Reviewerly/exHarmony huggingface dataset:
An Analysis of Online Game Influence Toward the Biology Learning Achievement of Students at SMAN 1 Suhaid and SMPN 1 Suhaid, Kapuas Hulu

This study aims to investigate the influence of online game toward biology learning achievement, dealing with the school environment and family environment of senior high school and junior high school students.The research method of this study is descriptive qualitative, which had been conducted at SMA N 1 Suhaid and SMP N 1 Suhaid, Kapuas Hulu, West Kalimantan.The sample is students who play online games.The technique of collecting data is using questionnaire in order to investigate online game users, interest factor in learning biology at school, and family factor.The technique of data analysis is using linear regression.Moreover, the result of the hypothesis test includes; (1) It is obtained that t > t table, or 2.789> 2.024.It means that there is an influence of online games toward biology learning achievement at SMA N 1 Suhaid in terms of the school environment.(2) The second hypothesis test results t < t table, or 0.903 <2.024.It means that there is no influence of online games in terms of family environment.(3) The result of the third hypothesis test results t > t table, or 2.132> 2.024.It means that there is an influence of online games toward the school environment.(4) The fourth hypothesis test results t > t, or 2.140> 2.024.It means that there is an influence of online game toward family environment.
Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

Abstract Cloud computing has become one of the major computing paradigms. Not only the number of offered cloud services has grown exponentially but also many different providers compete and propose very similar services. This situation should eventually be beneficial for the customers, but considering that these services slightly differ functionally and non-functionally -wise (e.g., performance, reliability, security), consumers may be confused and unable to make an optimal choice. The emergence of cloud service brokers addresses these issues. A broker gathers information about services from providers and about the needs and requirements of the customers, with the final goal of finding the best match. In this paper, we formalize and study a novel problem that arises in the area of cloud brokering. In its simplest form, brokering is a trivial assignment problem, but in more complex and realistic cases this does not longer hold. The novelty of the presented problem lies in considering services which can be sold in bundles. Bundling is a common business practice, in which a set of services is sold together for the lower price than the sum of services’ prices that are included in it. This work introduces a multi-criteria optimization problem which could help customers to determine the best IT solutions according to several criteria. The Cloud Brokering with Bundles (CBB) models the different IT packages (or bundles) found on the market while minimizing (maximizing) different criteria. A proof of complexity is given for the single-objective case and experiments have been conducted with a special case of two criteria: the first one being the cost and the second is artificially generated. We also designed and developed a benchmark generator, which is based on real data gathered from 19 cloud providers. The problem is solved using an exact optimizer relying on a dichotomic search method. The results show that the dichotomic search can be successfully applied for small instances corresponding to typical cloud-brokering use cases and returns results in terms of seconds. For larger problem instances, solving times are not prohibitive, and solutions could be obtained for large, corporate clients in terms of minutes.
A class of linear codes and their complete weight enumerators

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ {\mathbb F}_q $\end{document}</tex-math></inline-formula> be the finite field with <inline-formula><tex-math id="M2">\begin{document}$ q = p^m $\end{document}</tex-math></inline-formula> elements, where <inline-formula><tex-math id="M3">\begin{document}$ p $\end{document}</tex-math></inline-formula> is an odd prime and <inline-formula><tex-math id="M4">\begin{document}$ m $\end{document}</tex-math></inline-formula> is a positive integer. Let <inline-formula><tex-math id="M5">\begin{document}$ \operatorname{Tr}_m $\end{document}</tex-math></inline-formula> denote the trace function from <inline-formula><tex-math id="M6">\begin{document}$ {\mathbb F}_q $\end{document}</tex-math></inline-formula> onto <inline-formula><tex-math id="M7">\begin{document}$ {\mathbb F}_p $\end{document}</tex-math></inline-formula>, and the defining set <inline-formula><tex-math id="M8">\begin{document}$ D\subset {\mathbb F}_q^t $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M9">\begin{document}$ t $\end{document}</tex-math></inline-formula> is a positive integer. In this paper, the set <inline-formula><tex-math id="M10">\begin{document}$ D = \{(x_1, x_2, \cdots, x_t)\in {\mathbb F}_q^t:\operatorname{Tr}_m(x_1^2+x_2^2+\cdots+x_t^2) = 0, \operatorname{Tr}_m(x_1+x_2+\cdots+x_t) = 1\} $\end{document}</tex-math></inline-formula>. Define the <inline-formula><tex-math id="M11">\begin{document}$ p $\end{document}</tex-math></inline-formula>-ary linear code <inline-formula><tex-math id="M12">\begin{document}$ {\mathcal C}_D $\end{document}</tex-math></inline-formula> by <p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ \begin{eqnarray*} {\mathcal C}_D = \{\textbf{c}(a_1, a_2, \cdots, a_t): (a_1, a_2, \cdots, a_t)\in {\mathbb F}_q^t\}, \end{eqnarray*} $\end{document} </tex-math></disp-formula> <p style='text-indent:20px;'>where <p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE2"> \begin{document}$ \textbf{c}(a_1, a_2, \cdots, a_t) = (\operatorname{Tr}_m(a_1x_1+a_1x_2\cdots+a_tx_t))_{(x_1, \cdots, x_t)\in D}. $\end{document} </tex-math></disp-formula> <p style='text-indent:20px;'>We evaluate the complete weight enumerator of the linear codes <inline-formula><tex-math id="M13">\begin{document}$ {\mathcal C}_D $\end{document}</tex-math></inline-formula>, and present its weight distributions. Some examples are given to illustrate the results.
Overview of Remote Sensing Efforts at University of Maryland Eastern Shore

Abstract Remote sensing and aerial imaging efforts at University of Maryland Eastern Shore (UMES) have been ongoing for over a decade. It was initiated with the UMESAIR (Undergraduate Multidisciplinary Earth Science Airborne Instrumentation Research) project in early part of the century as an exploratory experiential learning project as means to foster collaboration and provide exposure to science and engineering students to scientists and engineers at NASA’s Wallops Flight Facility which is within 50 miles of campus. Subsequently, with significant support from USDA’s National Institute of Food and Agriculture (NIFA) the remote sensing endeavors have been integrated with the smart farming and precision agriculture efforts closely aligned with the land grant mission of UMES and the regional emphasis in the Delmarva Peninsula. Maryland Space Grant Consortium (MDSGC) have also supported a synergistic project titled Aerial Imaging and Remote Sensing for Precision Agriculture and Environmental Stewardship (AIRSPACES) on an annual basis which has allowed continued involvement of multidisciplinary undergraduate students from the STEM fields to remain involved with the efforts.
People Detection and Tracking Using an On-Board Drone Camera

The problem of people detection and tracking in unmanned ground vehicles has been studied in depth in computer vision and autonomous robotics research communities. Different well-known proposals have already been proposed to solve the problem of people detection and tracking using machine vision algorithms. However, for unmanned aerial vehicles, it is still a subject of research today. The lack of high-quality sensors and on-board cameras and the capability to process the data collected in real-time makes it difficult to achieve optimal solutions in real-time. In this work, we propose to use machine vision algorithms to process in real-time the images collected by the camera of a drone and subsequently performing the detection and tracking of people who are located in the environment. The proposal was experimentally evaluated comparing different semantic segmentation techniques. Finally, to validate the proposal, a real scenario was created and carried out, which consisted of detecting and tracking people with a drone autonomously in a controlled environment.
Saliency Optimization Integrated Robust Background Detection with Global Ranking

Image saliency detection plays an important role in the field of computer vision. In order to make the saliency detection achieve better results, this paper proposes an algorithm that combines the global cue with the robust background prior measurement. Specifically, we first get a global ranking of superpixels by absorbing time in Markov chain, which is the absorbing nodes represent the superpixels of the fictitious boundary, and the transient nodes denote the others. Then, the global similarity of transient node can be measured by its absorbing time, so a global ranking for each transient node can be calculated, which is called as global cue in this paper. Finally, considering the remarkable energy optimization model, we integrate the robust background prior measurement with calculated global cue to form a new optimization model for saliency detection. In conclusion, our method is better than some typical significant detection algorithms on several datasets through the experimental verification.
Implementation Of K-Means Clustering Algoritm To Determine Stunted Status In Children Under Two Years Old

The stunting prevalence in Indonesia ranks fourth in the world, meaning that 37% of Indonesian toddlers are stunted. Stunting is a chronic nutritional deficiency problem caused by lack of nutritional intake for a long time, resulting in a disturbance to the child's growth, which is a lower or shorte
Explaining Content-Based Recommendations with Topic Models

Recommendation systems play a key role in current online commerce enterprises. Despite their success, they usually behave like black-boxes from the user perspective, typically failing to produce high quality human-computer interactions; interpretability is thus a major concern for the next generation of recommendation systems. In this paper we propose a model-agnostic method based on topic models that generates explanations for content-based recommendation systems.
ANALISIS KEMAMPUAN PEMAHAMAN KONSEP MATEMATIS SISWA YANG MENGGUNAKAN MODEL PEMBELAJARAN RECIPROCAL TEACHING

The process of student understanding of mathematical concepts is still low, this happens to most students at SMP Nasional Bandung . Students and students experience obstacles in learning and understanding mathematical concepts, especially when the material provided is changed to different problems. One of the reasons students find it difficult to understand mathematical concepts is the use of conventional learning models. Therefore, the use of the Reciprocal Teaching learning model using experimental research methods is expected to make it easier for students to understand mathematical concepts, especially when the material provided is changed into various forms. Class VII students at SMP Nasional Bandung became the population in the study, while the sample was class VII-A and VII-E students who were randomly selected. The test used is the type of description as an instrument and will be tested on class VIIIA. After the analysis is carried out, produce the feasibility of all test questions worthy of being used in research. Analysis of the trial data is carried out using a t-test using the SPSS 23.0 (Independent Sample T-Test) application. The conclusion is the use of the Reciprocal Teaching learning model can improve the ability of students to understand mathematical concepts compared to conventional learning models. Therefore, the use of the Reciprocal Teaching learning model becomes an alternative for teachers when learning about students so that learning becomes more effective, active and enjoyable. Keywords: Mathematical Concept Understanding, Reciprocal Teaching, Mathematical Proficiency
Explore the Application of Block Chain Technology in Financial Shared Service Center

With the gradual progress of economy, the level of financial content is increasing, and the complexity is also greatly improved. Traditional financial methods can no longer deal with a lot of financial information, we must find new ways to deal with complex financial data. The progress of science and technology promotes the emergence of block chain technology, which makes traditional financial transactions change. Under the influence of block chain technology, a system different from traditional online payment transactions and value transfer emerges, and the scope of financial shared service center is gradually expanded. Financial shared service center can reduce costs and maximize the benefits of enterprises. By combining block chain technology with the application of financial shared service center, the working efficiency of financial shared service center can be improved and the optimization of financial shared service center can be realized. Based on block chain technology, this paper explains relevant concepts of block chain technology and financial shared service center, and discusses the application of block chain technology in financial shared service center by using relevant construction algorithms. These are of great importance to the scientific construction of financial shared service center.
Fine-tuning Resource Allocation of Apache Spark Distributed Multinode Cluster for Faster Processing of Network-trace Data

In the field of network security, the task of processing and analyzing huge amount of Packet CAPture (PCAP) data is of utmost importance for developing and monitoring the behavior of networks, having an intrusion detection and prevention system, firewall etc.In recent times, Apache Spark in combination with Hadoop Yet-Another-Resource-Negotiator (YARN) is evolving as a generic Big Data processing platform.While processing raw network packets, timely inference of network security is a primitive requirement.However, to the best of our knowledge, no prior work has focused on systematic study on fine-tuning the resources, scalability and performance of distributed Apache Spark cluster (while processing PCAP data).For obtaining best performance, various cluster parameters like number of cluster nodes, number of cores utilized from each node, total number of executors run in the cluster, amount of main-memory used from each node, executor memory overhead allotted for each node to handle garbage collection issue, etc., have been finetuned, which is the focus of the proposed work.Through the proposed strategy, we could analyze 85GB of data (provided by CSIR Fourth Paradigm Institute) in just 78 seconds, using 32 node (256 cores) Spark cluster.This would otherwise take around 30 minutes in traditional processing systems.
New Approaches to Cyber Defense

As noted in Chap. 3 , conventional cyberspace defense technologies focus on bolstering the target system externally and trying to test, discover, and eliminate known threats. In spite of the substantial and fruitful researches in the field of vulnerability detection and backdoor testing, we are still far from the ideal goal of rooting out vulnerabilities and backdoors. Both the academia and the industry have realized that the conventional static or coordinated approaches of defense are not well adept at handling Advanced Persistent Threat (APT). In response, many advanced countries have launched research programs based on new defense approaches (such as moving target defense, or MTD) [1] in an attempt to change the rules of the game. The nature of cyberspace is such that it is easy to attack but difficult to defend. By increasing the dynamism, randomness, and redundancy of the system or network against external attacks, the new defensive technologies render attackers unable to effectively sustain the cognitive advantage over the target system and the available resources over time and space, making it difficult to accumulate information, duplicate the mode of attack, recreate the effect, or continue the method of attack, which significantly increases the cost of the attack and hopefully would change the rules of the game.
Designing Machine Learning Method for Software Project Effort Prediction

Software project effort Prediction is the most challenging and important activities in software project development. In software Industry when the size of the project and number of developers increases the project become complex, at this point accuracy prediction is strongly required during the early stages of project development. But to predict at early stages data and information are not available at the preliminary phases of project as well as the data is not complete, consistent and certain. In this research work we uses Artificial Neural network, Fuzzy logic, use case point model, modified environmental factor and revised use case point to predict software project effort at early stages. Artificial Neural Network has the ability to learn from previous data and fuzzy logic deals with uncertainty and also it provides a technique to deal with imprecision and information granularity. Also the modified environmental factor and revised use case point are used to determine the effort at early stage of the project development. In our experiment we compares each models, fitting accuracy using MMRE and PRED (0.25). The fitting accuracy of the models in terms of MMRE for Neuro-Fuzzy-UCBEM is 0.03, Neuro_UCBEM 0.13, Fuzzy-UCBEM 0.12 and UCBEM 0.22 and the fitting accuracy of the models in terms of Pred (0.25) for Neuro-Fuzzy-UCBEM is 1, Neuro_UCBEM 0.93, Fuzzy-UCBEM 0.93 and UCBEM 0.8. So our experiment result shows that Neuro-fuzzy logic model using revised use case point and modified environmental is best out performing model for software project development effort prediction at early stage than another models. Since the Neuro-fuzzy-UCBEM shows low value of MMRE (Mean of Magnitude of Relative Error) and high value prep (0.25) than Neuro-UCBEM, UCBEM and Fuzzy-UCBEM models.
Simulation of multi-agent interaction in the system “labor exchange - enterprise”

Abstract The work simulates a game with full information with two agents in the supply chain: one labor exchange - one enterprise. The strategy of the enterprise is the demand for labor resources. The labor exchange strategy is the party of labor resources. The company provides the labor exchange with information on the demand for workers, the labor exchange decides on the procurement and delivery of batches of workers. Labor resources as stocks are managed by the labor exchange. Thus, the model proposed in the work obeys the VMI (Vendor Managed Inventory) concept and is a model of cooperation between the labor exchange and the enterprise. A multi-product EOQ (Economic Order Quantity) model for a two-tier supply chain is proposed. The task is to minimize the value of the supply chain of labor resources, in which the optimization variable is the maximum level of labor shortages. The task takes into account the shortage of labor resources, monetary fines for the deficit, restrictions on the space of the labor exchange and the number of orders.
A Study on System and Application Performance Monitoring System Using Mass Processing Engine(ElasticSearch)

Infrastructure is rapidly growing as Internet business grows with the latest IT technologies such as IoT, BigData, and AI. However, in most companies, a limited number of people need to manage a lot of hardware and software. Therefore, Polestar Enterprise Management System(PEMS) is applied to monitor the system operation status, IT service and key KPI monitoring. Real-time monitor screening prevents system malfunctions and quick response. With PEMS, you can see configuration information related to IT hardware and software at a glance, and monitor performance throughout the entire end-to-end period to see when problems occur in real time.
Infographics and their Role in the IS Propaganda Machine

Although infographics form only a small proportion of IS propaganda output, they provide an intimate insight into the functioning and agenda of the IS. This research paper analyses more than 400 infographics published by official IS media units against the thematic framework set out by Winter (2018). The three principal IS propaganda themes identified by Winter of Victimhood, Utopia and War feature to varying extents of prominence in IS infographics, with multiple subthemes respectively. This paper’s analysis shows that Religious Life and Summaries make up the majority of infographic subthemes, while the primary theme Victimhood is almost non-existent.
Upaya Preventif Perilaku Menyontek Siswa Melalui Layanan Dukungan Sistem

The purpose of this study was to find out about the efforts made by teacher guidance and counseling at MTs. Roudlotul Banat Sidoarjo to prevent student cheating behavior. Based on the results of the teacher's guidance and counseling research at MTs. Roidlotul Bannat Sidoarjo has made several efforts that are expected to prevent students from cheating. By using system support in the form of information services and orientation guidance which is expected to provide understanding to students in cognitive, affective and psychomotor aspects regarding cheating behavior
Eksplorasi Serat dan Kain Kenaf Dengan Teknik Tekstil Pada Produk Fesyen

Abstract Kenaf (Hibiscus cannabinus L) is a plant that is widely cultivated in Indonesia, especially in Java and Kalimantan. This plant is a fiber-producing plant. Initially in 1978 kenaf fiber was only used as a sack in ISKARA program. Every part of the kenaf plant could be used, starting from the stem, bark, leaves and roots. Currently its use is widely used in the automoyive, electronics, pulp and paper industries. But it still lacking in the textile field. Thought this fiber has the potential to be developed in textile field. The research method used is a qualitative method by collecting primary and secondary data through literature study, observation, interviews, and experiments. Experiments carried out to provide variations on weaving using kenaf fibers and combining with kenaf fabrics to increase innovation in the use of kenaf fibers and fabrics. The result of the study offer alternative fiber materials and kenaf fabrics then made into fashion accessories product in form of bags. Keywords : Kenaf, Weaving, Bag
An Experimental Analysis of the Applications of Datamining Methods on Bigdata

&lt;p&gt;&lt;em&gt;Data mining is a procedure of separating covered up, obscure, however possibly valuable data from gigantic data. Huge Data impactsly affects logical disclosures and worth creation. Data mining (DM) with Big Data has been broadly utilized in the lifecycle of electronic items that range from the structure and generation stages to the administration organize. A far reaching examination of DM with Big Data and a survey of its application in the phases of its lifecycle won't just profit scientists to create solid research. As of late huge data have turned into a trendy expression, which constrained the analysts to extend the current data mining methods to adapt to the advanced idea of data and to grow new scientific procedures. In this paper, we build up an exact assessment technique dependent on the standard of Design of Experiment. We apply this technique to assess data mining instruments and AI calculations towards structure huge data examination for media transmission checking data. Two contextual investigations are directed to give bits of knowledge of relations between the necessities of data examination and the decision of an instrument or calculation with regards to data investigation work processes.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;�&lt;/em&gt;&lt;/p&gt;