Reviewerly/exHarmony huggingface dataset:
Implementation of the Perceptron Method for Recognizing the Patterns of Types of Intestinal Nematode Worms

The purpose of pattern recognition is do the process of classifying an object into one particular class based on the pattern it has, so it can be used to recognize patterns of intestinal nematode worm types. One of the methods used in pattern recognition is by utilizing the artificial neural network method, the artificial neural network is able to represent a complex Input-Output relationship. For that the algorithm used is the perceptron algorithm. Perceptron is one method of Artificial Neural Networks. In the introduction of types of intestinal nematode worms, a computer must be trained in advance using training data and test data, this study discusses how a computer can recognize a pattern of types of intestinal nematode worms using the perceptron method. Based on the results of testing trials with input in the form of worm image scan results, based on the results of the perceptron method testing is able to recognize the pattern recognition of the types of intestinal nematode worms and be able to analyze with the right results of 100% for pinworms patterns, hookworm patterns, and 40- 50% for roundworms, by comparing the output value and the target value entered first.
A Denoising PDE Model based on Isotropic Diffusion and Total Variation Models

In this paper, a denoising PDE model based on a combination of the isotropic diffusion and total variation models is presented. The new weighted model is able to be adaptive in each region in accordance with the image’s information. The model performs more diffusion in the flat regions of the image, and less diffusion in the edges of the image. The new model has more ability to restore the image in terms of peak signal to noise ratio and visual quality, compared with total variation, isotropic diffusion, and some well-known models. Experimental results show that the model is able to suppress the noise effectively while preserving texture features and edge information well.
Petrographical and mineral chemistry characteristics of mafic dykes in Nhi Ha area, Ninh Thuan and its meaning

The NE-SW and NW-SE mafic dyke rocks in Nhi Ha area intruded through Cretaceous granitoid Dinh Quan or overlapped by Quaternary sediment materials. It has block type and blue grey in color in the field. Under microscope, based on minerals and textures, divided into 2 types of mafic dyke rock, such as diabase and porphyritic diabase. Minerals chemical analysis by EPMA method: plagioclase almost are labradorite (An 60.80%), oligoclase (An from 12.31 to 14.29%), some grain had albitation (An from 6.75 to 8.46%); pyroxene with augite (Wo41,80 En44,22 Fs13,98 to Wo43,40 En37,50 Fs19,10) and diopsite composition (Wo45,45 En42,48 Fs12,07 to Wo48,16 En40,02 Fs11,81); amphiboles in basalt are magnesio-hastingsite (Ca-amphibole group). Popular alteration minerals are chlorite, epidote and sericite. Chlorite is pycnochlorite type, ratio Fe/(Fe+Mg) &lt;0,6 show that it formed by hydrothermal alteration process with temperature from 150 to 3250C. Opaque minerals are pyrite, chalcopyrite, ilmenite, magnetite, titanomagnetite, titanite (sphene) and a little bit of rutile.&#x0D;
22: Algorithmen II, Vorlesung, WS 2018/19, 08.01.2019

22 | 0:00:00 Start0:00:33 2D Bereichssuche0:01:30 Wavelet Tree0:10:21 Bitvektoren0:12:16 Onlinealgorithmen0:17:10 Competitive analysis0:22:46 online problem: ski rental0:31:16 Paging0:42:50 Comparison of algorithms0:48:43 A general lower bound0:55:01 Resource augmentation0:57:08 Conservative algorithms1:05:36 New results1:07:48 Radomized algorithms1:09:04 Three types of adversaries1:11:34 Marking Algorithm1:13:52 Competetive ratio of RMARK
성인의 국어 듣기 능력 실태 조사 결과 분석 - 2018년 국민의 국어능력 실태 조사를 중심으로 -

The purpose of this study is to analyze the current status of Korean adults’ speaking ability of the Korean language and compare it with results from 2013, based on the findings of “The 2018 National Assessment of Korean Language Ability (NAKLA),” to determine academic and educational implications. This study examined the speaking ability of 2,939 people aged 20 to 69 years, sampled by probabilities proportional to size. The level of speaking ability was analyzed based on variables such as age group, gender, district size, occupation, level of education, major, reading amount, and internet usage. The analysis of differences by demographic characteristics found statistically significant differences in the variables of gender, age, occupation, and level of education, but not in district size. Furthermore, an analysis by background variable found statistically significant differences in the variables of reading amount and internet usage, but not in major. The study was meaningful as 2,939 adults in Korea were selected using a “sampling with probabilities proportional to size” method to directly evaluate their speaking ability. The implementation of the assessment and grading can contribute to basic data at both educational and policy levels as a wide variety of group variables has been established on a national scale to identify differences between the groups.
Introduction to Machine Learning

Machine learning (ML) is a subfield of artificial intelligence, the science and engineering of making intelligent machines.2 One of the pioneers of artificial intelligence, Arthur Samuel, defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed.”3 Figure 1-1 shows the relationship between artificial intelligence, machine learning, and deep learning. Artificial intelligence (AI) encompasses other fields, which means that while all machine learning is AI, not all AI is machine learning. Another branch of artificial intelligence, symbolic artificial intelligence, was the predominant AI research paradigm for much of the 20th century.4 Symbolic artificial intelligence implementations are referred to as expert systems or knowledge graphs which are in essence rules engines that use if-then statements to draw logical conclusions using deductive reasoning. As you can imagine, symbolic AI suffers from several key limitations; chief among them is the complexity of revising rules once they are defined in the rules engine. Adding more rules increases the knowledge in the rules engine, but it cannot alter existing knowledge.5 Machine learning models on the other hand are more flexible. They can be retrained on new data to learn something new or revise existing knowledge. Symbolic AI also involves significant human intervention. It relies on human knowledge and requires humans to hard-code the rules in the rules engine. On the other hand, machine learning is more dynamic, learning and recognizing patterns from input data to produce the desired output.
Development of Airborne Ultrasonic Haptic Feedback System

As from the previous haptic feedback method, which is using the device attachment method to detect the tactile sensation on the hand. The tactile sensation only can be felt on hand when the user wears the haptic device, without the haptic device attached on hand, the user cannot feel the haptic feedback in the mid-air. Thus, this project is to develop ultrasound haptic feedback in the mid-air gesture without using an attachment device such as a glove. At the frequency of 40kHz of the ultrasound transducer, the mechanoreceptor of human hand cannot sense the wave from the ultrasound. Therefore, the objective of this project is to design the hardware of transducer array to implement the simulation model to perform volumetric haptic shape rendering. The principle of the acoustic radiation force used to create the tactile sensation from the ultrasound wave. By modulating the phase and amplitude of the ultrasound wave, the acoustic field can be created in a certain area, which called as ‘focal point’. The focal point can be created in form of three dimensions in the mid-air, corresponding each focal point can be creating a certain shape. Thus, by modulating the phase of the ultrasound at 200Hz, we can produce haptic feedback shape in the mid-air which the mechanoreceptor of the human hand can sense the ultrasound wave. The hardware designed requires a driver board to give external power signal to the transducer array which controlled by the Mega Arduino. The simulation will be designed for sending the instruction to the Mega Arduino to give a different signal of phase and amplitude to each transducer. As the experiment set up is placing the transducer array 30 degrees facing the oil surface. Shape rendering in the simulation will be sent to the Arduino then it will produce the focal point on the oil surface for observation and comparing the results between the simulation and actual haptic feedback system. As a result, the driver board able to control the power up to 3Watts to provide a stable transducer signal. The best results for the focal point to be felt on the hand when the period set at 40ms with ON and OFF the transducer array. But for the shape rendering results, in 1 second, the transducer array can create maximum up to 4 points only, which cannot clearly observe the complete shape at a time. This is because of the limitation of the instruction uploading time from the computer to Arduino for controlling the phase and amplitude of each transducer. The application of this project can be improving the realistic of the VR gaming and can be applied to the touchless interface, which can control the system without any control panel that will reduce the space in the control room.
LibGuides: ACL 2020: Beyond All Limits!: Poster Printing @ Dugan Library

A guide to all things ACL 2020 Annual Conference at Newman University in Wichita, Kansas. We look forward to hosting the conference at our school and we are ready to welcome you to our school and our community. We can print your poster and have it ready to pick up on Wednesday, June 10th
Evaluating speech samples designed for the Voice Profile Analysis Scheme for Brazilian Portuguese (BP-VPAS)

The present study aims at evaluating a corpus designed for voice quality settings analysis (Laver, 1980).It addresses the role of keyspeech segments for the identification of voice quality settings.The corpus, recorded by 60 speakers, contained repetitions of 10 keyspeech sentences, and semi-spontaneous speech samples.These samples were evaluated by two expert subjects according to the BP-VPAS Profile (Camargo, Madureira, 2008).These data were analyzed by means of an artificial neural network and statistically by multiple regression analysis.The data reinforce the importance of the description of voice quality based on the phonetic model and the relevant role of keyspeech segments.
Lingua, conversazione e modalità comunicative in tribunale: uno studio sull’area toscana

The paper offers an overview of some linguistic, communicative and interactional occurrences in the minutes of criminal court hearings, transcribed from audio recordings. The analysed transcripts represent about 16 hours of speech by Italian speakers and all minutes are from Tuscany. After some preliminary considerations relating to the transcription of texts, the work focuses on the variational aspects linked to the relationship between language and dialect, as well as between legal language and general language. Next, it offers reflections about the types of conversation and the interactional strategies employed in a court room, this is a place in which one needs to have good communicative competence if one is to participate in the interaction. Finally, it seeks to prove that transcripts of hearings should be considered real legal texts, although they do feature some specific aspects that are by no means negligible.
The Issues and Challenges in Data Pre-Processing and Pattern Discovery for Web Mining Algorithms

Web mining is an exciting discipline in the area of data mining as well as classification or clustering. Detecting the usage patterns of the users is significant in using tremendous data accessible in the World Wide Web. Web Usage Mining involves distinguishing usage patterns and has numerous pragmatic applications. It concentrates around methods that can possibly predict the behaviors of the users interacting with the web. It is a nontrivial procedure of mining implicit, unknown and conceivably valuable patterns from a huge database. Web mining can be commonly characterized as the application of data mining procedures to extract the knowledge from the text data from the web. Web mining can be additionally sorted as web content that incorporates text, image, audio, and video etc. Web usage includes log files HTTP, app server logs, etc. In this paper, we aim to give a general view on web usage mining and its significance for originators and those interested in e-commerce and website personalization. Further, the phases involved in web usage mining and the challenges involved in data pre-processing and pattern discovery are presented. Also, it illustrates the various applications and tools along with commonly used algorithms for web usage mining are discussed. Finally, it clarifies a few current research issues and gaps in web usage mining such as privacy and scalability.
Where to Go from Here

Security operations is a mindset. Healthcare entities do not necessarily need to have a dedicated security operations center or contract with a managed service provider delivering virtual security operations center (SOC) services. But the mindset needs to be there. Once the overarching cybersecurity program is designed and implemented, the real work on implementing security begins. In the forward to Snort: IDS and IPS Toolkit, Stephen Northcutt talks about the difference between managing security via checklists and policies and understanding how to implement security. This means frameworks like the NIST Cybersecurity Framework (CSF) are great tools to guide entities in implementing security, risk assessment, and management strategies – but more is needed. Entities must perform threat assessments and in-depth analysis of vulnerabilities and attack vectors to implement high-functioning detection capabilities. This comes from knowledge of the network traffic characteristics and how to protect it from malicious use. As the security operations center grows in maturity, higher levels of visibility and understanding create opportunities for granular detection of specific activities in the environment.
대학교수의 플립드러닝 수업 운영의 내러티브: “의심에서 진심으로의 여정”

The objective of this study is to summarize a new professor’s experience of developing and operating flipped learning courses and presenting a case study on the courses with an aim of sharing the experience with other instructors. This researcher had to develop a flipped learning course immediately after being appointed at J University. Owing to the lack of knowledge and know-hows of flipped learning on part of both the instructor and students, three hurdles needed to be overcome for its effective operation: instructors’ doubts on the efficacy of flipped learning, students’ concerns about operating in an unfamiliar class, and doubts among the instructors and students. According to the previous studies on flipped learning that required that each lesson be prepared in the form of unique instructional design considering the characteristics and expectations of the curriculum, this researcher has strived to create classes for successful operation, wherein all students can participate, learn, and discuss rather than classes led by only some of the students. For instance, this study has developed and applied the following major strategies: to develop an operation structure of the flipped learning class that focuses on discussion learning, to develop evaluation strategies to facilitate discussion learning, to develop attractive educational content, and to gradually adjust the difficulty level of discussion topics, allowing students to adapt to discussion learning. The class operation results revealed the change in the learning attitudes of students from overdependence on the right answers and intentions by instructors to free and cooperative participation in discussion learning in addition to enhancement of the satisfaction levels before and after the class from 4.24 to 4.27, respectively, on average. The researcher's experience is significant in that successful flipped learning requires unique approach under a situational context rather than formalized design and operation. In conclusion, based on the researcher's experience, this study suggests the necessities of optimal design support considering the characteristics and expectations of each subject, collecting and sharing various case studies on flipped learning classes, and preparing supportive measures based on the instructor’s experience.
Pengembangan Media Pembelajaran Berbasis Mobile Learning Pada Mata Pelajaran Dasar Listrik Dan Elektronika Sekolah Menengah Kejuruan

In the learning process Basic Electricity and Electronics material, students are constrained in understanding the subject matter, because it uses formulas that are difficult to understand without adequate supporting learning media. Besides the learning time Basic Electricity and Electronics material which is only available 4 hours per week, so that not all materials can be delivered optimally to students. Therefore learning needs to be supported by suitable and adequate media. The purpose of this research is to produce a learning media based on mobile learning that is valid, practical and effective for Basic Electricity and Electronics subjects.&#x0D; The method used in this research is Research and Development (R&amp;D). The development model used follows the Borg &amp; Gall model which is arranged in several research steps. The instrument used in the form of a validation questionnaire was given to material experts and media experts. Questionnaire to measure practicality is given to students and teachers of the subject matter. To measure the effectiveness of the Quasi experiment research design used by comparing student learning outcomes of the experimental class and the control class.&#x0D; The results showed that the learning media based on mobile learning was declared valid, practical and effective for use in the Basic Electricity and Electronics subjects.
The Probability Prediction Model of Motorcyclist Accident Against IRSMS and AIS from the Police Department, East Java (A Case Study in Kediri Regency and Surabaya City)

According to traffic accident data collected by the East Java Police Department through IRSMS and AIS portal in 2018, the number of accidents tended to incline from 2015 to 2017.The objective of this research focusing on Kediri and Surabaya area is finding the characteristic of motorcycle riders in the study locations, looking for the characteristic of motorcycle-related accidents in both cities, and creating a motorcycle-related accident probability model in the targeted cities.The primary data was obtained using questionnaires and interview with 200 participants.The analysis method used in this research was descriptive frequency analysis and logistic regression analysis.The result of the descriptive analysis shows that 112 respondents has been in a traffic accident before.Based on the social economy characteristic, riders aged under 25 (50%) are the biggest group.Also, based on the motorcycle movement aspects, the most influential group was the one with more than 5 km mileage a day (68%).While based on logistic regression analysis, it resulted that the influencing factor are X2 (ages) and X12 (mileage of travel).Overall, the probability of motorcycle-related accident probability of riders under 25 whose mileage more than 5 km a day is 91.5 %.
04: Robotik II: Humanoide Robotik, Vorlesung, SS 2019, 20.05.2019

04 | 0:00:00 Starten0:02:48 Cognitive Grasping0:05:12 What is a grasp ?0:07:33 What properties are essential to grasps 0:12:12 Grasp analysis and grasp synthesis0:14:30 Grasp contacts0:20:56 What influences the generation of grasp hypotheses ? 0:37:11 Object classes for robot grasping0:42:58 Human Hand Models0:48:29 Human hand models in the literature0:58:58 Grasping in Humans1:10:10 haptics in human grasping1:17:30 Grasping taxonomies
Korean Language Textbook Development for Mission Purpose and Application for Migrant Mission: Focusing on Application of Evangelism for Foreign Students through 『Learning Korean with Worldviews』

Today, the number of Korean language learners is increasing rapidly. Migrant mission should be approached differently depending on the type of migrants. Since there is a great need for studying Korean language in the early settlement process of foreign students in Korea, studying Korean language is a good point of contact for mission. However, compared to the high demand, there is no supplement of Korean textbooks for mission purpose. Korean language textbook development for mission purpose should consider foreign students as learners, Korean teachers, text of biblical and general contents, and the context of foreign students. Based on this, I analyzed 『Learning Korean with worldviews』 as Korean language textbook for evangelism and present an evangelism plan by using it for foreign students.
On Improper Machine Translations in Press Reports

Machine translation, also known as automatic translation, is the process of converting one natural language (source language) into another natural language (target language) by using networks. There are some language errors in current machine translation in news releases. Having compared human translators’ translation texts and machine translation results, improper machine translation results are found. They are inaccurate use of words, rigid sentence patterns and unclear expression of specific cultural meanings. Accurate machine translation needs the assistance of human translators.
13: Industrie 4.0, Vorlesung, WS 2018/19, 25.01.2019

13 | 0:00:00 Start0:00:07 Syllabus - Winter term 2018/190:01:48 Literature for Today's class0:02:41 Video: Simple Outlier Detection 0:06:25 Motivation: Predictive Maintenance 0:09:51 Machine Learning Algorithms for Preditictive Maintenance 0:16:27 Anomaly Detection 0:18:26 Related Problems 0:20:18 Relationship Among Data Instances0:21:21 Rule-based Anomaly Detection0:29:01 Representation Matters 0:31:02 Anomalies in a Single Time Series Signal 0:32:28 Anomalies in a Multiple Time Series Signals 0:34:20 Criteria for Anomaly Detection 0:39:42 Characteristics of Univariate, Multivariate and Hybrid Anomaly Detection Methods0:49:31 Characteristics of Univariate, Multivariate and Hybrid Anomaly Detection Methods0:54:12 Type of Anomalies 0:55:23 Contextual Anomalies 0:57:13 Collective Anomalies 0:58:42 Output of Anomaly Detection 0:59:22 Evaluation of Anomaly Detection - F-value 1:01:33 Accuracy of Anomaly Detection - F-value 1:03:26 Evaluation of Outlier Detection - ROC & AUC 1:07:04 General Scheme for Anomaly Detection 1:08:48 Taxonomy1:12:11 Variants of Anomaly Detection Problem