Reviewerly/exHarmony huggingface dataset:
Dongeng Sebagai Sosialisasi Mitigasi Bencana Kebakaran di Pemukiman Padat Penduduk Kelurahan Petukangan Utara, Pesanggrahan Jakarta Selatan

DKI Jakarta is the capital of the Republic of Indonesia which has a great risk of fire disasters in densely populated settlements, especially during the long dry season. Petukangan Utara Village, Pesanggrahan Subdistrict, South Jakarta, one of the pockets of dense settlements in the South Jakarta area that was targeted by the community service locations. The area of Petukangan Utara 2.99 Km2 consists of 5,075 families with 119 RTs and 11 RWs. The impact of settlement density has been a source of reduced number of infiltration wells and loss of water discharge around the settlement thereby increasing the risk of fire in dense residential areas. However, the high risk of fire disaster is not accompanied by a strong briefing on disaster mitigation. During this time, the community carries out more post-disaster activities in the form of emergency response and recovery rather than pre-disaster activities in the form of disaster reduction / mitigation and disaster preparedness as in neighboring countries. So the community service program focuses on the development of disaster mitigation, especially fire disasters with the goal of questioning children through reading fairy tales. The activity partner is Rumah Yatim Nurul Hasanah in the densely populated settlement of Petukangan Utara Village. The reason for cooperating with Rumah Yatim is because children are the most vulnerable group to become victims. It is hoped that campus activism through fairy tales in developing socialization of fire disaster mitigation from an early age can contribute to increasing disaster literacy and preventing fire threats in densely populated areas of the Jakarta Region
Improved DNN-HMM English Acoustic Model Specially For Phonotactic Language Recognition

The now-acknowledged sensitive of Phonotactic Language Recognition (PLR) to the performance of the phone recognizer front-end have spawned interests to develop many methods to improve it. In this paper, improved Deep Neural Networks Hidden Markov Model (DNN-HMM) English acoustic model front-end specially for phonotactic language recognition is proposed, and series of methods like dictionary merging, phoneme splitting, phoneme clustering, state clustering and DNN-HMM acoustic modeling (DPPSD) are introduced to balance the generalization and the accusation of the speech tokenizing processing in PLR. Experiments are carried out on the database of National Institute of Standards and Technology language recognition evaluation 2009 (NIST LRE 2009). It is showed that the DPPSD English acoustic model based phonotactic language recognition system yields 2.09%, 6.60%, 19.72% for 30s, 10s, 3s in equal error rate (EER) by applying the state-of-the-art techniques, which outperforms the language recognition results on both TIMIT and CMU dictionary and other phoneme clustering methods.
International Journal of Economic and Environmental Geology (IJEEG)

International Journal of Economic and Environmental Geology (IJEEG) is widely circulated Journal. The aim of this journal is to provide a platform for scientists, academicians, and technologists from all over the world to publish research work falling within the scope of IJEEG. This journal publishes original and review articles (full-length paper/short communication) within the fields of Economic and Environmental Geology.Â Economic Geology topics include but not restricted to Petroleum Geology, Mineral Exploration, Coal Geology, Ore Geology, Gemology, Industrial Rocks and Minerals, Industrial Applications, Mining Geology, Mineral Processing, and Mining Engineering etc. Environmental Geology topics include Water Resources, Drinking Water Quality, Ground/Surface Water Pollution, Medical Geology, Wastewater Treatment, Rainwater Harvesting, Solid Waste Management, Geohazards, Disaster Management, Climate Change, Soil Salinity, Environment Pollution, Environment Impact Assessment, and Renewable Energies etc.
Research on the Transformation of Confucianism, Buddhism and Taoism Aesthetics in Artistic Practice

The core of Lao Zi's natural philosophy is inaction. Lao Zi's aesthetic view of nature based on this is prominently and directly manifested in two aspects: void-quietness, inaction and beauty of simplicity. Integration of Nature literally means law of Tao is nature. The research on the transformation of the aesthetic thoughts of Confucianism, Buddhism and Taoism in the modern singing system can make up for the deficiency of the application of philosophical methods in vocal music teaching and enrich the current singing training system.
A Study on the Establishment of Intelligent National Crisis Management System in the Intelligent Information Society

정부는 4차 산업혁명기의 전개에 따라 초연결화를 바탕으로 초지능화를 확보하여 지능정보사회를 이루고 지능형 정부를 구현하기 위한 다양한 정책을 추진하고 있다. 따라서 국가위기관리도 환경변화에 대응하고 주요 정보통신기술(ICT)에 의한 지능화가 필요하다. 본 연구의 목적은 지능정보사회의 전개에 따라 국가위기관리의 지능화를 위한 정책적 방안을 모색하는데 있다. 이를 위해 인공지능(AI)을 기반으로 하는 국가위기관리의 필요성에 대하여 고찰하고, 주요 국가들의 인공지능(AI) 정책을 살펴 본 후, 인공지능(AI) 기반의 국가위기관리체계 구축을 위한 방안을 연구하였다. 연구 결과, 위기 유형별 데이터베이스화를 통해 위기관리체제의 정보화가 우선 필요하며, 이러한 정보화를 달성한 기관 간에 인프라를 구축하고, 구축한 인프라는 동일한 위기관리 플랫폼에서 운용되어야 하며, 이러한 플랫폼의 운용을 위한 전문 인력 양성과 조직 편성이 필요하다는 것을 알 수 있었다.Within the fourth industrial revolution, the South Korean government has employed various policies to establish an intelligent information society and intelligent government by securing super-intelligence based on hyper-connections. National crisis management needs to respond to environmental changes and become more intelligent using ICTs. The purpose of this study is to explore policy measures for securing intelligence for national crisis management in accordance with the development of an intelligent information society. To this end, we examined the need for AI (Artificial Intelligence)-based national crisis management and developed ways to establish AI-based crisis management system based on the review of AI policies in other major countries. The findings suggest, first setting up the IT-based system of crisis management, building and operating infrastructure among the IT-based agencies within the same platforms, and training professional manpower and restructuring organization for the operation of these platforms.
A Systematic Investigation of Neural Models for Chinese Implicit Discourse Relationship Recognition

The Chinese implicit discourse relationship recognition is more challenging than English due to the lack of discourse connectives and high frequency in the text. So far, there is no systematical investigation into the neural components for Chinese implicit discourse relationship. To fill this gap, in this work we present a component-based neural framework to systematically study the Chinese implicit discourse relationship. Experimental results showed that our proposed neural Chinese implicit discourse parser achieves the SOTA performance in CoNLL-2016 corpus.
Examination of International Collaborative Research Status and Co-Authorship Network Visualization of Korean Researchers in the Computer Field

Background/Objectives: According to a recent WEF report, Korea's competitiveness ranking in the Information & Communication Technology (ICT) sector, which provides the foundation technology of the Fourth Industrial Revolution, ranks among the highest in the world. It is very important to analyze the research behavior in the computer field, which is a core discipline in the ICT field. In this paper, to investigate the current status of international joint research by computer researchers in Korea, this study analyzed the current status of cooperative relations between domestic and foreign author-affiliated institutions by using the papers of Korean computer researchers published in core journals. Methods/Statisticalanalysis: Author's organization and country information were extracted from the collected papers by preprocessing. Based on these, the rankings by institution, country ranking, and the status of collaboration between countries and institutions were statistically analyzed. Thirdly, a network was created and visualized on a map based on the co-authorship between the author's institution and the country of each article examined in the previous step. Findings: The findings of this study are as follows. First, popular SCI journals preferred by Korean researchers were analyzed. Second, the statistics of author's affiliated institutions (author's organization, author's country) were counted by year of publication and period of year of publication. The rankings of domestic and overseas author-affiliated institutions and the rankings of countries were calculated. In the case of the United States, which has the highest rate of deductions, the rankings were also calculated by US states. Third, international collaborative networks of Korean computer researchers were created and visualized by author-level institutions and country levels. Improvements/Applications: The results of this study can help us to identify trends in computer researchers and research institutes in Korea. To this end, it is necessary to identify and collect key academic resources in the computer field as a whole.
An Economical and High-Quality Encryption Scheme for Cloud Servers with GPUs

Motivated by cloud servers undertaking heavy encryption of outsourced data for diverse devices, an Economical and High-Quality Encryption Scheme is proposed to alleviate the burden of energy consumption of the servers meanwhile to keep high-quality services. The objective of the scheme is to minimize the cost that combines economy and service quality. For achieving this objective, a two-phase scoring mechanism is proposed. And then based on the above methods and the scoring mechanism, an algorithm achieving the scheme is designed. To evaluate the scheme, some experiments are performed on a heterogeneous platform. The experimental results show that the encryption algorithm can save energy consumption by 47.8% and slightly improve delay rate by 0.93/10000 on average compared with the original one.
LibGuides: Online Reading Suggestions - Middle School: Home

Mine your home library for hidden treasures. Explore these online reads. Just keep reading!
Research on Optical Rainfall Calculation Method Based on FPGA

For the forecast and detection of disastrous precipitation weather, the most important thing is the detection of rainfall. However, the rainfall sensors commonly used at present, tipping bucket or weighing rain sensor, cannot meet the exist actual situation owing to the long-time of manual maintenance, measurement distance is short and the real-time performance is poor. The optical rain gauge has obvious advantages since its introduction. It can monitor the raindrop size and measure the speed of raindrops in real time. It has strong adaptability and can be free of maintenance for a long time. It can be applied to real-time monitoring of rainfall in harsh environments and remote areas. This paper mainly introduces the image algorithm processing on the raindrop image collected by the CCD camera. Initially use the acquisition card of the CCD camera to capture the original image data, and the image processing algorithm matching research is using the matlab. Then, after the algorithm obtained by the research is mapped to the FPGA, an automatic measurement, recognition, and discriminant rainfall model is generated by acquiring the image data collected by the line CCD and transmitting it to the FPGA. By taking advantage of the parallel processing of FPGA, the FPGA can quickly filter, binarize and shape the precipitation particle image data, and properly solve the problem of overlapping and occlusion between precipitation particles due to single plane acquisition, and finally deal with it. The image of a good precipitation particle is further calculated by the relationship between the plane equivalent diameter and its solid diameter, and the three-dimensional diameter of the precipitation particle is further calculated.
Lightweight Real-Time Object Detection Based on Deep Learning

Real-time object detection plays a significant role in the field of computer vision. Advanced object detection networks combine with the distribution characteristics in the image, while exposing detecting small targets as a bottleneck. In this paper, a novel network YOLO-light, can real-time detect and accurately detect objects in embedded system or portable devices. Firstly, in order to gaining better priori boxes, the clustering analysis is applied to pre-processing. Secondly, inspired from the multi-scale connection in the Feature Pyramid Networks (FPN) algorithm, YOLO-light enables multi-view and integrate features of various scales. With this design, YOLO-light can end-to-end training with low latency and higher average precision. The experiments testify that YOLO-light algorithm reveals satisfactory performance both in speed and accuracy.
Virtual Machine based Hybrid Auto-Scaling for Large Scale Scientific Workflows in Cloud Computing

Scheduling the Scientific Workflows (SWf) task onto ideal cost-effective resources in a distributed system remains a significant problem that demand for auto-scaling in a cloud computing environment. Even though cloud computing has considered as a quintessential platform for scientific users to perceive deadline constrained large-scale computations. However, complexity increases with dynamic increase in network size which makes mapping of resources a NP-hard problem. To overcome this situation, a novel hybrid auto-scaling strategy is proposed. The hybrid auto-scaling comprises of on-demand and spot instances pricing model for SWf computation under deadline and budget constraint. Many auto-scaling strategy have been proposed in the earlier works, but nevertheless, there is an ample scope for new auto-scaling strategy for efficient scheduling SWf in cloud environment. Moreover, hybrid auto-scaling approach for SWf considering spot and on-demand instances pose a challenge, since the approach has to estimate the proper amount and type of virtual machine instances to acquire and dynamically allocate the number of instances under spot or on-demand pricing model depending on the SWf need. A flexible hybrid auto scaling policy is proposed for scheduling SWf efficiently. Experimental results reveal the promising potential on the proposed algorithm with regard to minimization in makespan and cost of SWf under deadline and budget constraint in cloud environment.
A Study of Wet-Bulb Temperature and Approach Temperature Based Control Strategy of Water-Side Economizer Free-Cooling System for Data Center

Data centers are specific facilities gathering quantities of servers consuming large energy for which cooling part account two-thirds. Water-side economizer (WSE) system is an effective way to reduce the cooling part consumption. In this study, an ambient wet-bulb temperature $$\left( {T_{\text{wet}} } \right)$$ and approach temperature $$\left( {T_{\text{ap}} } \right)$$ control strategy for WSE system is simulated in five cities in China. The result shows that $$T_{\text{ap}}$$ affects the free-cooling hours and the performance characteristic of chiller and cooling tower. Under most circumstance, the optimal $$T_{\text{ap}}$$ is mainly determined by chiller-cooling tower system performance characteristic while the free-cooling hours are not the dominant factor. However, when the city-ambient $$T_{\text{wet}}$$ decrease, the free-cooling hours is more vital. When city-ambient $$T_{\text{wet}}$$ is low enough, the energy-saving rate is higher when $$T_{\text{ap}}$$ decrease because of more free-cooling hours.
A Cost-Reducing Partial Labeling Estimator in Text Classification Problem

The paper proposes a new approach to address the text classification problems when learning with partial labels is beneficial. Instead of offering each training sample a set of candidate labels, researchers assign negative-oriented labels to ambiguous training examples if they are unlikely falling into certain classes. Researchers construct two new maximum likelihood estimators with self-correction property, and prove that under some conditions, new estimators converge faster. Also the paper discusses the advantages of applying one of new estimators to a fully supervised learning problem. The proposed method has potential applicability in many areas, such as crowd-sourcing, natural language processing and medical image analysis.
Efficient Disaster Response Using Reserve Forces - Focusing on the US National Guard -

현재 우리가 살고 있는 사회는 기상이변으로 인한 자연재난과 산업발달로 인한 산업재난 등 크고 작은 재난이 끊임없이 발생하고 있다. 그리고 이러한 재난은 점점 대형화되어 이에 따른 사회적 피해와 복구비용도 천문학적으로 증가하고 있는 실정이다. 이와 같이 재난의 발생과 규모는 점차 증가하는데 비해, 재난에 대응하는 소방이나 지방자치단체의 능력은 한계성을 나타내고 있다. 이와같은 재난대처의 한계성은 엄청난 규모의 사회적 피해로 나타나며, 이러한 재난에 효율적으로 대처하기 위해서는 재난발생 초기에 신속한 재난대응과 피해에 대한 복구 활동이 필요하다. 하지만 재난대응을 위해 짧은 시간에 인원을 동원하기란 현실적으로 많은 어려움이 따르며, 이에 신속하게 동원할 수 있는 예비군조직을 활용한다면 증가하고 있는 재난에 대한 효율적인 대처방안이 될 것이다.The current society has experienced constantly small and large disasters including natural disasters (e.g. extreme weather) and industrial disasters due to industrial development. Moreover, the disasters are getting bigger and more serious and the social damage and recovery costs are increasing astronomically. Despite the increase of the occurrence and magnitude of various disasters, the ability of firefighting agencies and local governments to respond to disasters is still limited. Since the limited disaster response could cause enormous social damages, rapid and effective responses to cope with disaster and recover damages should be undertaken at the early stage of disaster cycle. However, mobilizing personnel to respond to disasters in a short period of time is a real challenge, and thus it would be an effective way to respond to a growing number of disasters using a reserve army which can be quickly mobilized.
Advanced Image Analysis for Learning Underlying Partial Differential Equations for Anomaly Identification

In this article, the authors adapt and utilize data-driven advanced image processing and machine learning techniques to identify the underlying dynamics and the model parameters for dynamic processes driven by partial differential equations (PDEs). Potential applications include non-destructive inspection for material crack detection using thermal imaging as well as real-time anomaly detection for process monitoring of three-dimensional printing applications. A neural network (NN) architecture is established that offers sufficient flexibility for spatial and temporal derivatives to capture the physical dependencies inherent in the process. Predictive capabilities are then established by propagating the process forward in time using the acquired model structure as well as individual parameter values. Moreover, deviations in the predicted values can be monitored in real time to detect potential process anomalies or perturbations. For concept development and validation, this article utilizes well-understood PDEs such as the homogeneous heat diffusion equation. Time series data governed by the heat equation representing a parabolic PDE is generated using high-fidelity simulations in order to construct the heat profile. Model structure and parameter identification are realized through a shallow residual convolutional NN. The learned model structure and associated parameters resemble a spatial convolution filter, which can be applied to the current heat profile to predict the diffusion behavior forward in time.
Ranking Based Opinion Mining in Myanmar Travel Domain

The travel and tour industry is one of the world’s largest service industries. It takes part as a vital role of state’s economy for the developing countries like Myanmar. Therefore, a travel information support system becomes essential that construction of Myanmar travel domain has been considered to be developed which is enriching with ancient heritage and known for many natural tourist destinations. Besides, it extracts travelers’ opinions with a purpose of promoting and improving all related services. We intend to implement the ontology-based opinion mining for Myanmar travel domain. The main contribution of this paper is collecting valuable travel information and tourists’ opinions of Myanmar by crowd sourcing method which has to be used for ranking based opinion mining to support travelers. This proposed method is important for travel industry in terms of services quality, promotion of the business and facilitate the industry that can be ranked based on the factors of determining many various opinions. Thus, opinion mining is affected not only the review comments created by visitors, but also the ranking of travel items given by every stake holder in the industry.
SISTEM AKUNTANSI PENGGAJIAN PADA CV. MAHKOTA SPORT DI SAMARINDA

notes, and report) and collect it tobecome financial information, then report it to easily management in company financial management, the sales system is a component that is paid by the company the employees as a service for the performance that has been given to the company, such compensation is usually given to employess. The purpose of this thesis research is to find out and analyze the implementation of the CV. Mahkota Sport accounting sistem. The author analysis the payroll system on the CV. Mahkota Sport by using comparing the system applied in the company with the actul system according to the theory and comparing the flow chart attached to the interview process in the form of aquestionnaire which means that according to data by using many answer or interview with part of the related part is accounting for 4 person Results analysis mentioning the accounting system at CV. Mahkota Sport is enough according to meed internal control which is supported with questionary results from 16 items questions with score answer yes 75% CV. Mahkota Sport satisfies the elements of a good internal control system. However, there are a number of things that need to be considered, namely in depth examination. Based system implemented by the company has been carried out quite well.
Dog Breed Classification Using Transfer Learning

This research focuses on the use of machine learning and computer vision techniques to predict the dog breed using a set of images. In this, the convolutional neural network is used as a base which is responsible for the prediction of dog breed. For making this model effective, there was further inclusion of two cases. The first case was that to give the human image as an input, and it will provide the resemblance breed of the dog as output, and the second was to give an image of things other than a dog or a human, and it will provide “something else” as an output. The test accuracy of the model is 84.578%.
Parametric Analysis on Enhanced Facial Emotion Recognition with Weight Optimized Neural Network

This paper intends to design a novel FER model that includes four phases such as (i) Face Detection, (ii) Feature extraction, (iii) Dimension reduction, and (iv) Classification. Here, Viola Jones (VJ) method is deployed for face detection, which is the initial technique to offer better object detection at real-time. Subsequently, feature extraction is carried out by means of Local Binary Pattern (LBP), and Discrete Wavelet Transform (DWT). As the “curse of dimensionality” seems to be a major fact, the dimension reduction of features is done using Principal Component Analysis (PCA). Finally, the classification is carried out by means of Neural Network (NN) with the new training algorithm called Probability based-Bird Swarm Algorithm (P-BSA), by which the weights are optimized. The performance of the proposed algorithm is done by making the algorithmic analysis. More importantly, the positive integer ( U) of the proposed algorithm is varied to certain values: 0.5, 1, 1.3, 1.5 and 1.8, respectively.