Reviewerly/exHarmony huggingface dataset:
864 Published By: Blue Eyes Intelligence Engineering &amp; Sciences Publication Retrieval Number: E6080018520 /2020©BEIESP DOI:10.35940/ijrte.E6080.018520 Journal Website: www.ijrte.org Multi Level Queue Scheduling With Particle Swarm Optimization (Mlqs-Pso) Of Vms in Queueing Heterogeneous Cloud Computing Systems

This article investigates in cloud computing systems about problem of delay optimal Virtual Machine (VM) scheduling holds constant resources with full infrastructure like CPU, memory and storage in the resource pool. Cloud computing offers users with VMs as utilities. Cloud consumers randomly demand different VM types over time, and the usual length of the VM hosting differs greatly. A scheduling algorithm for a multi-level queue divides the prepared queue towards lengthy and various queues. System is allocated with single queue in to several longer queues. The systems are allocated to one queue indefinitely, usually on any basis of process property, like memory size, process priority, or process sort. Every queue will have its self-algorithm for scheduling. Likewise, a system that’s taking in a less preference queue is so lengthy, a high-priority queue can be transferred. Using Particle Swarm Optimization Algorithm (MQPSO), Multi-level queue scheduling has been done. To evaluate the solutions, it explores both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) programming algorithms, i.e., SJF-MMBF. The scheme incorporating the SJF-ELM-specific scheduling algorithms depending SJF buffering and Extreme Learning Machine (ELM) is also being proposed to prevent work hunger in an SJF-MMBF system. Furthermore, the queues must be planned, which is usually used as a preventive fixed priority schedule. The results of the simulation show that the SJF-ELM is ideal inside strong duty as well as maximum is environment dynamically, with an efficient average employment hosting rate.
SISTEM INFORMASI GEOGRAFIS PEMETAAN BENCANA ALAM KOTA BREBES MENGGUNAKAN METODE EXTREME PROGRAMMING

Public service is an activity of service for every citizen and resident for goods, services, and / or administrative services provided by public service providers, namely the Regional Disaster Management Agency (BPBD) of Brebes Regency. The quality of public services must have the principle of openness or transparency, easily accessible, accountability and cling to the principles of efficiency and effectiveness. But in fact the current information and reporting of disasters is still conventional. This study aims to reflect and build a Web-Based Disaster Geographic Information System using Extreme Programming method. The research method uses qualitative methods with descriptive data analysis techniques. Data obtained by observation and interviews. The benefit of this application is to improve services to the Brebes Regional Disaster Management Agency also useful for internal from the Implementing Party or Admin who can manage Disaster Area data online, accurately, and quickly. As well as greatly helping the community to obtain maps of information on disaster-prone areas, the latest disaster information and the community can report directly on disaster events so that the Regional Government can directly handle the disaster.
Digital 2D Animation for Educational Visualization in Secondary School: A Development Courseware of Bintang Hati PT3 Novel

Multimedia technology has opened up new opportunities and exploration to be exploited in learning environment. This technology has the ability to integrate, combine, and deliver various media such as text, graphic, audio, video, animation and interactivity which can help make teaching and learning process more effective and rewarding. However, a multimedia courseware development high-quality and cost-effective is a major challenge that needs to be addressed and focused if we want to integrate the multimedia application a real success. This study presents development of a multimedia courseware using 2D animation for PT3 students in secondary school in assisting them to learn about the Bintang Hati novel using visualization technique. The development courseware is an alternative of the conventional learning method using chalk and talk. Integrating the multimedia elements in learning method will make the learning environment more interesting and presentable. ADDIE model has been used as guidelines in developing the courseware. Each phases and activities of the model are discussed in detail based on the development process involved. The methodology for courseware quality assurance and its relationship with the development process model have been presented. By using the observation, questionnaire, and interview method in data collection, the result of the successful courseware development is also presented.
DNS Security - Prevent DNS Cache Poisoning Attack using Blockchain

The block chain is attaining popularity day to day as it is acting as distributed ledger for Cryptocurrency such as Bitcoin and ripple. This research paper has focused on the Blockchain and its working pattern with technical implementation of block creation. This technical paper is considering the prevention of DNS Cache poisoning attack in Blockchain which is known as larger class of name-based attacks. DNS Packet interceptions may be made using various attacks like Cache poisoning attack, man-in-the-middle attacks etc. As there are numerous security mechanisms to secure the Blockchain but in order to make Blockchain immune from cache poisoning attack, there is need to update the block creation module. Therefore, this research work is proposed to make reduction in probability of data corruption that can be created from different attacks. It resolves the issue of cache poisoning attacks using user defined port instead of predefined port. However, the initialization of transmission is performed here using predefined port number. In second step, the encrypted port number is decrypted to initiate communication using user defined port number. The use of port with IP address would restrict attacks during data transmission. The paper has presented the comparative analysis of existing DNS attacking prevention mechanism to proposed work.
AN IMPROVED ACTION KEY FRAMES EXTRACTION ALGORITHM FOR COMPLEX COLOUR VIDEO SHOT SUMMARIZATION

Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection.The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation.The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames.The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm.We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots.The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot.We then evaluated the Improved AKF algorithm with the-state-of-theart algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset.The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots.Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems.
Prevalence of Malaria and Typhoid Coinfection in Relation to Haematological Profile of University Students in Akure, Nigeria

Introduction: Malaria and typhoid fever are endemic febrile diseases with overlapping signs and symptoms notably fever, diarrhoea, vomiting and headache. Objective:The study was aimed to investigate the level of co-infection of malaria and typhoid fever and the haematological parameters among students of the Federal University of Technology Akure. Methodology:A cross-sectional study was conducted on Two hundred students.Venous blood samples were collected for Widal and Malaria test.The Widal agglutination test was performed by the rapid slide titration method using commercial antigen suspension (Cal-Test Diagnostic Inc. Chino, U.S.A.) and Malaria diagnosis was carried out using antigen based-Rapid Diagnostic Test (RDT).Results: Of the 200 examined students, 121 (60.5%) were positive for malaria and 161 (80.5%) were positive for typhoid fever.Male were more infected with both malaria and typhoid fever (43% and 40.5% respectively), however, the difference in the prevalence of infections between the genders were not statistically significant (P > 0.05).103 (51.5%) students had co-infection out of which 78 (39%) males were more infected than 25 (12.5%)females.Age group 21-25 years had the highest co-infection (31%).There is a significant difference between the numbers of students with co-infection and those without co-infection (P < 0.05).In the haematological analyses, this study showed that a reasonable percentage of malaria and typhoid fever infected patients were anaemic (15.5%), 5.8% had higher than normal leucocyte count, 13.6% with lymphocyte count lower than normal and 7.8% of the co-infected students had monocyte count higher than the normal range. Conclusion:The incidence of typhoid and malaria co-infection will greatly reduce when there is improved personal hygiene, targeted vaccination campaigns and intensive community health education on preventive and control measures against malaria and typhoid fever.
Low Memory Footprint CNN Models for end-to-end Driving of Autonomous Ground Vehicle and Custom Adaptation to Various Road Conditions.

The Idea presented in this paper is to establish that different types of Convolution Neural Network (CNN) model are required to be used for different types of terrains, to optimally drive the vehicle. The Layers and the trainable variables required to be configured as per the road conditions. For simpler roads, lighter and simpler networks and for complicated road, a CNN model with heavier network is required. Existing CNN models were used to build the performance baseline with respect to simple and complicated road conditions. In the proposed CNN models, layers and trainable parameters are adjusted according to the road conditions. The objective of the proposed solution is to successfully drive with minimum number of convolution layers and trainable variables, fit for deployment on computer consuming less than 100 watts, without GPU, for moderate speed autonomous vehicle. These new sets of proposed CNN models are either equal or smaller in network size and trainable parameters, memory footprint and refresh rate compared to Alexnet and Nvidia model. The overall requirement of computational power, cost, and size is also reduced. In this paper, we recommend designing AV software with multiple CNN models to address different road conditions instead of on one Model for all road conditions. Finally, we establish that when the terrain is more complex and requires more features to be extracted then the CNN layers need to be tweaked to extract more features and the speed of the vehicle needs to be reduced.
Attribution in the Statistics Mirror Anonymous Articles in the “Vremya” and “Epokha” Journals Published by the Dostoevsky Brothers’

The attribution of content of Vremya (Time) and Epokha (Epoch) journals, in which many anonymous and pseudonymous articles have been published, presents many challenges for the researchers. In the XIX–XX centuries, a corpus of texts, which were unambiguously attributed to F. M. Dostoevsky, M. M. Dostoevsky, A. A. Grigoriev, N. N. Strakhov and other Vremya and Epokha authors, was formed on the basis of traditional documentary (letters, payroll, income and expense book, memoirs), ideological and thematic and stylistic evidence. The following types of texts were isolated within the corpus of anonymous and pseudonymous articles, author's and collective articles, editorial inserts, notes; by type of writing - authorship, co-authorship, editing and adaptation of others texts. Modern methods of statistical analysis of formal grammatical and syntactic parameters in the text can provide sufficient grounds for expanding the source attribution base. This article presents the results of a study of a number of anonymous articles in the Dostoevsky brothers' journals using three statistical methods of attribution: linguistic and statistical analysis according to the G. Kjetsaa's method, the Transformer neural network and the Random forest algorithm. These results need to be correlated; they should be taken into account as complementary to traditional attribution methods.
地球物理三维电磁反演方法研究动态

Abstract There is no abstract available for this article. 地球物理 正则化 三维正反演 各向异性反演 多参数联合反演 × Update Abstract English Chinese Update
Agent-based asynchronous training in distributed software development

In the changing software development, training on tools and techniques are necessary for the developers for better quality. Global software development faces great challenge for employee training as project members are scattered geographically. Developers should aware of the custom and culture of co-developers to improve their social and interpersonal competencies such as negotiation, teamwork, conflict resolution, time management, leadership, and communication using common language. The paper discussed a framework for agent based training model that can simulate global software development from different cultures; allows learners to train asynchronously as the agents are always available, and permits to play different roles in the various stages of project development. The framework also provides effective client training on product and effective for the discussion between the client and the developer and thus reduces the effort. The model was evaluated with online geographically separated trainees and trainers and analysed the feedback.
A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
Improving Students Speaking Skills by Recount Text

The aim of this study was to find out whether there was a students ability to speaking after learning recount text. This type of research is quantitative. The data used is the value of students in knowing improvenment in speaking students to understanding recount text. Data collection techniques in this study were interviews as introductory stage with students and teacher to find out the improvenment in students speaking skills, observation, questionnaire and data collection. The conclusion of this research is that after doing cycle 1 and cycle 2 the recount text can to improve students ability&#x0D; Key words: speaking skills, recount text.&#x0D;
An Optimized CNN based Real World Anomaly Detection in Surveillance Videos

Anomaly detection in automated surveillance video is an extremely monotonous process for monitoring for crowded scenes and surveillance videos are capable to incarcerate a mixture of sensible anomalies. An appropriate machine learning technique can help to train the Anomaly Detection System (ADS) in identifying anomalous activities during surveillance. To this end, we present an anomaly detection system that can be used as a tool for anomaly detection in surveillance videos using the concept of artificial intelligence. The main intention of the proposed anomaly detection system is to improve the detection time and accuracy by using the concept of Convolutional Neural Network (CNN) as artificial intelligence technique. In this paper we present a CNN based Anomaly Detection System (CNN-ADS), which is the combination of multiple layer of hidden unit with the optimized MSER feature by using Genetic Algorithm (GA). Here CNN is used for classifying the activity into normal and abnormal from the surveillance videos based on the fitness function of GA which is used for the selection of optimal MSER feature sets. Further, Self adaptive genetic algorithm (SAGA) is adopted to efficiently solve optimization problems in the continuous search domain to select the best possible feature to segregate the pattern of normal and abnormal activities. The main contribution of this research is validation of proposed system for the large scale data and we introduce a new large-scale dataset of 128 hours of videos. Dataset consists of 1900 long and untrimmed real-world surveillance videos, with 13 sensible anomalies such as road accident, burglary, fighting, robbery, etc. as well as normal activities. The experimental results of the planned system show that our CNN-ADS for anomaly detection achieve essential improvement on anomaly detection presentation as compared to the state-of-the-art approaches. The dataset is available at: https://webpages.uncc.edu/cchen62/dataset.html. In this paper, to validate the proposed ADS we provide the comparison of existing results of several recent deep learning baselines on anomalous activity detection. The real-time ADS in surveillance video sequences using SAGA based CNN with MSER feature extraction technique is implemented using Image Processing Toolbox within Matlab Software.
Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

&lt;div&gt;Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist.&lt;/div&gt;&lt;div&gt;To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. &lt;/div&gt;&lt;div&gt;After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. &lt;/div&gt;&lt;div&gt;Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. &lt;/div&gt;&lt;div&gt;Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.&lt;/div&gt;
Cybersquatting: Threat to Domain Name

The present article deals with conflicts arising out of registration of domain names of existing trade names with the intention to resell it and/or encash the goodwill. Such practice is known as ‘Cybersquatting’. Registration of Domain names and acquiring a domain name of choice has become a rage over the time. It is the first come first get thing for getting registered. In this paper the researcher has explained what a cybersqatting is alongwith various types of cybersquatting and its prevention and targets. This paper also suggests remedial measures to deal with cybersquatting. The Anti Cybersquatting Consumer Protection Act (ACPA), an enactment by United States, needs a special applaud for being the first country to have introduced a special act for dealing with the menace of cybersquatting which was the need of the hour. It so happens that people opt to buy the domain name from squatters as it being cheap instead of seeking remedy from the court of law
Sindhi Stemmer using Affix Removal Method

Stemming is the process of mapping various inflections of a word to its base form.Stemmer is an essential component of Information Retrieval (IR) systems and different Natural Language Processing pipelines.This research reports the development and evaluation of stemmer for a resource poor language Sindhi.The stemmer is using a lexicon-based affix removal technique for stemming.The developed lexicon represents the base forms and the algorithm uses this lexicon during the affix removal process.The overall performance accuracy is evaluated, and stemmed error rate is calculated.The results show 89.57% overall performance accuracy.
TRAFFIC LIGHT CONTROL USING FUZZY LOGIC MAMDANI METHOD

Traffic congestion is a common occurrence in Indonesia. Traffic congestion is increasing from year to year, causing many things to happen, such as longer travel time, increased transportation costs, serious disruptions to transporting products, decreasing levels of work productivity, and wasteful use of labor energy. Congestion is also caused by a traffic light control system that is made with a fixed time so it can not detect the density of certain paths. Traffic lights in Indonesia, frequent damage that makes the density and the flow of his road vehicles can not be controlled. From these problems, conducted research to reduce the density of vehicles using infrared sensors and see the waiting time of the vehicle when the red light. The traffic light control system will use Fuzzy Logic Mamdani method. In Mamdani method by applying fuzzy into each variable and will be done matching between rule with condition which fulfilled to determine contents of output to be executed by prototype. This congestion detection will help the system in controlling the green light time by looking at stable, medium, and traffic jams. When the bottleneck starts to detect, the prototype will add a green light time according to the condition that is 0 seconds, 5 seconds, 10 seconds, and 15 seconds. However, when the streets are not detected by traffic jams, the green light will be back to normal at 15 seconds without additional time
Sentiment Analysis and Opinion Summarization of Product Feedback

With the exponential growth of online shopping platforms, user interaction is made direct through their reviews and ratings. User’s opinions and experiences are a significant source of valuable information in decision making process. In recent days, almost every website encourages users to express and exchange their views, suggestions and opinions related to product, services, policies, etc. publicly. Opinion mining is an extensive branch of Artificial Intelligence and a form of Natural Language Processing which illustrates the attitude of the customers, in specific services or products. Also known as Sentiment Analysis, it aims at determining the response and mood or attitude of the speaker or the overall contextual and emotional polarity or reaction. Existing algorithms determine sentiment by training on datasets, lexicon-based approach by calculating polarity and rule-based approach for classification. Opinion Summarization is the process of consolidating a large amount of sentiments and opinions into a clear and brief statement for an easier grasp on the underlying context. Major summarization methods include, Extractive method, Sentence Ranking, Abstractive method and Clustering of Textual Segments. Hence it is important to judge and classify these reviews and present a laconic opinion so it would be easier for users to obtain a gist and overall polarity on the various reviews instead of going through all of them