Reviewerly/exHarmony huggingface dataset:
Multi-objective optimisation techniques for virtual machine migration-based load balancing in cloud data centre

This paper aims to balance the load in cloud data centre (CDC) by migrating virtual machines (VM) across hosts using multi-objective optimisation techniques. The unpredictable rate of demand for the cloud services leads to load fluctuation and subsequently load imbalance in cloud data centre. Hence, to balance the load in cloud data centre, this work presents multi-objective optimisation technique-based load balancing (MOOT-LB) method. This work proposes two multi-objective optimisation techniques namely, multi-objective particle swarm optimisation (MOPSO) and multi-objective differential evolution (MODE) for load balancing the cloud data centre. These techniques identify an optimal set of hosts and set of VMs to be migrated from the source hosts and identify the target hosts for migration in an efficient way. The objectives are to minimise the frequency of VM migration and migration time. To evaluate the performance of the proposed techniques ClouSim 3.0.3 simulator is used. The performance of the proposed techniques is compared, and the results show that MOPSO-based load balancing technique achieves better performance than MODE-based load balancing technique.
Building a Morphological Analyser for Laz

This study is an attempt to contribute to documentation and revitalization efforts of endangered Laz language, a member of South Caucasian language family mainly spoken on northeastern coastline of Turkey.It constitutes the first steps to create a general computational model for word form recognition and production for Laz by building a rule-based morphological analyser using Helsinki Finite-State Toolkit (HFST).The evaluation results show that the analyser has a 64.9% coverage over a corpus collected for this study with 111,365 tokens.We have also performed an error analysis on randomly selected 100 tokens from the corpus which are not covered by the analyser, and these results show that the errors mostly result from Turkish words in the corpus and missing stems in our lexicon.
Ecological and Faunistic Analysis of Spirurida Order Nematodes – Zooparasites of Uzbekistan

The purpose of the research is to study species diversity and some features of life history of Spirurida order nematodes - zooparasites of Uzbekistan. Materials and methods . Specimens of adult Spirurida order nematodes of domestic and wild animals’ population from all regions of Uzbekistan and Karakalpak Republic were collected and studied. Insects, the intermediate hosts of some species of concerned nematodes, were studied according to established methods. Some bioecological features of species Spirurata, Camallanata, Gnathostomata, Filariata suborders are analyzed. Results and discussion . Modern species diversity of Spirurida order nematodes of Uzbekistan fish, birds and mammals was studied. The order is presented by four species Spirurata, Camallanata, Gnathostomata, Filariata in studied animals. The total amount of studied order species was 145, where 16 species were recorded in fish, 81 species in birds, and 48 species in mammals. Species of Spirurata and Filariata which are widely distributed in desired hosts in Uzbekistan biogeocoenosis are the center of Spirurida fauna. Life cycles of indicated nematodes are carried out involving intermediate and reservoir (= paratenic) hosts. Specimens of Copepoda, Ephemeroptera, Orthoptera, Coleoptera and Diptera belong to the first, carp-like fish belong to the second. Evolution of infection transfer by intermediate host to definitive promotes formation and functioning of different types of parasitic systems.
Quality Analysis of Mobile Web Server

Abstract There are many mobile web servers that can be used, whether it is a mobile web server that is free or paid. This research aims to analyze the quality of three mobile web servers consisting of one paid mobile web server and two other web servers that are obtained free of charge. To measure the quality of a mobile web server in this study using Apache JMeter tools and Web Application Performance Testing (WAPT). Based on the results of testing, it was found that the quality of paid mobile web servers was better compared to the quality of free mobile web servers based on values obtained using Apache JMeter, paid mobile web server is faster looking from sample times, latency and connect time result. When tested using WAPT, paid mobile servers also have better results based on the value of successful hits and average response time. The results of this assessment will depend on the type of smartphone that uses in the implementation hardware, so the value of the test results can be different if implemented on a different smartphone.
Correction to: Retinal oximetry and fractal analysis of capillary maps in sickle cell disease patients and matched healthy volunteers

In the published online PDF version, Figure 3 was incorrectly captured the same as Figure 1.
IMPACT OF IMPUTATION ON CLUSTER-BASED COLLABORATIVE FILTERING APPROACH FOR RECOMMENDATION SYSTEM

The Collaborative Filtering (CF) widely used in Recommendation System commonly suffers the sparsity issue since the unobserved rating entries usually over dominance the observed ones. A clustering technique is an alternative solution that can solve the problem. However, no in-depth work has investigated how the missing entries should be mitigated and how the cluster-based approach can be implemented. In this study, we show how the imputed cluster-based approach deals with the missing entries, improving the recommendation quality. The framework of our method consists of four main stages: rating imputation to replace the missing entries, K-means clustering to group users or items based on the imputed rating data, CF-based prediction model, and generating the list of top-N recommendation. This paper uses three variations of imputation techniques, i.e., null, mean, and mode. The cluster-based approach is employedby using the K-Means as the clustering technique, and either the user-based or the items-based model as the CF approach. Experiment results show that the null imputation technique gives the best results when dealing with the missing entries. This finding indicates that the implementation of the clustering techniqueis sufficient for solving the sparsity issue such that imputing the missing entries is not necessary. We also show that our imputed cluster-based CF methods always outperform the traditional CF methods in terms of the F1-Score metric.
PROTOTIPE APLIKASI PENENTUAN TINGKAT KESEGARAN TEMPE BERBASIS CITRA DIGITAL

Consumption level of tempe in Indonesia is higher than chicken and meat. Tempe freshness level we consume is an important indicator of tempe quality itself. A fresh diet will provide nourishment and energy to our body. Tempe freshness level can be determined using automated application that is synchronized with the mobile phones. This application prototype was constructed based on Matlab and digital image. A smart detection system of tempe which can detect the freshness of tempe was constructed to ensure whether tempe can be eaten or discarded. An application is developed to decide the good or bad quality of tempe through its freshness.Keywords: Digital image, freshness, Matlab, RGB, tempe
PENGELOLAAN SUMBER MATA AIR DI DESA KEDUNGOLENG KECAMATAN PAGUYANGAN KABUPATEN BREBES

Mountain springs as artesian springs and in a condition of continuity provided they are assisted by normal rainfall and an environment that remains green. In any season the mountain water source will continue to provide adequate water supply for the community. Kedungoleng Village is a village located in the hills in the District of Paguyangan, Brebes Regency. The hilly topography of the village of Kedungoleng and far from the springs is the weakness of the village to get clean water. Kedungoleng residents rely on rainwater, dug wells and public baths to fulfil their daily needs. Clean water in Kedungoleng Village comes from a spring in the hills, which is then accommodated in a public bath. This public bath is the only source of water that is easily available. It's just that all residents use so that sometimes residents fight over and are ineffective. The long-distance also requires considerable time and energy to bring clean water to the house. Some residents have dug wells, but the water is yellow and smelly and not suitable for drinking water consumption. The purpose of community service is to meet the needs of clean water for the community adequately and sustainably both in terms of quantity, quality and continuity to improve health and quality of life for the people in Kedung Oleng Village. This method of implementing community service activities designed water sanitation improvements in the form of making water tanks. Building water reservoirs are expected to be able to meet the needs of clean water at any time.
TEACHING STUDENTS SPEAKING SKILL THROUGH FISHBOWL TECHNIQUE ON SUBJECT OF ENGLISH FOR TOURISM

The ability to speak is the main basis that must be possessed by every student, especially for students who majored in English education. However, most problems in the process of teaching English are closely related to the ability to speak itself. The formulation of the problem in this study are; is there a significant improvement in speaking skills when applying the fishbowl technique to the English for Tourist Guide course? and What are the classroom situations and conditions when the fishbowl technique is applied in the learning process in the English for Tourist Guide course? Therefore, the long-term goal of this research is to produce learning theories that can be applied by lecturers without changing their roles but can optimize the creativity and ability of students in good English and in accordance with applicable language rules. This research method is quantitative research and the design used in this study is Classroom Action Research (CAR) which consists of several cycles with a sample of one class, namely class A3 2016 IKIP SIliwangi. The results and conclusions in this study are the sum of all student scores in the pre-test is 122, and the total score in session 1 is 154, while the score in session 2 is 187, after averaged, the scores obtained are 4.5 for the scores pre-test, 5,7. for the value of session 1, and 6.9 for the value of session 2 from the analysis made, it can be concluded that the results obtained have not demonstrated the success of Fishbowl learning conducted by lecturers / researchers. Looking at the comparison of initial values, the value of the first cycle and the value of the second cycle, there was a significant increase, namely from the average initial value of 4.5 rising in cycle I to 6.3 and in cycle II rising to 7.9 This increase could not underestimated because this increase in value is from the maximum efforts carried out by researchers for the sake of improving the quality of education and the advancement of education, especially in English Language Study Program IKIP Siliwangi.
Selection of Optimal Sub-ensembles of Classifiers through Evolutionary Game Theory

Ensemble methodology combines multiple learning schemes in order to improve the classification performance. An ensemble made of a large number of classifiers entails an increase in the computational cost, memory storage, and even a reduction in the predictive performance. Ensemble pruning has become an important task that lives up to these challenges. The thrust consists of constructing a subset that maintains or improves the accuracy of the original set of classifiers while reducing the number of members that constitute the ensemble. Inspired by evolutionary game theory, we formulate ensemble pruning as a dynamical system. Our approach simulates the evolution of an ensemble according to the replicator dynamics. The selection mechanism is defined based on strategic interactions that favor complementary members. The survivors of the evolutionary process compose the final ensemble. In order to evaluate the proposed technique, we performed comparisons with some major state-of-the-art methods such as semi-definite programming, genetic algorithm, and orientation ordering, based on 30 UCI benchmark datasets. The results demonstrate the effectiveness of our approach in terms of accuracy performance, pruning ratio, and computational cost.
Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm

This paper considers the problem of estimating the distribution of returns in reinforcement learning (i.e., distributional RL problem). It presents a new representational framework to maintain the uncertainty of returns and provides mathematical tools to compute it. We show that instead of representing a probability distribution function of returns, one can represent their characteristic function instead, the Fourier transform of their distribution. We call the new representation Characteristic Value Function (CVF), which can be interpreted as the frequency domain representation of the probability distribution of returns. We show that the CVF satisfies a Bellman-like equation, and its corresponding Bellman operator is contraction with respect to certain metrics. The contraction property allows us to devise an iterative procedure to compute the CVF, which we call Characteristic Value Iteration (CVI). We analyze CVI and its approximate variant and show how approximation errors affect the quality of computed CVF.
Towards Adaptive Text Summarization: How Does Compression Rate Affect Summary Readability of L2 Texts?

This paper addresses the problem of readability of automatically generated summaries in the context of second language learning.For this we experimented with a new corpus of level-annotated simplified English texts.The texts were summarized using a total of 7 extractive and abstractive summarization systems with compression rates of 20%, 40%, 60% and 80%.We analyzed the generated summaries in terms of lexical, syntactic and length-based features of readability, and concluded that summary complexity depends on the compression rate, summarization technique and the nature of the summarized corpus.Our experiments demonstrate the importance of choosing appropriate summarization techniques that align with users needs and language proficiency.
Ensemble learning based multi-source information fusion

Aiming at the target recognition tasks of multi-source sensors, this paper proposes a decision-level information fusion model based on ensemble learning to improve the target recognition ability of distributed sensors. Based on distributed sensing data, feature analysis model is first constructed to reduce the dimension of original data. Then, target recognition model is constructed by data mining to realize the rapid identification by single classifier. On this basis, information fusion model based on ensemble learning is proposed to assist decision-making, combined with different ensemble strategies to improve the robustness and reliability of multi-source sensor target recognition. Finally, five public data sets are used to verify the effect of multi-source information fusion model under four homogeneous strategies and two heterogeneous strategies.
Differentiated education of children with peculiarities of intellectual development in institutions of general secondary education.

professor of the department of special, inclusive and health-
The application of transfer learning in film and television works

Many personalized advertisement recommendation studies suffer from the problem of only certain tagged items can be recommended in video playback, which mean it can’t recommend more produces to users that they really like . It also doesn’t know the users really like at the source. Due to the large number of scene changes in different video, the users can choose more items they like. This study attempts to adopt transfer knowledge to solve the problem of data volume to provide users with a variety of options. Aiming at the image classification model of learning on big data set, this paper proposes a method to solve the problem of scene object recognition in TV program,such as movies,TV plays, variety shows and short video, by transferring a pre-trained depth image classification model to a specific task. In a small training set, Learning high-level representations on a small training set to produce a task-specific target model. Experiments on small data sets and real face sets collected by myself show that the transfer learning is effective and efficient. In the application of video, this study provides a theoretical basis for personalized click recommendation of video users.
IP Cores for Graph Kernels on FPGAs

Graphs are a powerful abstraction for representing networked data in many real-world applications. The need for performing large scale graph analytics has led to widespread adoption of dedicated hardware accelerators such as FPGA for this purpose. In this work, we develop IP cores for several key graph kernels. Our IP cores use graph processing over partitions (GPOP) programming paradigm to perform computations over graph partitions. Partitioning the input graph into nonoverlapping partitions improves on-chip data reuse. Additional optimizations to exploit intra and interpartition parallelism and to reduce external memory accesses are also discussed. We generate FPGA designs for general graph algorithms with various vertex attributes and update propagation functions, such as Sparse Matrix Vector Multiplication (SpMV), PageRank (PR), Single Source Shortest Path (SSSP), and Weakly Connected Component (WCC). We target a platform consisting of large external DDR4 memory to store the graph data and Intel Stratix FPGA to accelerate the processing. Experimental results show that our accelerators sustain a high throughput of up to 2250, 2300, 3378, and 2178 Million Traversed Edges Per Second (MTEPS) for SpMV, PR, SSSP and WCC, respectively. Compared with several highly-optimized multi-core designs, our FPGA framework achieves up to 20.5× speedup for SpMV, 16.4× speedup for PR, 3.5× speedup for SSSP, and 35.1× speedup for WCC, and compared with two state-of-the-art FPGA frameworks, our designs demonstrate up to 5.3× speedup for SpMV, 1.64× speedup for PR, and 1.8× speedup for WCC, respectively. We develop a performance model for our GPOP paradigm. We then perform performance predictions of our designs assuming the graph is stored in HBM2 instead of DRAM. We further discuss extensions to our optimizations to improve the throughput.
QxSQA: GPGPU-Accelerated Simulated Quantum Annealer within a Non-Linear Optimization and Boltzmann Sampling Framework

We introduce QxSQA, a GPGPU-Accelerated Simulated Quantum Annealer based on Path-Integral Monte Carlo (PIMC). QxSQA is tuned for finding low-energy solutions to integer, non-linear optimization problems of up to 214 (16,384) binary variables with quadratic interactions on a single GPU instance. Experimental results demonstrate QxSQA can solve Maximum Clique test problems of 8,100 binary variables with planted solutions in under one minute, with linear scaling against key optimization parameters on other large-scale problems. Through the PIMC formulation, QxSQA also functions as an accurate sampler of Boltzmann distributions for machine learning applications. Experimental characterization of Boltzmann sampling results for a reinforcement learning problem showed good convergence performance at useful scales. Our implementation integrates as a solver within our QxBranch developer platform, positioning developers to efficiently develop applications using QxSQA, and then test the same application code on a quantum annealer or universal quantum computer hardware platform such as those from D-Wave Systems, IBM, or Rigetti Computing.
SISTEM PERINGATAN DINI KEBAKARAN HUTAN MENGGUNAKAN MODUL NODEMCU DAN BOT TELEGRAM DENGAN KONSEP INTERNET OF THINGS (IOT)

Forests have an important role in the life of living things. Nowadays forest fires (Karhutla) become a serious problem that can disrupt the symbiosis and life chain of living things. This problem has become a concern for the community, government and the world. Data obtained until August 2019 recorded 328,724 hectares and burned forest land. To overcome this problem, the government has made various efforts in the form of appeals or legal sanctions on actions that threaten forest sustainability whether carried out individually or in groups. Many cases of forest fires are known when a fire has occurred and little can be detected early. Information on the occurrence of many fires was obtained by residents around the location of the fire. To get the help of the fire department, community participation is needed, to contact the fire department so that they can anticipate the fire disaster early. The aim of this research is to develop a forest fire early warning system using the nodemcu module and the Telegram BOT with the Internet of Things (IOT) concept. Based on the test results of the Forest Fire early warning system using the Nodemcu module and the Telegram BOT with the concept of the Internet of Things (IOT) it is very helpful to provide quick information to find out fires that occur in the forest, by using the Internet of Things method, the officer will be able to know the conditions in real time, because this technology is capable of monitoring hardware using internet communication tools such as Telegram so that distance and location are not affected as long as the sensor used detects changes that occur.Keywords: Internet Of Things, Nodemcu, Telegram, Thingspeak, Forest fires
Using a web quest to form a professional self-determination of future primary education teachers

Тенденции развития науки и образования -5 -РАЗДЕЛ I. ПЕДАГОГИКА Алексеева О.В
Generating Challenge Datasets for Task-Oriented Conversational Agents through Self-Play

End-to-end neural approaches are becoming increasingly common in conversational scenarios due to their promising performances when provided with sufficient amount of data.In this paper, we present a novel methodology to address the interpretability of neural approaches in such scenarios by creating challenge datasets using dialogue self-play over multiple tasks/intents.Dialogue self-play allows generating large amount of synthetic data; by taking advantage of the complete control over the generation process, we show how neural approaches can be evaluated in terms of unseen dialogue patterns.We propose several out-of-pattern test cases each of which introduces a natural and unexpected user utterance phenomenon.As a proof of concept, we built a single and a multiple memory network, and show that these two architectures have diverse performances depending on the peculiar dialogue patterns.
Local Comparative Decimal Pattern for Face Recognition

Rapid growth of social networks has provided an extraordinary medium to share a large volume of photographs online. This calls for designing efficient face recognition techniques that are applicable to images with low resolutions and arbitrary poses. This paper proposes a new pose invariant face recognition method for low resolution images using only a single training sample. A 3D model, reconstructed using Generic Elastic Model (3D GEM) from a frontal view training sample, is used to generate a set of nonfrontal gallery face images. The face region of the nonfrontal query sample is then extracted using the same landmark detection technique as in the 3D GEM algorithm. Afterwards, a novel texture representation technique called Local Comparative Decimal Pattern (LCDP) is proposed to extract features from each of the training and query samples. A set of experimental results on the ORL, Georgia Tech (GT), and LFW face databases demonstrates the efficiency of the proposed method compared to other state-of-the-art approaches.
Analysis on the application of 5G technology in The emergency surveying and mapping

5G technology; The emergency surveying and mapping;Emergency response