Reviewerly/exHarmony huggingface dataset:
Peningkatan Keterampilan Membaca Pemahaman dan Hasil Belajar Melalui Strategi Direct Reading Thinking Activities (DRTA) Tema Daerah Tempat Tinggalku pada Siswa Kelas IV SDN Guli Boyolali Tahun Pelajaran 2018/2019

Etik Widyastuti / A510150071. IMPROVEMENT OF UNDERSTANDINGREADING SKILLS AND LEARNING OUTCOMES THROUGH DIRECTREADING THINKING ACTIVITIES (DRTA) STRATEGIES AREA PLACE TOSTAY AT THE CLASS IV STUDENTS OF SDN 1 GULI BOYOLALI STUDYYEAR 2018/2019. Essay. Teacher Training and Education Faculty,Muhammadiyah University Surakarta. March, 2019.This study aims to describe the improvement of reading skills in understandingand learning outcomes of students through the application of strategies forDirect Reading Thinking Activities to the content of Indonesian languagelearning. The subject of this study is the fourth semester students of semesterII of the 2018/2019 academic year. The type of research used is ClassroomAction Research. Data collection techniques using documentation, interviews,observation and tests. This research uses interactive analysis data analysistechniques, which include data reduction, data presentation, verificat ion. Thetechnique of checking the validity of the data using triangulation of sourcesand methods. The results of the study show that the application of the strategyof derect reading thinking activity (DRTA) can improve reading skills inunderstanding and learning outcomes of students on the theme area of thefourth grade students of SDN 1 Guli Boyolali 2018/2019 academic year.reading and learning outcomes of grade IV SDN 1 Guli academic year2018/2019.
ProWritingAid Review 2020 – Don't Buy Without Reading This!

ProWritingAid is only one of the many proofreading tools out there that can help you write better. If you are confused about some grammar rules, ...
5 Ways to Fix Error While Opening Sound Device in Audacity

Error opening sound device Audacity is an issue that arises if you select Edit Preferences under Devices. Yet, there are no devices available to choose ...
Improving the QoS of Recommender Systems using Adaptive Machine Learning

Recommendation based systems have gained a lot of popularity due to their wide range of applicability. From e-commerce-based product recommendation, to social media-based friend recommendation, these systems can be used for any kind of pattern analysis targeted to recommending data based on interlinked usage statistics. In order to improve the quality of such systems, they must have a strong pattern recognition engine, combined with a strong prediction engine. Because, a strong pattern recognition engine will be able to analyze and distinguish different patterns effectively, and the prediction engine will be able to merge these patterns together in order to predict the recommendation for the system. Generally, algorithms like neural network, k-means, kNN and SVM based pattern analyzers are combined with neighborhood-based, context-aware pattern analysis-based and collaborative filtering-based predictors in order to develop a complete recommendation system. Many authors have also combined recommender systems in order to generate a high-performance hybrid recommender. In this paper we have proposed a machine learning based recommender system which uses strengths of different recommender systems in order to improve the overall recommendation accuracy. Furthermore, the performance is compared with some state-of-the art systems in order to evaluate the performance of the proposed system
P1_6 Quick Quadcopters: Top Speed of a Racing Drone

Drone racing is becoming an increasingly popular sport. These drones are capable of flying above 100 MPH which poses the question, how fast can a racing drone fly? In this paper, we investigated this question, finding that a typical racing drone can fly up to 132 MPH.
Study on selection of a 6R manipulator's configuration in off-line programming

This paper presents a problem of selection of a robotic manipulator configuration in the course of an end- effector static path planning with the use of off-line programming technique. The aim of the research was to carry out exemplary analyses of path planning to study problems related to manipulator configuration selection. A kinematic structure of the articulated manipulator was chosen for the numerical simulation purpose. Two types of paths were considered. For analysis of the Point-to-Point (PTP) positioning a closed-loop path composed of 7 poses was chosen. A considerable variation of the end-effector orientation along the path was simulated. The author suggested determination of the shortest path as the one that is the most suitable for application. The conducted simulation showed that a change of configuration of the manipulator wrist (flip) was advantageous for shortening of PTP paths so it should be allowed during the PTP paths planning. The continuous positioning was analyzed on an example of a rectangular (linear-segment) path. Again, considerable variation of the end-effector orientation was assumed. The results of the continuous path planning showed that appropriate location of path in a robotic manipulator workspace is necessary for obtaining feasible continuous paths. The reported results of the study might help improving automatic configuration assignment procedures of the static path planning with the use of off-line programming technique.
Design and implentation of a mixed-reality robotic game

For years, technology has allowed us to sell increasingly sophisticated robot toys to the public, however it has been noticed that the public quickly lost interest even for the most sophisticated robot, in fact this technology was often expensive, decontextualized and left to itself, ultimately, after the functions that were already pre-programmed to the robot, it was no longer able to provide stimuli and keep up the customer's interest as it does in today's traditional video games. With the application of the PIRGs model it was discovered that robots, even out of production, took on a new life. In fact the PIRGs model with its guidelines aims to give a context to the robot that can better enhance its capabilities through the use of external tools such as augmented reality, an external tracking system that allows to interact with the robot in the most natural way possible or the use of physical objects that can interact with the virtual world. This thesis therefore deals with an implementation of a robotic game that follows the PIRGs model, in particular a Cozmo type robot was used, a Kinect V2 to implement external tracking and finally a projector to project the virtual dim area. Specifically, the aim of the thesis is to investigate how the perception of the game and of the robot through the use of tangible objects. To do this, three different versions have been developed and tests have been performed on volunteers to verify these hypotheses.
Kecerdasan Ekologis sebagai Modal Mitigasi Bencana: Studi Krisis Lahan Tani Desa Ranupani Kabupaten Lumajang

ECOLOGICAL INTELLIGENCE AS DISASTER MITIGATION CAPITAL: LAND CRISIS STUDY OF RANUPANI VILLAGE, LUMAJANG REGENCY. Ranupani village which is a village in Bromo Tengger Semeru National Park (TNBTS) currently has an ecological problem that needs to be considered, namely the crisis of agricultural land. Environmental awareness is a challenge for Ranupani residents to overcome the difficulties of the land caused by mistakes in planting patterns and inheritance distribution systems. Seeing this challenge, it is necessary to nurture ecological intelligence early on in order to overcome the challenges of living vulnerable to disasters. Research with qualitative considerations tries to discuss more about intelligence. The technique of collecting data using interviews with 30 Ranupani Elementary School students, observation, and literature study. This study shows Ranupani Elementary School students are still discussing ecological issues as a matter of littering and have not yet described the types of ecological problems that can cause disaster. Seeing these results, this study proves the need for early intelligence early in Ranupani Village.
Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

The paper proposes a novel approach for the identification of cancerous regions located in a dense part of a breast. This task is particularly challenging even for experienced radiologists due to lack of clear boundaries between the cancerous and normal tissue. Multi-scale analysis of structured micro-patterns generated from local binary patterns (LBP) was used to generate a very small number of features which allowed for successful detection of cancerous regions. The proposed technique was tested on two publicly available datasets: Digital Database for Screening Mammography (DDSM) and INbreast. The area under the receiver operating characteristic (AUC) curve for DDSM with 2 features only was 0.99 and 0.92 for INbreast with 3 features.
경제수업에서 활용한 교수학습방법의 효과 메타분석 : 인지ㆍ정의적 영역의 종합

As a follow-up study of Park and Seo(2018), the purpose of this study was to comprehensively examine the effect size of teaching methods used in economic classes on cognitive (economic literacy) and affective achievement. The results of the study are as follows. First, although the effect of teaching methods used in economic classes contributed to the improvement of the affective and cognitive areas of students was quite heterogeneous, the overall effect size was high. Second, the difference in the size of effectiveness of teaching methods used in economic classes was not significant, while the ''newspapers'' method was most effective in affective achievement and the ''ICT'' method was most effective in cognitive achievement. Third, looking at school level differences, there were no statistically significant differences in the affective achievement, significant differences in cognitive performance were found, and the meta-regression analysis found that the lower the age, the greater the effectiveness on cognitive achievement.This study is meaningful in that it has identified the impact on cognitive and affective achievement of teaching methods used in economic classes through research accumulated in the field of economic education.
A Fine Tuned Universal Language Model Fine-Tuning (ULMFiT) Approach for Airline Twitter Sentiment Analysis

More researches have shown that real-time Twitter data can be used to predict market movement of securities and other financial instruments. The Universal Language Model Fine-Tuning (ULMFiT) is a new approach which is based on training a language model and transferring its knowledge to a final classifier. We propose to fine tune the ULMFiT model by optimizing the parameters and training the model in a deterministic approach to increase the reproducibility. In this paper, we performed multi-class classification using Fine-Tuned ULMFiT, Naive Bayes, SVM, Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors, on the Twitter US Airline data set from Kaggle. A model is built firstly for six major U.S. airlines that performs sentiment analysis on customer reviews so that the airlines can have fast and concise feedback. Recommendations is made secondly on the most important aspect of services they could improve given customers complains. Significant accuracy has achieved, which shows that our models are reliable for future prediction. Also, the accuracy of different models is compared, and results show that Random Forest is the best approach.
PENGARUH PENGGUNAAN SISTEM KOMPUTERISASI TERHADAP KINERJA PEGAWAI PADA DINAS PENDIDIKAN, PEMUDA DAN OLAHRAGA KABUPATEN FAKFAK

Abstract; This study aims to determine the effect of the use of a computerized system to improve employee performance in the Department of Education, Youth and Sports in Fakfak Regency. Researchers use questionnaires as research instruments and regression analysis as an analysis method. After the data is collected and analyzed, it proves that the use of the computerized system has a positive and significant influence or contribution to the performance of employees at the Department of Education, Youth and Sports in Fakfak Regency. This is evident from the results of calculations, where the positive regression coefficient is 0.873 with a significance probability of 0.000. Thus it is recommended for employees to always look and show the performance or work to the maximum extent both in terms of quality and quantity of work. Keywords: computer, computerized system, employee performance
Research on Image Recognition Algorithm Based on Deep Convolution Neural Network

In order to improve the performance and robustness of traditional image recognition algorithms based on manually designed features, this paper analyzes and studies convolutional neural network, and constructs a deep convolutional neural network based on AlexNet network. The method of converting single-channel depth data into three-channel data is adopted to solve the problem that the model can only accept three-channel data during fine tuning, thus realizing the object recognition function on the object database. The experimental results show that the deep convolution neural network algorithm has better convergence effect, faster convergence speed, higher recognition rate and stronger generalization ability. At the same time, the application of deep convolution neural network algorithm avoids the local optimal problem in the training process and further improves the training speed.
Community Policing with support of digitalcommunication channels

While today's police forces are decreasing in number and their resources are limited, population is at the same time increasing. This means that other types of efforts and strategies are nee ...
Library Guides: Odegaard Online Reading Recommendations: National Poetry Month Highlights

A collection of online poetry collections at UW Libraries that were highlighted on social media for National Poetry Month.
Emotional Intelligence of People Towards Political Parties in Andhra Pradesh Based on Twitter Data

Social networking facilitated better approach for correspondence innovation for individuals to impart their insights, intrigue, and assumptions. Massive measure of unstructured information is created from online networking like Facebook, twitter, LinkedIn, which is warehouse of helpful bits of knowledge. Analysis can be connected to remove different valuable bits of knowledge frame this. The main aim of this paper is to extricate the information from substantial internet based life information, recognize the general population estimations and conduct to settle on aware choices. Powerful estimation investigation of individuals’ feelings is important for complex points like legislative issues, government, present patterns and medicinal services. These goals are accomplished by constant recovery of twitter information what’s more, perform conclusion investigation. It orders feelings of the general population are certain, negative, cheerful, tragic or neutral towards National and Regional political parties in India like BJP, Congress TDP, YSRCP and Janasena. Thinking about individuals’ feelings, gatherings can distinguish their shortcoming and take care of enhancement to satisfy society needs and work towards fulfilling individuals prerequisites. In this paper, utilizing content mining and unsupervised lexical technique grouped tweets identified with these parties to distinguish public perception towards parties.
Controlled hardware architecture for fractal image compression

Fractal image compression utilising algorithms have a high demand on the memory interface and the processor's arithmetic unit, which in turn fails to utilise the full capabilities of a general purpose processor.Since the algorithm is repetitive, the parallelisation reduces the time complexity of the otherwise expensive coding scheme.The design for FIC is proposed in this paper.It is based on the fact that the algorithm requires only integer arithmetic with repetitive use of the same data set.Making use of multiple functional units, controlled parallelism is introduced in this process.This makes encoding time 80 times faster than high level software implementation.It is 25 times faster than the assembly level implementation on a DSP processor.
The Role of Supply Chain Strategy and Affiliate Marketing in Increasing the Demand for E-Commerce – Social Media POV

Abstract - The study examined the most influential affiliate marketing and supply chain strategies that are most beneficial and usable through social media websites which can increase the demand on e-commerce. Through employing the quantitative approach; the researchers distributed total of (95) questionnaires on marketing managers within cosmetics companies in Jordan- Amman during the fiscal year (2018-2019). Results of study indicated a high level of awareness and dependency of cosmetics companies on marketing through social media websites as a form of endorsement. In addition to that, it appeared that among the chosen affiliate marketing strategies CPC/PPC was the most influential and usable strategy through social media as a form of endorsement and marketing
Multi-channel Security Through Data Fragmentation

This chapter presents a novel security framework developed for a multi-channel communications architecture that achieves security by distributing messages and their authentication codes across multiple channels at the bit level. This method of transmission provides protection from confidentiality and integrity attacks without relying on encryption. The two communicating parties utilize existing key exchange mechanisms to pass initialization information. The framework operates by assigning to each message bit a fragment identifier using a hardware-based stream cipher as a pseudorandom number generator, and transmitting specific message fragments across each channel. This prevents the entirety of a message from being transmitted over a single channel and spreads the authentication across the available channels, enabling the sender and receiver to identify a compromised channel even in the presence of a sophisticated man-in-the-middle attack where the adversary forces message acceptance at the destination, perhaps by altering the message error detecting code. Under some conditions, the receiver can recover the original message without retransmission. The holistic framework is attractive for critical infrastructure communications because it provides availability while defending against confidentiality and integrity attacks.
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How to Uninstall Grammarly (Step by Step Guide) – 501 Words

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Proposal for an incremental learning method for CNN-based breast cancer tumor detection CAD development

In recent years, convolutional neural networks (CNN) have found increasingly active application in the field of computer-aided diagnosis (CAD) research. Typically, general-use, high-performance detectors are designed using machine learning, the training of which is conducted by applying comprehensive sets of case images having various variations. In this study, we show that, when configuring CNN training data, dividing the data into multiple subsets and adjusting their ratios, instead of providing the data uniformly, has the potential for effective learning. We propose in this study a learning method by which CNN learning using these subsets is incrementally repeated. In this study, subsets of breast cancer mass learning data based on mass size and intensity were created. Using multiple data sets prepared for use in the evaluation of a CNN that had been subjected to learning, optimal ratios were considered and, based on this, performance evaluations using actual unknown data were conducted. Next, the ratios of evaluation data subsets having numerous detection errors were raised and relearning conducted. This process was repeated multiple times, as long as increases in the area under curve (AUC) were observed, thus enabling the design of a high-performance CNN. As a result of applying unknown data to this CNN, we found that it exhibited a higher AUC than a CNN to which learning data was simply provided comprehensively, demonstrating the effectiveness of the proposed learning method.
Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series.

Increasingly large observational datasets from healthcare and social media may allow new types of causal inference. However, these data are often missing key variables, increasing the chance of finding spurious causal relationships due to confounding. While methods exist for causal inference with latent variables in static cases, temporal relationships are more challenging, as varying time lags make latent causes more difficult to uncover and approaches often have significantly higher computational complexity. To address this, we make the key observation that while a variable may be latent in one dataset, it may be observed in another, or we may have domain knowledge about its effects. We propose a computationally efficient method that overcomes latent variables by using prior knowledge to reconstruct data for unobserved variables, while remaining robust to cases when the knowledge is wrong or does not apply. On simulated data, our approach outperforms the state of the art with a lower false discovery rate for causal inference. On real-world data from individuals with Type 1 diabetes, we show that our approach can discover causal relationships involving unmeasured meals and exercise.
Research Guides: Japanese Kuzushiji & Hentaigana Resources: Benkyōkai Materials

A guide for the Kuzushiji Benkyōkai group hosted by Drs. C. Laffin and J. Mostow in Summer 2020. NB: Kuzushiji is a general term that includes hentaigana and cursive kanji; hentaigana applies specifically to kana.
DSC Library: Falcon Self-Service- Faculty Center: Attendance Verification

This is an info-guide created for the Faculty Center Located in the Falcon Self Service area. This was creaed as a resource for IT Training. This provides and overview of the Attendance Verification in the Faculty Center of the Falcon Self Service system. It also provides step by step instructions on how to submit attendance.
An emotional model for swarm robotics

Swarm robotics is a system of multiple robots where a desired collective behavior emerges from the interactions between the robots and with the environment. This paper proposes an emotional model for the robots, to allow emerging behaviors. The emotional model uses four universal emotions: anger, disgust, sadness, and joy, assigned to each robot based on the level of satisfaction of its basic needs. These emotions lie on a spectrum where depending where the emotion of the robot lies, can affect its behavior and of its neighboring robots. The more negative the emotion is, the more individualistic it becomes in its decisions. The more positive the robot is in its emotion, the more it will consider the group and global goals. Each robot is able to recognize another robot's emotion in the system based on their current state, using the AR2P recognition algorithm. Specifically, the paper addresses emotions’ influence on the behavior of the system, at the individual and collective levels, and the emotions’ effects on the emergent behaviors of the multi-robot system. The paper analyses two emergent scenarios: nectar harvesting and object transportation, and shows the importance of the emotions into the emergent behavior in a multi-robot system