Reviewerly/exHarmony huggingface dataset:
SISTEM PAKAR DIAGNOSA PENYAKIT DAN HAMA PADA TANAMAN LADA DENGAN METODE FORWARD CHAINING BERBASIS ANDROID

Pepper Plantation is currently experiencing a significant decline from both the quality as well as the difficult treatment. As for some of the factors affecting the decrease in the form of disease and pests. These problems are difficult to overcome because of the limited number of experts who are able to identify and provide solutions to pepper plants. So it takes an expert system capable to identify diseases and pests as well as handling solutions by leveraging the android-based technology using forward chaining. This research was conducted using the survey directly into the object of research and interviews to experts. The testing is done by testing the accuracy of data involving 35 reached 97,14%. with the accuracy results prove that the method of forward chaining can work well in the expert system of diagnosis of disease and pest of pepper plants.
The enduring potential of data: An assessment of researcher data stewardship practices at the University of Victoria

We would like to acknowledge the Canadian Research Data Management Survey Consortium and the Portage Research Intelligence Expert Group (portagenetwork.ca) for the development and on-going management of the online survey adapted for this study.The survey data associated with this report has been published, as follows:Quinless, Jacqueline, 2018, Multidisciplinary Survey of Research Data Management (RDM) Practices of University of Victoria Researchers, https://doi.org/10.5683/SP2/1L8NKY, Scholars Portal Dataverse, V3
NACO/CONSER/SACO Listen-and-Learn Session

This listen-and-learn session was primarily intended for participants in the Atla funnels for the NACO, CONSER, and SACO programs of the Program for Cooperative Cataloging. It provided an update from the PCC meeting in May, with an opportunity for participants to ask questions about the programs.
숙달도에 따른 중국어권 한국어 학습자의 응결장치 선택 양상 연구 -연결어미와 접속부사를 중심으로-

The study polled 165 Chinese-speaking Korean learners with TOPIK certificates: 55 beginning, 23 intermediate, 32 level 4, 26 level 5, and 55 level 6 learners. By type, it was found that the ratio of the use of the connective endings gradually increased as the proficiency level increased, but the ratio of use of conjunctive adverbs decreased. At all levels, it seems that Korean learners in Chinese language used more connections than those in Chinese. In particular, when advanced learners connect sentences to beginning and intermediate learners, they tend to prefer linking word endings before conjunctive adverbs. By definition, we could see that Korean learners in Chinese language made more use of their conjunctive endings at all levels than those in connection, regardless of their proficiency in conflict, home, condition, and yield relationships. However, more conjunctive adverbs were used in beginning, intermediate and advanced levels for causative and selective relationships, rather than conjunctive endings. Through this, we found that Korean learners in Chinese language did not prefer connective endings at all levels, and we found that there were some cases in which they chose connecting adverbs before connecting them at certain stages. (Dongguk University)
The Teaching Content Reform for Information and Communication Engineering Graduate Degree Course “Broadband Mobile Communication Networks” Based on Knowledge System Construction

â€œBroadband Mobile Communication Networksâ€ is a graduate degree course of information and communication engineering major in a large numbers of universities. But there are some problems about this course such as old teaching content and without constructing knowledge system. In order to solve these problems, we take the enhancement of broadband mobile communication network performance and capacity as the goal, construct the knowledge system and redesign the teaching content. After implementation, good teaching results have been achieved.
A DTW-Based Representation to Support Static Sign Language Recognition from Binary Images

Sign language recognition (SLR) from binary images is a challenging task due to the huge amount of information required to process and the complex variations among classes. Here, we introduced a dynamic time warping (DTW) based representation approach to reveal consistent static SLR patterns from binary images. Indeed, we estimated curvature coefficients sequences (CCSs) from a contour filtering using different step length values. In turn, a DTW feature space is built from CCSs attributes, and a Relief-F-based feature selection stage is carried out to highlight discriminative DTW attributes. Achieved results on a publicly available dataset probe that our strategy attains an $$85\%$$ classification performance on average. Further, to the best of our knowledge, this is the first attempt to apply dissimilarity-based representations for codifying binary images in SSLR.
PARALLEL VERIFICATION EXECUTION WITH VERIFY ALGEBRA IN A CLOUD ENVIRONMENT

Soft-as-a-Service (SaaS) is a software delivery model that contains composition, development and execution on cloud platforms.And massive SaaS applications need verifying before deployed.To get the verify results of a large quantity of applications in a tolerate time, verify algebra (VA) is used to cut down the number of combinations to be verified.VA is an effective way to acquire the verify statue by using previous results.In VA, the verify result is calculated without knowing the process of verification.In this way, the verification task can be distributed to servers and executed in any order.This paper proposes method called component disassembly tree to decompose a complex SaaS application.And designs a parallel verification framework in cloud environment.The Optimization of execution is discussed.The proposed parallel schema is simulated in MapReduce.
Theoretical Bases of the System of Teachers` Professional Development in Vocational Education Functioning

The essential concept of «system» is analyzed. The concept of «pedagogical system» is defined and the theoretical principles of its functioning are presented in a number of scientists` researches. It has been established that the system of teachers` professional development in vocational (professional) education should be a dynamic formation of different, but united by common goals of the components, which develops in accordance with new realities and socio-economic`s perspectives development of the country, and functions in order to promote the professional development of teachers, an increase in the level of their qualifications, competence and professionalism, the expansion of the professional-pedagogical profile.
Event detection in sports video based on audio-visual and support vector machine. Case-study: football

In this paper, we propose a novel audio-visual feature-based framework for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded football video. Specifically, the features gathered by various feature detectors is combined by means of a support vector machine which infers the occurrence of an event based on a model generated during a training phase, utilising a corpus of 2.5 hours of content. The system is evaluated using 2.5 hours of separate test match content.
Sacred concert «In the Beginning You, Lord» («V nachalih Ty, Hospody») by S. Dehtiarev: Questions of Authorship

We analyze Sacred concert «In the beginning you, God» («V nachaleh Ty, Hospody»), which has a triple authorship in handwritten and printed sources – M. Berezovskyi – A. Vedel – S. Dehtyarevskyi. The concert cycle is analyzed. There are arguments according to which the concert belongs to the creative heritage of S. Dehtyarevskyi.
Polarized wavefront measurement using an electrically tunable focused plenoptic camera

Performing optical imaging of relatively small or weak targets in complicated background, as a primary approach used to explore the nature, is limited in performance by atmosphere-induced optical aberrations. Methods that characterize wavefront aberration have improved our understanding of how wavefront distortions degrade image quality. However, these approaches are unable to show the light-field and polarization information except for the wavefront aberration. The integrated imaging chip or microsystem that can acquire more dimensional information is crucial for researchers to see more clearly into the scene. To address this question, an electrically tunable focused plenoptic camera that is used to visualize objects directly in a snapshot with multi-dimension perception such as wavefront, polarization, and light-field information, is proposed and demonstrated. This prototyped camera, which is composed of a twist nematic liquid-crystal microlens array (TN-LCMLA) and a common CMOS sensor array, can directly sense two orthogonally polarized lightfield images via switching the working stated of TN-LCMLA. This prototype records not only intensity and polarization information but also the direction and position of incident beams, so as to indicate that the localized wavefront slope of incident beams can be extracted directly. In this study, the theoretical analysis, reconstruction algorithm and experiment results show that a high performance and functional detection are obtained by this novel configuration. Then, with the proposed algorithm, the prototyped camera has a potential to be used in adaptive imaging application and many other optical systems.
Cost-based constrained task scheduling in cloud environment

In the cloud environment, all the tasks are allocated and executed by using the available services in order to achieve higher performance, least total time for computation, shortest period of response time and utilisation of resources and so on. There are some serious challenges that the existing models could allocate the task to the resources without the profit maximisation scheme. The main motivation that is behind this paper is to design and develop a Cloud Manager (CM) to efficiently manage the cloud resources and also completing the jobs for the allocated resources for the given task in the cloud environment. It is implemented using a prior resource optimisation (PRO)-based allocation algorithm that takes into account execution time, transmission time and cost. The desirable feature of this paper is that the resources are allocated as per the cloud consumer request. The potency of this is presented in the cloud environment by finding the optimal and suboptimal allocation scheme of resources which maximise the profit of cloud provider and also improve the quality of scheduling solution.
Upon Experience (Some Views)

The review was prepared in the framework of the problem of experience discussion at the initial stage of the scientific and pedagogical research related to the didactic conditions of learners’ experience. In the modern world it is important to consider experience as a multidimensional, complex phenomenon of a person’s mental life from the viewpoint of psychological science; to study experience as a phenomenon of social life; as a phenomenon and as a factor in the economy in the sense of productive activity. For carrying out pedagogical research, it is necessary to foreground the views on experience in the history of science, and this, of course, it should be carried out in a historical and philosophical context. Interpreting experience in the philosophy of the second half of the 20th and the beginning of the 21st centuries, the author notes the lesser attention of philosophers to the concept of experience and to the almost unlimited power of experience in a postmodern view of the world. One of the objectives of the discussion is to review how the ideas of experience developed in the prospective that is necessary for the study. The main conclusions about the understanding of experience in philosophy are presented.
Research and Practice of Teaching Mode in Total-English for Foreign Students in Agricultural Colleges and Universities

In the past, overseas students who studied in China mainly studied in liberal arts and medicine. With the improvement of China's scientific and technological innovation ability and economic strength, agricultural colleges have gradually become the growth point attracting foreign students to China. The biggest learning obstacle for foreign students in China is the language barrier. All the time, total-English teaching has become the main mode for foreign students in China. There are some problems when teaching in full English for foreign students to study in China. The teaching mode of foreign students in English is studied and analyzed combined with the authorâ€™s own teaching experience in this paper. At the same time, how to improve the training quality of foreign students in agricultural universities in China from the perspective of language is discussed and Suggestions for improvement are presented, which provides reference for improving the training quality of foreign students in agricultural universities. 1.
Research on Information Construction and Management of University Library

In the rapid development of the Internet today, Information technology is gradually infiltrating into various industries. Library is the core of colleges and universities and an important place for students to learn knowledge. To promote the construction and management of university library informatization plays an important role in the improvement of students' learning ability and the expansion of their knowledge. Informatization refers to the adoption of information technology for the rational use and development of information resources, through information technology to achieve information exchange and information sharing, so as to promote the quality of information and promote the rapid development of social economy.
Service-Agent-Based Model Composition

This chapter aims to deal with the automatic model composition problem by jointly using service-oriented technology and agent technology. Most existing composition methods focused on model reusability are unable to self-adapt to dynamic environments. The concept of “service-agent (SA)” is introduced to enable the automatic composition of component models. The main idea of the method proposed in this chapter is to encapsulate a component model into an SA so that agent and service-based composition methods can be exploited to deal with the model composition problem. The SA-based method can improve the efficiency of model composing, especially, in dynamic environments. Finally, an SA software prototype for model composition is built and a case study is given with the prototype.
Prediksi Pesediaan Stock Pulsa Menggunakan Fuzzy Linear Regresion

Electronic phone credit (pulsa) sales at this time showed an outstanding increase, because of the needs of phone credit usage every day by the society that has now became a need. Phone credit business will never stop because human always communicate with one another. To ensure the availability of the product in the phone credit server, the phone credit server manager routinely, in a particular period, inventorize the purchase of phone credit products. From the exploration above, the market conditions in a business are always changing, In a business. Process prediction is important for every business person in decision-making. Then, there is a need to build a prediction system that could predict phone credit transactions in the next period. Results or output of this system is predictive value of transactions in the next period with average error value of 4% using β1=0,5 and β2=0,2. Therefore, it can be used as a reference for the management in the decision-making.
Future Directions: What the Future Holds for TERM

TERM field promised to generate new tissues using the principles of engineering in combination with an understanding and application of the biologic sciences, defying the boundaries of biomedicine. Twenty five years have passed with scientific, regulatory and societal challenges, a few translational successes and new roadmaps to be explored hasten by an increasingly aging population. From the conceptual mouse with a human ear up to new biofabrication platforms and stem cell technologies, the future is now and offers great opportunities to pursue and delivery exciting solutions for failing tissues and organs. This article overviews the developments on TERM, their impact in therapeutic strategies and society, and anticipates new realities and paradigms that will take the TERM landscape into the 22th century.
Event Generation and Statistical Sampling with Deep Generative Models and a Density Information Buffer

We present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but also to ensure these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes $e^+e^-\to Z \to l^+l^-$ and $p p \to t\bar{t} $ including the decay of the top quarks and a simulation of the detector response. We find that the tested GAN architectures and the standard VAE are not able to learn the distributions precisely. By buffering density information of encoded Monte Carlo events given the encoder of a VAE we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g. for the phase space integration of matrix elements in quantum field theories.
Study on Intelligent Management System of Electrical Safety

This paper introduces the present situation of electric fire protection, expounds the concept of fire early warning, briefly introduces the research work being carried out in this field, introduces the intelligent management system of electric safety developed recently, and on this basis, analyzes the fire prevention and control problems that are expected to be solved by the electric safety intelligent management system for further study. To enhance the level of electrical fire protection in China, a new prevention and control method is proposed.
RESPONSE TO SPOTLIGHT ON PROMOTION LETTERS: THE DEVIL IS IN THE (FINANCIAL) DETAILS

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.
Development of Fast and Memory-Safe Operating System Kernel

Microkernels are considered to be more secure by design than monolithic OS kernels. Furthermore, small size of a microkernel codebase allows developers to maximize its performance, to make its architecture perfect or even to apply formal verification methods. The latter makes it possible to guarantee various important characteristics of an OS kernel, but it’s very expensive in terms of time and effort needed. There are projects presenting another approaches, which allow to guarantee (with some assumptions) memory-safety of a kernel. Usually, these methods are based on using programming languages, which provide this property by design. The problem is that it’s hard to find such a language, that ensures memory-safety and at the same time is suitable for OS kernel development. In this paper we present a possible approach to development of a microkernel, which is not expensive in terms of time spent and ensures both memory safety and high performance.
Neutrosophic -Based Bayesian Neural Network for Handling Data Overlap Problem In Classification

Overlapping region is a region in data where at least two different classes Ca and Cb simultaneously exhibit probability densities greater than zero.Overlapping problem has been a serious problem in classification system shown to have negative effect on the performance of even re-known classifiers by several researchers.Neural network is very poor in representing uncertainty as well as having uninterpretable black-boxes, lacking in transparency, difficult to trust.It is therefore pertinent to develop a system to work making use of neural network to effectively handle cases of non-linear decision boundaries poised with overlap problem amidst the varied causes of overlapping problems in datasets as well as augment it with transparency and trust metric so as ensure that it is interpretable to an extent.Object Oriented Analysis and Design Methodology was used while the implementation was done using WEKA and Java on a Netbeans platform.Experimental results show that Neutrosophic-Based Bayesian Neural Network (|NBBNNS) with an accuracy of 96.41% using a confusability measurement threshold of 0.003278 performed better than the conventional Neural Network had an accuracy of 72.45% using ILPD dataset; Accuracy of 100% was observed for both Wine and Iris datasets respectively.Our system was also used to differentiate between ILDP positive instances and Hepatitis Positive instances shown to have concept overlapping problem with accuracy of 97.30%.This clearly shows that Neutrosophic-Based Bayesian Neural Network (BNNS) is a promising classifier or system for classification of datasets prone to overlap problems.
Distributionally Robust Multi-instance Learning with Stable Instances.

Multi-instance learning (MIL) deals with tasks where data consist of set of bags and each bag is represented by a set of instances. Only the bag labels are observed but the label for each instance is not available. Previous MIL studies typically assume that the training and test samples follow the same distribution, which is often violated in real-world applications. Existing methods address distribution changes by re-weighting the training data with the density ratio between the training and test samples. However, models are often trained without prior knowledge of the test distribution which renders existing methods inapplicable. Inspired by a connection between MIL and causal inference, we propose a novel framework for addressing distribution change in MIL without relying on the test distribution. Experimental results validate the effectiveness of our approach.
Optimizing Support Vector Machine Classification Based on Semantic-Text Knowledge Enrichment

In this research, we enhanced the performance of Support Vector Machine (SVM) in text classification by applying semantic-knowledge enrichment. We propose using semantic-knowledge enrichment scheme to inject new concepts into the original contents of the text documents. A pre-processing technique is proposed for cleaning and extracting features for generating semantic concepts through using WordNet database and the open source Natural Language Toolkit (NLTK). Additionally, the combined online variation Bayes algorithm and the Latent Dirichlet Allocation model are used as a dimensionality reduction technique to generate abstract concepts from the raw text. In our experiment, we clarified the process of preparing data for cleaning, transformation and weighting the features vectors in a multi-dimensional space as a step to measure the performance metrics of SVM, before and after applying our proposed approach on two different datasets. K-Fold Cross-Validation technique is used to validate our proposed approach. Moreover, a confusion matrix is implemented to measure the accuracy and macro-averages of precision, recall and f1 measurements. The result of the evaluation showed improvements in term of accuracy from 94% to 98.3% for the dataset-1, and from 88% to 93% for dataset-2. Moreover, the training time of the classifier in terms of seconds was reduced to 32% and 17% for dataset-1 and dataset-2 respectively, in comparison with the training time of the original data before applying our proposed enrichment scheme.