Computer Science

The art of recovering an image from damage in an undetectable form is known as inpainting. The manual work of inpainting is most often a very time consuming process. Due to digitalization of this technique, it is automatic and faster. In this paper, after the user selects the regions to be reconstructed, the algorithm automatically reconstruct the lost regions with the help of the information surrounding them. The existing methods perform very well when the region to be reconstructed is very small, but fails in proper reconstruction as the area increases. This paper describes a Hierarchical method by which the area to be inpainted is reduced in multiple levels and Total Variation(TV) method is used to inpaint in each level. This algorithm gives better performance when compared with other existing algorithms such as nearest neighbor interpolation, Inpainting through Blurring and Sobolev Inpainting.

Indonesia's good economic development helped create the development of diverse micro, small & medium enterprises better known as UMKM in Indonesia. UMKM is present in the midst of the community to help meet the needs of the community in the local area where UMKM are located. Unfortunately when people want to find UMKM to meet the needs, faced with the problem of location search & categorization of UMKM that are around their environment. From the problems presented, the research aims to create a directory system for MSMEs that store location data & web-based UMKM categorization with the aim of facilitating the public in finding information related to UMKM data. In developing the system, this study uses the waterfall method approach. This method used because it is easier to understand with good documentation.

8th International Conference on Computer Science, Engineering and Applications (ICCSEA 2018) Will Provide An Excellent International Forum For Sharing Knowledge And Results In Theory, Methodology And Applications Of Computer Science, Engineering And Applications. The Conference Looks For Significant Contributions To All Major Fields Of The Computer Science And Information Technology In Theoretical And Practical Aspects. The Aim Of The Conference Is To Provide A Platform To The Researchers And Practitioners From Both Academia As Well As Industry To Meet And Share Cutting-Edge Development In The Field.

BIG DREAMS, 3 BEST TOOLS for Creativity, Innovation, Design
1000 questions to answer
1500 parameters to apply
4000 references to read and surpass.
Say you have read the "top 50" books on amazon about creativity-design-innovation.  Very encouraging stuff, filled with TIPS.  {No one created anything with the help of such tips--oh well).  WHY do all readers of such books, never create worth a damn? WHY? 
This book BIG DREAMS is the answer.  For the lack of the vastness of each tool herein, for the lack of the levels of detail of each tool herein, for the last of the guts-perseverence-effort handling such vastness of detail entails--THAT is WHY.
This book comes from an enormously expanded Cognitive List Limit--shared by nearly all top creators in 63 fields and 41 nations studied in the University of Chicago's EXCELLENCE SCIENCES Research Project, set up by the author of BIG DREAMS.  Your cognitive list limit is the number and diversity of alternatives you generate hundreds of time each day of your life to quotidian choice situations--where to go tonight to eat, what to order, who to invite, how to travel, when to leave, and zillions of others.  Some societies average 3, other societies average 9, the rest are in between.  Top creators average between 7 and 8. 
A thought experiment--take someone 50 times a day generating 10 alternatives compared to someone 50 times a day generating 5 alternatives.  5 times 50 = 250 per day x 100 days = 25,000 in 1/3+ of a year, x 350 days = 88,000 per year, approx.  THAT IS 88,000 alternatives explored per year MORE than the other person.  Now times 5 years, 10 years, 20 years, an entire career. 
This fits the PRODUCTIVITY MODEL OF CREATIVITY presented in the world's best book on creativity by Simonton Origins of Genius.  Get that book.  For cogntive list limits are a kind of basis of mental productivity and creativity comes ONLY from lots of tries, that are diverse from each other, that cover all dimensions of a topic at all levels of detai.  GUESS WHAT--that is what all 3 tools in this book--are all about--MORE questions exposing GAPS between how top creators think and how you think, MORE parameters that create creations-designs-innovations than any other published book or published ten books for that matter better ordered with more detail, MORE references on MORE diverse sorts of creativity-design-innovating than any other list of references in the world except maybe the entire listings of the Library of Congress, the British Library, and such.
8000+ eminent creators were studiend in the EXCELLENCE SCIENCE research project producing 42 models of creativity various of them exhibited.  Those 42 models were refined, fine tuned, via 4000 references (tool 3 of this BIG DREAMS book).  20 of those 42 models were elaborated in great detail (tool 2 of this book) in multi-level checklist format for ease of learning and assessing and use.  Those 42 models, with 20 thusly elaborated, generated the 1000 questions (tool 1 of this BIG DREAMS book). 
Use the 1000 questions to tell you the differences in how top creators work/think and how you work/think.
Use the 1500 parameters from 20 models to tell you more variety in HOW to create than you dreamed of before or observed around you.
Use the 4000 references to grow rapidly beyond your present self in a variety of directions till you have made and bought and read and diagrammed your own 4000 favorite books.

P versus NP is considered as one of the most important open problems in computer science. This consists in knowing the answer of the following question: Is P equal to NP? A precise statement of the P versus NP problem was introduced independently in 1971 by Stephen Cook and Leonid Levin. Since that date, all efforts to find a proof for this problem have failed. Another major complexity class is coNP. Whether NP = coNP is another fundamental question that it is as important as it is unresolved. In 1979, Fortune showed that if any sparse language is coNP-complete, then P = NP. We prove there is a possible sparse language in coNP-complete. In this way, we demonstrate the complexity class P is equal to NP.

P versus NP is considered as one of the most important open problems in computer science. This consists in knowing the answer of the following question: Is P equal to NP? A precise statement of the P versus NP problem was introduced independently in 1971 by Stephen Cook and Leonid Levin. Since that date, all efforts to find a proof for this problem have failed. Another major complexity class is UP. Whether UP = NP is another fundamental question that it is as important as it is unresolved. To attack the UP = NP question the concept of NP-completeness is very useful. If any single NP-complete problem is in UP, then UP = NP. Quadratic Congruences is a well-known NP-complete problem. We prove Quadratic Congruences is also in UP. In this way, we demonstrate that UP = NP.

The mobile phones are becoming now an irreplaceable utility of every household. It serves as wall clock, alarm clock, calculator, calendar, timer and many more, but have this multi-functionality overloaded the interface of the new generation of the mobile phones. The youth have adapted well to these multiple functionalities graphical user interface, but the interface has now haunting effects for usage by the two age groups i.e. elderly and kids. The interface may end up leading the new generation mobiles in the market useless or of very little use to the elderly and kids. This leads towards the need of age based user interface in the mobile operating system which will consist of interface selection home screen which further directs to age oriented interfaces.

The method of the learning change with the time and advancement in the world. But with the change in learning methods the increase in the learning choices, it becomes highly difficult for the learners to know that which learning method is more effective and improves their skills. Many of the studies stated that e-learning is far better than the traditional learning while some stated that traditional learning is better. So, because of this rising conflict this study examines which learning method is better and analyze is the e-learning method is better than the method of traditional learning in order to help learners, educators, and the institutions to adopt the best method. To examine we compare the benefits of both the learning methods and also the need for and the changing trend of the market and use the interview approach for collecting the data. The literature study also proved much helpful in constructing the result of this study. From all the analyses the review of the literature and interview responses revealed that e-learning is better learning method than the traditional learning and much gives better results according to students and the learners. The result reveal that e-learning offer various benefits like it is convenient, learner can learn from anywhere at any time, engagement level of the learners is also high in it, the cost is low, flexibility is high, offer the learners to learn new skills with managing their professional and personal life effectively, allow learners to take lecture number of times through audio-video recording of lectures, provide access to update the content, ensure quick delivery of the lessons, high consistency, scalability, less influence on the environment and positive impact on the organization profitability because of its effectiveness. All of these are not available in traditional learning and reduce its effectiveness in this global and complex world. Therefore, it is stated that because of all these benefits its adoption among the students and organizations all over the world enhance as compared to traditional learning.

Software Testing ensures delivery of good quality software to the customer. With the increase of product functionality, testing becomes challenging as it is time-consuming to perform manually, cost also increases as test suite size grows and human error can creep into a system which may lead to losses for the organization. Therefore, automation of software testing for the product is needed using appropriate Automation tool, which can enable developers and testers to easily automate the entire flow or process of automation testing. The objective of this paper is to conduct a comparative study of automated tools i.e. the OpenScript and Selenium-based on differentcriteriàs.

With increase in loan seekers around the world while bank assets remain the same, it becomes utmost important for banks to find out the credible customers. In this paper, we have taken the previous records of people to whom the loan was granted and on the basis of those records, the system is trained using the machine learning model. The implementation has been done in four main sections i.e., Data Visualization, Data Munging, Building the Predictive Model and Testing. The main objective that is fulfilled here is, by using this system, we will easily be able to identify the credible customers, which otherwise involves rigorous procedure by bank officials, in the present scenario.

This issue's Transactions section is the second installment (of two) of papers that arose from the UK CREATOR project: New Research Models and Processes for the Creative Industries. (See Leonardo Vol. 43, No. 1 for the first installment.) CREATOR is a joint EPSRC/AHRC/ ...

This issue's Transactions section is the second installment (of two) of papers that arose from the UK CREATOR project: New Research Models and Processes for the Creative Industries. (See Leonardo Vol. 43, No. 1 for the first installment.) CREATOR is a joint EPSRC/AHRC/ ...

Przedstawiony artykuł zawiera jedno z pierwszych polskich opracowań na temat diagnostyki wibroakustycznej układów mechanicznych. Wychodząc z założenia, że kluczem do prawidłowej diagnostyki jest znalezienie w rozważanym sygnale cech pozwalających rozróżnić stan prawidłowej pracy mechanizmu oraz stany przedawaryjne oraz awarie - skoncentrowano uwagę na odpowiedniej komputerowej analizie  sygnału dźwiękowego.

MC methods learn directly from episodes of experience MC is model-free: no knowledge of MDP transitions / rewards MC learns from complete episodes: no bootstrapping MC uses the simplest possible idea: value = mean return Caveat: can only apply MC to episodic MDPs All episodes must terminate