Reviewerly/exHarmony huggingface dataset:
Cloud Data Retrieval for Multi related keyword based on Clustering Technology 

Cloud computing is a rapidly growing technology highly preferred by small and medium business where resources such as storage media, platform and applications shared over the Internet is used by multiple users in an organization. The common pay-as-you-go subscription model of Cloud computing makes it a successful technology, on the other hand, prerequisite conditions to impose confidentiality on cloud data and scalability requirement increases the complexity of cloud computing. Traditional algorithms provide either Boolean or ranked search result for the given single keyword does not retrieves accurate data on end-users interest in searching Cloud data with multiple related keywords during the same transaction. In this paper, an efficient clustering technique is used to retrieve encrypted cloud data for multiple related keywords. Inclusion of clustering technique to group related keywords together retrieves efficient and accurate cloud data. The proposed system ranks cloud data based on end user feedback on top of existing ranking algorithms which simply relies on keyword occurrence in a document increases the accuracy of data retrieved
Video Summarization using Keyframe Extraction Methods

Video summarization plays an important role in too many fields, such as video indexing, video browsing, video compression, video analyzing and so on. One of the fundamental units in the video structure analysis is the keyframe extraction, Keyframe provides meaningful frames from the video. The keyframe consists of the meaningful frame from the videos which help for video summarization. In this proposed model, we presented an approach that is based on Convolutional Neural Network, keyframe extraction from videos and static video summarization. First, the video should be converted to frames. Then we perform redundancy elimination techniques to reduce the redundancy from frames. Then extract the keyframes from video by using the Convolutional Neural Network(CNN) model. From the extracted keyframe, we form a video summarization.
Dynamic slicing of aspect-oriented software: an UML-based approach

In this paper, we propose a technique to compute the dynamic slice of UML models for aspect-oriented software. The UML architectural model for aspect-oriented software represents the 'core' components, 'aspectual' components and the relationship between them. Firstly, we construct aspect-oriented model dependence graph (AOMDG) as an intermediate representation of the UML model for aspect-oriented software. AOMDG is a combination of aspect-class dependence graph (ACDG) and aspect-sequence dependence graph (ASDG). ACDG is constructed by using a set of class and set of aspect class diagrams and ASDG is constructed by using a set of sequence and aspect sequence diagrams from the UML architectural model. Then, we propose an algorithm to compute the dynamic slice by traversing AOMDG by marking and unmarking the nodes for a particular scenario during run-time.
Analysing the Drum Kit in Performance

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USING THE WHOLE GROUP DISCUSSION METHOD AGAINST SPEAKING SKILLS OF STUDENTS OF CLASS VIII SMP

Speaking skill is the main capital in communication. However, the problem that exists in the field and is often encountered in the classroom is that not all students, especially those in SMP POMOSDA, have good speaking skills. The purpose of this study was to determine how influential the whole group group discussion method was in developing speaking skills, especially for class VIII SMP POMOSDA students. This study uses an experimental method (true experimental research). In this study using experimental techniques with a posttest-only control design pattern. Sampling using purposive sampling technique because the sample from the population is determined by the researcher himself. It can be seen that the difference in speaking skills of the experimental class and control class students is seen where the value (tcount) = 7,350 is greater than ttable = 2,000 with df = 60 (7,350 2,000) and sig.(2-tailed) = 0.000 where ( 0.000 &lt; 0.05) with a 95% confidence level, the research data is significant, so it can be concluded that H0 is accepted and H1 is rejected, and it is stated that there is an effect of the whole group group discussion method on the speaking skills of eighth grade students of SMP POMOSDA Tanjunganom Nganjuk.
Conceptual and Logical Data Model Production

In this chapter, we are going to really wind things up and begin to apply the skills that were covered in the previous chapters and create a data model from a set of requirements. The data model I create will not be ready to be implemented by any means, as there are more refinement steps to come in the next couple of chapters, but the goal of this model will be to serve as the basis for the eventual model that will get implemented. Personally, I both love and loathe this particular step in the process because this is where things get complicated. All the requirements and documents need to be considered, which hopefully reflect a complete understanding and desires of the people who actually want your software.
Toward Developing a Real-World Computational Thinking Test Tool from Existing Models

Various researches are always being carried out to measure the effectiveness of software education. We analyzed previously developed computational thinking tools and studied their practical application and verification methods. Using this information, we developed a 20-item questionnaire to categorize the tools by the abilities they measured: analysis, design, implementation, and reasoning. We surveyed college freshman and 204 students in computer programming subjects in liberal arts and then conducted an exploratory factor analysis to analyze the validity and reliability of our questionnaire test tool. Our test showed that previously used computational testing tools lacked the ability to measure problem-solving processes based on computational thinking. To solve this problem, we revised the questionnaire items to consider the problem-solving process based on computational thinking and proposed a tool that can check the computational thinking through the material of real life using the students’ empirical knowledge. The statistical analysis was as follows: analysis ability (reliability α = .895); design ability (reliability α = .727); implementation ability (reliability α = .745), and reasoning ability (reliability α = .833). To measure computing errors, you need a testing tool that can address real-world problems. We aimed to develop a research tool for measuring computational thinking based on the case of applying and revising existing test tools.
Cross-cultural advertising campaigns and the translation of humour

With the Internet and globalization, companies are able to reach wider audiences than ever before. Cultural adaptation has become a necessary strategy for advertisers in order to reach cross-cultural audiences without creating new advertising concepts. Although shown to be an effective and popular method, using humour in advertising can be difficult for cross-cultural campaigns because humour is difficult to translate and culturally based. Using Robertson’s theory of glocalization, advertisers can use cultural adaptation to create effective humorous cross-cultural advertising campaigns. In order to determine an effective method of cultural adaptation, this paper analyses Apple Inc.’s successful “Get a Mac” television advertising campaign, focusing on the use of slang, characters, and cultural humour characteristics. The results of the analysis, which examines both the culturally adapted commercials as well as the localized one-off commercials, suggest that advertisers should use a blended approach to glocalization. This approach incorporates both glocalized advertisements (using the same concept for different cultures with necessary modifications made) as well as localized advertisements (unique advertisements for each culture). By using this approach, advertisers can create effective humorous advertising campaigns for cross-cultural audiences.
A Social Media Recommender System

Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.
Online Community

The reason you are constructing customer support, a mailing list, social media, and a website is to cultivate an online community. An online community is a large group or groups of users who talk about your app, suggest features, and helpfully guide development. An organized community is an invaluable resource in making users happy.
Iterative SARSA: The Modified SARSA Algorithm for Finding the Optimal Path

This paper presents a thorough comparative analysis of various reinforcement learning algorithms used by autonomous mobile robots for optimal path finding and, we propose a new algorithm called Iterative SARSA for the same. The main objective of the paper is to differentiate between the Q-learning and SARSA, and modify the latter. These algorithms use either the on-policy or off-policy methods of reinforcement learning. For the on-policy method, we have used the SARSA algorithm and for the off-policy method, the Q-learning algorithm has been used. These algorithms also have an impacting effect on finding the shortest path possible for the robot. Based on the results obtained, we have concluded how our algorithm is better than the current standard reinforcement learning algorithms.
Shift to all‐virtual communications to reach prospective students

March 13, 2020, is a day Francesca Reed will always remember.
Image Steganography

Steganography is the practice of concealing a file, message, image, or video within another file, message, image, or video. The advantage of steganography is that the intended secret message does not attract attention to itself as an object of scrutiny. Steganography is concerned both with concealing the fact that a secret message is being sent and its contents. The change is so subtle that someone who is not specially looking for it is unlikely to notice the change. We intend to perform image steganography by designing a neural network that prepares the secret jpg image and hides the prepared jpg image in a cover jpg image.
Benchmarking of semantic annotation systems

In this research, an effort has been made to evaluate the semantic annotators with a systematic subjective evaluation technique. So far, most of the previous evaluation efforts have involved creation of gold standards and by measuring basic metrics, the performance of semantic annotators has been analysed. But in this work, a subjective evaluation technique has been applied to evaluate some of the publicly available semantic annotation systems. In this method, 60 participants have been involved in the evaluation. A survey has been carried out to collect the response from participants about what they think how well the annotators perform on different types of texts (e.g. long texts, short texts and tweets). Their responses have been analysed using standard statistical tests. Using this approach, it has been concluded that Wikipedia Miner performs better on long texts and Tag Me performs better on short texts and tweets than other systems.
Optimization of Energy Consumption in KUKA KR 16 Articulated Robot Manipulator

A study for optimal energy consumption in KUKA KR 16 articulated robot for pick-and-place task was introduce in this paper. In order to achieve the optimal energy consumption, an improve trajectory planning is required. Essentially, trajectory planning encompasses path planning in addition to planning how to move based on velocity, time and kinematics. Trajectory planning gives a path from a starting to a goal point by avoiding collisions in a 2D or 3D space. Therefore, this paper is focus on analyze the PTP motion and Linear motion in order to determine which is the best motion that can improve the trajectory planning. The optimal energy consumption to minimizing the movement based on three main axes where it used a big motors used to drive the axes. This method is much simpler in terms of development process and did not require any additional hardware to be install to the robot’s system. KUKA KR 16 is use to study optimal energy consumption and analyze PTP and Linear motion. The energy performance is measures with respect to two categories of movements known as Default and Optimal movement which do the same task repetitively within specific time. The result show that PTP motion consumed 6% more energy than Linear motion but completed 773 cycles within one hour whereas Linear motion only completed 492 cycles. Energy performance between Default and Optimal movement shows that Optimal movement recorded 21.8% less energy usage when compared to Default movement although the total cycles completed for both movement almost the same.
Adversarial Attack on Machine Learning Models

Machine Learning (ML) models are applied in a variety of tasks such as network intrusion detection or malware classification. Yet, these models are vulnerable to a class of malicious inputs known as adversarial examples. These are slightly perturbed inputs that are classified incorrectly by the ML model. The mitigation of these adversarial inputs remains an open problem. As a step towards understanding adversarial examples, we show that they are not drawn from the same distribution than the original data, and can thus be detected using statistical tests. Using this knowledge, we introduce a complimentary approach to identify specific inputs that are adversarial. Specifically, we augment our ML model with an additional output, in which the model is trained to classify all adversarial inputs.
Implementasi Kebijakan Bendungan di Kabupaten Kampar

The purpose of this study was to determine the implementation of thedam policy (study on the impact of the existence of floating net cages). The method used in this research is a qualitative method using descriptive case study methods with data collection techniques through interviews and documentation, using the theory of policy implementation from Van Metter and Van Horn. The results showed that the implementation of the dam control policy was carried out by applying measures and policy objectives, resources, characteristics of the executing agent, attitudes / tendencies of implementing agents, communication, social, economic and political environment. Then there are inhibiting factors in the implementation of dam control policies (a case study of the impact of the existence of floating net cages) in Kampar Regency, namely the increase in river water discharge which threatens the safety of floating net cages, the anticipation of the cage community is still very minimal in the face of high water discharge during the rainy season, depth. The unstable water greatly affects the floating net cage system, there is still a lack of communication between the Plta Koto Panjang and the floating net cage community, the floating net cage community has experienced a drastic economic decline due to the release of dam water during the rainy season, mass fish die.
Teaching Electronic Journals: Finding, Using, and Citing Them

Today’s library users are Web savvy but not always Web educated. As more and more of a library’s print journal collection becomes part of the library’s electronic collection, we must take on the roles of guiding and teaching students how to navigate and learn from their interactions with electronic journals. The Teaching Electronic Journals workshop discussed the opportunities we have to educate our users about electronic journals and how to use and cite them effectively. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;http://www.HaworthPress.com&gt;]
MANAGING THE PANDEMIC – EASING THE CRISIS

We have been under MCO since Jun 2020. All due to the unwanted spread of the deadly pandemic Covid 19 originated from Wuhan China. When the order was about to be lifted came the new Covid super spreader that has the impact of affecting those within the vicinity of the infected person. These phenomena are expected to continue hence the new normality need to be adopted. The movement had to be restricted in some way or another. The question thus arises to what extent does restriction to movement is allowed as freedom of movement is rights guaranteed in our constitution. Restricting movement during MCO is similar to restricting fundamental liberties provided under Art 9 freedom of movement of the Federal Constitution. Though morality is something personal but law-making may be influenced by morality standards. Despite law having the force of law yet no sensible people would tolerate immorality. This paper will discuss how morality can be aligned to legality as well as how legality can pose the opposite. The rationale of both sides can give us some views of how the line is treaded and provide guidance in combatting the pandemic in a correct way.
Denoising of image using bilateral filtering in multiresolution

One of the very efficient and resource conservative image processing methodology is with the help of bilateral filters. This technique filters the image without the help of edge smoothing but it does employs spatial averaging in a non-linear way. The filtering technique discussed above is very much dependent on the parameters of its filters. A very slight change in filter parameter values effects the outputs and results in a most drastic manner. In this paper, the author has worked on two contributions. In the applications concerning image denoising, the author has contributed in study of the parameter selection of bilateral filters which are optimal in nature. The contribution number two is about extending the present work i.e. extension of the filters which are bilateral in nature. In this process, the bilateral filtering of images is applied to the lower frequency sub-bands which is also known as approximation sub-band. This sub-band is obtained by using the wavelet transformations. Hence, a new framework for image denoising will be created which will be combination of multiresolution bilateral filtering and wavelets transformation techniques. As a matter of fact, this combination is efficient in contradicting noise from an image.
A comprehensive comparison on evolutionary feature selection approaches to classification

© 2015 Imperial College Press. Feature selection is an important data preprocessing step in machine learning and data mining, such as classification tasks. Research on feature selection has been extensively conducted for more than 50 years and different types of approaches have been proposed, which include wrapper approaches or filter approaches, and single objective approaches or multi-objective approaches. However, the advantages and disadvantages of such approaches have not been thoroughly investigated. This paper provides a comprehensive study on comparing different types of feature selection approaches, specifically including comparisons on the classification performance and computational time of wrappers and filters, generality of wrapper approaches, and comparisons on single objective and multi-objective approaches. Particle swarm optimization (PSO)-based approaches, which include different types of methods, are used as typical examples to conduct this research. A total of 10 different feature selection methods and over 7000 experiments are involved. The results show that filters are usually faster than wrappers, but wrappers using a simple classification algorithm can be faster than filters. Wrappers often achieve better classification performance than filters. Feature subsets obtained from wrappers can be general to other classification algorithms. Meanwhile, multi-objective approaches are generally better choices than single objective algorithms. The findings are not only useful for researchers to develop new approaches to addressing new challenges in feature selection, but also useful for real-world decision makers to choose a specific feature selection method according to their own requirements.
Design of Ensemble Classifier Selection Framework Based on Ant Colony Optimization for Sentiment Analysis and Opinion Mining

Ensemble Classifier provides a promising way to improve the accuracy of classification for sentiment analysis and opinion mining. Ensemble classifier should combine with diverse base classifiers. However, establishing a connection between diversity and accuracy of ensemble classifier is tedious task because of sensitivity between diversity and accuracy. In this paper an Ensemble classifier selection (ECS) framework based on Ant Colony Optimization (ACO) algorithm is presented. The framework provides a subset of base classifiers from a given set of classifiers with maximum possible diversity and accuracy to design an ensemble classifier for sentiment analysis and opinion mining. This framework uses diversity measures and accuracy as selection criteria for classifier selection for ensemble creation. The experimental result shows that the ensemble classifiers provided by this framework presents an efficient way for sentiment analysis and opinion mining.
Binary image classification: A genetic programming approach to the problem of limited training instances

© 2016 by the Massachusetts Institute of Technology. In the computer vision and pattern recognition fields, image classification represents an important yet difficult task. It is a challenge to build effective computer models to replicate the remarkable ability of the human visual system, which relies on only one or a few instances to learn a completely new class or an object of a class. Recently we proposed two genetic programming (GP) methods, one-shot GP and compound-GP, that aim to evolve a program for the task of binary classification in images. The two methods are designed to use only one or a few instances per class to evolve the model. In this study, we investigate these two methods in terms of performance, robustness, and complexity of the evolved programs. We use ten data sets that vary in difficulty to evaluate these two methods. We also compare them with two other GP and six non-GP methods. The results show that one-shot GP and compound-GP outperform or achieve results comparable to competitor methods. Moreover, the features extracted by these two methods improve the performance of other classifiers with handcrafted features and those extracted by a recently developed GP-based method in most cases.