Reviewerly/exHarmony huggingface dataset:
Monitoring of Students’ Quality in the Context of Economic Education

The article deals with the monitoring as the way of measuring students’ quality in learning the socio-humanities disciplines in the context of economic education. It is proved that it can become a way of measuring and evaluation of the efficiency of educational process, gathering practical information concerning improvement of the quality of learning. The monitoring helps to find defects and weak points in educational process. The theoretical fundamentals of the investigated problem give an opportunity to the authors to present their own interpretation of the monitoring of the quality of learning as permanent, systematic supervision, control and diagnostics of the quality of future specialists’ educational achievements. The analysis of pedagogical scientific studios gave an opportunity to define the following indexes of quality of learning: quality of educational process; a motivational sphere; creation of comfort educationally-emotional climate. The quality of educational process by means of the system of mutual visits of lessons of the social-humanities disciplines according to the detailed indicators of quality of learning has been analyzed. Special attention has been paid to the motivational sphere to the learning by the students of economic specialties as one of the main sphere that influences the quality of knowledge of future economic experts. The existence of emotionally favorable climate has been investigated at the lessons of the social-humanities disciplines. The author’s methodical recommendations for improvement defects according to the indicators of quality of learning have been offered. The research results show the urgent necessity to find the efficient ways of reorganization and updating the existing mechanism of evaluating the quality of learning the social-humanities disciplines by students of economic specialties. The efficiency of the received results is confirmed with the help of pedagogical experiment data.
Discover your best self at Wanneroo Aquamotion

A revamped Wanneroo Aquamotion is ready to go, with renovated indoor lap andleisure pools open to the public from Friday 26 April 2019.
Credit card fraud detection using Random Forest Algorithm

This Project is focused on credit card fraud detection in real-world scenarios. Nowadays credit card frauds are drastically increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed project we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions. To come up with the solution one can make use of technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology, it becomes feasible to automate this process and to save some of the intensive amounts of labor that is put into detecting credit card fraud. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a random forest algorithm and decision trees. Using this feasible algorithm, we can analyze the larger data-set and user provided current data-set. Then augment the accuracy of the result data. Proceeded with the application of processing of some of the attributes provided which can find affected fraud detection in viewing the graphical model of data visualization. The performance of the techniques is gauged based on accuracy, sensitivity, and specificity, precision. The results is indicated concerning the best accuracy for Random Forest are unit 98.6% respectively.
Parallels between Engineering Graphics and Data Visualization: A First Step toward Visualization Capacity Building in Engineering Graphics Design

The definition of visualization in engineering graphics has evolved over the years due in part to extensive research in visualization by the engineering design graphics profes¬sion. The literature indicates research in the areas of engineering graphics and the use of visualization has grown from improvement of visualization skills and the development of visualization tools, to the exploration of spatial ability and perception in engineering graphics.  The goals of this paper are three-fold: (1) to provide a high level overview of how the definition of visualization has historically been defined and shaped in engineering graph¬ics, (2) provide an introduction to data visualization as a process, and (3) explore the parallels between the process of visualizing data and the engineering design process as first steps toward visualization capacity building in engineering design.
Energy and Area Effective Hardware Design of Lifting Approach Discrete Wavelet Transform

&lt;span lang="EN-US"&gt;This paper presents low power Discrete Wavelet Transform DWT architecture, comprising of forward and inverse multilevel transform for 5/3 lifting scheme LS based wavelet transform filter. This LS filter consists of integer adder units and binary shifter rather than multiplier and divider units as in the convolution based filters; hence it is more adaptable to energy efficient hardware performance. The proposed architecture is described using the VHDL based methodology. This VHDL code has been simulated and synthesized to achieve the gate level building design which can be organized to be effectively developed in hardware environment. The Quartus II 9.1 software synthesis tools were employed to implement 2D-DWT VHDL codes in Altera Development board DE2, with Cyclone II FPGA device. The proposed LS wavelet architectures can be attained by focusing on the physical FPGA devices to considerably decrease the needed hardware expenditure and power consumption of the design. The utilized logic and register elements of the architecture are 127 slices (only 1%) usage from 33216 and the architecture consumes only 0.033 W. Simulations were performed using different sizes of gray scale images that authenticate the proposed design and attain a speed performance appropriate for numerous real-time applications.&lt;/span&gt;
Communication as Gesture

This book critiques current assumptions about 'communication', particularly digitally mediated communication, by re-examining conceptual foundations in rhetoric, linguistics, semiotics, information theory, and cybernetics. The result is a dimensional account of interaction that is at once both intuitive and revolutionary.
Learning by Doing: Buckling of Corrugated Fiberboard and Box

PDF file of Extended Abstract [Extended Abstract], Power Point Presentation mixed with Author's narration [PPT MP3] and Power Point PDF [PPT PDF] are available only for Registered Users Only.To view/download all the conference open access materials you must be Registered with the IESTOC SCHOLARLY REFEREED ONLINE CONFERENCE SYSTEM.Registration is Free..Register Now..Register Yourself and get full access of IESTOC published materials...
AUTOMATED ATTENDANCE ENTRY SYSTEM BY DETECTION AND RECOGNITION OF THE FACE USING OPEN CV

In contemporary world, image processing is used in many fields like medical field, pattern recognition, color processing, etc.The vintage attendance methods like manual entry, finger print scanning, iris scanning are time consuming.Therefore we proposed a system which uses face recognition for attendance entry.We use Opencv-python in Raspbian platform to reduce the cost of the system.Haarcascade algorithm is used as feature for detection. LBPH(Local Binary Pattern Histogram) is used for better recognition of human face
A Comment-Based Algorithm for Post-Ranking Rapprochement on Facebook

This work investigates the effects of a comment, in an individual post, voted by a reputed person. The proposed algorithm utilized 10 variables for ranking comment's owner represented by the value of \(\textit{Cor}\) variable. Then the model will analyze how such a vote will affect the rank of that post by increasing the upvotes or by increasing the downvotes. Eight variables are proposed to evaluate the rank of the post represented by the value of \(GW_p\) variable. At the end, the overall score of the post will be calculated represented by \(GS_p\) variable. Being simple and easy to implement, the proposed method is expected to measure the post-sensitive influence on participants on that given post. However, introducing user's weight (ranking) as a new parameter for the evaluation of post's weight, could highly correct the whole evaluation of post's ranking. As commenters vary in their weights (rankings), posts can be upvoted or downvoted because of commenter's opinion and thought on the given post. This work is novel and aimed at introduce a new method for post ranking that can be utilized for different purposes in different disciplinarians.
An End-User Pipeline for Scraping and Visualizing Semi-Structured Data over the Web

The Web is a vast source of semi-structured datasets that are made readily available to support the construction of new knowledge. Information visualization techniques have been demonstrated as a suitable alternative for allowing users to analyze and understand a large amount of data. However, the steps required for visualizing semi-structured data obtained from the Web is not straightforward, and it requires proper treatment before information visualization techniques could be applied. In this work, we present a visualization pipeline for describing the fundamental operations required for visualizing semi-structured data over the Web. We employ Web Scraping and Web Augmentation techniques for supporting interactive visualizations and solving tasks without changing the context of use of the data. Our approach is duly supported by a framework including scraping-, augmenting- and visualization-tools and it has been applied to different kinds of websites to demonstrate its validity and feasibility. Our ultimate goal is to expand the limits of our technology for improving the user interaction with websites and creating new experiences for a better understanding of large datasets.
Load balance in cloud on the basis of size of packet

Cloud computing helps to share data and also provide many resources to users so, the users can pay only for those resources as much they used. The aim of cloud computing is to stores the data and distributed resources in the open environment over the network, where the users can get data easily. The goals of cloud computing are to use the resources efficiently and gain maximum profit. Multiple users request for information at a time. Due to such a huge amount of data request from users, the problem of load balancing arises which becomes the main challenge in the field of cloud computing. To handle such requests efficiently and effectively, various load balancing approaches have been proposed in the past years. Scheduling of incoming users request is a critical problem in cloud computing because a cloud provider has to serve many users in cloud computing. A good scheduling technique is used to bits of help in proper and efficient utilization of the resources. This system designed a better scheduling technique. Here we also use the VMs for handling the user’s requests. Scheduling is performed on the basis of available disk space and file size to be processed.
Effects of Exercise Encouragement on Health-Related Factors and Blood BDNF Level in Participated in Boarding Health Promotion Program

In order to clarify the combined effect of vegetarian diet and physical exercise on mental health, we tried to analyze the influences of the physical exercise program with regular vegetarian diet during 10 days on blood BDNF level and its associated factors in Korean collegiate students. A total of 25 male Korean collegiate students(mean age: 20.9 ± 1.9 years) indicated in this study. They were divided into three groups including control group(n=7, CG), the intervention group without emcouragement(n=8; NEG group) and the intervention group with encouragement(n=10; EG). The intervention groups(n=18, encouragement group) irrespective of encouragement performed the regular physical exercise program with vegetarian diets(named as Nutrition, Exercise and Trust in God(NET) program), and was staying at the dormitory together for a total of 10 days. The variables including body composition, physical fitness factors and blood BDNF level were measured twice before and after the intervention. Blood BDNF level indicated the significant difference among three groups, respectively(p.05). The intervention group with encouragement indicated the highest increase in blood BDNF level after this study program, while the intervention group without encouragement showed the steeper decrease in blood BDNF level after this study program than controls. Therefore, our data suggest that the intervention with encouragement may be one of the useful tools for the mental health improvement of Korean collegiate students.
Interval and Value Fundamentals Mutual Authorization Executive for Period Severe Essentials

The new perspective of re-appropriating data to the cloud is a twofold edged sword. From one perspective, it frees data proprietors from the specific organization, and is less requesting for data proprietors to bestow their data to proposed customers. Of course, it introduces new challenges on assurance and security protection. To anchor data mystery against the genuine anyway curious cloud master association, different works have been proposed to enable fine grained data to get the opportunity to control. In any case, till now, no plans can support both fine-grained get the chance to control and time-fragile data conveying. In this paper, by introducing arranged release encryption into CP-ABE (Ciphertext-Policy Attribute-based Encryption),we propose some other time and quality parts joined access control on time-sensitive data for open dispersed capacity. In perspective of the proposed arrangement, we further propose a beneficial method to manage setup get to methodologies looked with grouped access essentials for time-sensitive data. Expansive security and execution examination exhibits that our proposed arrangement is extremely gainful and satisfies the security necessities for time sensitive data storing out in the open cloud.
The Necessary Consideration for More Effective Location Information on E911

A father and his son were missing and found dead 24 days later in May 2018. They understood their own location and talked with family by the cellular phone initially, but the rescue teams were not able to find them. Actually the emergency calls are mainly from cellular phones and the location information can be sent together. We should check the usage status and helpfulness of location information on E911 to understand the problems and improvement point. We would like to have discussion for more effective E911 system providing and the improvement agenda to save human life.
Meteorology and Myth: The Thunderstorm and Tornado Deities of Japan

Folklore and mythology are not to be interpreted as proper history or science fact. There may be scientific reasons why the narrative themes in Japanese folklore persist. Raijin is the god of thunder and lightning, and Fujin is a god of windstorms and tornadoes. Syncretic representation of these sky deities are ubiquitous in Japanese art and cultural landscapes. This presentation will attempt to explain the science link between Japan’s weather and its folklore. Stories such as Raijin’s penchant for eating the navels of children, or why Fujin’s skin is green, have a plausible meteorological explanation. The purpose of this qualitative research is for teaching by stimulating interest and discussion. Learning outcomes including the reinforcement of basic weather science and an appreciation of Japan’s unique culture.
Analysis of Physical Education Teachers’ Language-behaviors Patterns in Middle School

The purpose of this study was to explore the middle school physical education teachers’ language behavior patterns in physical education classes. Participants were 4 physical education teachers from ‘B’middle school in Gyeonggi Province. For data collection, 3 classes for each teacher was recorded by the videocamera and the language behavior pattern analysis instrument was used for data analysis. Prior to data analysis, inter-reliability was checked 5 times and confirmed by 91% at the final check-up. The results of this study showed experienced teachers ‘A’and ‘B’ used more operational languages than novice teachers. Novice teachers used more language related to task of contents than career teachers. Language behavior patterns related to task showed that both career teachers and novice teachers looked like long-winded explanations. Also, Language patterns related to the tasks appeared that they used more negative feedback and preferred to corrective and concrete feedback. The results of this study revealed that physical education teachers should have proper language behavior pattern in the class. Physical education teacher education program should offer more thorough language behavior training program which enhance preservice teachers’ understanding and commitment of it in the field.
Early Sepsis Detection with Deep Learning on EHR Event Sequences

Background: Sepsis is a clinical condition involving an extreme inflammatory response to an infection, and is associated with high morbidity and mortality. Without intervention, this response can progress to septic shock, organ failure and death. Every hour that treatment is delayed mortality increases. Early identification of sepsis is therefore important for a positive outcome.&#x0D; Methods: We constructed predictive models for sepsis detection and performed a register-based cohort study on patients from four Danish municipalities. We used event-sequences of raw electronic health record (EHR) data from 2013 to 2017, where each event consists of three elements: a timestamp, an event category (e.g. medication code), and a value. In total, we consider 25.622 positive (SIRS criteria) sequences and 25.622 negative sequences with a total of 112 million events distributed across 64 different hospital units. The number of potential predictor variables in raw EHR data easily exceeds 10.000 and can be challenging for predictive modeling due to this large volume of sparse, heterogeneous events. Traditional approaches have dealt with this complexity by curating a limited number of variables of importance; a labor-intensive process that may discard a vast majority of information. In contrast, we consider a deep learning system constructed as a combination of a convolutional neural network (CNN) and long short-term memory (LSTM) network. Importantly, our system learns representations of the key factors and interactions from the raw event sequence data itself.&#x0D; Results: Our model predicts sepsis with an AUROC score of 0.8678, at 11 hours before actual treatment was started, outperforming all currently deployed approaches. At other prediction times, the model yields following AUROC scores. 15 min: 0.9058, 3 hours: 0.8803, 24 hours: 0.8073.&#x0D; Conclusion: We have presented a novel approach for early detection of sepsis that has more true positives and fewer false negatives than existing alarm systems without introducing domain knowledge into the model. Importantly, the model does not require changes in the daily workflow of healthcare professionals at hospitals, as the model is based on data that is routinely captured in the EHR. This also enables real-time prediction, as healthcare professionals enters the raw events in the EHR.
Sequence management UX patterns

With recent advances in natural language understanding techniques and farfield microphone arrays, natural language interfaces, such as voice assistants and chatbots, are emerging as a popular new way to interact with computers. They have made their way out of the industry research labs and into the pockets, desktops, cars and living rooms of the general public. But although such interfaces recognize bits of natural language, and even voice input, they generally lack conversational competence, or the ability to engage in natural conversation. Today's platforms provide sophisticated tools for analyzing language and retrieving knowledge, but they fail to provide adequate support for modeling interaction. The user experience (UX) designer or software developer must figure out how a human conversation is organized, usually relying on commonsense rather than on formal knowledge. Fortunately, practitioners can rely on conversation science. This book adapts formal knowledge from the field of Conversation Analysis (CA) to the design of natural language interfaces. It outlines the Natural Conversation Framework (NCF), developed at IBM Research, a systematic framework for designing interfaces that work like natural conversation. The NCF consists of four main components: 1) an interaction model of "expandable sequences," 2) a corresponding content format, 3) a pattern language with 100 generic UX patterns and 4) a navigation method of six basic user actions. The authors introduce UX designers to a new way of thinking about user experience design in the context of conversational interfaces, including a new vocabulary, new principles and new interaction patterns. User experience designers and graduate students in the HCI field as well as developers and conversation analysis students should find this book of interest.
Cientifizando McLuhan

In Laws of Media, written with his son Eric, Marshall McLuhan aimed to assign scientific status to his ideas, presenting his Tetrad model and offering several examples of its application. Mcluhanism places great emphasis on media ecology specific perceptual bias; authors such as Maurice Merleau-Ponty, Humberto Maturana, Francisco Varela, David Chalmers and Andy Clark allow us to bridge it to the research on consciousness and brain carried through the last decades. If media creates an experience related to its epistemic context, it is possible to approach the forms of human-machine coupling – especially in digital culture –, by examining the hypothesis of a triplex isomorphism between &lt;brain&gt;&lt;apparatus&gt;&lt;experience&gt; that would turn mcluhanism into hard science. This hypothesis’ aspects are discussed, alongside further problems related to contemporary artwork creation.
Probabilistic Fault-Tolerant Universal Quantum Computation and sampling problems in Continuous Variables

We define a quantum computational model with continuous variables composed of vacuum input states, a finite set of gates and homodyne detection. We prove universality, fault tolerance and, as a sampling problem, quantum advantage.