Reviewerly/exHarmony huggingface dataset:
Semantic and Syntactic Interoperability Issues in the Context of SDI

Interoperability is one of the core concepts of Spatial Data Infrastructure due to the fact that exchange and access to spatial data is the foremost aim of any SDI. These issues are closely related to the concept of application schema that plays a significant role in interchanging spatial data and information across SDI. It is the basis of a successful data interchange between two systems as it defines the possible content and structure of data, thus it covers both semantic and syntactic interoperability. These matters also appear in a couple of questions concerning SDI including, among others, a model‑driven approach and data specifications.&#x0D; Spatial data exchange through SDI involves UML and GML application sche­mas that comprise semantic and syntactic interoperability respectively. How­ever, working out accurate and correct application schemas may be a challenge. Moreover, their faultiness or complexity may influence the ability to valid data interchange.&#x0D; The principal subject of this paper is to present the concept of interoperability in SDI, especially semantic and syntactic, as well as to discuss the role of UML and GML application schemas during the interoperable exchange of spatial data over SDI. Considerations were conducted focusing on the European SDI and the National SDI in Poland.
STUDENTS’ PERCEPTION ON THE TEACHER’S NON-VERBAL COMMUNICATION IN TEACHING ENGLISH AT SMAN 14 BOMBANA

Many teachers understand that the use of nonverbal communication in teaching process is a good way to support the students’ understanding. By this effort, students may reach good comprehension through their belief for every act of teaching and learning process. This belief can be scripted to picture their perception. The students’ perception is all the students’ mental images and feeling which happen during their activities in the school. This means that any activities where the students gain information, the perception exist there. In this thesis, the students’ perception determines the function of the teacher’s nonverbal communication. The aim of this research was to investigate the students’ perception on the teacher’s nonverbal communication in teaching English at SMAN 14 Bombana. The research used a qualitative design with the subject of the study was class X and XI IPA. The total numbers of these two classes were 47. The researcher used classroom observation sheet and semi-structure interview as the instrument of the research, the interview which was the questions were formulated but, in the field, the interviewer might modify the format or questions during the interview process. This process took place to both of the students and the teacher. After collecting data, the data was analyzed descriptively. The result showed that the students agreed that the use of nonverbal communication in the teaching and learning process works. All of the expected meaning behind the nonverbal communication could be understood by the students well. The entire teacher’s nonverbal communication where the researcher focused namely gestures, eye contact, and tone of voice functioned well. Most of the students agreed that the teacher’s nonverbal communication made them motivated to learn, focused their attention, and made them eagerly to speak, and sometimes, it made the students proud
Advanced Approach for Animal Recognition System Based On Convolution Neural Network

In this paper, the animal images are processed using convolution neural network (CNN).This method is compared with animal recognition methods like Linear discriminate analysis (LDA),Principal compone -nt analysis(PCA), Support vector machine(SVM),Local binary pattern histogram (LBPH).For many years in order to study the eco-system of the endangered species we were relaying only on our physical presence.There was huge risk of life and on top of that due to the lack of the automated systems, overall data gathered was not sufficient to conduct the thorough study on the endangered species.For experimental purpose we are creating database of the animals in which there are separate class for each and every animal has 100 images.
Co-designing tools to empower further, independent co-design: collaborating with diverse individuals with lived experience of food poverty

Co-designing tools to empower further, independent co-design: Co-designing tools to empower further, independent co-design: collaborating with diverse individuals with lived experience of collaborating with diverse individuals with
Verifiable Hierarchical Key Assignment Schemes

A Hierarchical Key Assignment Scheme (HKAS) is a method to assign some private information and secret keys to a set of classes in a partially ordered hierarchy, so that the private information of a higher class together with some public information can be used to derive the keys of all classes lower down in the hierarchy. Historically, HKAS has been introduced to enforce multi-level access control, where it can be safely assumed that the public information is made available in some authenticated form. Subsequently, HKAS has found application in several other contexts where, instead, it would be convenient to certify the trustworthiness of public information. Such application contexts include key management for IoT and for emerging distributed data acquisition systems such as wireless sensor networks. In this paper, motivated by the need of accommodating this additional security requirement, we first introduce a new cryptographic primitive: Verifiable Hierarchical Key Assignment Scheme (VHKAS). A VHKAS is a key assignment scheme with a verification procedure that allows honest users to verify whether public information has been maliciously modified to induce an honest user to obtain an incorrect key. Then, we design and analyse VHKASs which are provably secure. Our solutions support key update for compromised secret keys by making a limited number of changes to public and private information.
Ontological approach to Chinese text processing

To implement natural language user interface and an intelligent answer to questions, the knowledgebased semantic model for Chinese language processing is proposed. The article gives careful consideration to the existing methods and various knowledge bases for natural language processing. The analysis of these methods has led to the conclusion that in natural language processing, the knowledge base is the most fundamental and crucial part. The knowledge base makes it possible to ensure processing of a natural language based on initially described knowledge and to explain the processing operations. By virtue of the analysis of various methods for constructing knowledge bases about the English and Chinese languages, an ontological approach to the Chinese language processing was proposed. The Chinese language processing model has two main aspects: the design of knowledge base about the Chinese language and the development of ontology-based knowledge processing machine. The proposed approach is aimed at developing a semantic model of knowledge on the Chinese language. As a stage in the implementation of the approach, I designed the ontology of the Chinese language that can be applied for further processing of the language. This paper considers the preliminary version of the ontology and the principle of building a knowledge base about the Chinese language. There are no uniform standards and evaluation system for designing an ontology. Expansion, refinement and evaluation of the ontology require further research.
Multi-Robot Motion Planning of k-Colored Discs Is PSPACE-Hard

In the problem of multi-robot motion planning, a group of robots, placed in a polygonal domain with obstacles, must be moved from their starting positions to a set of target positions. We consider the specific case of unlabeled disc robots of two different sizes. That is, within one class of robots, where a class is given by the robots' size, any robot can be moved to any of the corresponding target positions. We prove that the decision problem of whether there exists a schedule moving the robots to the target positions is PSPACE-hard.
Arterial Spin Labeling Image Synthesis From Structural MRI Using Improved Capsule-Based Networks

Medical image synthesis receives much popularity in recent years, and ample medical images can be synthesized by diverse deep learning models to alleviate the problem of lack of data in many medical imaging utilizations. However, most medical image synthesis methods still incorporate the well-known pooling operation in their convolutional neural networks-based/generative adversarial networks-based models, from which image details will be inevitably lost due to the pooling operation. In order to tackle the above problem, improved capsule-based networks, in which no pooling operation is executed and spatial details of images can be effectively preserved thanks to the equivariance characteristics of capsule models, are proposed in this paper to synthesize arterial spin labeling images, for the first time. Technically, three important issues in constructing improved capsule-based networks, including the depth of basic convolutions, the layer of capsules, and the capacity of capsules, are thoroughly investigated. Comprehensive experiments made up of region-based/voxel-based partial volume corrections and dementia diseases diagnosis based on two different datasets are conducted. The superiority of improved capsule-based networks introduced in this paper is substantiated from the statistical point of view.
Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints

Accurate deformable image registration is important for brain analysis. However, there are two challenges in deformation registration of brain magnetic resonance (MR) images. First, the global cerebrospinal fluid (CSF) regions are rarely aligned since most of them are located in narrow regions outside of gray matter (GM) tissue. Second, the small complex morphological structures in tissues are rarely aligned since dense deformation fields are too blurred. In this work, we use a weakly supervised registration scheme, which is driven by global segmentation labels and local segmentation labels via two special loss functions. Specifically, multiscale double Dice similarity is used to maximize the overlap of the same labels and also minimize the overlap of regions with different labels. The structural similarity loss function is further used to enhance registration performance of small structures, thus enhancing the whole image registration accuracy. Experimental results on inter-subject registration of T1-weighted MR brain images from the OASIS-1 dataset show that the proposed scheme achieves higher accuracy on CSF, GM and white matter (WM) compared with the baseline learning model.
MARAPU VAḺI EḺUTUTAL TIṞAṈUM TAṞKĀLA EḺUTUTAL TIṞAṈUM ORU PĀRVAI [A STUDY ON MODERN AND TRADITIONAL WRITING SKILL]

This paper discuss about the modern and traditional writing. The modern writing is fully based on keyboarding. Traditional writing comprised of paper and pen writing. The question is how effective does keyboarding and traditional writing enhancing the thinking skills. Handwriting has begun to fall as students become increasingly reliant on communication via digital device. As a result, some educators started to shift their focus from handwriting instruction to teaching keyboarding. So, this study attempts to analyse the effectiveness of computer typing and handwriting in promoting thinking skills. From this discussion it has been concluded that the complex thinking skills such as analysing, evaluating and creating were not reflected completely in computer typing compared to handwriting skills.
Fleet Control using Coregionalized Gaussian Process Policy Iteration

In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect through cloud-based architectures in order to share information about their status and environment. Such an infrastructure allows seamless data sharing between fleet members, which could greatly improve the sample-efficiency of reinforcement learning techniques. However in practice, these machines, while almost identical in design, have small discrepancies due to production errors or degradation, preventing control algorithms to simply aggregate and employ all fleet data. We propose a novel reinforcement learning method that learns to transfer knowledge between similar fleet members and creates member-specific dynamics models for control. Our algorithm uses Gaussian processes to establish cross-member covariances. This is significantly different from standard transfer learning methods, as the focus is not on sharing information over tasks, but rather over system specifications. We demonstrate our approach on two benchmarks and a realistic wind farm setting. Our method significantly outperforms two baseline approaches, namely individual learning and joint learning where all samples are aggregated, in terms of the median and variance of the results.
Thinking fast and slow about words and voices: RT-distributional analyses of voice-specific priming in auditory word recognition.

Studies have demonstrated that listeners can retain detailed voice-specific acoustic information about spoken words in memory. A central question is when such information influences lexical processing. According to episodic models of the mental lexicon, voice-specific details influence word recognition immediately during online speech perception. Another view, the Time-Course Hypothesis, claims that voice-specific details influence word recognition only when processing is slow and effortful. The present study investigates the latter proposal by employing reaction time (RT)-distributional analyses. A long-term repetition priming experiment was conducted using an auditory lexical-decision task. In 2 blocks, participants made speeded responses to existing and nonexisting spoken words. In the second block, stimuli consisted of items that had not been presented in the first block and of items that were either repeated in the same voice or in a different voice. Ex-Gaussian and Vincentile analyses of the RT distributions in the second block revealed that voice-specific priming is reflected in distributional shifting rather than in distributional skewing. This indicates that voice-specific priming is not limited to very slow responses but that it affects both fast and slow responses. This finding is inconsistent with a strict version of the Time-Course Hypothesis, which claims that voice-specific priming occurs only during offline processing. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
THE AMBIGUITY OF ENGLISH ADVERTISEMENT

This is a qualitative research on applied linguistic which is conducted to find applied semantic theory about ambiguity, a condition where an utterance has two or more interpretations. The writers uses Kreidler’s theory which classifies ambiguities into three kinds; lexical, referential, and syntactic which is devided into two types; surface structure and deep structure. The discussion includes kinds of ambiguity that are found and their interpretations. The data sources is English advertisement and the data is English advertisement utterances in banners, posters, and billboards. The writers collects the datas by taking in a picture, sellecting, and presenting. The next is analysis and conclusion. There are 33 datas that are found, they are twelve banners, eleven posters, and ten billboards. From banners, there are three lexical, no referential, six surface structure, and three deep structure ambiguities. From posters, there is no lexical, one referential, four surface structure, and six deep structure ambiguities. From billboards, there are two lexical, two referential, four surface structure, and two deep structure ambiguities. Generally, the ambiguities are dominated by surface and deep structure ambiguity.
Leukocyte super-resolution via geometry prior and structural consistency

Significance: Researchers have made great progress in single-image super-resolution (SISR) using deep convolutional neural networks. However, in the field of leukocyte imaging, the performance of existing SISR methods is still limited as it fails to thoroughly explore the geometry and structural consistency of leukocytes. The inaccurate super-resolution (SR) results will hinder the pathological study of leukocytes, since the structure and cell lineage determine the types of leukocyte and will significantly affect the subsequent inspection. Aim: We propose a deep network that takes full use of the geometry prior and structural consistency of the leukocyte images. We establish and annotate a leukocyte dataset, which contains five main types of leukocytes (basophil, eosinophil, monocyte, lymphocyte, and neutrophil), for learning the structure and geometry information. Approach: Our model is composed of two modules: prior network and SR network. The prior network estimates the parsing map of the low-resolution (LR) image, and then the SR network takes both the estimated parsing map and LR image as input to predict the final high-resolution image. Result: Experiments show that the geometry prior and structural consistency in use obviously improves the SR performance of leukocyte images, enhancing the peak-signal-to-noise ratio (PSNR) by about 0.4 dB in our benchmark. Conclusion: As proved by our leukocyte SR benchmark, the proposed method significantly outperforms state-of-the-art SR methods. Our method not only improves the PSNR and structural similarity indices, but also accurately preserves the structural details of leukocytes. The proposed method is believed to have potential use in the wide-field cell prescreening by simply using a low-power objective.
Environmental safety and living in a smart city

The present study examines the environmental safety and liveability of today's metropolises and cities by discussing the present situation regarding technical sciences, accidents, disasters and waste management, sharing some philosophical thoughts on the future, describing the technical problems, human failures and errors, and finally, by proposing some possible solutions.It is necessary and of upmost importance to address the issues of security, environmental safety and protection, as well as long-term, sustainable liveability of the smart cities of the future by considering the latest technology available today, as proposed in the present study.It outlines some of the guidelines aiming to change human behaviour, the necessity and possibility of ensuring order and eliminating insecurity, chaos and disorder in future smart cities.Furthermore, it wishes to bring attention to the lack and need of scientific research of the elements providing security and liveability of today's cities and metropolises, the absence of which is causing suffering and unbearable living conditions for millions of people.Finally, it proposes an approach of engineering and careful, responsible and forward-looking planning to ensure that people will live in security and dignity in the smart cities of the future.
Addressing the Challenges Facing Decentralized Identity Systems

With data breaches becoming an increasingly prominent topic within society, businesses have begun directing resources towards enhanced security systems, while governments have seemingly attempted to grant individuals more rights over their personal data. With the development of new data management systems, organizations have the opportunity to create technologies that enhance and not endanger the privacy of data owners (Nyst et al., 2016). This paper discusses the concept of a decentralized identifier (DID) system, which shifts the control of personal data from centralized entities to the identity owners themselves. It places a focus on user data privacy by leveraging distributed ledger technology. Although a DID system presents numerous opportunities for data protection, it must first overcome a number of obstacles before replacing its centralized counterpart. This paper addresses two key challenges that DID systems face: scalability and interoperability. The proposed solutions to the scalability challenge include the implementation of a second layer protocol atop the distributed ledger, as well as a simplified user experience provided by a digital wallet application. Meanwhile, the key strategies for addressing the interoperability challenge include the use of a Universal Resolver, integration of distributed ledgers, and facilitation of compatible digital wallets and identity hubs. By addressing these challenges through collaborative efforts, developers may be one step closer to granting everyone control over their personal data.
Correction Simulation of Three Dimensional Image of Table Tennis Players Serve Position

Traditional method of three-dimensional (3D) image correction of serving form of table tennis always neglects to carry out gradient weighted mean filtering for image detail, which leads to low correction precision. In order to solve the problem, based on trilateral filtering, a new correction method is proposed in this research. Correction model of motion artifact of the 3D image kept by structure of serving was built. The model consisted of regular term of non-local means and fidelity term of piece similarity. The 3D image was decomposed into elementary component and detail component. The gradient weighted mean filtering was carried out for time domain curve of the detail component. It is proved by experimental results that the method can reserve detail feature of the image preferably and improve various objective evaluation parameters by a large margin.
Image cipher applications using the elliptical curve and chaos

Abstract A novel symmetric cryptosystem of the substitution permutation network type is presented for image encryption in 14 rounds. An algorithm is developed to generate 15 keys to encrypt images ...
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models.

Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods generate adversarial texts in a pair-wise way, that is, an adversarial text can only be created from a real-world text by replacing a few words. In many applications, these texts are limited in numbers, therefore their corresponding adversarial examples are often not diverse enough and sometimes hard to read, thus can be easily detected by humans and cannot create chaos at a large scale. In this paper, we propose an end to end solution to efficiently generate adversarial texts from scratch using generative models, which are not restricted to perturbing the given texts. We call it unrestricted adversarial text generation. Specifically, we train a conditional variational autoencoder (VAE) with an additional adversarial loss to guide the generation of adversarial examples. Moreover, to improve the validity of adversarial texts, we utilize discrimators and the training framework of generative adversarial networks (GANs) to make adversarial texts consistent with real data. Experimental results on sentiment analysis demonstrate the scalability and efficiency of our method. It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.