Reviewerly/exHarmony huggingface dataset:
Learning Theory Convergence Rates for Observers and Controllers in Native Space Embedding

This paper derives rates of convergence of approximations of observers and controllers arising in the native space embedding method for adaptive estimation and control of a class of nonlinear ordinary differential equations (ODEs) that feature functional uncertainty. The native space embedding method views the nonlinear ODE as a type of distributed parameter system (DPS), and ideal controllers are derived from the DPS representation. Implementable estimators or controllers for the ODE are obtained by approximation of the DPS using history-dependent, scattered bases in the native space. The basis functions are defined in terms of their centers of approximation. This paper shows that for a large collection of choices of the native space, it is possible to derive convergence rates for implementable schemes that are expressed in terms of the fill distance of the centers of approximation in a subset that supports the observation or measurement process. The error bounds are derived in terms of the power function of the reproducing kernel and resemble those derived recently in machine learning theory and Bayesian estimation as applied to discrete stochastic systems.
Accelerated Primal-dual Scheme for a Class of Stochastic Nonconvex-concave Saddle Point Problems

Stochastic nonconvex-concave min-max saddle point problems appear in many machine learning and control problems including distributionally robust optimization, generative adversarial networks, and adversarial learning. In this paper, we consider a class of nonconvex saddle point problems where the objective function satisfies the Polyak-Łojasiewicz condition with respect to the minimization variable and it is concave with respect to the maximization variable. The existing methods for solving nonconvex-concave saddle point problems often suffer from slow convergence and/or contain multiple loops. Our main contribution lies in proposing a novel single-loop accelerated primal-dual algorithm with new convergence rate results appearing for the first time in the literature, to the best of our knowledge.
Modeling the Development of Energy Network Software, Taking into Account the Detection and Elimination of Vulnerabilities

This paper solves the problem of modeling the scheme for developing software systems, which can be used in building solutions for secure energy networks. A development scheme is proposed in a set of representations through which each program of the software complex passes, namely the following representations: idea, conceptual model, architecture, algorithm, source code, graphic code, abstract syntax tree, assembler code, machine code, byte code, executed code. The main properties of each representation are indicated, such as the form (text, graphic, programming language, binary, and decoded), development (transformation) methods, as well as vulnerabilities that are detected in it. An example of each representation is given, particularly as applied to the energy networks. The scheme elements (representations, vulnerabilities, forms, etc.) and the main operations for working with their elements (representation transformation, vulnerability injection, and detection) are presented in an analytical form. An example of a development scheme for a simple software complex of energy networks is given. The classification of vulnerabilities is introduced; it divides the vulnerabilities according to the structural level, functioning disruption, and information impact. The vulnerabilities in each of the views are substantiated using the common vulnerabilities and exposures (CVE) database. An experiment was conducted to demonstrate the vulnerability spread across representations during the development of a software complex example for the energy network. The features of the applications of the obtained results for energy networks are taken into account. The advantages, disadvantages, and limitations of the study, as well as ways to eliminate them, are discussed.
Risk-Sensitive Policy with Distributional Reinforcement Learning

Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the Q function generally standing at the core of learning schemes in RL by another function, taking into account both the expected return and the risk. Named the risk-based utility function U, it can be extracted from the random return distribution Z naturally learnt by any distributional RL algorithm. This enables the spanning of the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, with an emphasis on the interpretability of the resulting decision-making process.
A Global Trajectory Planning Framework Based on Minimizing the Risk Index

At present, autonomous mobile robots are widely used in industrial and commercial fields. However, although the global path searched by existing mobile robot path planning methods has no collision with obstacles, there is a problem in that the path is close to obstacles and is not smooth, and there is a collision safety risk when the robot is actually moving. To solve the above problems, this paper proposes a global path planning method based on minimizing the risk index. Firstly, the distance calculation method of the heuristic function of the traditional graph search algorithm is improved to reduce the number of nodes in the search space. Additionally, by selecting the appropriate search neighborhood, the search efficiency and path smoothness of the algorithm are improved. Thirdly, to increase the distance between the original search path and obstacles, the risk index path search strategy is proposed. Finally, the minimized snap trajectory smoothing method with a safe corridor is used to smooth the original waypoint. Both simulation and real robot experimental results show that the minimum distance between waypoints and obstacles is increased by 43.72% on average, and the number of trajectory inflection points are reduced by 75.12% on average after optimization. As such, the proposed method can fully guarantee safety and generate smooth mobile robot paths in global trajectory planning tasks.
Enhancing Abstractive Summarization with Extracted Knowledge Graphs and Multi-Source Transformers

As the popularity of large language models (LLMs) has risen over the course of the last year, led by GPT-3/4 and especially its productization as ChatGPT, we have witnessed the extensive application of LLMs to text summarization. However, LLMs do not intrinsically have the power to verify the correctness of the information they supply and generate. This research introduces a novel approach to abstractive summarization, aiming to address the limitations of LLMs in that they struggle to understand the truth. The proposed method leverages extracted knowledge graph information and structured semantics as a guide for summarization. Building upon BART, one of the state-of-the-art sequence-to-sequence pre-trained LLMs, multi-source transformer modules are developed as an encoder, which are capable of processing textual and graphical inputs. Decoding is performed based on this enriched encoding to enhance the summary quality. The Wiki-Sum dataset, derived from Wikipedia text dumps, is introduced for evaluation purposes. Comparative experiments with baseline models demonstrate the strengths of the proposed approach in generating informative and relevant summaries. We conclude by presenting our insights into utilizing LLMs with graph external information, which will become a powerful aid towards the goal of factually correct and verified LLMs.
How Chatbot Language Shapes Consumer Perceptions: The Role of Concreteness and Shared Competence

In service settings, chatbots frequently are associated with substandard care, depersonalization, and linguistic misunderstandings. Drawing on assemblage theory (i.e., the examination of how heterogeneous parts, through their ongoing interaction, create an emergent whole with new capacities that the parts themselves do not have), the authors investigate how chatbots’ language concreteness—the specificity of words used during interactions with consumers—can help improve satisfaction, willingness to use the chatbot, and perceived shopping efficiency. Across three experiments, the findings reveal a psychological mechanism driven by concrete chatbot language that makes chatbots seem competent and reinforces consumer self-competence, in turn boosting satisfaction, willingness to use the chatbot, and perceived shopping efficiency. This pattern of results contributes to consumer behavior by providing evidence of the chatbot language concreteness effect on consumer–chatbot interactions. For practitioners, the authors outline conversational designs that could help optimize implementation of chatbots in customer service.
A Dynamic and Adaptive Cybersecurity Governance Framework

Cybersecurity protects cyberspace from a wide range of cyber threats to reduce overall business risk, ensure business continuity, and maximize business opportunities and return on investments. Cybersecurity is well achieved by using appropriate sets of security governance frameworks. To this end, various Information Technology (IT) and cybersecurity governance frameworks have been reviewed along with their benefits and limitations. The major limitations of the reviewed frameworks are; they are complex and have complicated structures to implement, they are expensive and require high skill IT and security professionals. Moreover, the frameworks require many requirement checklists for implementation and auditing purposes and a lot of time and resources. To fill the limitations mentioned above, a simple, dynamic, and adaptive cybersecurity governance framework is proposed that provides security related strategic direction, ensures that security risks are managed appropriately, and ensures that organizations’ resources are utilized optimally. The framework incorporated different components not considered in the existing frameworks, such as research and development, public-private collaboration framework, regional and international cooperation framework, incident management, business continuity, disaster recovery frameworks, and compliance with laws and regulations. Moreover, the proposed framework identifies and includes some of the existing frameworks’ missed and overlapped components, processes, and activities. It has nine components, five activities, four outcomes, and seven processes. Performance metrics, evaluation, and monitoring techniques are also proposed. Moreover, it follows a risk based approach to address the current and future technology and threat landscapes. The design science research method was used in this research study to solve the problem mentioned. Using the design science research method, the problem was identified. Based on the problem, research objectives were articulated; the objective of this research was solved by developing a security governance framework considering different factors which were not addressed in the current works. Finally, performance metrics were proposed to evaluate the implementation of the governance framework.
Syntactic complexity in translated eHealth discourse of COVID-19: a comparable parallel corpus approach

In the pandemic era, the efficient management of COVID-19 by public health authorities is dependent upon the effective dissemination of health-related information. Due to the global nature of COVID-19, translations of healthcare information from the source language to the languages of target users are common. The current study discusses the findings from a comparative corpus analysis of a self-built corpus based on the translated version of eHealth information and the Coronavirus corpus, which contains 12 million words and serves as a reference corpus. Revealing a significant difference between the two corpora, the study shows that the overall sentence complexity of translated texts is significantly lower than non-translated texts even though it deploys longer length of production. The results also show that the translated e-Health information encompasses less coordination structures. In addition, the findings regarding the use of subordination and specific structures are rather mixed and the translated texts appear to be less complicated in the measure of T-units per sentence and verb phrases per T-unit. This study highlights that critical information such as medication adherence must be communicated clearly and understandably to the general public. The findings of the study offer support for the use of plain English in the dissemination material provided by the websites of the leading healthcare public institutions
An Innovative Teaching Model

The degree to which a virtual content layer exists between the viewer and the real world, and how the real world is augmented, can vary greatly across tools, and use cases, but is essential to all forms of virtual reality (VR). The degree of overlap with the real world, as well as the importance of location, cooperation, and mobility, can vary dramatically from one VR experience to the next. Teachers will need support in figuring out how to best utilize AI for the benefit of student learning because of the plethora of available AI options. In this chapter, the authors share the results of a poll of students, both in the field and those hoping to enter it, to learn their thoughts on VR in the classroom. To gauge participants' thoughts on VR potential in the classroom, they gave them the VR platform and had them create a VR experience on their mobile devices. In virtual classrooms, 165 students can interact with the VR and other students as avatars, ask questions, write conversations, identify vocabulary connected between VR and material, and provide suggestions for improving classroom management.
Emotions in Requirements Engineering: A Systematic Mapping Study

The purpose of requirements engineering (RE) is to make sure that the expectations and needs of the stakeholders of a software system are met. Emotional needs can be captured as emotional requirements that represent how the end user should feel when using the system. Differently from functional and quality (non-functional) requirements, emotional requirements have received relatively less attention from the RE community. This study is motivated by the need to explore and map the literature on emotional requirements. The study applies the systematic mapping study technique for surveying and analyzing the available literature to identify the most relevant publications on emotional requirements. We identified 34 publications that address a wide spectrum of practices concerned with engineering emotional requirements. The identified publications were analyzed with respect to the application domains, instruments used for eliciting and artefacts used for representing emotional requirements, and the state of the practice in emotion-related requirements engineering. This analysis serves to identify research gaps and research directions in engineering emotional requirements. To the best of the knowledge by the authors, no other similar study has been conducted on emotional requirements.
A scale conjugate neural network learning process for the nonlinear malaria disease model

&lt;abstract&gt; &lt;p&gt;The purpose of this work is to provide a stochastic framework based on the scale conjugate gradient neural networks (SCJGNNs) for solving the malaria disease model of pesticides and medication (MDMPM). The host and vector populations are divided in the mathematical form of the malaria through the pesticides and medication. The stochastic SCJGNNs procedure has been presented through the supervised neural networks based on the statics of validation (12%), testing (10%), and training (78%) for solving the MDMPM. The optimization is performed through the SCJGNN along with the log-sigmoid transfer function in the hidden layers along with fifteen numbers of neurons to solve the MDMPM. The accurateness and precision of the proposed SCJGNNs is observed through the comparison of obtained and source (Runge-Kutta) results, while the small calculated absolute error indicate the exactitude of designed framework based on the SCJGNNs. The reliability and consistency of the SCJGNNs is observed by using the process of correlation, histogram curves, regression, and function fitness.&lt;/p&gt; &lt;/abstract&gt;
From Equity of Opportunity to Equity of Development: A Comparative Analysis of Large-Scale Online Education in Urban and Rural K-12 Schools in China during COVID-19

AbstractAbstractFaced with the sudden outbreak of the COVID-19 pandemic, Chinese K-12 schools pivoted to online teaching to "suspend classes without stopping learning" and to ensure continued learning in the first half of 2020. As an emergency measure to combat the pandemic, the implementation of online teaching met a range of challenges. This study randomly distributed approximately 420,000 questionnaires to primary and secondary students, teachers, and administrators across all provinces in China, with the aim of investigating online teaching during the pandemic from the perspectives of teaching organization, policy implementation, and learning experience, as well as comparing the ways urban and rural schools responded to COVID-19 from the perspective of balanced education development. Results show that schools across different regions in the country are equipped with essential facilities for online teaching, guaranteeing the successful rollout of online teaching. It is also found that schools need to enhance their capacity for contextualized decision making and innovation in teaching practice and that additional support should be given to rural schools to improve their access to resources and their online teaching experience. Significant differences between urban and rural schools are found to exist in such areas as students' learning interest and effectiveness, information technology literacy, and psychological well-being. Moreover, there also exists considerable discrepancy in teachers' digital teaching competence between urban and rural schools. We discuss the implications from the findings of this large-scale study and make suggestions for future directions of online teaching development in the digital age.Keywords: Balanced education developmentCOVID-19digitalization leadershipeducational equityICT in educationonline educationonline learningsuspending classes without stopping learning Additional informationNotes on contributorsYiling HuYiling Hu is affiliated with the Department of Education Information Technology, East China Normal University, Shanghai.Jing NieJing Nie is affiliated with the Department of Education Information Technology, East China Normal University, Shanghai.Xiaoqing GuXiaoqing Gu is affiliated with the Department of Education Information Technology, East China Normal University, Shanghai.
Piecewise polynomial approximation of spatial curvilinear profiles using the Hough transform

Given a curvilinear profile P represented as a set of points in the space R3 and four families of low-degree polynomial curves that respectively depend on the parameters in the space R4, our goal is to identify the piecewise space polynomial curve best fitting the profile P. We use a parametric representation of the space curves and subdivide the profile into smaller portions that can be fitted with regular curves. We provide theoretical guarantees to the existence of such an approximation and an algorithm for the profile approximation. We take advantage of the implicit function theorem to locally project a space curve on at most two planes and to locally recognise it with a low-degree polynomial curve obtained by applying the Hough transform. Finally, we recombine the curve expressions on the two planes backwards in the space R3. The outcome of the algorithm is thus a piecewise polynomial curve approximating the profile. We validate our approach to approximate curvilinear profiles extracted from 3D point clouds representing real objects and to simplify and resample point clouds.
FRIDA: A Collaborative Robot Painter with a Differentiable, Real2Sim2Real Planning Environment

Painting is an artistic process of rendering visual content that achieves the high-level communication goals of an artist that may change dynamically throughout the creative process. In this paper, we present a Framework and Robotics Initiative for Developing Arts (FRIDA) that enables humans to produce paintings on canvases by collaborating with a painter robot using simple inputs such as language descriptions or images. FRIDA introduces several technical innovations for computationally modeling a creative painting process. First, we develop a fully differentiable simulation environment for painting, adopting the idea of real to simulation to real (real2sim2real). We show that our proposed simulated painting environment is higher fidelity to reality than existing simulation environments used for robot painting. Second, to model the evolving dynamics of a creative process, we develop a planning approach that can continuously optimize the painting plan based on the evolving canvas with respect to the high-level goals. In contrast to existing approaches where the content generation process and action planning are performed independently and sequentially, FRIDA adapts to the stochastic nature of using paint and a brush by continually re-planning and re-assessing its semantic goals based on its visual perception of the painting progress. We describe the details on the technical approach as well as the system integration. FRIDA software is freely available at: https://github.com/cmubig/Frida.
Surrounding-aware representation prediction in Birds-Eye-View using transformers

Birds-Eye-View (BEV) maps provide an accurate representation of sensory cues present in the surroundings, including dynamic and static elements. Generating a semantic representation of BEV maps can be a challenging task since it relies on object detection and image segmentation. Recent studies have developed Convolutional Neural networks (CNNs) to tackle the underlying challenge. However, current CNN-based models encounter a bottleneck in perceiving subtle nuances of information due to their limited capacity, which constrains the efficiency and accuracy of representation prediction, especially for multi-scale and multi-class elements. To address this issue, we propose novel neural networks for BEV semantic representation prediction that are built upon Transformers without convolution layers in a significantly different way from existing pure CNNs and hybrid architectures that merge CNNs and Transformers. Given a sequence of image frames as input, the proposed neural networks can directly output the BEV maps with per-class probabilities in end-to-end forecasting. The core innovations of the current study contain (1) a new pixel generation method powered by Transformers, (2) a novel algorithm for image-to-BEV transformation, and (3) a novel network for image feature extraction using attention mechanisms. We evaluate the proposed Models performance on two challenging benchmarks, the NuScenes dataset and the Argoverse 3D dataset, and compare it with state-of-the-art methods. Results show that the proposed model outperforms CNNs, achieving a relative improvement of 2.4 and 5.2% on the NuScenes and Argoverse 3D datasets, respectively.