Reviewerly/exHarmony huggingface dataset:
Development of an ontology for modelling medieval manuscripts: the case of Progetto IRNERIO

The catalogue of digitized medieval texts managed by Progetto IRNERIO contains a rich amount of legal, cultural and historical data that is neither easy to access nor linked to relevant external information. This paper introduces an ontology, called Medieval Manuscripts Ontology (MeMO), to model these texts so as to allow researchers to represent, identify, analyse and retrieve the information within and related to them. MeMO has been designed with a solid methodology in order to be compliant with the requirements of the domain experts (the preservation of the historical narrative, the representation of the context and the description of the collection). Also, it is flexible and richly documented so as to be easily reusable and extensible for further expansions. Thus MeMO allows one to model the resources managed by Progetto IRNERIO with precise semantics so as to avoid information loss and to support a better representation of the conceptual complexities that characterize the collection.
Algebraic Signature Based Data Possession Checking Method with Cloud Storage

Cloud computing has been envisioned as a next generation information technology (IT) paradigm. The risk of losing data stored with any untrustworthy service provider is the key barrier to widespread uptake of cloud computing. This paper proposes an algebraic signature based remote data possession checking (RDPC) scheme to verify the integrity of the data stored in the cloud. This scheme integrates forward error-correcting codes to enhance the data possession guarantee, which can recover the data when a small amount of file has been deleted. The scheme allows verification without the need for the auditor to compare against the original data, which reduces the communication complexity dramatically. The storage complexity of cloud user is reduced to several bytes’ information. Extensive security analysis and simulation show that the proposed scheme is highly provably secure. Finally, experiment results reveal that the computation performance is effective, and bounded by disk I/O.
Distributed rule execution for smart home system

Smart home system is one of the most popular applications and research interests in the past decade. This system can give many features such as home monitoring, appliance control, and automation system, energy-saving, and home security. A smart home system normally has a centralized architecture. This architecture gives the advantage of being easy to manage. However, the centralized architecture has a fault-tolerance problem. If the center of the system (master station) is down, then the entire system will be failed. A smart home system needs a mechanism to avoid system failure when the master station is down. Furthermore, for the big scale smart home/building system with many devices, the delay time of information transmission and processing can affect the service provision time. To solve the problem, a distributed rule execution mechanism for the smart home system is proposed in this paper. Practical experiments show that the proposed mechanism can avoid the fault-tolerance problem in the centralized smart home system. The system still provides the right services even though the master station is off. The mathematical analysis of the delay time also shows that the proposed mechanism can reduce the delay time of service provision.
Application of TPRS Teaching Method in Chinese Teaching in Mea Fah Luang Middle School of Thailand

The purpose of TPRS teaching method is to encourage teachers to create situations through a large number of questions and improve their own stories. This paper takes the students of grade two in the second grade of Mea Fah Luang Middle School in Thailand as the research object. Starting from the research situation of TPRS teaching method at home and abroad, this paper shows the application of TPRS teaching method in Chinese teaching, designs teaching plans and carries out practical feedback, summarizes the advantages and disadvantages of TPRS teaching method applied in Thai primary Chinese classroom, and gives corresponding optimization suggestions, so as to explore the application way of TPRS teaching method in Chinese classroom of Thailand.
Morlet-Kernel Principal Component Analysis Features for Speech Recognition

The aim of this paper is to present the application of Morlet wavelet to extract the speech features in place of MFCC features. KPCA is applied for selecting and reducing the large features obtained from Morlet wavelet. NLMS (Normalized Least Mean Square) filter is used to reduce additive noise levels ranging from ±5 dB to ±15 dB. Features are modeled using Ensembled Support Vector Machine classification model for FSDD and Kannada multi speaker data sets. The comparative results are discussed over logistic regression model. The proposed model reduces the noise with 99% of recognition rate for isolated words. The efficiency of ensembled classification model is explored.
Application of Back Propagation to Hospital Patient Outcomes

We applied back propagation to patient's outcomes in a hospital. There is an outcome of discharge from hospital to the patient's home. Another case is change of hospital. A back propagation learned to predict an outcome from patient's vital data.
Creating a Custom Virtual Private Cloud and Launch an Elastic Compute Cloud (EC2) Instance in Your Virtual Private Cloud

Cloud Computing is regarded to as putting away and getting to data over the web. The hard disk of your PC doesn’t hold this data. In Cloud computing, you can get to information from a remote server. Amazon Web Services (AWS) enables adaptable, solid, versatile, simple to-utilize and practical Cloud computing arrangements. AWS is an extensive, simple to utilize processing stage offered Amazon. A virtual private cloud (VPC) is devoted to the AWS account which is in the AWS cloud that acts coherently detached with different virtual systems. Amazon EC2 is a secure web administration which allows register with the modifiable limit in the cloud .In this work, we are giving route subtleties of Creating a custom VPC and dispatch an EC2 Instance in your VPC.
Classification of flower images using SVM method through the colour, texture and shape using Histogram, Haar Wavelet and Robert Cross methods

Abstract Flowers are one of the plants that offer a beauty that makes the environment more beautiful and attractive. Not only a decoration, but flowers are also used as cut flowers, sowing flowers and herbal medicine. flowers that grow in Indonesia have two forms, namely the form of a flower and a bunch. In the classification of flower characteristics taken from the flower, images are characteristics of colour, texture and shape. The purpose of this system design is to make a classification of flowers that grow in Indonesia that have different colour, texture and shape characters using the Histogram, Haar Wavelet and Robert Cross methods to get the value of features to be made vector and used on SVM method to determine the types of flowers. SVM is a classifier that has the advantage of being able to process high-dimensional data, without eliminating significant performance degradation. From the experiment of this Indonesian flower classification application, the accuracy of classification success results in the test interest of 81.67% by using the colour, texture and shape features of the extraction results with the Histogram, Haar Wavelet and Robert Cross methods and classified by the SVM method with Linear, Polynomial kernel and RBF.
The Intelligent Power Wheelchair Upgrade Kit

This paper presents an update on the research and development of the Intelligent Power Wheelchair (IPW) upgrade kit. First, we review the last three years of trials, then we describe the improvements proposed for the next prototype. The 2020 IPW edition is designed to be more light weight, modular, and multi-functional than it's predecessor. The assembly process has been streamlined, and there is now greater ability to tailor the system to the needs of the individual user. The brand new, self-leveling scanner has broader applications as an aid for people with visual impairment. Keywords: Smart Wheelchair, Human Trials.
A LSTM Neural Network for On-site Earthquake Early Warning

&amp;lt;p&amp;gt;On-site Earthquake Early Warning (EEW) systems estimate possible destructive S-waves based on initial P-waves and issue warnings before large shaking arrives. On-site EEW plays a crucial role to fill up the &amp;amp;#8220;blind zone&amp;amp;#8221; of regional EEW systems near the epicenter, which often suffers from the most disastrous ground shaking. Previous studies show that peak P-wave displacement amplitude (Pd) may provide a possible indicator of destructive earthquakes. However, the attempt to use a single indicator with fixed thresholds suffers from inevitable misfits, since the diversity in travel paths and site effects for different stations introduce complex nonlinearities. To overcome the above problem, we present a deep learning approach using Long-Short Term Memory (LSTM) neural networks. By utilizing the properties of multi-layered LSTM, we are able to train a highly non-linear neural network that takes initial waveform as input and gives an alert probability as the output on every time step. It is then tested with several major earthquake events, giving the results of a missed alarm rate less than 0.03 percent and false alarm rate less than 15 percent. Our model shows promising outcomes in reducing both missed alarms and false alarms while also providing an improving warning time for hazard mitigation procedures.&amp;lt;/p&amp;gt;
Efficient Computation of the Large Inductive Dimension Using Order- and Graph-theoretic Means

Finite topological spaces and their dimensions have many applications in computer science, e.g., in digital topology, computer graphics and the analysis and synthesis of digital images. Georgiou et. al. [11] provided a polynomial algorithm for computing the covering dimension dim(X; 𝒯) of a finite topological space (X; 𝒯). In addition, they asked whether algorithms of the same complexity for computing the small inductive dimension ind(X; 𝒯) and the large inductive dimension Ind(X; 𝒯) can be developed. The first problem was solved in a previous paper [4]. Using results of the that paper, we also solve the second problem in this paper. We present a polynomial algorithm for Ind(X; 𝒯), so that there are now efficient algorithms for the three most important notions of a dimension in topology. Our solution reduces the computation of Ind(X; 𝒯), where the specialisation pre-order of (X; 𝒯) is taken as input, to the computation of the maximal height of a specific class of directed binary trees within the partially ordered set. For the latter an efficient algorithm is presented that is based on order- and graph-theoretic ideas. Also refinements and variants of the algorithm are discussed.
A Conceptual Framework for Conversational APIs

Application programming interfaces (APIs) are everyday tools for any programmer. During its usage, an API mediates the communication process between designers and users. This mediation may be implemented in three different ways: the source code, the documentation, and the API behavior. According to the resources used, an API may reach higher or lower levels of conversation with its users. In this paper, we propose a conceptual framework for the definition and classification of conversational APIs. A conversational API is one capable of communicating its internal logic of operation and how to be properly used. Through the proposed framework, we introduce how to classify an API according to its level of conversation. Besides, we use our framework for evaluating three common Java APIs for managing date/time. Our experience indicates that the proposed conceptual framework may be useful for supporting designers in implementing their API interfaces accordingly to the proper needs of communication required by their different types of users.
Populating the Peephole Optimizer of a Smart Contract Compiler.

Developing compiler optimizations, especially for new, rapidly evolving smart contract languages, can be onerous and error-prone, but is especially important for smart contracts, where deployment and execution directly translate to monetary cost and which cannot change once deployed. One common optimization technique is the use of peephole optimizations, replacement rules that are applied using pattern-matching. These rules are normally constructed using human expertise, which is both time-consuming and far from systematic in exploring opportunities for optimization. In this work we propose a pipeline to automatically populate the peephole optimizer of a smart contract compiler. We apply superoptimization to an existing code base to obtain sequences of instructions, which can be replaced by cheaper, observationally equivalent instructions. We then generate peephole optimization rules by extracting the underlying patterns of these optimizations. We provide a case study of our approach and a prototype implementation for bytecode of the Ethereum Virtual Machine, the tool ppltr, which combines the superoptimizer ebso and the rule generator sorg. Then we evaluate our approach by generating and applying nearly 1k peephole optimization rules extracted from 2k optimizations obtained from deployed bytecode.
SEQUENTIAL PROCEDURE FOR NONPARAMETRIC ESTIMATION OF STOCHASTIC DE-PENDENCE

A technique is proposed for constructing a nonparametric model of a multidimensional dependence in conditions of small volumes of initial statistical data. The method is based on a sequential procedure for building a nonparametric model in the space of argument lists of the restored dependency. The as-ymptotic properties of the developed model are investigated and the results of computational experi-ments are presented.
Analysis on the relationship between stroke rate and distance per stroke and the effect on boat speed of Jiangsu rowing athletes of lightweight men's double sculls

Using the rowing stroke-watch SEIKO S141 to record the time and 10 stroke rates in each 500 meters split of the lightweight men's double sculls on water 2000 meters test competition, and calculate the average boat speed, stroke rate and distance per stroke in each 500 meters split. The results show that: the boat speed of each 500 meters split was descending gradually; the stroke rate changed reversely compared to distance per stroke in each 500 meters split; the boat speed of the last 500 meters split did not ascend following the ascending of the stroke rate; the average stroke rate of our athletes was significantly lower than the average stroke rate of the world’s elite athletes during the whole 2000 meters.
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation.It extends XLNet (Yang et al., 2019) introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidence, and the answer memory working as a buffer continually refining the generated answers.Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset.An additional qualitative analysis revealed the interpretability introduced by the memory module.
A General Framework for Chinese Domain Knowledge Graph Question Answering Based on TransE

Abstract This paper introduces a general question answering (QA) framework for Chinese domain knowledge graphs to serve various industries. The question is analyzed by shielding invalid characters in this framework, and the TransE calculation of entities and relations is used to search answer, rather than traditional logical queries that rely heavily on predefined rules. In this way, the QA framework can be easily applied to different fields with little manual participation, and reduce the migration cost caused by the lack of scalability of the existing QA systems, which are designed only for specific domains. Then, the proposed framework is operated on the space launch knowledge graph and has achieved HITS@1, HITS@3, HITS@5 scores of 60.60%, 80.87%, 85.61% separately, which is competitive with other restricted domain knowledge graphs. In addition, numerical results on the knowledge graph of the Three Kingdoms and the COVID-19-Character verified the scalability of the QA framework.
Performance Analysis of Noise Channel Model for Quantum Noise Stream Cipher

This paper presents a noise channel model to analyses the channel characteristics between legitimate receiver and eavesdropper in quantum noise stream cipher system. The simulation results show that the channel advantage for legitimate receiver over eavesdropper can be formed by combining proper channel codes with quantum noise. Due to the fluctuation of the noise, the eye pattern degradation of the signals increased rapidly with the increasing of modulation order. In addition, the security gap in this paper is obtained by theoretical calculation. And with the increase of number of modulation order, the security gap increases significantly. After the number of modulation order goes beyond 32, the security gap is more than 0, when the number of modulation order goes up to 1024, the security gap is more than 12, which means the security and communication integration security and communication integration can be achieved.
VRF-Based Mining Simple Non-outsourceable Cryptocurrency Mining

This paper introduces VRF-based mining, a simple and effective way of making pooled mining impossible. Instead of using hash functions, VRF-based mining uses Verifiable Random Functions (VRFs) for Proof-of-work (PoW)-based consensus. As VRF binds an output with a secret key, a pool operator should reveal its secret key to outsource the mining process to miners, and miners can anonymously steal cryptocurrency in the pool operator’s wallet. We revisit the definition of non-outsourceability in existing research, and identify two properties, namely punish-mining-reward and stealing-unlinkability. Punish-mining-reward specifies that if a pool operator outsources mining to a miner, then the miner can steal mining reward. Stealing-unlinkability specifies that the pool operator cannot identify miners stealing mining reward. We show that VRF-based mining satisfies punish-mining-reward, but not stealing-unlinkability. Nevertheless, the pool operator should take extra effort – which makes maintaining mining pools expensive – to identify mining reward stealers and transfer mining reward before being stolen. In addition, we discuss several considerations on instantiating VRFs for VRF-based mining. Moreover, we experimentally show that VRF-based mining is simple to implement and introduces negligible overhead compared to hash-based mining.
DIACR-Ita @ EVALITA2020: Overview of the EVALITA2020 Diachronic Lexical Semantics (DIACR-Ita) Task

This paper describes the first edition of the “Diachronic Lexical Semantics” (DIACR-Ita) task at the EVALITA 2020 campaign. The task challenges participants to develop systems that can automatically detect if a given word has changed its meaning over time, given contextual information from corpora. The task, at its first edition, attracted 9 participant teams and collected a total of 36 submission runs.
Accuracy comparison methods between putro’s method with husain’s method for validating damaged physical documents

Abstract To face with digital divide challenge in e-government implementation, we can combine manual and electronic processes in government administration. To be able to validate in an electronic scheme two methods have been proposed each by Putro and Husain. This study will compare the accuration the validation method between perceptual hash proposed by Putro with the method proposed by Husain for validating damaged physical documents, especially those caused by a disaster. This quasi-experimental study was conducted by comparing the hash value of the original document with those of six variations of the document’s damage. While the type of document for testing includes: text, image, and combined text-image documents. As a result, Perceptual hash proposed by Putro is better than Husain’s method for validating physical documents because of its tolerant 4 out of 6 types of damage.
VR Design and Display System of Ceramic Products Based on Cloud Service Platform

The development of Virtual Reality (VR) technology will bring a new round of technological revolution to the internet industry. Using VR technology to realize the virtual design and display of ceramic products can improve the interactive efficiency of ceramic product design and let users experience the fun of product design. Therefore, this paper designs and implements a VR design and display system of ceramic products based on cloud design service platform. In this paper, we propose the architecture and decoupling ideas of the VR design and display system of ceramic products. For the front end of the system, we use Unity as the client development tool, use the Facade Pattern to split different functional modules, and apply asynchronous method to load FBX model and process JSON information. For the back end of the system, the Socket and PHP Web framework are deployed on the server-side, in which Socket is responsible for processing delay-sensitive functions, and PHP is responsible for processing delay-insensitive functions. The application effect of the implemented systems shows that it can improve the interactive efficiency of designing ceramic products.
Development of smart english classroom system based on FPGA software and hardware co-simulation test

Improving English proficiency for professional students is a significant challenge in the AEC and the Renaissance. The problem found in the Polytechnic Gland Shipping Research Institute is the lack of time motivation and insufficient time for students to study outside the classroom lack of time. Therefore developed an Android-based “Technical English” application that supports PPN, English teaching, and learning processes. Development and Smart Classroom List (SCI) Verification. SCI is a learning environment tool that integrates existing technologies, including TROFLEI, TICI and CCEI (ergonomics). SCI is included to learn the functions, materials to describe flexibility, and experience factors in the smart classroom. The information technology detail used in some projects has also been adapted to the elegant classroom environment. Older classmates check the tool and reveal ten balances: body design, flexibility, and use of technology, learning data diversity, inquiry, collaboration, student drive, honesty and learning experience. The results of the Acid Ceasefire Agreement prove that is a simple tool for assessing the learning environment in a smart classroom. Student Differences, Flexibility, Technology Application, and Learning Data Factors There are four significant practical priority differences in the learning environment in a smart classroom. FPGA has received a lot of attention from the nuclear industry as an alternative platform for logical programming controllers for digitalization and control. The FPGA development software feature includes several steps for integration and refinement, and each such step requires a different simulation verification step. This research proposes an integrated software testing framework that simulates all the glitches created by FPGA software and evaluates whether it can work in general, using the standard Oracle programs for all glitches simultaneously.