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Ip23 24 refine cots website #34

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c42d520
FIX routing errors when deployed
Feb 28, 2024
38baea5
CHANGE banner size to 30% of the screen height
Feb 29, 2024
900a53b
DELETE unnecessary files
Feb 29, 2024
5f054e6
FIX some syntax errors
Feb 29, 2024
ec4550c
FIX some minor errors
Feb 29, 2024
8959062
TRY to fix news page
Feb 29, 2024
e6807c6
FIX team card link reference
Feb 29, 2024
564d54c
FIX sample md files
Feb 29, 2024
95d1474
FIX news page and some other things
Feb 29, 2024
424cd53
MAKE home, news pages more consistent with the rest
Feb 29, 2024
6541612
INTEGRATE tagging - works only in _post folder
Mar 2, 2024
1fcdfd5
ADD some news, and some tweaks to home page
Mar 7, 2024
753392e
REMOVE project pagination in home page
Mar 7, 2024
5303f88
CHANGE footer size and REMOVE some contents
Mar 7, 2024
6dfe278
CHANGE tags link layout in tag-pages
Mar 7, 2024
fa4e003
CHANGE tags appearance
Mar 7, 2024
43ca526
FIX image sizes in banner
May 12, 2024
7412f3b
FIX pading issues with layouts --> news, projects
May 12, 2024
6d5788f
FIX tags in news layout
May 12, 2024
d622448
ADD social media links and update the themify icons
May 12, 2024
2a4f1e0
FIX nav-bar brand logo redirecting issue
May 12, 2024
65c7cc6
ADD team member Sahan Dissanayaka
May 12, 2024
536a4be
ADD new 'doctoral students' section to team page
May 12, 2024
b74ef0a
ADD people details
May 12, 2024
b31f57a
ADD some publications and fixed issue with links in publication
May 12, 2024
6ba8f67
ADD some publications and fixed issue with links in publication
May 12, 2024
dc3ce53
CHANGE pub paper 1,2 to proper names
May 15, 2024
bd8a0e8
FIX dates in publications
May 15, 2024
37ed30e
FIX contents and links on publications
May 15, 2024
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FIX contents and links on publications
  • Loading branch information
WKDKavishka committed May 15, 2024
commit 37ed30e0553800c8103234a1873229bed07ab083
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ title: "WebDraw: A Machine Learning-Driven Tool for Automatic Website Prototypin

year: 2023
month: November
Day:
Day:

image: "assets/images/publications/default.png"
thumbnail: "assets/images/publications/default-thumbnail.png"
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Original file line number Diff line number Diff line change
Expand Up @@ -9,14 +9,25 @@ Day:
image: "assets/images/publications/default.png"
thumbnail: "assets/images/publications/default-thumbnail.png"

paperurl: "#"
platform: "ACM Digital Library"
paperurl: "https://www.sciencedirect.com/science/article/abs/pii/S2590118423000126?via%3Dihub"
platform: "sciencedirect"
---

#### Abstract
###### Authors :

[Thisaranie Kaluarachchi]()

Title : A systematic literature review on automatic website generation
[Manjusri Wickramasinghe]()

#### Abstract

Abstract : Since machine learning became a prominent feature in the modern-day computing landscape, the urge to automate processes has increased. One such process of particular interest has been the automatic generation of websites based on user intention. Though the requirement of such automatic generation is a modern-day need, the quality of the automatic generation still provides a unique set of challenges. As such, to analyze these unique challenges and viable opportunities in automatic website generation, this survey systematically reviews research on the topics of automatic website generation. The analysis initially segments state-of-the-art into three categories based on the dominant strategy used for automatic generation. These strategies are examples-based, mock-up-driven, and artificial intelligence-driven automatic website generation. When considering the example-based strategy, the emphasis is on analyzing how manual design aspects of a professionally developed website are incorporated into generation models and the challenges that arise. Similarly, transformation methods from website visual design into functional GUI code are investigated for the mock-up-driven strategy with a particular reference to the six underlying conversion mechanisms. Finally, artificial intelligence website builders are analyzed based on their ability to build customizable websites to user preferences. Based on this systematic review of 47 research works on the three dominant strategies, this survey outlines unique challenges and future research endeavors that researchers would encounter when developing models that generate websites automatically and provides insights to researchers on selecting a website generation strategy based on user intention appropriately.
Authors : Authors: Thisaranie Kaluarachchi , Manjusri Wickramasinghe
Publication Link : https://www.sciencedirect.com/science/article/abs/pii/S2590118423000126?via%3Dihub
Since machine learning became a prominent feature in the modern-day computing landscape, the urge to automate processes has increased.
One such process of particular interest has been the automatic generation of websites based on user intention.
Though the requirement of such automatic generation is a modern-day need, the quality of the automatic generation still provides a unique set of challenges.
As such, to analyze these unique challenges and viable opportunities in automatic website generation, this survey systematically reviews research on the topics of automatic website generation.
The analysis initially segments state-of-the-art into three categories based on the dominant strategy used for automatic generation.
These strategies are examples-based, mock-up-driven, and artificial intelligence-driven automatic website generation.
When considering the example-based strategy, the emphasis is on analyzing how manual design aspects of a professionally developed website are incorporated into generation models and the challenges that arise.
Similarly, transformation methods from website visual design into functional GUI code are investigated for the mock-up-driven strategy with a particular reference to the six underlying conversion mechanisms.
Finally, artificial intelligence website builders are analyzed based on their ability to build customizable websites to user preferences.
Based on this systematic review of 47 research works on the three dominant strategies, this survey outlines unique challenges and future research endeavors that researchers would encounter when developing models that generate websites automatically and provides insights to researchers on selecting a website generation strategy based on user intention appropriately.
Original file line number Diff line number Diff line change
Expand Up @@ -4,19 +4,23 @@ title: "Improving Automatic Music Genre Classification Systems by Using Descript

year: 2023
month: April
Day:
Day:

image: "assets/images/publications/default.png"
thumbnail: "assets/images/publications/default-thumbnail.png"

paperurl: "#"
platform: "ACM Digital Library"
paperurl: "https://link.springer.com/chapter/10.1007/978-3-031-29956-8_26"
platform: "springer"
---

#### Abstract
###### Authors :

[Ravindu Perera]()

[Manjusri Wickramasinghe]()

Title: Improving Automatic Music Genre Classification Systems by Using Descriptive Statistical Features of Audio Signals
[Lakshman Jayaratne]()

#### Abstract

Abstract : Automatic music genre classification systems are vital nowadays because the traditional music genre classification process is mostly implemented without following a universal taxonomy and the traditional process for audio indexing is prone to error. Various techniques to implement an automatic music genre classification system can be found in the literature but the accuracy and efficiency of those systems are insufficient to make them useful for practical scenarios such as identifying songs by the music genre in radio broadcast monitoring systems. The main contribution of this research is to increase the accuracy and efficiency of current automatic music genre classification systems with a comprehensive analysis of correlations between the descriptive statistical features of audio signals and the music genres of songs. A greedy approach for music genre identification is also introduced to improve the accuracy and efficiency of music genre classification systems and to identify the music genre of complex songs that contain multiple music genres. The approach, proposed in this paper, reported 87.3% average accuracy for music genre classification on the GTZAN dataset over 10 music genres.
Authors: Ravindu Perera, Manjusri Wickramasinghe, Lakshman Jayaratne
Publication Link : https://link.springer.com/chapter/10.1007/978-3-031-29956-8_26
Original file line number Diff line number Diff line change
Expand Up @@ -4,19 +4,25 @@ title: "Protecting Copyright Ownership via Identification of Remastered Music in

year: 2024
month: January
Day:
Day:

image: "assets/images/publications/default.png"
thumbnail: "assets/images/publications/default-thumbnail.png"

paperurl: "#"
platform: "ACM Digital Library"
paperurl: "https://www.techrxiv.org/users/712936/articles/697329-protecting-copyright-ownership-via-identification-of-remastered-music-in-radio-broadcasts?commit=7d0fd87941564c4b1871a40e4f613387f0176a41"
platform: "techrxiv"
---

#### Abstract
###### Authors :

[Pasindu Marasinghe]()

[Manjusri Wickramasinghe]()

Title: Protecting Copyright Ownership via Identification of Remastered Music in Radio Broadcasts
[Lakshman Jayaratne]()

[Shakya Abeytunge]()

#### Abstract

Abstract: In radio broadcasting, the crucial task of monitoring becomes evident for protecting musical work copyrights and ensuring the fair distribution of royalties. Manual monitoring, due to its time-consuming and unreliable nature, necessitates an automated approach. The challenges in automated monitoring arise mainly from the practice of broadcast stations remastering songs before airing. These alterations introduce complexities that complicate the identification process for existing music identification techniques. This paper tackles this challenge by exploring the feasibility of employing computer vision techniques on STFT spectrograms from ongoing audio streams. The objective is to identify similar spectrogram representations by comparing them with previously registered key features extracted from STFT spectrograms generated for original song tracks. This aims to unveil the identity of content being broadcast on radio and, consequently, safeguard the rightful ownership of copyrighted songs. The proposed approach achieved an accuracy of over 97% for tempo alterations up to 20% and over 95% accuracy for pitch alterations up to 20%.
Authors: Pasindu Marasinghe, Lakshman Jayaratne, Manjusri Wickramasinghe, Shakya Abeytunge
Publication Link : https://www.techrxiv.org/users/712936/articles/697329-protecting-copyright-ownership-via-identification-of-remastered-music-in-radio-broadcasts?commit=7d0fd87941564c4b1871a40e4f613387f0176a41
In radio broadcasting, the crucial task of monitoring becomes evident for protecting musical work copyrights and ensuring the fair distribution of royalties. Manual monitoring, due to its time-consuming and unreliable nature, necessitates an automated approach. The challenges in automated monitoring arise mainly from the practice of broadcast stations remastering songs before airing. These alterations introduce complexities that complicate the identification process for existing music identification techniques. This paper tackles this challenge by exploring the feasibility of employing computer vision techniques on STFT spectrograms from ongoing audio streams. The objective is to identify similar spectrogram representations by comparing them with previously registered key features extracted from STFT spectrograms generated for original song tracks. This aims to unveil the identity of content being broadcast on radio and, consequently, safeguard the rightful ownership of copyrighted songs. The proposed approach achieved an accuracy of over 97% for tempo alterations up to 20% and over 95% accuracy for pitch alterations up to 20%.
Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,8 @@
layout: publication
title: "Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection"

author1: Sahan Dissanayaka
author2: Dr Manjusri Wickramasinghe
author1: Sahan Dissanayaka
author2: Dr Manjusri Wickramasinghe
author3: Pasindu Marasinghe

year: 2023
Expand All @@ -13,15 +13,14 @@ Day: 12
image: "assets/images/publications/2019Author1.jpeg"
thumbnail: "assets/images/publications/2019Author1-thumbnail.jpeg"
paperurl: https://www.sciencedirect.com/science/article/pii/S2666764921000485
paperurl: "#"
platform: "ACM Digital Library"
platform: "sciencedirect"
---

#### Authors

1. [__SAHAN DISSANAYAKA__]()
2. [__MANJUSRI WICKRAMASINGHE__](/team/dr-manju/)
3. [__PASINDU MARASINGHE__](/team/pasindu-marasinghe/)
1. [**SAHAN DISSANAYAKA**]()
2. [**MANJUSRI WICKRAMASINGHE**](/team/dr-manju/)
3. [**PASINDU MARASINGHE**](/team/pasindu-marasinghe/)

#### Abstract

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