Reviewerly/exHarmony huggingface dataset:
與「口」共起之搭配詞的考察 —以品詞為中心—

&lt;p&gt;本論文以語料庫(NLB：NINJAL-LWP for BCCWJ)中「動詞口」的搭配詞為 研究對象，調查與「口」共起動詞的類型、分布情形外，有關語義用法也一併 探討。 調查結果：與「口」共起動詞的類型有「動詞過去＋口」「動詞基本形＋口」 「動詞連用形ていた＋口」「動詞連用形ている＋口」「動詞連用形＋口」五種 類型。 五種類型的語義用法總共有＜口的基本語義＞、＜說法＞、＜種類・類型 ＞、＜人、物的出入場所＞、＜安定之處＞5 種。＜口的基本語義＞使用量最 多；而＜安定之處＞只有 1 例而已。 而比較各品詞（形容詞、形容動詞、名詞、動詞）)與「口」之搭配詞的語 義用法發現：4 品詞都有＜口的基本語義＞、＜說法＞，而＜安定之處＞限於 「名詞＋口」「動詞＋口」；＜人・人類＞限於「形容動詞＋口」，另外，＜魚＞ ＜味＞＜固有名詞＞則只限於「名詞＋口」使用。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;This paper is taking &amp;ldquo;verb + KUCHI&amp;rdquo; as a research target in the corpus (NLB: NINJAL-LWP for BCCWJ) in order to investigate the &amp;ldquo;KUCHI&amp;rdquo; in different types, distribution of co-existing words, and semantic usage. The results of KUCHI with verbs co-exist types are &amp;ldquo;past verb + KUCHI&amp;rdquo;, &amp;ldquo;the basic form of verb + KUCHI&amp;rdquo;, &amp;ldquo;the continuative form(ren-you-kei) TE I TA (ていた) + KUCHI&amp;rdquo;, &amp;ldquo;the continuative form TE I RU (ている) + KUCHI &amp;quot;, and &amp;ldquo; the continuative form + KUCHI&amp;rdquo;. These five types of co-exist words have five semantic usages can be categorized into , , , and &lt; place to stay &gt;. The &lt; basic semantics of KUCHI &gt; is the most frequently used, while the&lt; place to stay &gt; is only one case used. Comparing the semantic usage of various parts of speech (adjectives, adjective verbs, nouns, verbs) and KUCHI collocations of semantic usage. It is found that the four parts of speech have two usages of and . &lt; place to stay &gt; is only used for &amp;ldquo;noun + KUCHI&amp;rdquo; and &amp;ldquo;verb + KUCHI&amp;rdquo;. only for &amp;ldquo;adjective verb + KUCHI&amp;rdquo;. , and are only used for &amp;ldquo;noun + KUCHI&amp;rdquo;.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Face Mask Detection Using YOLOv3

The COVID-19 is an unprecedented crisis that has resulted in several security issues and large number of casualties. People frequently use masks to protect themselves against the transmission of coronavirus. In view of the fact that specific aspects of the face are obscured, facial identification becomes extremely difficult. During the ongoing coronavirus pandemic, researchers’ primary focus has been to come up with suggestions for dealing with the problem through rapid and efficient solutions, as mask detection is required in the current scenario, whether in public or in some institutions such as offices and other workplaces. Only detecting whether a person wears mask or not is not enough. There is another aspect of wearing the mask properly such that it covers all the required portion of the face to ensure there is no exposure to any viruses. To address this, we proposed a reliable technique based on image classification and object localization, which can be accomplished using YOLO v3's object detection in machine learning.
Fuzzy 2-Partition Kapur Entropy for Fruit Image Segmentation Using Teacher-Learner Optimization Algorithm

For the past few years, image classification has been a rapidly growing computer vision application. For the extraction and detection of objects from images, image segmentation has been a popular digital image processing method. Type-II partition fuzzy is an excellent strategy for selecting threshold values. To choose the best threshold values for the Fuzzy-II partition, various parameters must be optimized. Partitioned fuzzy-II-oriented Kapur entropy and teacher-learner-based optimization (TLBO) were used to investigate possible parameters and subsequent appropriate threshold values. The fuzzy-II partition entropy is used in the proposed study approach to evaluate its potential for determining the best threshold value using the TLBO algorithm. Fruit images are utilized in experimental work to segment based on optimal threshold values. The SSI, PSNR, and uniformity parameters have been used to compare the segmentation performance of the proposed approach with the existing approaches.
Nonlinear function‐on‐scalar regression via functional universal approximation

We consider general nonlinear function-on-scalar (FOS) regression models, where the functional response depends on multiple scalar predictors in a general unknown nonlinear form. Existing methods either assume specific model forms (e.g., additive models) or directly estimate the nonlinear function in a space with dimension equal to the number of scalar predictors, which can only be applied to models with a few scalar predictors. To overcome these shortcomings, motivated by the classic universal approximation theorem used in neural networks, we develop a functional universal approximation theorem which can be used to approximate general nonlinear FOS maps and can be easily adopted into the framework of functional data analysis. With this theorem and utilizing smoothness regularity, we develop a novel method to fit the general nonlinear FOS regression model and make predictions. Our new method does not make any specific assumption on the model forms, and it avoids the direct estimation of nonlinear functions in a space with dimension equal to the number of scalar predictors. By estimating a sequence of bivariate functions, our method can be applied to models with a relatively large number of scalar predictors. The good performance of the proposed method is demonstrated by empirical studies on various simulated and real datasets.
Research progress on coupling artificial intelligence and eco-environmental models.

Artificial intelligence (AI) has been widely used in the eco-environment field, but with shortcomings in revealing the laws of natural science, such as insufficient generalization ability and poor interpretability. In order to overcome these shortages and tap into complementary advantages, coupling AI and eco-environmental models containing physical mechanism has been a new research method with fast development in recent years. We introduced the classifications of AI used in eco-environmental field, outlined its applications, and mainly illustrated the progresses, status and inadequacies for the coupling research. Based on all the summaries, we proposed a new coupling method of physical mechanism and AI for reconstructing mechanism processes, followed by analyses of theoretical significance of partial parameters, feasibility of better generalization and interpretability, as well as prospection of imitating physical mechanism. At the end of the review, we discussed the trend of the coupling method of AI and eco-environment models.人工智能算法在生态环境领域已有广泛应用,但在揭示自然科学现象规律时存在泛化能力不足、可解释性差等问题。为弥补这些不足,实现优势互补,将人工智能算法与具有物理机制的生态环境模型耦合研究已成为近些年快速发展的一种新型研究方法。本文从应用在生态环境领域的人工智能算法出发,概述了其分类和应用情况,重点梳理了人工智能算法与生态环境模型耦合研究的发展、现状及不足,提出了一个将人工智能与机理模型紧密耦合以重构机理过程的思路,分析了该网络部分参数的理论意义,提高可解释性和泛化能力的可行性,以及模拟机理过程运行的应用前景,并展望了人工智能算法与生态环境模型耦合研究的发展趋势。.
Learning to Embed Knowledge for Medical Dialogue System

The medical dialogue system (MDS) is committed to constructing an intelligent platform to complete an automatic medical online consultation. Most previous researchers have focused on using the dialogue history between doctors and patients to yield an accurate and informative response, while neglecting that most patients lack professional medical knowledge, which leads to oral expressions during their self-report period. As a result, there are many medical terms that fail to utter formally in the dialogue, which leads to performance degradation of conventional MDS. In the paper, we propose a learning to embed KnowledGe for Medical Dialogue system (KGMD). We first construct an entity normalized knowledge graph, which intuitively helps to unify the deviation caused by oral expressions. Then, we embed knowledge information into corresponding entities to complete entity alignment. However, additional knowledge incorporation may divert the sentence from its correct meaning, which is called knowledge noise. To overcome knowledge noise, KGMD introduces Knowledge-Masked (KM) to complete information hiding. Thus, doctor response is generated within unambiguous entity knowledge. Experimental results show that the knowledge introduced effectively boosts the robustness of dialogue systems. Especially, our proposed model achieves supreme performance on MedDG.
Small but Mighty

Sensys Networks’ new compact and robust vehicle-detection sensor can be installed in under five minutes
The Necessity of Forming Multilingual Competencies in the Educational Process Digitalization

The pandemic caused by the COVID-19 virus affected all spheres of society and had a significant impact on the education system, forcing an urgent transition to distance learning formats. As practice has shown, distance learning forms are especially difficult when working in a foreign language environment. The present article aims to highlight critically important to discuss multilingualism in combination with online learning the process of an emergency transition to remote work and distance learning through the eyes of teachers working in multilingual student classrooms and a multilingual educational environment where students are both taught in Russian as a foreign language and in English, which is not native to them. An empirical study using the authors’ questionnaire, in which representatives of the “cadre core” took part, showed the professional and psychological readiness of teachers for the transition, on the one hand, and a number of problems associated with the level of administrative support, a decrease in the quality of education and technical problems of the transition, on the other. However, it should be emphasized that, despite some clear advantages of distance working format in a multilingual environment, the assessment of the organization of the transition and its effects should be the subject of close attention, because online learning promotes the development of multilingual skills and competencies and increases the requirements for teaching and strengthens the role of the teacher as their guide. Distance learning formats should be developed in a comprehensive manner, from technical and organizational aspects to content and distance learning technologies. The results of this work are part of a large-scale study aimed at implementing a multilingual approach to learning in new historical conditions caused by global transformations of the existing global world order.
An Action Research on the Application of Production-oriented Method in the Teaching of Continuation Writing Task in Senior High School English

At present, when it comes to the module of writing after reading, high school English teachers usually only complete the existing writing requirements, and will not expand to carry out more English writing skills training.In the case of improper use of teaching methods and insufficient class hours, the teaching effect of reading after writing is not ideal.When students use traditional writing thinking, they use the characteristics of accumulation of language and freedom of expression of personal opinions, which leads to the situation that students' composition results deviate from the main ideas conveyed in the original text and the degree of harmony is not high.Although new teaching methods emerge one after another, front-line teachers cannot select effective teaching methods that are suitable for students' current English level in time according to the current situation of students.In accordance with the English curriculum standards of senior high schools and the requirements of core literacy of English subject, this study carried out an action research on the application of production oriented method in the teaching of follow-up writing after reading English in senior high schools by combining with the theoretical system of "production oriented method".It aims to improve students' writing ability, improve students' writing habits, enrich students' language expression forms and language structure, build cultural confidence and nourish students with international culture through the teaching of production oriented mode after high school English reading.
The power low in information geometry: Attempt from the viscoelastic relaxation of rock

It is known that power law exists in the background of various natural phenomena. One example is the viscoelastic behavior of rocks. In the flow laws of high temperature of rocks, strain rate is in proportion to the power of stress. It can be replaced by the one that relaxation modulus (the ratio of stress to strain) is in proportion to the power of time with fractal dimension as power exponent. From Laplace transform for the relaxation modulus, the distribution of relaxation time (relaxation spectrum) with the power of relaxation time is derived. It indicates the existence of fractal distribution of different relaxation times in material elements in rocks. On the other hand, these strain-relaxation modulus-stress relations can be recaptured as the input-response-output relation in an ideal complex system with the power law of component. When input and output are stochastic with probability functions, the response corresponds to the change in differential geometric structure on a statistical manifold with a point as a probability function. Although previous studies suggested the correspondence between the power exponent (fractal dimension) and the constant (alpha) characterizing invariant geometric structure (alpha-connection), its details have not been discussed yet. In this presentation, we would reveal the correspondence between the power exponent (fractal dimension) and the constant (alpha) based on q-exponential family in information geometry, which is a more general exponential family.
Coregionalization models of geophysical data for site effect assessment at Anse-à-Veau, Haiti

The multivariate generalization of the kriging, in geostatistical analysis, called cokriging makes it possible to take advantage of the relationships between several variables. The information carried by a secondary variable may improve the precision of the estimation of the main variable. The multivariate structural model between the variables is then built from the spatial joint analysis of the data which can be realized by coregionalization linear models.&amp;#160;&amp;#160;The present study aims at assessing the site effects from the results of multivariate analysis of seismic data collected at Anse-&amp;#224;-Veau, a municipality in the Nippes Department of Haiti, characterized by a relatively high seismic activity &amp;#8211; during the measurement campaign in August 2021 this region had been hit by the 2021 Nippes Earthquake. The surveys carried out include ambient noise and seismological recordings, seismic tests as well as electrical resistivity measurements along profiles. The two first were processed, respectively, in terms of Horizontal to Vertical Spectral Ratios (HVSR) and Standard Spectral Ratios (SSR), the seismic tests both as Seismic Refraction Tomography (SRT) and by Multichannel Analysis of Surface Waves (MASW) and the last measurements as Electrical Resistivity Tomography (ERT). In total, more than 100 HVSR recordings, 7 115m-long seismic profiles and 8 ERT profiles have been completed. All related results were then compiled within one multi-data (including also geological, geomorphic and geomechanical information) 3D geomodel and submitted to a spatial analysis. The coregionalization modelling applied within this analysis is expected to take advantage of the relationships between the different type of seismic data in order to better estimate the potential site effects in the study area.Keywords: coregionalization model, site effects, multi-geophysics, geomodelling, Haiti. &amp;#160;&amp;#160;&amp;#160;
Challenges and New Opportunities in Diverse Approaches of Big Data Stream Analytics

The continuous generation of data develops new data analysis challenge called stream data analysis. However, most of the real-time applications require effective processing of diverse, fast, continually generating, and huge volume data. Data pre-processing plays an efficient role in producing high-quality input data. Applications in which the notion of incoming data changes over time, a single classifier could not perform better for constantly changing input, leading to the ensemble learning strategy, in which we handle stream data with multiple weak learners rather than a single strong learner. Since the properties of the streams are generally unknown, selecting an appropriate method or model is a crucial task. Meta Learning approach helps in performing stream analytics by separating the base level characteristics of input data and convert it into meta-information so that the dynamic model can be selected as per the available meta-information. Deep learning has proven to be the most promising approach specifically for high dimensional big data streams. This research study briefly describes about the different approaches involved in big data stream analytics.
Summarization of News Articles Using Transformers

It is essential to provide a better mechanism for swiftly and effectively extracting information in the modern age when there is so much information available online. The summary of a lengthy written document can be difficult for humans to manually extract. There is a variety of textual content available online. Identifying relevant documents in the large amount of available documents and learning crucial information from them are very challenging undertakings. Automatic text summarization is essential for addressing the two issues highlighted above. Text summarization is the process of taking the most important and relevant information from a document or collection of related papers, condensing it, and keeping the main ideas. Abstract Based Text Summarizer takes the input text from the user and provides a summarized text as output. It uses transformers that use thousands of pretrained models to classify the text. Transformers classify the text and Hugging Face is used to arrange those classified texts in a correct order, so that the meaning of the text given as input does not change and the summary would be more effective. The proposed system performs better in terms of rouge parameters.
Channel Modeling Based On Quantum Generative Adversarial Network

Channel modeling is indispensable in a communication system. In this paper, a novel scheme for channel modeling using quantum generative adversarial model was proposed. A quantum generative adversarial network is a generative adversarial model with a quantum circuit as the generative module and a deep neural network as the discriminant module, thereby exploiting the privilege of quantum algorithms in simulating probability distributions to stochastic channel models. Experiments were conducted on IBM QX quantum computing platform. The gradient descent of the cost function and Kullback-Leibler divergence were analyzed. Results verify the feasibility and superiority of the quantum generative adversarial network for channel modeling.
Abstract Categorical Logic

We present in this talk an abstract categorical logic based on an abstraction of quantifier. More precisely, the proposed logic is abstract because no structural constraints are imposed on models (semantics free). By contrast, formulas are inductively defined from an abstraction both of atomic formulas and of quantifiers. In this sense, the proposed approach differs from other works interested in formalizing the notion of abstract logic and of which the closest to our approach are the institutions, which in addition to be semantics free do not also impose any syntactic contingencies on the structure of formulas. To define the semantical framework in which formulas will be interpreted, we propose to follow the idea from categorical logic which defines the semantical interpretation of formulas from context and as subobjects of an object of a given category. In the spirit of Lawvere’s hyperdoctrines, we use a more abstract notion which generalizes the notion of subobject, standard in category theory: Pitt’s prop-categories. Always in the spirit of categorical logic, we propose a sequent calculus of which we show correctness and completeness for all semantical frameworks defined over any prop-categories. We then study some conditions which allow us to get this completeness result for particular classes of prop-categories.
:<i>Dream-Child: A Life of Charles Lamb</i>

Previous articleNext article No AccessBook ReviewsEric G. Wilson, Dream-Child: A Life of Charles Lamb. New Haven, CT: Yale University Press, 2022. xxi+521 pp. US$35.00.David GarcíaDavid GarcíaCarthage College Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Wordsworth Circle Volume 53, Number 4Fall 2022 Article DOIhttps://doi.org/10.1086/723477 Views: 4Total views on this site For permission to reuse, please contact [email protected]PDF download Crossref reports no articles citing this article.
Evaluation of Marker-Based AR-Tracking with Vuforia in the Context of Rail Vehicle Maintenance

Maintenance and repair jobs present a multitude of challenges. Digital assistance systems (DAS) can be used to support workers in tackling these challenges. One way of achieving this is by using a tablet with AR-technology to display necessary information seemingly in the real world. In order to place information at the correct positions, the DAS needs to know exactly where it is currently located. Multiple approaches are available to achieve this. One of those approaches is to use a marker which is placed somewhere in the real world as an anchor point for the system. In the context of rail vehicle maintenance, the workspace is inherently large with rail wagons having lengths of more than 10 m. This means that a marker that is placed somewhere on the wagon will not always be in the field of view of the DAS’s camera, resulting in a possible reduction of the precision with which virtual objects can be displayed. This paper examines the viability of the marker-based approach under realistic circumstances. It was found that with a distance of multiple meters between marker and a spot that is to be highlighted, the precision will decrease significantly.
Peningkatan Keterampilan Menulis Karangan Narasi Melalui Pendekatan Contextual Teaching and Learning di Kelas V Sekolah Dasar

&lt;p&gt;This study aims to improve the process of learning to write narrative essays in Indonesian language learning through the Contextual Teaching and Learning (CTL) approach and improve learning outcomes of narrative essay writing skills through the CTL approach to the fifth grade students of SD Negeri Bojongjengkol 03 Ciampea District, Bogor Regency. This type of research is Classroom Action Research. The researcher uses an action research model developed by Kemmis and Mc. Taggart which includes the stages of planning, implementing actions, observing, and reflecting. The subjects of this study were the fifth grade students of SD Negeri Bojongjengkol 03, Ciampea District, Bogor Regency, totaling 25 students. This classroom action research was carried out in two cycles, namely the first cycle consisting of 3 meetings and the second cycle consisting of 2 cycles. Data collection techniques using tests and observations. The data collection instrument used learning outcomes tests and observation sheets. To test the validity of the instrument in this study using construct validity using expert judgment. Research data obtained from observation and test learning outcomes. The data obtained were analyzed descriptively quantitatively and descriptively qualitatively. In the learning process in cycle I, the teacher applied the CTL approach. In the first cycle, the students' narrative essay writing skills increased from the pre-cycle results, the students' average score was 64.84 and increased to 66.12. Learning in cycle II the teacher applies the CTL approach combined with group discussions. Cycle II the average score of students increased to 75.16. In the first cycle, only 54.29% of students who finished learning increased to 100% in the second cycle. The results showed that the CTL approach could improve narrative essay writing skills.&lt;/p&gt;