Sunday, February 28, 2021

MATCHCUT ASSIST: A MOBILE SYSTEM TO AUTOMATE MATCHCUT PROCESS USING COMPUTER VISION

Author :  Harrison Zhou

Affiliation :  Irvine High School, Irvine, CA 92604

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

In film industry, camera angle and smooth transition between shots are very important and time-consuming manual process. Match Cut is most frequently used technique in this industry to achieve seamless transition. This paper proposes an app that offers solution to assist match cut technique using edge detection algorithm on mobile device. Users can use the app to take two different shots which can be used to match cut in future. It is helpful to take different shots without manual efforts and heavy equipment.

Keyword :  Match Cut, Android Application, OpenCV, Photography, Photo Edit, Canny Edge Detection

For More Details https://aircconline.com/csit/papers/vol9/csit91209.pdf

Friday, February 26, 2021

Call for Papers - 12th International Conference on Communications Security & Information Assurance (CSIA 2021)

 12th International Conference on Communications Security & Information Assurance (CSIA 2021)

November 20 ~ 21, 2021, Zurich, Switzerland

https://iccsea2021.org/csia/index

Important Dates


Submission Deadline:March 06, 2021
Authors Notification:May 06, 2021
Final Manuscript Due:May 14, 2021


Contact Us: csia@iccsea2021.org




9th International Conference on Information Technology in Education (ICITE 2021) - Call for Papers

 9th International Conference on Information Technology in
Education (ICITE 2021)

July 10~11, 2021, Toronto, Canada

https://acsit2021.org/icite/index

Important Dates

Submission Deadline:March 06, 2021
Authors Notification:March 29, 2021
Final Manuscript Due:April 07, 2021

Contact Us: icite@acsit2021.org






AN ADAPTIVE AND SMART SYSTEM FOR PARENTAL CONTROL ON DIGITAL GAMES

Author :  Clark Ren

Affiliation :  California State Polytechnic University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

As more and more students get access to computers to aid them in their studies, they also gain access to machines that can play games, which can negatively affect a student's academic performance. However, it is also debated that playing video games could also positively affect a student’s academic performance. In order to address both sides of the argument, we can create an app that limits the amount of time a student has to play games while not completely removing the ability for students to play games.

Keyword :  Parental Control, Smart System, Digital Games, Web Service

For More Detailshttps://aircconline.com/csit/papers/vol9/csit91208.pdf

Sunday, February 21, 2021

A DIET CONTROL AND FITNESS ASSISTANT APPLICATION USING DEEP LEARNING-BASED IMAGE CLASSIFICATION

Author :  Tianren Dong

Affiliation :  Northwood High School, Irvine, CA, 92620

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

With more and more attentions paid on health, people begin to care about healthy diet options created by experts on nutrition. However, it will take a long time to observe the effects by taking healthy diet. This causes great difficulty for users to follow the healthy diet strictly. Most existing applications are not user-friendly in inputting information to the application. Then it becomes difficulty to track for exact health status. This paper proposes an android application which can be trained to recognize different kinds of food and facilitate the information input through phone camera using machine learning algorithms. Thus, nutritional information can be fed in application accurately.

Keyword :  Machine learning, Android application, Image recognition

For More Detailshttps://aircconline.com/csit/papers/vol9/csit91207.pdf

Saturday, February 20, 2021

2D IMAGE FEATURES DETECTOR AND DESCRIPTOR SELECTION EXPERT SYSTEM

Author :  Ibon Merino

Affiliation :  Tecnalia Research and Innovation, Donostia-San Sebastian

Country :  Spain

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower.

Keyword :  Computer vision, Descriptors, Feature-based object recognition, Expert system

For More Details https://aircconline.com/csit/papers/vol9/csit91206.pdf


Friday, February 19, 2021

AN INTELLIGENT INTERNET-OFTHINGS(IOT) DOOR BELL SYSTEM FOR SMART NOTIFICATION ALERT

 Author :  Melissa Qian

Affiliation :  Northwood High School, Irvine, CA 92620

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

This paper presents an innovative redesign of a doorbell system in order to eliminate unnecessary ringing noise from users’ daily life. Employing artificial intelligence for face recognition, the IoT doorbell system define the visitors as complete strangers or someone who is expected. The next step operates based on this result; the doorbell system will either ring or send out notification to the users’ phone depending on the familiarity of the visitor and the user.

Keyword :  Machine Learning, Deep Learning, Artificial Intelligence, Wireless Network

For More Details https://aircconline.com/csit/papers/vol9/csit91205.pdf

Wednesday, February 17, 2021

#BREXIT VS. #STOPBREXIT: WHAT IS TRENDIER? AN NLP ANALYSIS

 Author :  Marco A. Palomino

Affiliation :  University of Plymouth, Drake Circus, Plymouth, PL4 8AA, United Kingdom

Country :  India

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

Online trends have established themselves as a new method of information propagation that is reshaping journalism in the digital age. We argue that sentiment analysis—the classification of human emotion expressed in text—can enhance existing algorithms for trend discovery. By highlighting topics that are polarised, sentiment analysis can offer insight into the influence of users who are involved in a trend, and how other users adopt such a trend. As a case study, we have investigated a highly topical subject: Brexit, the withdrawal of the United Kingdom from the European Union. We retrieved an experimental corpus of publicly available tweets referring to Brexit and used them to test a proposed algorithm to identify trends. We validate the efficiency of the algorithm and gauge the sentiment expressed on the captured trends to confirm that highly polarised data ensures the emergence of trends.

Keyword :  Twitter; sentiment analysis; world clouds; text mining; information retrieval.

For More Details https://aircconline.com/csit/papers/vol9/csit91203.pdf

Tuesday, February 16, 2021

INCLUDING NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING INTO INFORMATION RETRIEVAL

 Author :  Piotr Malak

Affiliation :  University of Wrocław

Country :  Poland

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

In current paper we discuss the results of preliminary, but promising, research on including some Natural Language Processing (NLP) and Machine Learning (ML) approaches into Information Retrieval. Classical IR uses indexing and term weighting in order to increase pertinence of answers given to users queries. Such approach allows for matching the meaning, i.e. matching all keywords of the same or very similar meaning as expressed in user query. For most cases this approach is sufficient enough to fulfil user information needs.

Keyword :  Enhanced Information Retrieval, Contextual IR, NLP, Machine Learning

For More Detailshttps://aircconline.com/csit/papers/vol9/csit91202.pdf

Monday, February 15, 2021

FLEXIBLE LOG FILE PARSING USING HIDDEN MARKOV MODELS

Author :  Nadine Kuhnert

Affiliation :  Friedrich-Alexander University

Country :  Germany

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 12, September, 2019

Abstract :

We aim to model unknown file processing. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by focusing only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Second, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file processing. With changes in the raw log file distorting learned patterns, we aim the model to adapt automatically in order to maintain high quality output. After training our model on one system type, applying the model and the resulting parsing rule to a different system with slightly different log file patterns, we achieve an accuracy over 99%.

Keyword :  Hidden Markov Models, Parameter Extraction, Parsing, Text Mining, Information Retrieval

For More Details https://aircconline.com/csit/papers/vol9/csit91201.pdf

Tuesday, February 2, 2021

Call for Papers - 4th International Conference on Natural Language Processing and Trends (NATAP 2021)

 4th International Conference on Natural Language Processing and
Trends (NATAP 2021)

https://csita2021.org/natap/index

May 22 ~ 23, 2021, Zurich, Switzerland

Important Dates

Submission Deadline:February 06, 2021
Authors Notification:March 20, 2021
Final Manuscript Due:March 28, 2021


Contact Us : natap@csita2021.org





REDCLAN - RELATIVE DENSITY BASED CLUSTERING AND ANOMALY DETECTION

Author :  Diptarka Saha

Affiliation :  2WalmartLabs, Bengaluru, Karnataka

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 13, September, 2018

Abstract :

Cluster analysis and Anomaly Detection are the primary methods for database mining. However, most of the data in today's world, generated from multifarious sources, don’t adhere to the assumption of single or even known distribution - hence the problem of finding clusters in the data becomes arduous as clusters are of widely differing sizes, densities and shapes, along with the presence of noise and outliers. Thus, we propose a relative-KNN-kernel density-based clustering algorithm. The un-clustered (noise) points are further classified as anomaly or nonanomaly using a weighted rank-based anomaly detection method. This method works particularly well when the clusters are of varying variability and shape, in these cases our algorithm not only finds the “dense” clusters that other clustering algorithms find, it also finds low-density clusters that these approaches fail to identify. This more accurate clustering in turn helps reduce the noise points and makes the anomaly detection more accurate.

Keyword :  Clustering, Relative KNN – kernel density, Varying density clusters, Anomaly Detection, DBSCAN

For More Detailshttps://airccj.org/CSCP/vol8/csit89303.pdf