2022 IEEE International Conference on Computing (ICOCO)最新文献

{"title":"An End-To-End Cyber Security Maturity Model For Technology Startups","authors":"A. Selamat, Mohamed Noordin Yusuff Marican, S. H. Othman, S. Razak","doi":"10.1109/ICOCO56118.2022.10031900","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031900","url":null,"abstract":"Cybersecurity is increasingly becoming an important discussion topic in the boardroom of companies, regardless of the size or industry. Hackers nowadays are becoming increasingly smart. Instead of attacking big multi-national companies, international banks and government organisations which have built strong protection against cyber threats, the perpetrators now placed their focus on smaller and medium size businesses like technology start-ups through a variety of attacks from phishing, ransomware to the exploitation of vulnerabilities in the web or mobile applications. Therefore, it is imperative that technology start-ups have the capability in assessing their cyber security maturity to combat against cyber threats. However, for technology start-ups, it is especially imperative as cyber-attacks or data breaches could undeniably result in the loss of customers’ confidence, regulatory implications and revenue loss which could eventually result in the start-up untimely closure. Although there are available security frameworks commonly used in the industry by cyber security practitioners, these frameworks are not suitable for technology start-ups as they tend to be broad and generic, taking a long time to conduct the assessment requiring adequate manpower or even the need for a budget to hire external consultants to help in conducting the assessment. This study seeks to analyse the current cyber security frameworks and introduce an end-to-end Cyber Security Maturity Model, which can be used specifically for technology start-ups. The proposed model not only provides an end-to-end maturity assessment of the start-up’s cyber security posture but also coupled with an existing quantification model to justify the investments allocated in implementing cyber security measures for the start-up. Right-sizing the cyber security measures for the start-up in the different stages of the start-up lifecycle could allow reasonable controls to be implemented at the appropriate phase.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115398906","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}

{"title":"fProSentiment Analysis on Mobile Phone Brands Reviews using Convolutional Neural Network (CNN)","authors":"Noor Izati Abdul Hamid, N. Kamal, H. M. Hanum, Noor Latiffah Adam, Z. Ibrahim","doi":"10.1109/ICOCO56118.2022.10031660","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031660","url":null,"abstract":"Due to the rapid growth of online e-commerce, customers can now voice and express their reviews and thoughts on online products. Therefore, companies who market their product on the e-commerce website will receive thousands of reviews and feedback from their end-users directly on this platform. As the amount of textual data grows tremendously, developing sentiment analysis that automatically analyses text data becomes increasingly vital. It is because reading every review manually can be a tedious task and time-consuming. Analyzing the sentiment for all reviews can provide the companies with an overview of how positive or negative the customers are about their products. The convolutional neural network (CNN) has recently been used for text classification tasks and has achieved impressive results. Hence, this study proposes a CNN method for sentiment analysis to classify the reviews on mobile phone brands. The customer reviews dataset is obtained from the Amazon website. This study combined the Word2Vec-CNN model to predict the sentiment of mobile phone reviews effectively. Pre-trained Word2Vec model is utilized to generate word vectors in word embedding. CNN layers are used to extract better features for sentence categorization to identify the sentiment polarity of the reviews, whether positive or negative. The obtained results give us 88% accuracy and the developed application can also function well in analyzing the sentiment of customers’ reviews.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121627746","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}

{"title":"Impact of Disruptive Technologies on Customer Experience Management In ASEAN: A Review","authors":"Vyankatesh Adke, Priti Bakhshi, Muniza Askari","doi":"10.1109/ICOCO56118.2022.10031882","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031882","url":null,"abstract":"The financial services sector in the Association of South East Asian Nations (ASEAN) region has seen significant growth, driven by digitalization and the rise of fintech firms. Financial services accounted for about 8% of the overall Gross Domestic Product (GDP) at around ${$}$ 3 Trillion in 2021 [1]. While the GDP contracted slightly due to the COVID-19 pandemic, the overall outlook over the next five years remains positive.To further boost this growth, and foster innovation, regulators across ASEAN are establishing foundations for open finance, as is clear from policies in Singapore [2], the Philippines [3], and Indonesia [4].The main objectives of the open finance framework are to offer integrated financial services by making customer experiences that are fully digital, frictionless, empathetic, and anticipatory to customer needs.Customers today are more digitally empowered, expect personalized service, and often maintain relationships with multiple retail banks. As such, Customer Experience (CX) management is a top priority for retail banks to ensure overall brand recall, customer loyalty, and growth.This however also poses a new challenge to incumbent banks, as they need to embark on complex digital transformation journeys to stay relevant and competitive with due consideration for costs and accrued benefits.In this context, this study explores the impact of cloud, Artificial Intelligence (AI), and digital channels, collectively referred to as disruptive technologies, on customer experience management.It does so by critically examining existing literature on the evolution of digital technologies, their applications for customer engagement and the consequent impact on customer behaviours, and customer experience measures such as the Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS). Based on the review, the study identifies opportunities for future research in the form of research questions, which include factors like experience quality, behaviour traits, and customer segmentation attributes that impact customer experience.The study contributes by providing insights to retail banks on key factors to consider while embarking on digital transformation projects to improve customer experience. While the study focuses on retail banking, its contributions could be beneficial to adjacent financial services like lending and insurance in ASEAN.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"496 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126563056","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}

{"title":"Understanding Public Sentiment Towards a Public Rally Using Text and Social Media Analytic","authors":"Sian Lun Lau, Marvin J. H. Lee, Min Xuan Teoh","doi":"10.1109/ICOCO56118.2022.10031692","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031692","url":null,"abstract":"The use of social media for analysing behaviours and trends of the public is a growing research area. In 2021, the #LAWAN social movement emerged in Malaysia as a result of discontentment of the people towards the government. This study intends to study and discover the public perception and sentiment towards the #LAWAN rally. The investigation also questions whether is the civil society supportive of such social movement and rally, especially when rally, protests and demonstration are traditionally and often seen as a negative and non-constructive approach in the country. Tweets with the hashtag #LAWAN over a day on and before the rally day on 31st July 2021 have been collected and analysed. Sentiment analysis helped to identify the public sentiments towards the rally, and topic modelling helped to discover common topics from the 5000+ tweets scrapped from the social media.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127706858","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}

{"title":"Bibliometric Analysis of Global Scientific Literature on Robust Neural Network","authors":"Tengku Nurul Aimi Balqis Tengku, Malim Busu, Saadi Ahmad Kamarudin, N. Ahad, Norazlina Mamat","doi":"10.1109/ICOCO56118.2022.10031676","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031676","url":null,"abstract":"The study aims to present a bibliographic review of publications from the Scopus database related to the robust neural network topic. As of 13th September 2022, this study managed to gather 16 articles from 2019-2023 based on the keywords of robust neural network used for the searching process. The three tools have been used to analyze the gathered Scopus database, which are Microsoft Excel, VOSviewer software and Harzing’s Publish and Perish software. This study reports the findings in terms of the current trend and the impact of publications of robust neural network studies. According to bibliometrics analysis, the number of publications has been increasing over time. This study focuses only on the Scopus database. For future research, other databases like PubMed, Lens, Dimensions, and Web of Science could be considered so the findings will be more meaningful and impactful. This study is the first article to do a bibliographic review related to the neural network.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712170","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}

{"title":"Weight Perception Simulation in Virtual Reality with Passive Force using Force Sensing Resistors","authors":"Woan Ning Lim, Yunli Lee, K. Yap, Ching-Chiuan Yen","doi":"10.1109/ICOCO56118.2022.10031797","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031797","url":null,"abstract":"There is a rise of interest in promoting Virtual Reality (VR) in many industries since the VR headsets released as consumer products and getting affordable. The advantage of VR lies in its capability in creating a sense of presence and immersion, however it is still a major challenge to enable humans to feel the weight of the object in VR. There have been remarkable advancements in the development of haptic interfaces throughout the years. However, a number of challenges limit the progression to enable humans to sense the weight of virtual objects. Pseudo-haptic approach is a less costly alternative with better mobility compared to haptic interfaces. It is a software approach seeks to use the overall dominance of the visual system to create haptic illusions to render the perception of weight. In this paper, a pseudo-haptic model using passive force to simulate weight perception is proposed. The hand pressures are captured during the interaction to simulate the objects’ behavior to create the pseudo-weight illusion. The design and implementation of the force detection and visual feedback modules are discussed, and the preliminary evaluations of the force sensing resistors are presented.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116400489","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}

{"title":"A Student’s Perspective On The Evaluation Of Teaching And Learning Using Student Feedback Online (SuFO)","authors":"Zan Azma Nasruddin, Norafifa Mohd Ariffin","doi":"10.1109/ICOCO56118.2022.10031637","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031637","url":null,"abstract":"Nowadays, students are the main stakeholders in any educational setting. They actively participate in the transmission of knowledge. The university’s future planning heavily relies on their feedback on the current teaching and learning techniques. The Learning Management System (LMS), an i-Learn system, has been used by Universiti Teknologi MARA (UiTM) to implement blended learning in their teaching and learning methods. Student Feedback Online (SuFo), which UiTM has made available to its students, allows them to evaluate the teaching and learning process. However, there are issues with the validity and reliability of the SuFo question in evaluating the course, the lecturer’s performance, the classroom environment, and scepticism in the students’ responses brought up by the previous study. Therefore, this study aims to understand how students perceive SuFo questions and investigate how students feel about SuFo questions and any potential biases in student evaluations. This study analyses data using quantitative methods and a survey along with SPSS. One hundred students from various faculties have fully responded to the survey. The results demonstrate that there are no gender-based biases in student ratings and that the students strongly agree if the SuFo questions are modified and changed. There are also some suggestions for future planning such as changes of SuFo questions and reduced the number of SuFo questions.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130242192","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}

{"title":"Smart Homes in The Age of IoT","authors":"M. Ati, Arooba Khalid","doi":"10.1109/ICOCO56118.2022.10031788","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031788","url":null,"abstract":"With lifestyle changes, digital transformations, and increasing use of the internet, technology has become a part of our daily lives. It has become an essential factor used in all fields such as industry, commerce, education, and entertainment. Technology has also touched areas directly related to human life, such as health, medicine, and food. We are constantly engaging with technology: through smartphones and applications or even in-home devices and systems. We can control and give orders to various electrical smart devices from inside or outside the home; they all work in an interconnected way. For example, we can control the temperature of the house and water during the shower, raise the curtains, or turn on and off modern media and communication devices. This technology contributes to facilitating the user’s daily life and saving time and effort. The smart home contains remote control devices to operate and monitor electrical and electronic devices such as interior lighting, outdoor garden lighting, electric blinds, air conditioning, and TV, in addition to controlling audio systems, cameras, and electric doors using smartphones or screens on the wall. Also, this house contains remote control devices to protect against theft and fire. This study focuses on exploring the idea of smart homes and the related security techniques whilst shedding light on some work related to privacy and security in smart homes.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130405189","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}

{"title":"ATT&CK Behavior Forecasting based on Collaborative Filtering and Graph Databases","authors":"Masaki Kuwano, Momoka Okuma, Satoshi Okada, Takuho Mitsunaga","doi":"10.1109/ICOCO56118.2022.10032036","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10032036","url":null,"abstract":"Cyber attacks are causing tremendous damage around the world. To protect against attacks, many organizations have established or outsourced Security Operation Centers (SOCs) to check a large number of logs daily. Since there is no perfect countermeasure against cyber attacks, it is necessary to detect signs of intrusion quickly to mitigate damage caused by them. However, it is challenging to analyze a lot of logs obtained from PCs and servers inside an organization. Therefore, there is a need for a method of efficiently analyzing logs. In this paper, we propose a recommendation system using the ATT&CK technique, which predicts and visualizes attackers’ behaviors using collaborative filtering so that security analysts can analyze logs efficiently.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130634621","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}

{"title":"Evaluating the Predictive Ability of the Bipartite Dengue Contact Network Model","authors":"J. Labadin, B. H. Hong, W. Tiong, B. Gill, D. Perera, A. Rigit, Sarbhan Singh, Tan Cia Vei, S. M. Ghazali, J. Jelip, Norhayati Mokhtar, Wan Ming Keong","doi":"10.1109/ICOCO56118.2022.10031962","DOIUrl":"https://doi.org/10.1109/ICOCO56118.2022.10031962","url":null,"abstract":"This paper presents the predictive power analysis of the bipartite dengue contact (BDC) network model for identifying the source of dengue infection, defined as dengue hotspot. This BDC network model was earlier formulated, verified and validated using data collected in Sarawak, Malaysia. Then, a web-based BDC network system was implemented and subsequently tested by 7 other areas in Malaysia. The data collected using the system was then used to further evaluate the predictive ability of the BDC network model. The validity period of the dengue hotspots identified by the BDC network model was measured based on the accuracy of the predictive power analysis and Spearman’s Rank Correlation Coefficient (SRCC). Based on the results, using prior one-week data was sufficient to predict the dengue hotspot for the following week and subsequent two weeks. This shows that the hotspots are valid for two weeks. The accuracy for the outbreak areas is above 60%. Most of the model reported an SRCC above 0.70 which indicated a strong positive relationship between the hotspots in the targeted model and the validated model. Due to the accuracy and SRCC values obtained, it is suggested that the BDC network model can proceed further with retrospective data for other dengue outbreak areas in Malaysia and a prospective study for the areas that participated in this study.","PeriodicalId":319652,"journal":{"name":"2022 IEEE International Conference on Computing (ICOCO)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128783399","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}