling importance (CNSI)) were combined into twelve explanation approaches. These approaches were used to compute explanations for simulated data, and real-world industrial data with simulated responses. The approaches were ranked by how well they predicted the contributions according to the true models. For the simulation experiment, the generalized linear methods provided best explanations, while gradient boosting with either TreeExplainer or CNSI, or random forest with CNSI were robust for all relationships. For the real-world experiment, TreeExplainer performed similarly, while the explanations from CNSI were significantly worse. The generalized linear models were fastest, followed by TreeExplainer, while CNSI and Kernel SHAP required several orders of magnitude more computation time. In conclusion, local explanations can be computed for high-dimensional data, but the choice of statistical tools is crucial.
SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech

We introduce a new paradigm for single-channel target source separation where the sources of interest can be distinguished using non-mutually exclusive concepts (e.g., loudness, gender, language, spatial location, etc). Our proposed heterogeneous separation framework can seamlessly leverage datasets with large distribution shifts and learn cross-domain representations under a variety of concepts used as conditioning. Our experiments show that training separation models with heterogeneous conditions facilitates the generalization to new concepts with unseen out-of-domain data while also performing substantially higher than single-domain specialist models. Notably, such training leads to more robust learning of new harder source separation discriminative concepts and can yield improvements over permutation invariant training with oracle source selection. We analyze the intrinsic behavior of source separation training with heterogeneous metadata and propose ways to alleviate emerging problems with challenging separation conditions. We release the collection of preparation recipes for all datasets used to further promote research towards this challenging task.
Pre-processing of handwritten signature images for following recognition

In the process of handwritten signature recognition, preprocessing is an important step before calculation its informative features. Signatures of any person always have some differences, in addition, they can be of different colors, sizes and orientations. After signature digitization, their images may contain some noise. The purpose of signature image preprocessing is to obtain the most invariant representation of the digital image of a person's signature, which will allow us to identify him or define that the signature is forged. This paper describes a sequence of transformations necessary to perform preprocessing of the signature image and form its representation of a single orientation and size. It is assumed that there are no graphic elements in the image that are not related to the signature and the background is relatively uniform. The transformations under consideration sequentially perform binarization of the signature image, its filtering, rotation, cropping of the circumscribed rectangle and scaling to a fixed size. The described preprocessing procedures were applied to a number of available digital signature image databases, such as CEDAR, BHSig260-Bengali, BHSig260-Hindi. Experiments on signature recognition confirm that the presented approach to the signature image preprocessing leads to increasing of the recognition accuracy.
CNN-Based Approach for Overlapping Erythrocyte Counting and Cell Type Classification in Peripheral Blood Images

Classification and counting of cells in the blood is crucial for diagnosing and treating diseases in the clinic. A peripheral blood smear method is a fast, reliable, robust diagnostic tool for examining blood samples. However, cell overlap during the peripheral smear process may cause incorrectly predicted results in counting blood cells and classifying cell types. The overlapping problem can occur in automated systems and manual inspections by experts. Convolutional neural networks (CNN) provide reliable results for the segmentation and classification of many problems in the medical field. However, creating ground truth labels in the data during the segmentation process is time-consuming and error-prone. This study proposes a new CNN-based strategy to eliminate the overlap-induced counting problem in peripheral smear blood samples and accurately determine the blood cell type. In the proposed method, images of the peripheral blood were divided into sub-images, block by block, using adaptive image processing techniques to identify the overlapping cells and cell types. CNN was used to classify cell types and overlapping cell numbers in sub-images. The proposed method successfully counts overlapping erythrocytes and determines the cell type with an accuracy rate of 99.73\%. The results of the proposed method have shown that it can be used efficiently in various fields.
The impact of ICT utilization to improve the learning outcome: A meta-analysis

&lt;span lang="EN-US"&gt;This study aimed to describe the impact of the utilization of technology on learning outcomes through a meta-analysis. Data was collected by documenting research that is resulted from various sources collected through internet. These articles were published from 2006 to 2019, in United Nations Language. The article discusses the influence of information and communication technology (ICT) on learning achievement, analyzed qualitatively, describing the data in the form of sample size, standard deviation, and average, and published in journals indexed in Google Scholar. The analysis design employed a contrast group with the random effect model that the effect size is corrected. The analysis was used JASP software for calculating the average aggregate difference, drawing a forest plot, and publication bias. The results of the analysis show that there is a significant difference between groups using ICT and non-ICT in the learning process (SE=1.13), groups of students who utilizing ICT in the learning have better learning outcomes than those who do not use ICT. Based on the results of the analysis, it is very clear the urgency of implementing ICT in supporting the learning process. Schools should conduct an assessment of the condition of the school and students and then choose the appropriate ICT implementation. Thus, the quality of the learning process and students' technological literacy improve.&lt;/span&gt;
Mapping the Design Space of Human-AI Interaction in Text Summarization

Automatic text summarization systems commonly involve humans for preparing data or evaluating model performance, yet, there lacks a systematic understanding of humans' roles, experience, and needs when interacting with or being assisted by AI.From a human-centered perspective, we map the design opportunities and considerations for human-AI interaction in text summarization and broader text generation tasks.We first conducted a systematic literature review of 70 papers, developing a taxonomy of five interactions in AI-assisted text generation and relevant design dimensions.We designed text summarization prototypes for each interaction.We then interviewed 16 users, aided by the prototypes, to understand their expectations, experience, and needs regarding efficiency, control, and trust with AI in text summarization and propose design considerations accordingly.
Accountable Private Set Cardinality for Distributed Measurement

We introduce cryptographic protocols for securely and efficiently computing the cardinality of set union and set intersection. Our private set-cardinality protocols ( PSC ) are designed for the setting in which a large set of parties in a distributed system makes observations, and a small set of parties with more resources and higher reliability aggregates the observations. PSC allows for secure and useful statistics gathering in privacy-preserving distributed systems. For example, it allows operators of anonymity networks such as Tor to securely answer the questions: How many unique users are using the network? and How many hidden services are being accessed? We prove the correctness and security of PSC in the Universal Composability framework against an active adversary that compromises all but one of the aggregating parties. Although successful output cannot be guaranteed in this setting, PSC either succeeds or terminates with an abort, and we furthermore make the adversary accountable for causing an abort by blaming at least one malicious party. We also show that PSC prevents adaptive corruption of the data parties from revealing past observations, which prevents them from being victims of targeted compromise, and we ensure safe measurements by making outputs differentially private. We present a proof-of-concept implementation of PSC and use it to demonstrate that PSC operates with low computational overhead and reasonable bandwidth. It can count tens of thousands of unique observations from tens to hundreds of data-collecting parties while completing within hours. PSC is thus suitable for daily measurements in a distributed system.
Differentially Private Normalizing Flows for Synthetic Tabular Data Generation

Normalizing flows have shown to be a promising approach to deep generative modeling due to their ability to exactly evaluate density --- other alternatives either implicitly model the density or use approximate surrogate density. In this work, we present a differentially private normalizing flow model for heterogeneous tabular data. Normalizing flows are in general not amenable to differentially private training because they require complex neural networks with larger depth (compared to other generative models) and use specialized architectures for which per-example gradient computation is difficult (or unknown). To reduce the parameter complexity, the proposed model introduces a conditional spline flow which simulates transformations at different stages depending on additional input and is shared among sub-flows. For privacy, we introduce two fine-grained gradient clipping strategies that provide a better signal-to-noise ratio and derive fast gradient clipping methods for layers with custom parameterization. Our empirical evaluations show that the proposed model preserves statistical properties of original dataset better than other baselines.
PantheonRL: A MARL Library for Dynamic Training Interactions

We present PantheonRL, a multiagent reinforcement learning software package for dynamic training interactions such as round-robin, adaptive, and ad-hoc training. Our package is designed around flexible agent objects that can be easily configured to support different training interactions, and handles fully general multiagent environments with mixed rewards and n agents. Built on top of StableBaselines3, our package works directly with existing powerful deep RL algorithms. Finally, PantheonRL comes with an intuitive yet functional web user interface for configuring experiments and launching multiple asynchronous jobs. Our package can be found at https://github.com/Stanford-ILIAD/PantheonRL.
How Private Is Your RL Policy? An Inverse RL Based Analysis Framework

Reinforcement Learning (RL) enables agents to learn how to perform various tasks from scratch. In domains like autonomous driving, recommendation systems, and more, optimal RL policies learned could cause a privacy breach if the policies memorize any part of the private reward. We study the set of existing differentially-private RL policies derived from various RL algorithms such as Value Iteration, Deep-Q Networks, and Vanilla Proximal Policy Optimization. We propose a new Privacy-Aware Inverse RL analysis framework (PRIL) that involves performing reward reconstruction as an adversarial attack on private policies that the agents may deploy. For this, we introduce the reward reconstruction attack, wherein we seek to reconstruct the original reward from a privacy-preserving policy using the Inverse RL algorithm. An adversary must do poorly at reconstructing the original reward function if the agent uses a tightly private policy. Using this framework, we empirically test the effectiveness of the privacy guarantee offered by the private algorithms on instances of the FrozenLake domain of varying complexities. Based on the analysis performed, we infer a gap between the current standard of privacy offered and the standard of privacy needed to protect reward functions in RL. We do so by quantifying the extent to which each private policy protects the reward function by measuring distances between the original and reconstructed rewards.
Making Translations to Classical Planning Competitive with Other HTN Planners

Translation-based approaches to planning allow for solving problems in complex and expressive formalisms via the means of highly efficient solvers for simpler formalisms. To be effective, these translations have to be constructed appropriately. The current existing translation of the highly expressive formalism of HTN planning into the more simple formalism of classical planning is not on par with the performance of current dedicated HTN planners. With our contributions in this paper, we close this gap: we describe new versions of the translation that reach the performance of state-of-the-art dedicated HTN planners. We present new translation techniques both for the special case of totally-ordered HTNs as well as for the general partially-ordered case. In the latter, we show that our new translation generates only linearly many actions, while the previous encoding generates and exponential number of actions.
How to Find a Good Explanation for Clustering?

k-means and k-median clustering are powerful unsupervised machine learning techniques. However, due to complicated dependences on all the features, it is challenging to interpret the resulting cluster assignments. Moshkovitz, Dasgupta, Rashtchian, and Frost proposed an elegant model of explainable k-means and k-median clustering in ICML 2020. In this model, a decision tree with k leaves provides a straightforward characterization of the data set into clusters. We study two natural algorithmic questions about explainable clustering. (1) For a given clustering, how to find the ``best explanation'' by using a decision tree with k leaves? (2) For a given set of points, how to find a decision tree with k leaves minimizing the k-means/median objective of the resulting explainable clustering? To address the first question, we introduce a new model of explainable clustering. Our model, inspired by the notion of outliers in robust statistics, is the following. We are seeking a small number of points (outliers) whose removal makes the existing clustering well-explainable. For addressing the second question, we initiate the study of the model of Moshkovitz et al. from the perspective of multivariate complexity. Our rigorous algorithmic analysis sheds some light on the influence of parameters like the input size, dimension of the data, the number of outliers, the number of clusters, and the approximation ratio, on the computational complexity of explainable clustering.
Development of the trainer program based on neural network technologies for development of spatial thinking of students

Introduction. For the last thirty years, UNESCO has been repeatedly raising questions of accessibility and quality of education for every child, teen, and adult by implementing the “Education for All” program. The new concept of education proposed by UNESCO aims at transforming people’s lives and formulates the following sustainable development goal “To provide inclusive and equitable quality education and create lifelong learning opportunities for all. One strategy for achieving this goal is to use and develop information and communication technology (ICT) to strengthen education systems, knowledge dissemination, access to information, and quality and effective learning. The aim of the work is to increase the efficiency of students’ learning of geometrical and graphic disciplines by developing a pilot electronic course in LMS Moodle and special software implementing elements of individualization of the learning process. Materials and methods. Theoretical research methods included generalization of scientific and pedagogical literature on formation of spatial thinking. Empirical research methods included testing, interviewing, and questioning using Google forms. Mathematical methods of research included algorithmizing and programming. Python 3.9 language and PyCharm development environment were used for writing the trainer program. The OpenCV, Matplotlib and Keras libraries, Microsoft PowerPoint, Microsoft Visio and CAD Compass-3D were used to create methodological materials Results. The software simulator for developing spatial thinking of engineering students studying “Descriptive Geometry” discipline has been developed in three versions. Conclusion. The ideas about the level of spatial thinking of modern students are supplemented. The original programs for the implementation of the elements of individualization of the educational process in the discipline of “Descriptive geometry” and algorithms of its work were developed and introduced into the scientific turnover, as well as the proposed pedagogical conditions of their use. Practical significance lies in the creation of learning and teaching methods and program basic elements for the use of end-to-end digital technology in the learning process.
Semantic Feature Discovery with Code Mining and Semantic Type Detection

In recent years, the automation of machine learning and data science (AutoML) has attracted significant attention. One under-explored dimension of AutoML is being able to automatically utilize domain knowledge (such as semantic concepts and relationships) located in historical code or literature from the problem's domain. In this paper, we demonstrate Semantic Feature Discovery, which enables users to interactively explore features semantically discovered from existing data science code and external knowledge. It does so by detecting semantic concepts for a given dataset, and then using these concepts to determine relevant feature engineering operations from historical code and knowledge.
Meta-Learning for Decoding Neural Activity Data With Noisy Labels

In neural decoding, a behavioral variable is often generated by manual annotation and the annotated labels could contain extensive label noise, leading to poor model generalizability. Tackling the label noise problem in neural decoding can improve model generalizability and robustness. We use a deep neural network based sample reweighting method to tackle this problem. The proposed method reweights training samples by using a small and clean validation dataset to guide learning. We evaluated the sample reweighting method on simulated neural activity data and calcium imaging data of anterior lateral motor cortex. For the simulated data, the proposed method can accurately predict the behavioral variable even in the scenario that 36 percent of samples in the training dataset are mislabeled. For the anterior lateral motor cortex study, the proposed method can predict trial types with F1 score of around 0.85 even 48 percent of training samples are mislabeled.