Reviewerly/exHarmony huggingface dataset:
TiCA

This paper proposes a Tibetan text compression algorithm (TiCA), which is based on the fact that each Tibetan syllable is composed of one to seven components and each component has a unique Unicode encoding. First of all, through statistical analysis of 20G Tibetan text corpus, a fault-tolerant mapping dictionary is established and used as the dictionary of the TiCA. The TiCA then compresses the Tibetan text according to the mapping dictionary by mapping the original code to a single code. Finally, the experimental comparison shows that the Tibetan text compression algorithm proposed in this paper has achieved excellent results both in the compression rate and time consuming.
Enhance the perception of easy-to-fall and apply the Internet of Things to fall prediction and protection

The quality of medical care in Taiwan has been continuously improved. In particular, comprehensive medical care with scientific medical care, sickness and individualization has matured, and even technology-assisted medical care has become increasingly important. This study uses different aspects to explore how to strengthen the knowledge of patients and their companions during their stay and stay within 72 hours in a regional hospital of northern Taiwan from March 2018 to December 2018, so that the cognitive awareness rate is increased by 95%. Moreover, this study combines the concept of smart medical system with Internet of Things technology such as wireless data transmission and tilt sensor to predict the fall of hospitalized patients and immediately start protection. When the patient is about to fall, the sensor automatically detects the activation of the protective device, such as airbags, automatic inflatable clothing and other innovative anti-fall equipments. At the same time, the sensing data is uploaded to the nursing station electronic whiteboard and then display (easy to fall) the number of hospital bed through the cloud network. Finally, these sensing messages and processing methods are also stored in the cloud database to provide future analysis and verification for the improvement of patient safety.Biography:Shan-Ju Lin is a specialist nurse in Taipei City Hospital, Taipei City, Taiwan. She is also a master in Department of Nursing (Master postgraduate), University of Chang Gung University of Science and Technology,Taipei, Taiwan.       Shuo-Tsung Chen received the B.S. degree in Mathematics from National Cheng Kung University, Tainan in 1996 and M.S. degree in Applied Mathematics from Tunghai University, Taichung in 2003, Taiwan. In 2010, he received the Ph.D. degree in Electrical Engineering from National Chinan University, Nantou, Taiwan. Now he is an Assistant Professor in Department of Information Management, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan.
Comparative Analysis on Few Typical Algorithms of Gesture Recognition for Human-robot Interaction

Human-robot interaction (HRI) is considered as one of the key techniques of space intelligence robots. Few typical features of complicated human actions need to be captured and understood accurately by intelligent robot to ensure free communication and interaction between both above in real-time, especially for identifying and tracking hand state. There are four approaches for gesture recognition, including algorithm based on Kinect V2 SDK, model-based particle swarm optimization algorithm (PSO), deep learning algorithm and gesture module based on Baidu AI platform. These have been selected to compare in the form of principle, calculating time, robustness, range, environmental adaptability and correct rate respectively. The comparative results have generalized that both the second and the third algorithm have better performance than other algorithms in the above aspects such as calculating efficiency, robustness, detecting range, external disturbance and correct ratio. Particularly, the second algorithm is not only suitable for close range, but also suitable for multi-view cases. However, the third algorithm can have better performance, but depends on precise network model and weights by introducing lots of positive and negative gesture samples.
Research on the Combination of Information Technology and Oral English Teaching

Abstract The extensive application of information technology, the development of educational resources, the optimization of educational processes, and the improvement of education quality and efficiency in the field of education are the original driving force of education informatization, the promotion of education reform and development, and the cultivation of innovative talents that meet the requirements of the information society. Promote the foundation and premise of educational modernization. With the rapid development of social economy and culture, the integration of information technology in vocational English teaching has become the mainstream. Based on the problems faced by current oral English teaching, this paper explores the possibility of integrating information technology teaching in teaching and the places that need attention, and hopes to provide innovative countermeasures for vocational English teaching.
Intelligent Evaluation Model of Precision Mobile Network Software Evaluation Accuracy

The existing intelligent evaluation models of the evaluation accuracy of mobile network software all have some disadvantages, such as poor evaluation accuracy and large evaluation error. Therefore, the intelligent evaluation model of fine mobile network software evaluation accuracy is put forward to study on the intelligent evaluation model of the evaluation accuracy of mobile network software. The data acquisition card is used to collect the evaluation data of fine mobile network software, and it is pre-processed by data checking and data cleaning. Based on the pre-processed data, Bootstrap sampling method is used to obtain the evaluation data. Based on the accurate evaluation data of fine mobile network software, this paper uses integrated learning algorithm to intelligently evaluate the accuracy of fine mobile network software evaluation. The intelligent evaluation of the accuracy of fine mobile network software evaluation is realized. The experimental results show that compared with the existing intelligent evaluation model for the accuracy of fine mobile network software, the intelligent evaluation model for the accuracy of fine mobile network software improves the evaluation accuracy and reduces the evaluation error. It fully shows that the intelligent evaluation model of fine mobile network software evaluation accuracy has better evaluation performance
Path Planning Algorithm of Mobile Robot Based on IAFSA

In order to improve the search speed and shorten the search time of robot path planning, the algorithm and its characteristics of mobile robot in path planning are summarized. Firstly, the path planning of mobile robots is classified and summarized. From the perspective of mobile robots’ mastery of the environment, the path planning of mobile robots is replanned through IAFSA. Meanwhile, the development status, advantages and disadvantages of relevant algorithms are summarized. Then it points out the future development trend of robot path planning technology in improved algorithm, hybrid algorithm, multi-robot collaboration, complex environment and multi-dimensional environment.
Application of YOLO algorithm on student UAV

The main concern of this paper will be to explain the workflow of YOLO2 and YOLO3 object detection algorithms with regards to training on custom dataset for the purpose of deploying said algorithms on NVIDIA Jetson mini board that is placed inside the shell of an unmanned aerial vehicle made for a purpose of student unmanned aerial vehicle(SUAV) competition. A walk through the key concepts and intuition regarding neural nets, typical data augmentation processes, the training process and deployment, both from software and hardware point of view, will be given.
Measuring the Second Digital Divide: Education and Skills

As it is usually conceived, the digital divide between rich and poor countries refers to differences in access to digital technologies. The second digital divide on the other hand, is concerned with the factors that determine whether and how the adopted technologies are used and the extent of the benefits that are derived from them (one such factor, for example, is digital skills). The original feature of this chapter is that it seeks to measure the second digital divide between rich and poor countries and to compare the outcome with the more familiar divide in digital access (and the internet in particular). The former is measured with reference to two key technology complements, namely, attainments in reading and mathematics on the one hand and digital skills on the other. What I find is that even the acute divide in the Internet is surpassed by the figures representing the second divide in literacy and mathematics. This finding is attributed mainly to the acute crisis in learning that besets many schools in poor countries, especially those in Sub-Saharan Africa. Somewhat oddly, though, the divide in digital skills is less acute than for learning achievements. Apparently, certain digital skills can be acquired without prior competencies in basic education.
Adaptor Reconfiguration Based on Property Preserving Compliance in Evolving Cloud Computing

Existing approaches for adaptor reconfiguration in evolving cloud computing environments may be too restrictive in some situations, especially when a cloud service evolves into the one with less functionality. In these cases, preserving a certain property before and after reconfiguration may be acceptable for the designer. Thus, we present the approach for adaptor reconfiguration based on property preserving compliance. Firstly, FSM-based notations for describing cloud services, the adaptor, and temporal properties are defined. Further, by introducing the notion of property preserving compliance, the validity of an adaptor instance under the definition of a new adaptor version is determined. Then, the algorithms for reconfiguration of adaptors are presented and can be used for determining the migratability of an adaptor instance and the corresponding target state of reconfiguration. Finally, the effectiveness and feasibility of the approach is illustrated by a case study.
Relevant Content Selection through Positional Language Models: An Exploratory Analysis

This research results from work partially funded by Generalitat Valenciana (SIIA PROMETEU/2018/089) and the Spanish Government-ModeLang (RTI2018-094653-B-C22) and INTEGER (RTI2018-094649-B-I00). It is also based upon work from COST Action Multi3Generation (CA18231).
An Ontology-Aware Unified Storage Scheme for Knowledge Graphs

With the development of knowledge-based artificial intelligence, the scale of knowledge graphs has been increasing rapidly. The RDF graph and the property graph are two mainstream data models of knowledge graphs. On the one hand, with the development of the Semantic Web, there are a large number of RDF knowledge graphs. On the other hand, property graphs are widely used in the graph database community. However, different families of data management methods of RDF graphs and property graphs have been seperately developed in each community over a decade, which hinder the interoperability in managing large knowledge graph data. To address this problem, we propose a unified storage scheme for knowledge graphs which can seamlessly accommodate both RDF and property graphs. Meanwhile, the concept of ontology is introduced to meet the need for RDF graph data storage and query load. Experimental results on the benchmark datasets show that the proposed ontology-aware unified storage scheme can effectively manage large-scale knowledge graphs and significantly avoid data redundancy.
To Explore the Innovative Application of Computer Multimedia Technology in English Teaching Reform in Higher Vocational Colleges

Abstract It explains the various characteristics and advantages of advanced computer-related information technology in our current English teaching. In this paper, we introduce and explain many specific uses of this advanced technology in higher vocational English teaching. In addition, we take the English computer multimedia teaching system of Stanford University, a world-class famous school, as an example, and finally explain many opinions and suggestions on further deepening the English teaching reform in higher vocational colleges.
What Makes Angular Interseting

Angular JS is Single Page Application which is also a JavaScript library. It is a Open Source application which is used by the thousands of developers in the world. Angular is more responsive to action taken by the users. It is also licensed in Apache license version 2.0. The new mobile and web-based application frameworks has been released and it is used by the both the software development industry and research While some of these new frameworks are more popular than others, some are specialised in certain types of applications, and others have specific advanced features or outstanding capabilities that set them above others.The increase in the usage of mobile application has increased the demand of mobile application and has also increase the need for cross-platfrom framework. In this paper, we discuss the different criteria which identifies the strengths and weaknesses of using Angular frame work in developing mobile and web-applications. Finally, we focus more deeply on the version of the Angular framework showing the eminent capabilities and over its own earlier versions. Overall, our comparative analysis results in a few interesting findings regarding different version of Angular framework, leaving us to believe that a new generation might soon emerge from the exponential path of MVC, MV*/MVW and MVVM.
Developing a Framework for Acquisition and Analysis of Speeches

Speech plays a vital role for human communication. Proper delivery of speech can enable a person to connect with a large number of people. Nowadays, a lot of valuable speeches are being provided by many popular people throughout the world and it will be very helpful if important information can be extracted from those speeches by analyzing them. An automatic speech-to-text converter can facilitate the task of speech analysis. There have been carried out a lot of works for the conversion of speech to text in the last few decades. This paper presents a framework for the acquisition of speech along with the location of the speaker and then conversion of that speech into text. We have worked with speeches containing three different languages. To evaluate our framework, we collected speeches from several locations and the result shows that the framework can be used for efficient collection and analysis of the speeches.
Generative Adversarial Nets for Cost-Sensitive Face Recognition

Most face recognition studies are based on standard frontal face databases, but in real life, the images we obtain are profile face images of any angle in most instances. In this case, the traditional face recognition methods cannot achieve the lowest recognition cost. Therefore, how to use the obtained profile face images to synthesize the corresponding frontal face images is important in the face recognition system. Besides, most traditional face recognition systems are try to find an accurate classifier to achieve the lowest error rate, implicitly assuming that all misclassification costs are equal. It is an unreasonable assumption because almost in all face recognition systems, different types of misclassification errors often lead to different misclassification costs. To address the two issues, we propose a cost-sensitive face recognition method based on generative adversarial nets. First, generate frontal face images using the two-channel generative adversarial nets, and then introduce cost-sensitive learning in the recognition process to consider the cost imbalance problem. The experimental results demonstrate the effectiveness of the proposed method.
A Semantic Matchmaking Technique for Cloud Service Discovery and Selection Using Ontology Based on Service-Oriented Architecture

As we are moving ahead with cloud computing, preference for cloud services is getting increased day by day. These services mostly seem to be significantly identical in their functionality excepting their key attributes like storage, computational power, price, etc. As of now, there is no uniform specification for defining a service in the cloud domain. In order for the specification of the identical operations and publication of the services on the websites, different cloud service providers tend to use completely different vocabulary. The process of requesting for a cloud service becomes merely a challenging task as a result of increasing number of selection parameters and QoS constraints. Hence, a reasoning mechanism is very much required for service discovery that could resolve the resemblance appearing across different services with reference to the respective cloud ontology. In this paper, a semantic matchmaking technique is proposed for most relevant cloud service discovery and selection procedure based on Service-Oriented Architecture (SOA).
Pyramid M-Shaped Network for Ordered Dithering Block Truncation Coding Image Restoration

This paper presents a novel deep learning-based technique for reconstructing the Ordered Dithering Block Truncation Coding (ODBTC) decoded image. The proposed method inherits the effectiveness of wavelets transform and Convolutional Neural Networks (CNN). The proposed technique employs the two-dimensional Decimated Wavelet Transform (DWT) to decompose the ODBTC image into low and high frequency sub-bands over various resolution. These image sub-bands are progressively reconstructed with the Pyramid M-Shaped CNN consisting multiple input and output. This scheme considers the ODBTC decoded image as noisy image in Which the information of image can reconstructed by modifying the wavelets sub-bands using residual learning strategy. The produced sub-bands can be combined and transformed back to improve the quality of ODBTC reconstructed image. As documented in experimental results, the proposed method yields promising result for the ODBTC image reconstruction.
Evaluating Fault Tolerance of Distributed Stream Processing Systems

Since failures in large-scale clusters can lead to severe performance degradation and break system availability, fault tolerance is critical for distributed stream processing systems (DSPSs). Plenty of fault tolerance approaches have been proposed over the last decade. However, there is no systematic work to evaluate and compare them in detail. Previous work either evaluates global performance during failure-free runtime, or merely measures throughout loss when failure happens. In this paper, it is the first work proposing an evaluation framework customized for quantitatively comparing runtime overhead and recovery efficiency of fault tolerance mechanisms in DSPSs. We define three typical configurable workloads, which are widely-adopted in previous DSPS evaluations. We construct five workload suites based on three workloads to investigate the effects of different factors on fault tolerance performance. We carry out extensive experiments on two well-known open-sourced DSPSs. The results demonstrate performance gap of two systems, which is useful for choice and evolution of fault tolerance approaches.
Probability Programming and Control of Moving Agent Based on MC-POMDP

A design scheme for probabilistic planning and decision-making of mobile agents is proposed, which realizes the functions of probabilistic planning and grab control of mobile agents. How to use MC-POMDP algorithm to perform sensing and control in an unknown environment is described in detail. Combined with the particle filter algorithm to approximate the confidence state space, the existing POMDP technology was improved, and the probability planning was optimized. The actual operation results show the feasibility and effectiveness of the scheme. The system uses its own sensors to sense the environmental information, performs dynamic probabilistic path planning, successfully approaches the target object, and implements the mobile grabbing function of the mobile agent. Based on the example application of reinforcement learning principles, a new direction for the probability planner to deal with the generalized uncertainty of mobile agents is prospected.
Labeling of Multilingual Breast MRI Reports

Medical reports are an essential medium in recording a patient’s condition throughout a clinical trial. They contain valuable information that can be extracted to generate a large labeled dataset needed for the development of clinical tools. However, the majority of medical reports are stored in an unregularized format, and a trained human annotator (typically a doctor) must manually assess and label each case, resulting in an expensive and time consuming procedure. In this work, we present a framework for developing a multilingual breast MRI report classifier using a custom-built language representation called LAMBR. Our proposed method overcomes practical challenges faced in clinical settings, and we demonstrate improved performance in extracting labels from medical reports when compared with conventional approaches.
Key Rate Enhancement by Using the Interval Approach in Symmetric Key Extraction Mechanism

Wireless security is confronted with the complexity of the secret key distribution process, which is difficult to implement on an Ad Hoc network without a key management infrastructure. The symmetric key extraction mechanism from a response channel in a wireless environment is a very promising alternative solution with the simplicity of the key distribution process. Various mechanisms have been proposed for extracting the symmetric key, but many mechanisms produce low rates of the symmetric key due to the high bit differences that occur. This led to the fact that the reconciliation phase was unable to make corrections, as a result of which many key bits were lost, and the time required to obtain a symmetric key was increased. In this paper, we propose the use of an interval approach that divides the response channel into segments at specific intervals to reduce the key bit difference and increase the key rates. The results of tests conducted in the wireless environment show that the use of these mechanisms can increase the rate of the keys up to 35% compared to existing mechanisms.
A phase retrieval method for fringe projection profilometry based on the phase pyramid

High-precision phase retrieval is the key step of the fringe projection profilometry, while the traditional multifrequency phase-shifting method is easy to suffer from noise, which leads to a decrease in measurement accuracy. Based on the local similarity of phases, a new phase retrieval method is proposed in order to improve the accuracy of the phaseshifting and reduce the phase order error. The input images are down-sampled to obtain an image pyramid, and the phase of the high-resolution images is recovered from the low-resolution fringe patterns. Simulation and experimental results show that the proposed method can effectively reduce the phase error in the multi-frequency phase-shifting method and has good robustness and high accuracy