Reviewerly/exHarmony huggingface dataset:
A Deep Knowledge-Based Evaluation of Enterprise Applications Interoperability

Enterprise is a dynamic and self-managed system, and the applications are an integral part of this complex system. The integration and interoperability of enterprise software are two essential aspects that are at the core of system efficiency. This research focuses on the interoperability evaluation methods for the sole purpose of evaluating multiple enterprise applications interoperability capabilities in the model-driven software development environment. The peculiarity of the method is that it links the causality modeling of the real world (domain) with the traditional MDA. The discovered domain causal knowledge transferring to CIM layer of MDA form the basis for designing application software that is integrated and interoperable. The causal (deep) knowledge of the subject domain is used to evaluate the capability of interoperability between software components. The management transaction concept reveals causal dependencies and the goal-driven in-formation transformations of the enterprise management activities (an in-depth knowledge). An assumption is that autonomic interoperability is achievable by gathering knowledge from different sources in an organization, particularly enterprise architecture, and software architecture analysis through web services can help gather required knowledge for automated solutions. In this interoperability capability evaluation research, 13 different enterprise applications were surveyed. Initially, the interoperability capability evaluation was performed using four know edit distance calculations: Levenshtein, Jaro-Winkler, Longest common subsequence, and Jaccard. These research results are a good indicator of software interoperability capability. Combining these results with a bag of words library gathered from “Schema.org” and included as an addition to the evaluation system, we improve our method by moving more closely to semantic similarity analysis. The prototype version for testing of enterprise applications integration solution is under development, but it already allows us to collect data and help research this domain. This research paper summarizes the conclusions of our research towards the autonomic evaluation of interoperability capability between different enterprise applications. It reveals basic concepts on which we proved our assumption that enterprise application could be evaluated in a more objective, calculable manner.
Analysis of the Monetary and Financial System of the Dr Congo Place of Financial Intermediation on Economic Development

The purpose of this work is to evaluate the effect of financial on economic development in the DR Congo. This work is based on two economic approach, the first using ordinary least squares(OLS), and the second is the causality test Granger and the instrumental variable method (2SLS, GMM, LIML). The results of four econometric approach reinforce and suggest that financial through intermediation has a positive and significant effect on economic development. Directly or through an indirect channel through inflation, exchange rate and money supply before impacting on the level of overall economic activity. The results also show the vulnerability of the financial intermediation function by Congolaise banking system in financing growth sectors with high demand of Congolaise work (agriculture, industry….), indeed, the share of companies as well as individuals is low and short term finance. Based on these results we can note that the DR Congo gourvment in monetary authorities have an interest in implementing reforms to promote the emergence and efficiency of the financial system to grown the sector such as agriculture, industry.
Consideration of Disaster Prevention System using Service Design Management

In Japan's disaster prevention measures, rule making by law has been realized, but the construction of disaster prevention systems such as tools and support systems that implement disaster prevention measures based on the law is not sufficient. We considered that the cause was that the appropriate action to be taken when the disaster occurred was not yet "visualized". In other words, disaster prevention measures are largely accumulated by past experience, and systematization of measures is not sufficient. Therefore, in this study, referring to the records of the field survey after the occurrence of disaster, we will consider the way of disaster prevention measures using the approach of service design management emphasizing the concept of co-creation and ecosystem.
Developing Speed Reading Skills In Young Learners

T his study is aimed to develop the speed reading skills in young learners and some issues faced along the learning . Speed reading is usually conducted in adult students and very few applied in young learners. It is considered an important goal of reading instruction . This research applied descriptive qualitative method by conducting a close observation technique where researcher acted as a non-participant observer and didn't participate and interfere to 5 subjects in teaching. The findings showed that speed reading strategy surprisingly could be applied in young learners. and it helps the young learners to get better result and achievement in reading This strategy is so simple, effective and easy to apply and strongly recommended to be applied in teaching young learners.
Rgbd Based Generative Adversarial Network For 3D Semantic Scene Completion

3D scene understanding is of importance since it is a reflection about the real-world scenario. The goal of our work is to complete the 3d semantic scene from an RGB-D image. The state-ofthe-art methods have poor accuracy in the face of complex scenes. In addition, other existing 3D reconstruction methods use depth as the sole input, which causes performance bottlenecks. We introduce a two-stream approach that uses RGB and depth as input channels to a novel GAN architecture to solve this problem. Our method demonstrates excellent performance on both synthetic SUNCG and real NYU dataset. Compared with the latest method SSCNet, we achieve 4.3% gains in Scene Completion (SC) and 2.5% gains in Semantic Scene Completion (SSC) on NYU dataset.
Historical Document Processing: Historical Document Processing: A Survey of Techniques, Tools, and Trends.

Historical Document Processing is the process of digitizing written material from the past for future use by historians and other scholars. It incorporates algorithms and software tools from various subfields of computer science, including computer vision, document analysis and recognition, natural language processing, and machine learning, to convert images of ancient manuscripts, letters, diaries, and early printed texts automatically into a digital format usable in data mining and information retrieval systems. Within the past twenty years, as libraries, museums, and other cultural heritage institutions have scanned an increasing volume of their historical document archives, the need to transcribe the full text from these collections has become acute. Since Historical Document Processing encompasses multiple sub-domains of computer science, knowledge relevant to its purpose is scattered across numerous journals and conference proceedings. This paper surveys the major phases of, standard algorithms, tools, and datasets in the field of Historical Document Processing, discusses the results of a literature review, and finally suggests directions for further research.
Adaptive residual neural network for image super-resolution

Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single image super resolution (SISR) field. However, most of existing CNN-based SR models require high computing power, which is not conducive to daily use. In addition, these algorithms need to use a large number of CNN to obtain global features. Therefore, this paper proposes an image super-resolution framework based on adaptive residual neural network, using the adaptive framework to switch between global and local reasoning for internal features in a flexible way, it can extract a large number of global features without neglecting key information, which is conducive to the comprehensiveness of residual images. After the adaptive block, SENet is added to conduct channel modeling for the extracted features, and the importance of each feature channel is automatically acquired by learning method. Then, according to this importance, useful features are promoted and those that are not useful for the current task are suppressed. In this way, with more nonlinearity, the complex correlation between channels can be better fitted, and the number of parameters and computation can be reduced, which can improve the performance of super resolution to a certain extent.
"Surprised Ghost" Ask a librarian poster

These posters were created to encourage students to ask a librarian for help with research.
Shin Rip(申砬)'s number of troops in the Battle of Tangeumdae

신립이 서기 1592년 음력 4월 25일 충주성에 집결시켰던 총 병력 수는 8,000명이었다. 이 병력 가운데 신립 장군이 都城에서 동원하여 충주 성까지 인솔했던 병력 수는 보병 및 기병 4,366명이었다. 그리고 충청도 內 군현에서 동원하여 충주 성까지 이동시켰던 병력 수는 보병 3,634명이었다. 도성에서 인솔했던 4,366명 가운데 기병 수는 1,152명이었고 보병 수는 3114명이었으며 宗族은 100명이었다. 8,000명 가운데 보병 수는 6,748명이었다.The total number of troops that the Joseons(朝鮮) Commander Shin Rip (申砬) gathered in Chungju Castle(忠州城) on April 25, 1592(壬辰年 Imjin year), was 8000 men. Among these troops, the number of troops that General Shin-Rip(申砬) had mobilized from the capital Kyengseng(京城) and then led to Chungju Castle(忠州城) was 4366 infant ry and cavalry. And t he number of t roops t hat the governor(忠淸道監司) of Chungcheong province(忠淸道) mobilized from Chungcheong province(忠淸道) and then led to Chungju Castle(忠州城) was 3634 infantry. Among the 4366 soldiers who Shin-Rip(申砬) led from the Capital Kyengseng(京城) to Chungju Castle, the number of cavalry was 1,152(the Special Guard(別侍衛) 224 men + the Elite Troops(甲士) 928 men)men, the number o f infant ry was 3114 men, and t he number of Family Unit (宗族部隊) was 100 men.
Penerapan Strategi Operasional dan Pemeliharaan Drainase Berdasarkan Partisipasi Masyarakat

Blang Beurandang Village, BB1 Housing, Johan Pahlawan District, West Aceh Regency. Is a flood-prone area. This is due to the fact that many drainage canals are damaged due to frequent flooding so that drainage cannot function properly, other than that there is no public awareness in maintaining drainage. Based on this background, this study was made to directly examine the existing conditions of the field drainage, and the implementation of operational and maintenance strategies based on community participation. This research uses the Likert scale method, the data used are primary data and secondary data. Primary data includes the activity of distributing questionnaires to the community to find out community perceptions about optimizing drainage maintenance. While secondary data include the total population in the BB1 Blang Beurandang Housing Complex. From the research results obtained from the existing drainage conditions in Blang Beurandang Village, BB1 Housing, Johan Pahlawan District, West Aceh Regency, there is still a lot of sedimentation in the canal network that is very thick so that the drainage cross section can only hold a little rainwater, which overflows out, besides that at drainage network is still a lot of garbage buildup and grow thick grass that makes the drainage network clogged. In order to achieve good drainage maintenance, the strategy that must be implemented is to repair damaged drainage networks, normalize drainage networks, implement regular drainage maintenance in the form of mutual cooperation every two weeks, and periodic maintenance of drainage once a month together with the Government The area is by cleaning, socializing, and evaluating regularly about drainage cleaning that has been carried out so far through routine maintenance once a week.
Bias in Network Architectures and Platforms

Information networks always have an initial architecture, or at least some set of parameters. Their design is important: it enables and prohibits, slows downs or speeds up, certain kinds of activities. Protocols internal to the network might dictate who is in and who is excluded. Awareness of these architectures and features is useful on a variety of levels for journalists and others involved in communications: it can help facilitate community engagement and better sourcing while also overcoming biases that are inherent to networks.
Quantum Computing: A New Era of Computer Science

Quantum computers are the new emerging and exciting field of computer science. The Quantum computer technology is based on the laws of quantum physics which have high processing using the capability to be in multiple states, and simultaneously perform all possible permutations. At the time when Quantum computers will get started, it will become very easy to solve many problems which include the solving of very complex chemical processes with very high accuracy. This research paper will give an overview of the Quantum computers, the introduction of the Quantum Computers, description that how it works, brief knowledge about it’s features and benefits, and various future uses of the quantum computers.
Mother Tongue – Based Reading Materials for Grade III Pupils: A Proposal

This study was conducted to make one alternative Meranaw Reading Material in Reading Comprehension. The researcher utilized a descriptive research methodology in gathering data for this study. The descriptive design is deemed an appropriate method in the analysis and interpretation of the data from the self-made Meranaw Tutul Reading Comprehension. To obtain the data needed, the researcher made use of modified questionnaire. The data gathered were statistically treated using the Statistical Program for Social Sciences (SPSS). Frequency, percentage distribution, weighted mean, and standard deviation were used to analyze the data. Class discussion and observation were also used to further solicit the data. The result of the study could give benefits to the teachers on how to make necessary adjustments in the materials to suit the needs and interests of the pupils and to maximize its use in the delivery of teaching and learning. Based on the data gathered, it can be concluded that a pupil who was not competent in his/her first language always experience cognitive difficulties and understanding the text on reading comprehension. A strong foundation on mother tongue serves as a capital for learning the target language.
국립소방역사박물관의 전시 프로그램과 공간구성에 관한 연구

There have been numerous human casualties caused by various incidents around the world; overcrowding and convenience from urban development are becoming dangerous factors in people's lives. which leads to the purpose of the study; to plan the National Fire History Museum to enhance people's safety awareness from education and furthermore, prevent accidents. The study analyzes exhibition programs and spacial compositions of Fire History museums and proposes an exhibition plan for an efficient exhibition program and spatial composition. Fire museum cases overseas were analyzed with an exhibition system of narratives to identify problems from existing exhibition programs and spatial composition and propose a solution. The research methods were analyzing preceding studies, the constructive system of the exhibition, and composing elements of narratives. According to the analysis, fire museum overseas had simple narrative structure and text-oriented education programs?visitors can easily lose interest from the exhibition?lack of narrative characteristics was analyzed. Under the assumption that the museum program and space provides interesting experience to visitors from analysis of visitor's various sensual experiences and information can lead to a better educational effect than before. This study analyzes the program and spatial composition of fire museums overseas and structure of exhibition narratives to provide guidelines?structural elements of programs, spatial composition, and narratives?designing a Fire History museum. Furthermore, the study focuses on the problems that existing exhibition systems have and provide academic help to the National Fire History museum; Effective education of knowledge and thinking from the exhibition.
Mixture Approach To Move Package Approval

A file encryption plan is proposed based on features of the Cloud-specific file hierarchy. We recommend using Layer Access Structure to solve the problem of many hierarchical files under discussion. We conduct and implement comprehensive experiments for the Penguin Plan FH-Club. In the current system, the cost and time of file encryption is high, the system understands some time, computing, and the cost is very high. Layered access structures are combined into only one access structure, after which hierarchical files are encrypted using the integrated access structure. The cipher text components associated with attributes can be shared across files. Club-penguin-ABE plans are affordable which are more varied and therefore suitable for general applications. Many of the hierarchical files that are discussed are resolved by using the layer of access layer structure. In the proposed system, the storage of encrypted texts and the price for encoding files are preserved. As the number of files increases, the benefits of our plan become more evident. Therefore, the encoded text storage and file encryption time are saved. In addition, the proposed plan proved to be safe under the standard hypothesis.
READERS’ THEATRE TECHNIQUE; RELEVANCE OF NATURE OF CLASS FOR ENHANCING READING SKILLS IN SECONDARY SCHOOLS IN KISUMU COUNTY, KENYA

Learners in secondary schools ought to be provided with a learning environment which results in effective reading skills. Nature of class facilitates achievement in reading skills through group work, scaffold, motivation and enacting scripts. This study adopted social constructivism in a quasi-experimental design to establish influence of nature of class on achievement in reading skills in secondary schools. Data was collected from learners, n=205 (experimental groups), n=221 (control groups) and 19 teachers for a period of 8 weeks. Reading skills assessment tests, questionnaires, interviews, focus group discussions were used to collect data. The findings revealed a statistically significant relationship between nature of class and achievement in reading skills with p=0.00. The findings in regard to statistical analysis revealed nature of class had a significant impact on achievement in reading skills and interactive classroom environment would be valuable in the teaching and learning of reading skills. Article visualizations:
A Smart Search Functionality for a Memory Prosthesis: A Semantic Similarity-Based Approach

In this paper, we aim to offer a smart search functionality for a memory prosthesis for Alzheimer’s patients. This functionality is based on a multilingual semantic similarity-based approach. It estimates the similarity between the user’s search filed entry and the saved personal data of the user.
Discovering Semantic Relationships Among PDF Book Figures Using Contextual and Visual Similarity

Figures in books enhance the knowledge of users about facts, statistics, objects and concepts. Searching of figures and their associated textual information in the context of a specific topic from a collection of books needs plenty of time. Currently the solutions available in literature for figure searching are limited to scientific documents and are inapplicable to books due to their large sizes. These solutions are also domain dependent, uni-model, context ignorant and limited to their local repository due to which they give a large number of irrelevant search results and the figures are retrieved with limited information. Therefore, a generic, bi-model, dual repository based figure retrieval system is proposed to retrieve figures from books along with the contextual information to improve the understandability of the user. The system can establish semantic relationships between PDF book figures and can also relate figures to images on the web. The system can retrieve the figures with 91-96.5% precision and makes the navigation and searching of figures an effective and pleasant experience.
An adaptive local contrast enhancement method for low visibility aerial image

The contrast and color fidelity of aerial images are usually seriously weakened and many features are covered because of atmospheric scattering and other factors. By using global features, low contrast images can be improved globally and the enhanced images may have little noise and ringing artifacts, but overexposure or underexposure may occur on some parts. By using local features, the details appear better, but it may lead to noise and ringing artifacts when the contrast gain is too large. In the paper, a new contrast enhancement method with adaptive gamma correction based on the weighting of global and local gray-scale mean is proposed. The adaptive gamma parameter which is obtained by incorporating the global and local gray-scale mean into the weighting distribution, is used to correct the gray value of each pixel in the image. Aerial images taken by DJI unmanned aerial vehicle with Inspire 2 at an altitude of 500 meters have been processed in the proposed method. Experimental results indicated that the proposed algorithm performs even better than the current mainstream methods in contrast enhancement for low visibility aerial images.
A Performance Evaluation of Several Artificial Neural Networks for Mapping Speech Spectrum Parameters

In this work, we compare different neural network architectures, for the task of mapping spectral coefficients of noisy speech signals with those corresponding to natural speech. In previous works on the subject, fully-connected multilayer perception (MLP) networks and recurrent neural networks (LSTM & BLSTM) have been used. Several references report some initial trial and error processes to determine which architecture to use. Finding the best network type and size is of great importance due to the considerable training time required by some models of recurrent networks. In our work, we conducted extensive tests training more than five hundred networks, with several architectures to determine which cases present significant differences. The results show that for this application of neural networks, the architectures with more layers or the greater number of neurons are not the most convenient, both for the time required in their training and for the adjustment achieved. These results depend on the complexity of the task (the signal-to-noise ratio or SNR) and the amount of data available. This exploration can guide the most efficient use of these types of neural networks in future mapping applications, and can help to optimize resources in future studies by reducing computational time and complexity.
Seatbelt Recognition Method Based on Convolutional Attention Mechanism

In order to solve the problem of low accuracy of the driver's seat-belt identification and neglecting co-pilot, a method of driver's seat-belt identification based on deep convolutional channel modeling is improved. Firstly, the characteristic parameters of redundant channels are reduced by means of deep convolutional feature channel compression. Then, the feature weight of the deep convolutional channel is re-calibrated to improve the attention weight of the feature of the safety belt and suppress the expression of the interference feature. Finally, the central loss function is used to assist feature training of the model, so as to reduce the difference of feature distribution within the class and make the learned features easier to be classified and recognized. The experimental results show that, compared with other algorithms, the improved deep convolution driver seat belt identification model in this paper improves the recognition accuracy, recall rate, and F value by 6.9%, 4.32 and 5.5% respectively, and greatly improves the accuracy and practicability.