Reviewerly/exHarmony huggingface dataset:
RePair Grammars are the Smallest Grammars for Fibonacci Words

Grammar-based compression is a loss-less data compression scheme that represents a given string $w$ by a context-free grammar that generates only $w$. While computing the smallest grammar which generates a given string $w$ is NP-hard in general, a number of polynomial-time grammar-based compressors which work well in practice have been proposed. RePair, proposed by Larsson and Moffat in 1999, is a grammar-based compressor which recursively replaces all possible occurrences of a most frequently occurring bigrams in the string. Since there can be multiple choices of the most frequent bigrams to replace, different implementations of RePair can result in different grammars. In this paper, we show that the smallest grammars generating the Fibonacci words $F_k$ can be completely characterized by RePair, where $F_k$ denotes the $k$-th Fibonacci word. Namely, all grammars for $F_k$ generated by any implementation of RePair are the smallest grammars for $F_k$, and no other grammars can be the smallest for $F_k$. To the best of our knowledge, Fibonacci words are the first non-trivial infinite family of strings for which RePair is optimal.
Requirements Elicitation in Cognitive Service for Recommendation

Nowadays, cognitive service provides more interactive way to understand users' requirements via human-machine conversation. In other words, it has to capture users' requirements from their utterance and respond them with the relevant and suitable service resources. To this end, two phases must be applied: I.Sequence planning and Real-time detection of user requirement, II.Service resource selection and Response generation. The existing works ignore the potential connection between these two phases. To model their connection, Two-Phase Requirement Elicitation Method is proposed. For the phase I, this paper proposes a user requirement elicitation framework (URef) to plan a potential requirement sequence grounded on user profile and personal knowledge base before the conversation. In addition, it can also predict user's true requirement and judge whether the requirement is completed based on the user's utterance during the conversation. For the phase II, this paper proposes a response generation model based on attention, SaRSNet. It can select the appropriate resource (i.e. knowledge triple) in line with the requirement predicted by URef, and then generates a suitable response for recommendation. The experimental results on the open dataset \emph{DuRecDial} have been significantly improved compared to the baseline, which proves the effectiveness of the proposed methods.
Sinogram Enhancement with Generative Adversarial Networks using Shape Priors

Compensating scarce measurements by inferring them from computational models is a way to address ill-posed inverse problems. We tackle Limited Angle Tomography by completing the set of acquisitions using a generative model and prior-knowledge about the scanned object. Using a Generative Adversarial Network as model and Computer-Assisted Design data as shape prior, we demonstrate a quantitative and qualitative advantage of our technique over other state-of-the-art methods. Inferring a substantial number of consecutive missing measurements, we offer an alternative to other image inpainting techniques that fall short of providing a satisfying answer to our research question: can X-Ray exposition be reduced by using generative models to infer lacking measurements?
Study of the effect of quantum noise on the accuracy of the Schrodinger equation simulation on a quantum computer using the Zalka-Wiesner method

The study of the effect of quantum noise on the accuracy of modeling quantum systems on a quantum computer using the Zalka-Wiesner method is carried out. The efficiency of the developed methods and algorithms is demonstrated by the example of solving the nonstationary Schrodinger equation with allowance for quantum noise for a particle moving in the Poschl-Teller potential. The analysis and prediction of accuracy for the Zalka-Wiesner method are carried out taking into account the complexity of the quantum system and the strength of quantum noise. In our opinion, the considered problem provides a useful test for assessing the quality and efficiency of quantum computing devices on various physical platforms currently being developed. The obtained results are essential for the development of high-precision methods for controlling the technologies of quantum computations.
The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond

We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple box constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investigated due to the assumed triviality or insignificance of this question. Here, we demonstrate that, at least in algorithms based on Differential Evolution, this choice induces notably different behaviours - in terms of performance, disruptiveness and population diversity. This is shown theoretically (where possible) for standard Differential Evolution in the absence of selection pressure and experimentally for the standard and state-of-the-art Differential Evolution variants on special test function $f_0$ and BBOB benchmarking suite, respectively. Moreover, we demonstrate that the importance of this choice quickly grows with problem's dimensionality. Different Evolution is not at all special in this regard - there is no reason to presume that other heuristic optimisers are not equally affected by the aforementioned algorithmic choice. Thus, we urge the field of heuristic optimisation to formalise and adopt the idea of a new algorithmic component in heuristic optimisers, which we call here a strategy of dealing with infeasible solutions. This component needs to be consistently (a) specified in algorithmic descriptions to guarantee reproducibility of results, (b) studied to better understand its impact on algorithm's performance in a wider sense and (c) included in the (automatic) algorithmic design. All of these should be done even for problems with box constraints.
Truthful Online Scheduling of Cloud Workloads under Uncertainty

Cloud computing customers often submit repeating jobs and computation pipelines on \emph{approximately} regular schedules, with arrival and running times that exhibit variance. This pattern, typical of training tasks in machine learning, allows customers to partially predict future job requirements. We develop a model of cloud computing platforms that receive statements of work (SoWs) in an online fashion. The SoWs describe future jobs whose arrival times and durations are probabilistic, and whose utility to the submitting agents declines with completion time. The arrival and duration distributions, as well as the utility functions, are considered private customer information and are reported by strategic agents to a scheduler that is optimizing for social welfare. We design pricing, scheduling, and eviction mechanisms that incentivize truthful reporting of SoWs. An important challenge is maintaining incentives despite the possibility of the platform becoming saturated. We introduce a framework to reduce scheduling under uncertainty to a relaxed scheduling problem without uncertainty. Using this framework, we tackle both adversarial and stochastic submissions of statements of work, and obtain logarithmic and constant competitive mechanisms, respectively.
A Study on the Expansion of Local Literature Museums with the Development of Digital Media Technology

Today, Due to COVID-19, Digital media-based technical ability are emerging as a new paradigm. Cultural content has entered an era of innovative and diverse expansion. However, Is the local literature museum sensitive to the technology of digital media in this era? I sensed the urgent need to change and seek various contents for the popularization of local literature museums today. This is because the local literature museum should make the development of new content a task at hand to revitalize the public. I seriously asked what direction the local literature museum should go in this era and devised a plan. I suggested the ‘Metaverse Literature Museum’. This is because I see the most suitable expression technology for breaking the boundaries of local literature museums and creating programs where literary people and visitors interact with each other as Metaverse. Now, The local literature museum can open a virtual reality literature museum using augmented reality and virtual reality platforms by creating an access address and space for the local literature museum on the Metaverse platform. Programs such as performances, education, exhibitions, and experiences are available on the Metaverse platform. Now, you can try to change the expression technology and enjoyment method of literary works at the local literature museum. The most important point at this point is the collaboration between workers related to literature museums or local culture and artists and digital media content development experts. Only then can the development of digital media content at local literature museums be implemented. It is hoped that digital media will form various expression methods and spread to personalized literature centers tailored to digital media. Therefore, it is expected that the public s accessibility can be increased by revitalizing local literature halls through the application of digital media technology. In addition, it is expected that changes will be triggered in the way local literature museums are promoted, educated, and operated, and the aspect and support of specialized programs. Now, The local literature museum needs an attitude to counter changes according to the trend of the times.
Gaussian mixture modelling by exploiting competitive stop EM algorithm

Abstract To improve the robustness of order selection and parameter learning for Gaussian mixture model (GMM), this paper proposes a competitive stop expectation-maximization (EM) algorithm, which is based on two stop conditions. The first condition is a Lilliefors test based multivariate (MV) normality criterion, which is used to determine whether to split a component into two different components. The EM algorithm stops splitting when all components have MV normality. The minimum description length (MDL) criterion is used in the second condition, which competes with the first condition to prevent the EM algorithm from over-splitting. Simulation experiments verify the effectiveness of the proposed algorithm.
A Real-time Algorithm of Multiple Task Planning and Trajectory Planning for Drone Swarms

This paper proposes a real-time algorithm of multiple task planning and trajectory planning for drone swarms. The multiple task planning is based on the CAPT (Concurrent Assignment and Planning of Trajectories) algorithm, and especially, it plans multiple task considering task priority and a required minimum number of agents for each task. Also, the trajectory planning adopts minimum snap trajectory generation to produce dynamically feasible trajectories for drones of multi-rotor types, and it guarantees collision avoidance between neighboring agents in free aerial space. The proposed algorithm is implemented in the ROS (Robot Operating System) environment, and simulation results show feasibility and real-time capability of the proposed algorithm.
Nonclassical temporal correlation powers quantum random access codes

We explore the fundamental origin of the quantum advantage behind random access code. We propose new temporal inequalities compatible with noninvasive-realist models and show that any non-zero quantum advantage of n bits encoded to 1-bit random access code in the presence of shared randomness is equivalent to the violation of the corresponding temporal inequality. As an immediate consequence of this connection, we also prove that the maximal success probability of n bits encoded to 1-bit random access code can be obtained when the maximal violation of the corresponding inequality is achieved. We then show that any non-zero quantum advantage of n bits encoded to 1-bit random access code, or in other words, any non-zero violation of the corresponding temporal inequality can certify genuine randomness.
Augmentation-Free Graph Contrastive Learning

Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data. Despite its remarkable success, existing GCL methods highly rely on an augmentation scheme to learn the representations invariant across different augmentation views. In this work, we revisit such a convention in GCL through examining the effect of augmentation techniques on graph data via the lens of spectral theory. We found that graph augmentations preserve the low-frequency components and perturb the middle- and high-frequency components of the graph, which contributes to the success of GCL algorithms on homophilic graphs but hinders its application on heterophilic graphs, due to the high-frequency preference of heterophilic data. Motivated by this, we propose a novel, theoretically-principled, and augmentation-free GCL method, named AF-GCL, that (1) leverages the features aggregated by Graph Neural Network to construct the self-supervision signal instead of augmentations and therefore (2) is less sensitive to the graph homophily degree. Theoretically, We present the performance guarantee for AF-GCL as well as an analysis for understanding the efficacy of AF-GCL. Extensive experiments on 14 benchmark datasets with varying degrees of heterophily show that AF-GCL presents competitive or better performance on homophilic graphs and outperforms all existing state-of-the-art GCL methods on heterophilic graphs with significantly less computational overhead.
UV IDENTIFICATION FOR TARFAYAS OIL SHALE AND ASHING AT LOW TEMPERATURE OF Z1 LAYER (MOROCCO)

In order to elucidate our knowledge of oil shale, this work has been initiated. Morocco has large oil shale reserves, the most important deposits of which are in Timahdit, Tarfaya and Tangiers .As oil shale is first-round energy materials that can be used to further valorize these resources, we are interested in the research and development of oil shale-based materials capable of retaining pollutants present in wastewater. In this study, we discuss the role that oil shale can play in inhibiting the infiltration of micro pollutants into the water table. The part of this work presents a bibliographical study on the mineralogy, geochemistry and petro chemistry of oil shale. The use of these materials in the field of environmental protection is discussed. Firstly, an experimental identification work was carried out using the spectroscopic apparatus of our establishment to study the Z1 layer of the Tarfaya deposit in its raw state and in the screen solubilized with chloroform. The Z1 layer has a percentage of organic matter of 12.3% and mineral matter of 29.4% for a heating rate of 21Â°C/min (A.Attaoui:1999).Then, within the framework of a clean use of the material as an absorbent or depolluting(He.G.Liang et al:2019,S.Gamoudi et al :2019) we carried out the ashing at low temperature of the oil shale, which consists in carrying out combustions on this shale, more precisely on the organic part and not altering the mineral part. In this context, we carried out this incineration on the oil shale of Tarfaya (Z1) at temperatures ranging from ambient to 227Â°C. The samples were then subjected to HCl acid attacks, and the remaining part was measured and the quantity of the constituents sensitive to this acid attack was evaluated. Pure calcite was taken as a reference and the PH-meter was used to analyses the phenomenon.
Security, Privacy Issues, and Challenges in Adoption of Smart Digital Healthcare

Current mechanization has motivated innumerable segments of our day-to-day existence. With the development of Information and Communication Technologies (ICTs), smart medical management is using current age group data or information mechanization such as Internet of Things (IoT), Big Data, Cloud Computing, Blockchain and Artificial Intelligence (AI) to comprehensively transform the traditional medicinal arrangements and make medical treatment more environmentally friendly, appropriate and personalize. According to Blue Stream Consultancy, “Smart Healthcare is defined as technology that brings better diagnostic tools, better patient care, and equipment to improve the quality of life for all.” Smart Healthcare is no longer the simplest manner of adopting new merchandise and technologies for analysis and remedy, but it includes an extra trade of records a number of the events, a greater energetic function of patients during treatment and, in the end a higher management of medical facts. The Internet of Things (IoT) replace the commercial enterprise spectrum and its software varies extensively from agriculture to healthcare, to transportation, and so on. Health centre surrounding can be very worrying, particularly for elder residents and younger one. With continuous growth of global populace, the traditional make an appointment with the patient’s doctor has misplaced its efficiency. Therefore, Smart Healthcare becomes crucial. Smart Healthcare may be applied in any context, from temperature control for toddlers to caring for the aged. The difficulty and costs of implementation depend on the desired precision of the individual accuracy, functionality and complexity of the software involved. Security and privacy issues in smart healthcare is challenging as patient personal information are share among different parties. IoT-based smart healthcare systems involve authentication, data integrity, identification and scalability, as they use heterogeneous devices to collect patient vital information which need to be process in real time.This chapter analyzes the architecture and basic protocols used in smart healthcare systems, then identifies the security and privacy challenges in different layers, along with secure computational challenges that need to be addressed to accelerate the deployment and adoption of Smart Healthcare technologies for ubiquitous healthcare access. This chapter proposes blockchain-based architecture for addressing security and privacy issues existing within IoT to enable smart healthcare systems. The smart contracts are designed to implement the working logic of each user in the smart healthcare system.
A review of the purpose and aspect of the reduplication used in Kim Sang-heon’s Chinese poetry

The purpose of this study was to analyze the types and meanings of reduplication used in the Chinese poetry of Cheongeum Kim Sang-heon. Cheongeum reinforced the meaning of the poem she wanted to say by using 205 reduplication in a total of 624 times in 1,675 one poem. The overlapping vocabulary is intended to effectively express the poet s feelings and poetic mood by using various methods of spies, such as nouns, rhetoric, verbs, and adjectives. There is also a tendency to prefer certain squid. As shown in <Table-2> above, the 17 vocabularies in the top 10 that show the most frequency among all suppositions are high enough to reach 41% of the total number of uses. It was found that they were used in the meaning of expressing the poet s emotions or a lonely atmosphere, expressing the fleeting time or the passage of time, expressing the turbulent world affairs, and expressing a psychologically infinitely distant state.
Early Childhood Cooperative Behaviors through HighScope Approach in Thailand

Cooperative behaviors are very important for human lives, works and society in 21st century. It has been recognized by educators around the world that is a one of the most crucial factors for successful in complex problem solving and sustainable peaceful lives. This research was conducted in the context of Thai early childhood classroom. The objectives were to 1) compare the early childhood students’ scores of cooperative behaviors before and after the implementation of HighScope approach and to 2) analyze the early childhood students’ cooperative behaviors through the HighScope approach. Mixed research methodology was employed in this study. The pretest-posttest control group design was conducted mixing by the analytic description method. The experimental group consisted of 42 students from 84 students by cluster random sampling and the control group consisted of 42 students. They were studied in kindergarten during the first semester of the 2019 academic year at the Khon Kaen University Demonstration School. The research instruments consisted of lesson plans, cooperative behavior evaluation form, field notes, and a photo camera. Data were collected by 3 raters blind evaluating the students’ cooperative behavior, and 3 researchers taking field notes and photos. The data were analyzed by statistics (mean, S.D., t-test), and framework of Holz (2013). The results revealed that the experimental group students’ post- cooperative behavior score (mean = 3.71, S.D. = 0.06) higher than the pre-cooperative behavior score (mean = 3.45, S.D. = 0.06) with the statistical significance at the level of .01 and t-value of 5.46. Moreover, they demonstrated cooperative behaviors: social skills, expression of ideas, choice and decision making, feeling perception and conflict resolution, which are basis to form cooperative habit of mind. Teachers and educators could use HighScope approach to design instructional activities that promote students’ cooperative learning meaningfully
Bayesian Modeling and Computation in Python Learning

Journal of the Royal Statistical Society: Series A (Statistics in Society)Early View BOOK REVIEW Bayesian Modeling and Computation in Python Learning , Martin, Osvaldo A., Kumar, Ravin, Lao, Junpeng, 2021, Chapman & Hall/CRC Press, Boca Raton, FL, 422 pages, £74.99 (hardcover), ISBN: 978-0367894368 Stanley E. Lazic, Stanley E. Lazic stan.lazic@cantab.net Prioris.ai Inc., Ottawa, CanadaSearch for more papers by this author Stanley E. Lazic, Stanley E. Lazic stan.lazic@cantab.net Prioris.ai Inc., Ottawa, CanadaSearch for more papers by this author First published: 10 April 2022 https://doi.org/10.1111/rssa.12852Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
WERNER LOH's BIOGRAPHICAL THEORY OF UPBRINGING

The article is devoted to the biographical theory of upbringing developed by the German educator W. Loh in the 70-s of the twentieth century. Upbringing is analyzed by the author in the context of a person's life experience as the main anthropological concept of pedagogy. A person's life experience is considered as a certain scheme of education. Human upbringing is the assistance that helps the growing generation to overcome the biographical situations during the course of life. The article provides a description of the stages of a person's biographical development and the necessary competencies that allow him/her to overcome life and learning situations. The main role of an educator is to help a student in his or her idea of self-actualization and successful self-realization in life. An important condition of education is pedagogical conception that allows one to take a child in the context of his/her life experience and development tasks defined by the life age. An educator is seen as a significant Other that has the strongest influence on a student and becomes a symbolic figure in the long-life formation of a person.