2022 International Conference on Frontiers of Information Technology (FIT)最新文献

Cyber Automated Network Resilience Defensive Approach against Malware Images 针对恶意软件图像的网络自动网络弹性防御方法

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00051

Kainat Rizwan, Mudassar Ahmad, Muhammad Asif Habib

{"title":"Cyber Automated Network Resilience Defensive Approach against Malware Images","authors":"Kainat Rizwan, Mudassar Ahmad, Muhammad Asif Habib","doi":"10.1109/FIT57066.2022.00051","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00051","url":null,"abstract":"Cyber threats have been a major issue in the cyber security domain. Every hacker follows a series of cyber-attack stages known as cyber kill chain stages. Each stage has its norms and limitations to be deployed. For a decade, researchers have focused on detecting these attacks. Merely watcher tools are not optimal solutions anymore. Everything is becoming autonomous in the computer science field. This leads to the idea of an Autonomous Cyber Resilience Defense algorithm design in this work. Resilience has two aspects: Response and Recovery. Response requires some actions to be performed to mitigate attacks. Recovery is patching the flawed code or back door vulnerability. Both aspects were performed by human assistance in the cybersecurity defense field. This work aims to develop an algorithm based on Reinforcement Learning (RL) with a Convoluted Neural Network (CNN), far nearer to the human learning process for malware images. RL learns through a reward mechanism against every performed attack. Every action has some kind of output that can be classified into positive or negative rewards. To enhance its thinking process Markov Decision Process (MDP) will be mitigated with this RL approach. RL impact and induction measures for malware images were measured and performed to get optimal results. Based on the Malimg Image malware, dataset successful automation actions are received. The proposed work has shown 98% accuracy in the classification, detection, and autonomous resilience actions deployment.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123076758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of Latest CASE Tools for Database Engineering: A Systematic Literature Review 数据库工程最新案例工具的研究:系统的文献综述

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00012

Nazish Yousaf, Madeha Arif, M. Awan, Wasi Haider Butt

{"title":"Investigation of Latest CASE Tools for Database Engineering: A Systematic Literature Review","authors":"Nazish Yousaf, Madeha Arif, M. Awan, Wasi Haider Butt","doi":"10.1109/FIT57066.2022.00012","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00012","url":null,"abstract":"Computer Aided Software Engineering (CASE) tools aid in development of software at various stages. They provide automation of processes to some extent. Database handling requires reliability and integrity with security. Multiple CASE tools help in achieving all these, reduce the cost and time as well. Database implementations are made much easier with implementation of such tools. The purpose of this Systematic Literature Review (SLR) is to investigate various CASE Tools and analyze them categorizing into various domains. This paper has covered 49 researches between years 2015 to 2022, including 18 CASE tools belonging to 12 domains of Database development and maintenance in software development process. The paper highlights various domains in which CASE tools are being used. Moreover, multiple CASE tools for database handling that are frequently being used in the identified domains are also presented. After carrying out a parametric analysis, leading tools in top domains are identified. It is concluded that benefits of CASE tools in terms of database (DB) management automation, time and cost optimization are undeniable. They are the most important and core components of an efficient software development process.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115528958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a Machine Learning Model for Prediction of Colour Trends in Fashion Industry 用于预测时尚行业色彩趋势的机器学习模型的开发

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00061

Khadija Nadeem, Mudassar Ahmad, Zafar Javed, Muhammad Asif Habib

{"title":"Development of a Machine Learning Model for Prediction of Colour Trends in Fashion Industry","authors":"Khadija Nadeem, Mudassar Ahmad, Zafar Javed, Muhammad Asif Habib","doi":"10.1109/FIT57066.2022.00061","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00061","url":null,"abstract":"In the fashion industry, good trend prediction is the key to success for both manufacturers and retailers. Currently, the speed at which the industry is working is faster than ever before. The Colour forecasting process in many fashion organizations is not visible to the public. A forecasting method is proposed in this study to provide colour trends to industries in advance. Predicting colour trends would allow retailers to improve their logistics for the storage/shipping of clothes. This research proposes a systematic prediction model to forecast colour quickly and cost-effectively in real-time data. This research examines the colour forecasting process, its methodology, and how it is presented and used in the fashion industry. This study used Machine Learning (ML) to examine image data from the latest fashion trends to collect data via web-scraping images, extracting colours from images using k-means algorithms, and assessing the most trending 45 colours. We feed trendy clothing colours from different Pakistani brand websites to the algorithm. The algorithm predicts the frequency of each colour to either be positive or negative in the future. This research uses a forecasting model like ARIMA to forecast colour trends. Furthermore, the mean squared error is quite low, at 0.025. As a result, the current intelligent prediction system meets the criteria for capturing colour in trends for organizations. It would be a good development tool to maximize the profits of fashion companies and minimize the loss. Furthermore, it enables industries to make decisions for selecting colour trends.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129741695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Project Manager’s Personality is a Treasure Trove of Information 项目经理的个性是信息的宝库

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00065

Neelum Qasimi, Ali Afzal Malik

引用次数: 0

Message from the Program Chair 来自项目主席的信息

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/cgo.2009.39

Mary W. Hall

{"title":"Message from the Program Chair","authors":"Mary W. Hall","doi":"10.1109/cgo.2009.39","DOIUrl":"https://doi.org/10.1109/cgo.2009.39","url":null,"abstract":"Together with the program committee, it is my great pleasure to present this program for the 2009 Seventh International Symposium on Code Generation and Optimization – CGO ‘09. This year’s symposium continues its tradition of being a premier forum to bring together researchers and practitioners working on feedback-directed optimization and back-end compilation techniques. This year’s program includes a collection of 26 papers spanning a broad range of issues, reflecting the growing importance of code generation and optimization in this era of fundamental shifts in computer architecture. Beyond the typical areas for CGO of profile-directed optimization, dynamic optimization and program analysis, this year’s program includes sessions on analysis for concurrency, optimizations for stream programs, using intelligence in optimization, and programmer tools. We are pleased to include papers that consider optimizations for emerging architectures or provide insights on production systems. In addition to the papers, we are very fortunate to include two keynote talks from Joel Emer on the future of reconfigurable computing in mainstream architectures and Vikram Adve on the future of compiler research.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133840340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tri-model ensemble with Grid Search optimization for Bipolar Disorder Diagnosis 基于网格搜索优化的三模型集成双相情感障碍诊断

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00015

Syed Muhammad Zain, W. Mumtaz

{"title":"Tri-model ensemble with Grid Search optimization for Bipolar Disorder Diagnosis","authors":"Syed Muhammad Zain, W. Mumtaz","doi":"10.1109/FIT57066.2022.00015","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00015","url":null,"abstract":"Bipolar disorder is one of the common mood disorders and diagnosis is the most important part of mood disorders. This research involves sequence classification of bipolar 1, bipolar 2, and cyclothymia using psychiatric cliff notes, there were 200 samples of bipolar 1, 200 samples of bipolar 2, and 200 samples of cyclothymia. This work uses a novel tri-model based ensemble for the diagnosis of bipolar disorder with grid search based optimization. The paper involved several textual preprocessing techniques like lower casing, punctuation removal, and lemmatization and it involved the tfidf approach for feature extraction of important attention words from paragraph based textual data. After preprocessing the ensemble was created using three models Decision tree, Random Forest, and Adaboost. The ensemble was optimized with grid search optimization with an early stopping mechanism to prevent overfitting. The ensemble’s classification prediction was determined by the highest vote from the 3 individual models. The tri-model ensemble produced excellent results with an accuracy of 99% and precision, recall, and f1-score of 98%, outperforming other studies on text based mental health disorders. This work is the first work to include cyclothymia variants of bipolar disorder and involved complete coverage of all the 3 types of bipolar disorder. This work can help to facilitate bipolar patients and provide an extremely accurate diagnosis of all types of bipolar disorder in a real world scenario.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116979948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network Forensic Analysis of Twitter Application on Android OS Android平台上Twitter应用的网络取证分析

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00053

Alia Umrani, Yousra Javed, Muhammad Iftikhar

引用次数: 2

Application of NLP on student’s Discord messages for automatic Belbin role identification NLP在学生不和谐信息中的应用，用于自动识别Belbin角色

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00062

Konstantine Dichev, F. Bukhsh, Yeray Barrios-Fleitas

{"title":"Application of NLP on student’s Discord messages for automatic Belbin role identification","authors":"Konstantine Dichev, F. Bukhsh, Yeray Barrios-Fleitas","doi":"10.1109/FIT57066.2022.00062","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00062","url":null,"abstract":"Social media has found its way into education and, together with team formation, has started to play a significant role in students' university progress. Previous research has tried to generally analyze and give predictions about the influence of social media on students' learning curve but was not concentrated on understanding students' behavior within teams and directly linking it to Belbin roles, which is crucial for forming teams. In this paper, we are working with real-life data extracted from official university channels will allow us to propose a methodology and a pilot Belbin role automation tool to look further into the specifics of the problem. In addition, linking this to team roles and behavior concerns within the project teams will open the horizon for further research on how the performance of students within the teams is affected. We propose to create a primary tool and framework for validating Belbin roles through real-life social network data. Results show that it is possible to identify Belbin’s roles using natural language processing. The future work direction is to refine the words associated with every personality trait.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130138528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain Tumor Augmentation using the U-NET Architecture 使用U-NET架构的脑肿瘤增强

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00063

Mohsin Jabbar, M. Siddiqui, Farhan Hussain, Sultan Daud Khan

{"title":"Brain Tumor Augmentation using the U-NET Architecture","authors":"Mohsin Jabbar, M. Siddiqui, Farhan Hussain, Sultan Daud Khan","doi":"10.1109/FIT57066.2022.00063","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00063","url":null,"abstract":"Studies have found out that tumors in brain are one of the fiercest diseases which can ultimately lead to death. Gliomas are the most commonly found primary tumors that are very hard to predict and can be found anywhere in the brain. It is prime objective to differentiate the different tumor tissues such as enhancing tissues, edema, from healthy ones. To do this task, two types of segmentation techniques come into existent i.e. manual and automatic. The automation methods of brain tumor segmentation have gained ground over manual segmentation algorithms and further its estimation is very closer to clinical results. In this paper we propose a comprehensive U-NET architecture with modification in their layers for 2D slices segmentation as a major contribution to BRATS 2015 challenge. Then we enlisted different dataset that are available publicly i.e. BRATS and DICOM. Further, we present a robust frame- work inspired from U-NET model with addition and modification of layers and image pre-processing methodology such as contrast enhancement for visible input and output details. In this way our approach achieves highest dice score 0.92 on the publicly available BRATS 2015 dataset and with better time constraint i.e. training time decreases to 80-90 minute instead of previously 2 to 3 days.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126096782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On evaluation of dereverberation algorithms for expectation-maximization based binaural source separation in varying echoic conditions 不同回声条件下基于期望最大化的双声源分离去噪算法的评价

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00045

Muhammad Sheryar Fulaly, Sania Gul, Muhammad Salman Khan, Ata ur-Rehman, Syed Waqar Shah

{"title":"On evaluation of dereverberation algorithms for expectation-maximization based binaural source separation in varying echoic conditions","authors":"Muhammad Sheryar Fulaly, Sania Gul, Muhammad Salman Khan, Ata ur-Rehman, Syed Waqar Shah","doi":"10.1109/FIT57066.2022.00045","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00045","url":null,"abstract":"The outcome of source separation (SS) algorithms founded on spatial location cues, degrades in echoic conditions, due to corruption of these cues, that otherwise act as discriminative features for such systems. One of the solutions, for improving the performance of these systems, is to dereverberate the speech mixtures, ahead of the separation process. In this paper, we explore various dereverberation algorithms for preprocessing the reverberant speech mixture signal, before it can be given as an input to the model-based expectation-maximization source separation and localization (MESSL); a SS system based on location cues, working in varying echoic conditions. We then find the most optimum dereverberation algorithm, which can provide significant improvement in quality and intelligibility of the output speech signals from MESSL. It is found that the objective metrics advocate the use of the \"weighted prediction error (WPE)\" algorithm, providing an improvement of 3% in short term objective intelligibility (STOI) and 3.4 dB in signal to distortion ratio (SDR), while the subjective metrics favor the use of the \"precedence effect (PE)\" algorithm, which provides an improvement of 6% in average intelligibility score and 1% in average quality score, over the stand-alone MESSL system.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0