Reviewerly/exHarmony huggingface dataset:
Evaluasi Saluran Drainase Jalan Sukakarya Menggunakan Perogram Bantu Epa Swmm 5.0

ABSTRACTSukakarya Street Tampan District, Pekanbaru is one of the potential areas for flooding and inundation caused by rain. In the drainage channel, there is garbage that accumulates, and sedimentation is quite thick, so that the water collected exceeds the capacity of the drainage channel. In the rainy season, there are several puddles. This problem was identified because of the broken channel and some areas where there was no drainage channel. Then a study was carried out on the condition of the existing drainage system as a material consideration for its feasibility evaluation and drainage system planning that was able to overcome the inundation that occurred using EPA SWMM 5.0. With the expected flooding results obtained, it is necessary to prevent flooding by changing the dimensions of the channel. From the simulation results in the canal dimensions that can overcome the flood are the width of the channel (B) 1 m, and the depth of the channel (H) 1 m.Keywords: Flood, Drainage, EPA SWMM 5.0.
An Empirical Study on the Factors and Flexibility and Performance Measurement of Supply Chain Management in China's Food Industry

중국 식품산업의 구조는 글로벌 지역으로부터 식품원재료를 소싱하여 중국에서 식재료를 가공 생산하고 있다. 최근 중국에서는 식품산업에 대해 국민소득 증가, 국민건강 차원에서 식품의 안전성과 신뢰성이 중요 이슈로 부각되고 있다. 이러한 중국식품산업의 패러다임 변화는 중국식품기업들이 식품공급체인망(콜드체인)을 구축하여 고객만족 향상과 기업의 브랜드 가치, 신뢰성을 높이기 위하여 노력하고 있다. 따라서 본 연구의 목적은 중국 식품 기업의 콜드체인 구축이 유연성에 미치는 요인을 도출하고, 기업의 성과에도 어떤 영향관계를 미치는지 밝히는 것이다. 중국 연태시(Yantai, 煙臺) 지역의 식품기업을 대상으로 550부 설문지를 배포하여 실제 분석에는 126부의 설문지를 활용하여 실증분석하였다. 수집데이터로 SPSS Amos 25.0 Graphics을 활용한 실증분석으로 연구모형에 활용된 요인 신뢰성 및타당성 검증을 수행하였다. 본 연구는 다음과 같은 연구결과를 도출하였다. 공급체인관리 활성화 요인인 정보시스템 구축, 프로세스혁신, 대응성이 유연성에 긍정적인 영향을 미치는 것으로 파악되었다. 또한, 정보시스템 구축 활성화가재무성과에 유의한 정(+)의 영향을 미쳤으며, 유연성 요인도 재무성과에 유의한 정(+)의 영향을 나타냈다. 채택된 가설들을 바탕으로 중국 식품산업 내의 다각적 공급망 활성화 방안에 대해 살펴볼 것이며, 종합적으로 기업들이 정보시스템 구축을 통해 경쟁우위 확보와 유연한 대처능력 확보방안을 검토할 것이다.The structure of China s food industry is sourcing food materials from the global region and processing and producing them in China. Recently, the safety and reliability of food products in terms of national income and national health have been highlighted as important issues in China. This paradigm shift in the Chinese food industry is striving to improve customer satisfaction and increase the brand value and reliability of Chinese food companies by establishing a food supply chain (cold chain). Therefore, the purpose of this study is to derive factors that influence the construction of cold chain of Chinese food enterprises on flexibility, and to reveal how it affects the performance of enterprises. 550 questionnaires were distributed to food companies in Yantai, China, and for the actual analysis, 126 questionnaire were used to verify and analyze. An empirical analysis using SPSS Amos 25.0 Graphics was conducted to verify the reliability and feasibility of factors used in the research model. This study produced the following findings: It was understood that the establishment of information system, process innovation and responsiveness of supply chain management activation factors have a positive effect on flexibility. In addition, the activation of the information system deployment had a significant positive effect on financial performance, and the flexibility factor also indicated a significant positive effect on financial performance. Based on the assumptions adopted, we will look at ways to boost the diversified supply chain within the Chinese food industry, and comprehensively, companies will examine ways to secure competitive advantage and flexible coping capabilities through the establishment of an information system.
Young Scientist Awards at Geology 2020 for the best researches in Geology and Geochemistry

Conference series is announcing Young Scientist Awards through International Conference on Geology & Geochemistry (Geology 2020) which is scheduled at Dubai, UAE during November 23-24, 2020. This Geology 2020 focuses on “ Significance of tracking the planetary transformation and earth science ”. Geology 2020 and upcoming conferences will recognise participants who have significantly added value to the scientific community of environmental science and provide them outstanding Young Scientist Awards. The Young Scientist Award will provide a strong professional development opportunity for young researches by meeting experts to exchange and share their experiences at our international conferences.
C, C++, Java, Python, PHP, JavaScript and Linux For Beginners

This Book highlights some basic knowledge of artificial intelligence formal constructed programming languages (like C, C++, Java, PHP, Python, JavaScript and XML) designed to interact with the hardware in a more powerful way and to communicate instructions to a machine, particularly an electronic device which is capable of receiving information (data) in a particular form and of performing a sequence of operations in accordance with a predetermined but variable set of procedural instructions (program) to produce a result in the form of information or signals. If you read this book in a public place (on a commuter train, at the beach, or on the dance floor at the Restaurants, for example), you can read proudly, with a chip on your shoulder and with your head held high. C, C++, Java, PHP, Python and JavaScript are hot stuff, and you're cool because you're reading about it. If you are just learning what kind of animals C, C++, Java, PHP, Python and JavaScript are, this BOOK will make an excellent companion to any tutorial and serve as a source of knowledge to your specific questions. And, by reading this BOOK, you’ll have a broad, basic knowledge of C, C++, Java, Python, JavaScript [scripting language used in millions of Web pages such as Internet Explorer, Firefox, Chrome, Opera, and Safari] and PHP. This book is for all programmers, whether you are a novice or an experienced pro. The beginner will find its carefully paced discussions and many examples especially helpful.
Workshop 19: ScaDL Scalable Deep Learning over Parallel and Distributed Infrastructures

It is our great pleasure to welcome you to the second edition of the workshop on Scalable Deep Learning over Parallel and Distributed Infrastructure (ScaDL)! Recently, Deep Learning (DL) has received tremendous attention in the research community because of impressive results obtained for a large number of machine learning problems. The success of state-of-the-art deep learning systems relies on training deep neural networks over massive amounts of training data, which typically requires large-scale distributed computing infrastructure to run. It demands advancement along multiple research directions such as model/data parallelism, model/data compression, distributed optimization algorithms for DL convergence, synchronization strategies, efficient communication and specific hardware acceleration. This intersection of distributed/parallel computing and deep learning is thus becoming critical; this workshop aims to bring these two communities together to foster collaboration, discuss relevant topics and share results. In addition to five peer-reviewed research papers, ScaDL 2020 also features four invited presentations, from Dr. Manish Gupta (Google Research, India), Prof. Geoffrey Fox (Indiana University, USA), Prof. Wen-mei Hwu (UIUC, USA) and Dr. Minsik Cho (IBM, USA). We sincerely thank our invited speakers for their time and efforts.
Zero-Day Traffic Identification Using One-Dimension Convolutional Neural Networks And Auto Encoder Machine

Network traffic identification plays an important role in traffic engineering, anomaly detection and traffic billing. Recently, the machine learning and deep learning based algorithms have made a great success in identifying the known applications, where the training set and the test set are supposed to contain the same traffic classes. However, in a realistic scenario, the network traffic classifier may suffer from a low identification accuracy due to the substantial zero-day (unknown) traffic. The essential to solve this problem is to find the boundary between the known and zero-day traffic, which has not been well studied before. In this paper, based on the fact that for an Auto Encoder (AE) machine, the reconstruction error of a zero-day class is generally larger than that of a known class, we propose a zero-day traffic identification method using one-Dimension Convolutional Neural Networks (1D-CNN) and AE machine. In order to further improve the identification accuracy, we propose an algorithm to estimate the confidence possibility of the identification results based on the Extreme Value Theory. The experiments conducted on realistic traffic datasets demonstrate that our method has a great improvement in identification accuracy than the benchmarks.
Research on Correlation of Physical Examination Results Based on Improved Apriori Algorithm

The physical fitness test of college students is one of the manifestations of the achievements of colleges and universities in cultivating students' physical quality. Based on the improved Apriori algorithm for the physical fitness test scores and physical education course data of college students, this article explores the correlation between the two in depth and finds that physical education courses play a positive role in the growth of students' physical fitness. Among them, the promotion effect of students interested in courses is particularly prominent; at the same time, there is a strong correlation between endurance quality, speed quality and multiple physical qualities, they can effectively promote the improvement of other qualities. This article can provide a data basis for the study of college students' physical development and changes, and also provide constructive suggestions for the current talent training on the teaching arrangement and content of physical education courses.
한국의 억지 전략 진화: 3축 체계의 구축을 중심으로

North Korea’s weapons of mass destruction (WMD), like nuclear weapons and ballistic missiles, poses a serious threat to deterrence posture at the Korean Peninsula, which has been maintained for the past several decades. As a result, Republic of Korea (ROK) has been developing its own deterrence strategy, based on non-nuclear forces; known as “Three-Axis System”. At first, Three-Axis System of ROK emphasized capabilities for “deterrence by denial”; represented by Kill Chain and Korea Air and Missile Defense (KAMD). Later, the system is enhancing its integrity by adopting “deterrence by punishment”; represented by Korea Massive Punishment and Retaliation (KMPR). The establishment of Three-Axis System will strengthen ROK''s politico-military leverage to deter threes from North Korea’s WMD arsenal, as well as support diplomatic effort for denuclearization of North Korea.
COMMUNICATIVE APPROACH AND DISCOVERY LEARNING FOR TEACHING WRITING DESCRIPTIVE TEXT (A Quasi-Experimental Research at Seventh Grade Students of MTs AlBasyariyah Cigondewah in the Academic Year of 2018/2019)

Writing is one of the most important skills in language learning. Discovery learning is a model that emphasize that the learning participants have to find the concept or principle which has unknown to the students by themselves. This research was conducted at seventh grade students of MTs Al-Basyariyah. The sample of this research was students at class B and class E. This research aims: (1) to know whether or not there is significant different between communicative approach and discovery learning in teaching writing descriptive text. (2) To describe the implementation of communicative approach in teaching writing descriptive text. (3) to describe the students’ difficulties in writing descriptive text. The Researcher used Quantitative Research Metod as a Method and Quasi Experimental as a design of this research. The result of this research showed that the value of Posttest by using Mann-Whitney U score with the significance 2 tailed 0.013 is lower than 0.05. It showed the Null Hypothesis (H0) is rejected. It can be concluded that there is significant difference between Communicative Approach and Discovery Learning for teaching writing descriptive text. The result of the implementation of communicative approach showed that the students more active and have a good communicative competence. The students’ difficulties are in content, mechanic and grammar because their felt hard to develop the topic and their felt hard about the grammar.
Predictive Maintenance Proposal for Server Infrastructures

Predictive maintenance has played a key role in industry complexes for several years. The prediction of upcoming errors has the potential to increase the average productivity of any enterprise and to avoid losing opportunities due to a partial shut-down of the production system. Such ideas can be applied to large-scale server infrastructures achieving a fully automatic system which warns the administrators of a potential shut-down before it takes place. Mathematical algorithms and statistical tools can be used to model the standard behaviour of the system and, when an anomaly is detected, to warn the system operator. Furthermore, machine learning can also be used to model such a behaviour and to identify the most likely cause of the anomaly.
Brand Engagement, Brand Loyalty and Brand Satisfaction: Explanatory Role of Online Experience

The trend of online buying is increasing in Pakistan, however, majority of the online buyers are not happy with their online buying experience because of poor handling by the brands. Given that, this study argues that by creating more meaningful relations with customers (i.e. employee brand engagement) brands may have their loyalty and satisfaction. Therefore, this study aims at identifying the mediating impact of online brand experience between the association of customer brand engagement with brand satisfaction and brand loyalty. Considering this generic issue across the country, this study collected 190 responses from the people belonging to different walks of life including students, professionals, and business-personnel with the experience of online shopping. The findings of the study confirmed a partial mediating role of online brand experience between the association of customer brand engagement with brand satisfaction and brand loyalty. Practically, the significance of customer engagement in the E- commerce sector can be examined via this research and the entrepreneurs may enhance the level of customer brand engagement in the online selling which causes a habitual impact on the brand experience, sensations, feelings, cognitions and behavioral responses of the customers towards an online brand/store.
Research on User Interest Model Mining Method Based on Time-Tag

With the rapid growth of video resources in current IPTV services, it is difficult for users to find films of interest quickly. Personalized recommendation is an effective method to solve this problem. And building user interest model is the basis of personalized recommendation. Based on K-means and association rules algorithms, the user interest model based on time-tag is established. Firstly, working days and non-working days of the family are identified by K-means clustering algorithm. Secondly, the active intervals of working days and non-working days are extracted respectively. Finally, mine the viewing content in each active interval to complete the establishment of user interest model. The viewing data of 1000 IPTV users is used to carry on the experiment. The experimental results show that the user interest model based on time-tag has higher accuracy than the tag-based user interest model.
LibGuides: BUDGET PROJECT: Home

The Project is designed to assist students in considering their financial future after graduation.
Bayesian Decentralized Learning

Author(s): Kilinc, Osman Cihan | Advisor(s): Koushanfar, Farinaz | Abstract: The field of machine learning has grown tremendously in the past decade. It is utilized in many different industries with applications ranging from high-tech security systems to medical diagnosis. In order to train their models, technology companies aggregate vast amounts of data from their users and execute training in big data centers. In such an era, where the means of machine intelligence is gathered at the hands of the few and data privacy rights are continuously breached, the need for a more democratic and privacy-preserving machine learning method is exigent. Fortunately, advances in mobile computing is gradually moving the computations on the cloud to the devices. Decentralized learning reinforces these advances by enabling training on decentralized data. In decentralized learning, users train their models with their local datasets and share the acquired knowledge with each other. It mitigates data-sharing and provides a degree of freedom for model personalization. Thus, research on decentralized learning has gained pace. In this thesis, we explore decentralized learning, make an analysis from a bayesian perspective, explain the relevance of continual learning, and demonstrate empirical results from an up-and-coming decentralized learning method.
IMEXT Text Summarizer Using Deep Learning

This research focuses on the problem of creating a short summary for every page in a physical book. This will enable students, professors, lawyers and others to get the most important points of every page in the book in a summarized form. Motivated by the fact that users do not have the time to read lengthy materials, the research aims to make reading a less tedious activity by summarizing long paragraphs into short sentences. In this research, we model the summarizer using deep learning algorithms using bi-RNNs and attention mechanisms. Furthermore, a hardware model is made to turn and capture images of pages from a book automatically using raspberry pi.
An Enhanced Stochastic Gradient Descent Variance Reduced Ascension Optimization Algorithm for Deep Neural Networks

The goal of this paper is to compare the most commonly used first-order optimization techniques with proposed enhanced Gradient Descent-Based Optimization. The simplest optimization method is the gradient-based optimization technique. The optimization concerns instigated with Deep Neural Networks (NNs) are unraveled by the rest other techniques. The common technique used in deep neural network architectural setup is Stochastic Gradient Descent (SGD). In SGD there is a raise of variance which leads to slower convergence. This affects the performance of the system. So to address these issues the non-convex optimization technique with faster convergence using an enhanced stochastic variance reduced ascension approach is implemented. It enhances performance in terms of faster convergence.
The Effect of Booktrack on Students Reading Comprehension Ability

The purpose of this study was to determine the effect of Booktrack on listening to students in reading comprehension skills. Booktrack is a free web application that synchronizes digital books with audio, producing a deep reading experience. The sample of this study was class VII of Junior high school AL KAHFI SEPULU as an experimental class and as a control class that consists of 20 students. The method in this study is a quantitative method. In addition, the design used was pre-experimental research, and the instrument was tests. This research was taken using non-random sampling. In addition, this research was carried out through the following procedures: giving pre-test, applying treatment and giving post-test. Data were analyzed and processed using ANCOVA statistical data calculations by the SPSS 23 program. Significant shown by the average post-test of students in the experimental class was 50.00 and the post-test average in the control class was 48.00. The results of the statistical hypothesis test was 0.021 from the p-value. This was lower than the significant level of 0.05. If the p-value lof the significant level is 0.05. This means H1 is accepted and H0 is rejected. In conclusion, this Booktrack media is effective as a teaching medium to improve reading comprehension in grade VII students at Junior high school al Kahfi Sepulu.n
Proactive VoD delivery pattern reconfiguration based on temporal-spatial channel prediction

With the help of big data analytics, predictive resource allocation (PRA) techniques for video on demand (VoD) have been recognized as promising methods to save time-frequency resources, for a number of VoD packets can be transmitted in good channels in advance to avoid the predicted transmissions in bad channel conditions. With the increasing demands on a fantastic user quality of experience, a smooth playback and a low start-up delay are of equal importance to the emerging VoD with high fidelity, which inevitably leads to a critical delay requirement of VoD packets. However, the issue of resource estimation with quality of service (QoS) requirements is still an unsolved puzzle in PRA. In this paper, we propose a martingales-based physical resource block (PRB) abstraction method, where the random characteristics of the service process are embedded in the minimum PRB consumption. Based on the method, a proactive QoS-guaranteed reconfiguration algorithm is developed to optimize the multi-user delivery pattern applied in the prediction window, aiming to maximize spectrum efficiency. In this algorithm, since the delay sensitivity of VoD content transmitted in advance is dulled compared with the original VoD stream, we divide the original VoD slice into two sub-slices and derive a three-dimensional delivery pattern. The gain of resource saving and the capability of QoS guarantee brought by the reconfiguration have been demonstrated by the simulation results.
Ensuring License Compliance in Federated Query Processing

When two or more licensed datasets participate in the evaluation of a federated query, the query result must be protected by a license that is compliant with each license of the involved datasets. However, such a license does not always exist, and this leads to a query result that cannot be reused. We propose to deal with this issue during the federated query processing by discarding datasets of conflicting licenses. But then, a query with an empty result set can be obtained. To face this problem, we use query relaxation techniques. Our problem statement is, given a SPARQL query and a federation of licensed datasets, how to guarantee a relevant and non-empty query result whose license is compliant with each license of involved datasets? In a distributed environment, the challenge is to limit communication costs when the query relaxation process is necessary. In this paper, to detect and prevent license conflicts, we propose FLiQuE, a license aware query processing strategy for federated query engines. Experiments show that FLiQuE guarantees license compliance, and if necessary, can find relevant relaxed federated queries with a limited overhead in terms of execution time.
An Information Framing Through Newspaper Media: Knowledge Transfer on Children

The newspaper is known as a daily for adults, but there is a rubric aimed at children called the rubric. it is important to see how the framing, starting from the knowledge, information, messages, illustrations or drawings, writing, words and sentences that differ between children and adults. This study examines the transfer of knowledge through the Kompas Anak rubric. The purpose of this study is to describe the process of knowledge transfer to the child through mass media in the form of rubric named Kompas Anak by looking at the framing done on articles that containing knowledge for children. This qualitative research uses framing analysis method. Framing method is used to analyze knowledge transfer by the sender of knowledge through the rubric of Kompas Anak. The results showed that the process of knowledge transfer that occurs in the Kompas Anak rubric is a means to instill general knowledge in accordance with the understanding of children, so they can capture the knowledge transferred by the sender of knowledge.