Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences

Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 2311 - 2340 of 13822

Full-Text Articles in Entire DC Network

Invariant Training 2d-3d Joint Hard Samples For Few-Shot Point Cloud Recognition, Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang Oct 2023

Invariant Training 2d-3d Joint Hard Samples For Few-Shot Point Cloud Recognition, Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang

Research Collection School Of Computing and Information Systems

We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-pretrained 2D model. Surprisingly, such an ensemble, though seems trivial, has hardly been shown effective in recent 2D-3D models. We find out the crux is the less effective training for the “joint hard samples”, which have high confidence prediction on different wrong labels, implying that the 2D and 3D models do not collaborate well. To this end, our proposed invariant training strategy, called INVJOINT, does not only emphasize the training more on the hard …

Go to article

Residual Pattern Learning For Pixel-Wise Out-Of-Distribution Detection In Semantic Segmentation, Y Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro Oct 2023

Residual Pattern Learning For Pixel-Wise Out-Of-Distribution Detection In Semantic Segmentation, Y Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Semantic segmentation models classify pixels into a set of known ("in-distribution") visual classes. When deployed in an open world, the reliability of these models depends on their ability to not only classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects. Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., …

Go to article

Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu Oct 2023

Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Cryptocurrency’s pseudo-anonymous nature makes it vulnerable to malicious activities. However, existing deep learning solutions lack interpretability and only support retrospective analysis of specific malice types. To address these challenges, we propose Intention-Monitor for early malice detection in Bitcoin. Our model, utilizing Decision-Tree based feature Selection and Complement (DT-SC), builds different feature sets for different malice types. The Status Proposal Module (SPM) and hierarchical self-attention predictor provide real-time global status and address label predictions. A survival module determines the stopping point and proposes the status sequence (intention). Our model detects various malicious activities with strong interpretability, outperforming state-of-the-art methods in extensive …

Go to article

Heart Disease Prediction Under Machine Learning And Association Rules Under Neutrosophic Environment, Ahmed A. El-Douh, Songfeng Lu, Ahmed Abdelhafeez, Ahmed M. Ali, Alber S. Aziz Oct 2023

Heart Disease Prediction Under Machine Learning And Association Rules Under Neutrosophic Environment, Ahmed A. El-Douh, Songfeng Lu, Ahmed Abdelhafeez, Ahmed M. Ali, Alber S. Aziz

Neutrosophic Systems with Applications

Early identification and precise prediction of heart disease have important implications for preventative measures and better patient outcomes since cardiovascular disease is a leading cause of death globally. By analyzing massive amounts of data and seeing patterns that might aid in risk stratification and individualized treatment planning, machine learning algorithms have emerged as valuable tools for heart disease prediction. Predictive modeling is considered for many forms of heart illness, such as coronary artery disease, myocardial infarction, heart failure, arrhythmias, and valvar heart disease. Resource allocation, preventative care planning, workflow optimization, patient involvement, quality improvement, risk-based contracting, and research progress are …

Go to article

Enhancing Autism Education: Exploring Interactive Videos And Ai Integration For Effective Teaching, Fatima Ahmed Alraeesi Oct 2023

Enhancing Autism Education: Exploring Interactive Videos And Ai Integration For Effective Teaching, Fatima Ahmed Alraeesi

Theses

This research focuses on enhancing autism education by integrating interactive videos and AI solutions to improve teacher training. As the number of autistic students rises, it becomes crucial for special education teachers to employ effective teaching strategies tailored to individual needs. The most effective teaching methods for autistic students involve understanding the condition and incorporating customized instruction strategies, such as adapting assignments to suit the student's needs, assisting those with difficulty speaking, and employing visual aids for better organization. The proposed solution involves utilizing interactive video technology to train teachers, bridging the gap between research and practical implementation of educational …

Go to article

Optimal Agricultural Land Use: An Efficient Neutrosophic Linear Programming Method, Maissam Jdid, Florentin Smarandache Oct 2023

Optimal Agricultural Land Use: An Efficient Neutrosophic Linear Programming Method, Maissam Jdid, Florentin Smarandache

Neutrosophic Systems with Applications

The increase in the size of the problems facing humans, their overlap, the division of labor, the multiplicity of departments, as well as the diversity of products and commodities, led to the complexity of business and the emergence of many administrative and production problems. It was necessary to search for appropriate methods to confront these problems. The science of operations research, with its diverse methods, provided the optimal solutions. It addresses many problems and helps in making scientific and thoughtful decisions to carry out the work in the best way within the available capabilities. Operations research is one of the …

Go to article

Effective Entity Augmentation By Querying External Data Sources, Christopher Buss, Jasmin Mousavi, Mikhail Tokarev, Arash Termehchy, David Maier, Stefan Lee Oct 2023

Effective Entity Augmentation By Querying External Data Sources, Christopher Buss, Jasmin Mousavi, Mikhail Tokarev, Arash Termehchy, David Maier, Stefan Lee

Computer Science Faculty Publications and Presentations

Users often want to augment and enrich entities in their datasets with relevant information from external data sources. As many external sources are accessible only via keyword-search interfaces, a user usually has to manually formulate a keyword query that extract relevant information for each entity. This approach is challenging as many data sources contain numerous tuples, only a small fraction of which may contain entity-relevant information. Furthermore, different datasets may represent the same information in distinct forms and under different terms (e.g., different data source may use different names to refer to the same person). In such cases, it is …

Go to article

Experiences Of Autistic Twitch Livestreamers: “I Have Made Easily The Most Meaningful And Impactful Relationships”, Terrance Mok, Anthony Tang, Adam Mccrimmon, Lora Oehlberg Oct 2023

Experiences Of Autistic Twitch Livestreamers: “I Have Made Easily The Most Meaningful And Impactful Relationships”, Terrance Mok, Anthony Tang, Adam Mccrimmon, Lora Oehlberg

Research Collection School Of Computing and Information Systems

We present perspectives from 10 autistic Twitch streamers regarding their experiences as livestreamers and how autism uniquely colors their experiences. Livestreaming offers a social online experience distinct from in-person, face-to-face communication, where autistic people tend to encounter challenges. Our reflexive thematic analysis of interviews with 10 participants showcases autistic livestreamers’ perspectives in their own words. Our findings center on the importance of having streamers establishing connections with other, sharing autistic identities, controlling a space for social interaction, personal growth, and accessibility challenges. In our discussion, we highlight the crucial value of having a medium for autistic representation, as well as …

Go to article

Icl-D3ie: In-Context Learning With Diverse Demonstrations Updating For Document Information Extraction, Jiabang He, Lei Wang, Yi Hu, Ning Liu, Hui Liu, Xing Xu, Heng Tao Shen Oct 2023

Icl-D3ie: In-Context Learning With Diverse Demonstrations Updating For Document Information Extraction, Jiabang He, Lei Wang, Yi Hu, Ning Liu, Hui Liu, Xing Xu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

arge language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which involves inference based on a few demonstration examples. Despite their successes in NLP tasks, no investigation has been conducted to assess the ability of LLMs to perform document information extraction (DIE) using in-context learning. Applying LLMs to DIE poses two challenges: the modality and task gap. To this end, we propose a simple but effective in-context learning framework called ICL-D3IE, which enables LLMs to perform DIE with different types of demonstration examples. Specifically, we extract the …

Go to article

Masked Diffusion Transformer Is A Strong Image Synthesizer, Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan Oct 2023

Masked Diffusion Transformer Is A Strong Image Synthesizer, Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in an image, leading to a slow learning process. To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs’ ability to contextual relation learning among object semantic parts in an image. During training, MDT operates in the latent space to mask certain tokens. Then, an asymmetric masking diffusion transformer is designed to predict masked tokens from unmasked ones while maintaining the diffusion …

Go to article

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi Oct 2023

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi

Publications and Research

In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …

Go to article

Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal Sep 2023

Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal

Turkish Journal of Electrical Engineering and Computer Sciences

Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show …

Go to article

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma Sep 2023

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …

Go to article

Ai Vs. Ai: Can Ai Detect Ai-Generated Images?, Samah S. Baraheem, Tam Van Nguyen Sep 2023

Ai Vs. Ai: Can Ai Detect Ai-Generated Images?, Samah S. Baraheem, Tam Van Nguyen

Computer Science Faculty Publications

The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Net- works (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework …

Go to article

Evaluating A Large Language Model’S Ability To Solve Programming Exercises From An Introductory Bioinformatics Course, Stephen R. Piccolo, Paul Denny, Andrew Luxton-Reilly, Samuel H. Payne, Perry G. Ridge Sep 2023

Evaluating A Large Language Model’S Ability To Solve Programming Exercises From An Introductory Bioinformatics Course, Stephen R. Piccolo, Paul Denny, Andrew Luxton-Reilly, Samuel H. Payne, Perry G. Ridge

Faculty Publications

Life scientists frequently write computer code when doing research. Computer programming can aid researchers in performing tasks that are not supported by existing tools. Programming can also help researchers to implement analytical logic in a way that documents their steps and thus enables others to repeat those steps. Many educational resources are available to teach computer programming, but this skill remains challenging for many researchers and students to master. Artificial-intelligence tools like OpenAI’s ChatGPT are able to interpret human-language requests to generate code. Accordingly, we evaluated the extent to which this technology might be used to perform programming tasks described …

Go to article

A Gentle Introduction To Chatgpt, Steven W. Holloway Sep 2023

A Gentle Introduction To Chatgpt, Steven W. Holloway

Libraries

A guest lecture on the state of commercial generative transformer technology, mid-2023, to a general audience at Staunton Public Library.

Go to article

Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang Sep 2023

Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang

Journal of System Simulation

Abstract: To address the problem of the loss of local style details in cross-domain image simulation, a ChinOperaGAN network framework suitable for opera makeup is designed from the perspective of protecting the excellent traditional culture. In order to solve the style translation of differences in two image domains, multiple overlapping local adversarial discriminators are proposed in the generative adversarial network. Since paired opera makeup data are difficult to obtain, a synthetic image is generated by combining the source image makeup mapping to effectively guide the transfer of local makeup details between images. In view of the characteristics of opera makeup …

Go to article

A First Look Into Software Security Practices In Bangladesh, Ankit Shrestha, Tanusree Sharma, Pratyasha Saha, Syed Ishtiaque Ahmed, Mahdi Nasrullah Al-Ameen Sep 2023

A First Look Into Software Security Practices In Bangladesh, Ankit Shrestha, Tanusree Sharma, Pratyasha Saha, Syed Ishtiaque Ahmed, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

Software security practices are critical in minimizing vulnerabilities and protecting unauthorized access to the code and the system. However, software security practices outside Western countries need to be better understood. This need for understanding security practices is further necessitated by the increased outsourcing of software development that can result in vulnerabilities on a global scale. This article addresses this gap, focusing on Bangladesh, a country that represents a booming software industry in the Global South. In this study, we conducted semi-structured interviews with 15 developers to understand their security perceptions and identify the factors influencing software security practices in Bangladesh. …

Go to article

Sentiment Analysis Of Public Perception Towards Elon Musk On Reddit (2008-2022), Daniel Maya Bonilla, Samuel Iradukunda, Pamela Thomas Sep 2023

Sentiment Analysis Of Public Perception Towards Elon Musk On Reddit (2008-2022), Daniel Maya Bonilla, Samuel Iradukunda, Pamela Thomas

The Cardinal Edge

As Elon Musk’s influence in technology and business continues to expand, it becomes crucial to comprehend public sentiment surrounding him in order to gauge the impact of his actions and statements. In this study, we conducted a comprehensive analysis of comments from various subreddits discussing Elon Musk over a 14-year period, from 2008 to 2022. Utilizing advanced sentiment analysis models and natural language processing techniques, we examined patterns and shifts in public sentiment towards Musk, identifying correlations with key events in his life and career. Our findings reveal that public sentiment is shaped by a multitude of factors, including his …

Go to article

Synthesizing Of Alginate Coated-Silver Nanoparticles Core-Shell For Detecting Of S-Naproxen Enantiomer In Pharmaceutical Formulation, Abdolrasol Barazandeh, Ghasem Najafpour-Darz, Afshar Alihossein, Sohrab Kazemi Sep 2023

Synthesizing Of Alginate Coated-Silver Nanoparticles Core-Shell For Detecting Of S-Naproxen Enantiomer In Pharmaceutical Formulation, Abdolrasol Barazandeh, Ghasem Najafpour-Darz, Afshar Alihossein, Sohrab Kazemi

Karbala International Journal of Modern Science

Core-shell nanoparticles were used in a unique direct UV-Vis spectrophotometric technique to detect the S-naproxen enantiomer. Silver nanoparticles (Ag NPs) covered with sodium alginate (Alg) shell, a naturally occurring biopolymer, made the core of the nanostructure. To name a few, TEM, FESEM, UV-Visible, DLS, ZP, AFM, XRD, EDX and FT-IR are among the analytical methods used to analyze the creation of the core-shell structure. Based on obtained results, spherical Ag@ Alg NPs with an average size of 27.4 nm (DTEM), hydrolytic size of 46 nm (DDLS) and surface charge of -60.9 mV were successfully synthesized. The effect of various reaction …

Go to article

Overview Of The Clef-2023 Checkthat! Lab Task 4 On Factuality Of Reporting Of News Media, Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Dilshod Azizov, Rabindra Nath Nandi, Panayotov Panayot Sep 2023

Overview Of The Clef-2023 Checkthat! Lab Task 4 On Factuality Of Reporting Of News Media, Preslav Nakov, Firoj Alam, Giovanni Da San Martino, Maram Hasanain, Dilshod Azizov, Rabindra Nath Nandi, Panayotov Panayot

Natural Language Processing Faculty Publications

We present an overview of the CLEF-2023 CheckThat! lab Task 4, which focused on predicting the factuality of reporting of entire news outlets. This is a different level of granularity compared to previous efforts, which focused on fact-checking, where the target is a claim, or fake news detection, where the target is an article. We briefly summarize the participating systems and discuss the dataset, the task, and the evaluation setup. The task attracted a large number of registrations, and eventually five teams made submissions. All participants improved over the baseline by a margin using both deep learning and traditional machine …

Go to article

Asset-Based Approaches To Multilingual Students’ Computer Science Identity Development, Sharin Rawhiya Jacob, Mark Warschauer Sep 2023

Asset-Based Approaches To Multilingual Students’ Computer Science Identity Development, Sharin Rawhiya Jacob, Mark Warschauer

Journal of Computer Science Integration

While computer science identity development has been examined in several studies, there is much to learn about the development of multilingual students’ computer science (CS) identities. To develop strong CS identities, multilingual students must engage in culturally and linguistically sustaining curriculum, pedagogy, and interaction that draws from their rich and varied resources. This theoretical paper is grounded in a justice-centered, asset-based framework that views the traditions and practices in students’ cultures and communities as strong contributors to knowledge construction in STEM. We draw on multiple studies exploring multilingual student CS identity development to better understand how their personal, familial, community-based, …

Go to article

U.S. Parents' Intentions To Use Anti-Bullying Apps: Insights From A Comprehensive Model, Brittany Wheeler, Katie Baumel, Deborah Hall, Yasin Silva Sep 2023

U.S. Parents' Intentions To Use Anti-Bullying Apps: Insights From A Comprehensive Model, Brittany Wheeler, Katie Baumel, Deborah Hall, Yasin Silva

Computer Science: Faculty Publications and Other Works

Cyberbullying has become a prominent risk for youth and an increasing concern for parents. To help parents reduce their child's cyberbullying risk, anti-bullying apps (ABAs)—mobile applications for identifying and preventing instances of cyberbullying—have been developed in recent years. Given that ABAs are an emerging technology, limited research has been conducted to understand the factors predicting parents' intentions to use them. Drawing on three interdisciplinary theoretical frameworks, a sample of parents in the U.S. recruited through Amazon Mechanical Turk completed an online survey to assess parents' knowledge of, attitudes about, and intentions to use ABAs. Participants also rated the importance of …

Go to article

Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa Sep 2023

Machine Learning Techniques For The Identification Of Risk Factors Associated With Food Insecurity Among Adults In Arab Countries During The Covid-19 Pandemic, Radwan Qasrawi, Maha Hoteit, Reema Tayyem, Khlood Bookari, Haleama Al Sabbah, Iman Kamel, Somaia Dashti, Sabika Allehdan, Hiba Bawadi, Mostafa Waly, Mohammed O. Ibrahim, Stephanny Vicuna Polo, Diala Abu Al-Halawa

All Works

BACKGROUND: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to …

Go to article

Blended Learning In The Wake Of Ict Infrastructure Deficiencies: The Case Of A Zimbabwean University, Lucia Makwasha, Sam Jnr Takavarasha, Hazel Mubango Sep 2023

Blended Learning In The Wake Of Ict Infrastructure Deficiencies: The Case Of A Zimbabwean University, Lucia Makwasha, Sam Jnr Takavarasha, Hazel Mubango

African Conference on Information Systems and Technology

In the wake of debates between actors in the Zimbabwean higher education sector about the effectiveness of e-learning models, it is important to investigate the effectiveness of using blended learning at a time when infrastructure challenges are disrupting ICT access. This paper aims to address this quest for a deeper understanding by investigating students' perceptions of blended learning at a selected Zimbabwean university. Twelve in-depth interviews were conducted with students from a Zimbabwean university that employs blended learning under an interpretivist paradigm. Vygotsky's Zone of Proximal Development (ZPD) was used for conceptualising students' cognitive development and Engestrom's (2003) Third-generation Activity …

Go to article

Factors Influencing User Adherence Towards Privacy Standards Of Internet Of Things Devices, Philip Bazanye, Walter Uys, Wallace Chigona Sep 2023

Factors Influencing User Adherence Towards Privacy Standards Of Internet Of Things Devices, Philip Bazanye, Walter Uys, Wallace Chigona

African Conference on Information Systems and Technology

The upsurge in the use of Internet of things (IoT) devices increases the likelihood of cyber-attacks on end users. The objective of the study reported here was to investigate the factors that influence IoT device users’ adherence to privacy standards. This interpretivist exploratory research was guided by a three-phased approach using activity theory. The interview questions were derived from the conceptual model and themes analysed using deductive thematic analysis. The findings indicate that a lack of adherence is driven by a lack of trust in IoT devices and service providers, as well as convenience and health factors. In addition, users’ …

Go to article

Social Media & Privacy: Understanding Privacy In The Age Of Content Creator Culture, Robert Tagoe, Raphael Amponsah, Emmanuel Awuni Kolog, Eric Afful-Dadzie Sep 2023

Social Media & Privacy: Understanding Privacy In The Age Of Content Creator Culture, Robert Tagoe, Raphael Amponsah, Emmanuel Awuni Kolog, Eric Afful-Dadzie

African Conference on Information Systems and Technology

In today's digital age, content creators are gaining public attention and becoming highly influential. With that increased influence, it is important to acknowledge the privacy concerns within this culture. This interpretive research study seeks to identify and understand the dynamics of privacy within the content creator culture. This research will leverage information from interviews with content creators from various social media platforms such as YouTube, Instagram, Facebook, and TikTok. Using theories to understand the phenomena, theories of privacy calculus, privacy paradox and self-disclosure, will be used to view how content creators define and navigate privacy, strategies employed to control personal …

Go to article

Digital Transformation In Government: A Bibliometric Scoping Of African Aspirations And The Realities, Hossana Twinomurinzi, Sibukele Gumbo Sep 2023

Digital Transformation In Government: A Bibliometric Scoping Of African Aspirations And The Realities, Hossana Twinomurinzi, Sibukele Gumbo

African Conference on Information Systems and Technology

African countries have persistently ranked poorly in the United Nations E-Government Development index (EGDI). This study therefore sought to make sense of the nature and form of digital transformation (DX) in African governments. It used a bibliometric analysis and a critical realist lens to explore some of the underlying structures and generative mechanisms that influence DX. The key findings from 606 scholarly publications over the three decades of DX research reveals marked sharp contrasts, with South Africa dominating the research on DX, an underrepresentation of DX research from most African nations, and some influence from non-African nations, particularly the United …

Go to article

Codesigning A Big Data Analytic Tool For Girl Child Learner Drop Out From Eastern Cape Province -South Africa, Nobert Rangarirai Jere, Nosipho Carol Mavuso, Nelly Sharpley Sep 2023

Codesigning A Big Data Analytic Tool For Girl Child Learner Drop Out From Eastern Cape Province -South Africa, Nobert Rangarirai Jere, Nosipho Carol Mavuso, Nelly Sharpley

African Conference on Information Systems and Technology

Developing sustainable solutions is critical for adoption of digital solutions. As the high number of learners dropping out of school continues to increase, it is critical to find innovative ways of predicting and preventing high drop out. Current literature has documented a number of factors that influence learner drop out. Innovative ideas, techniques and activities have been undertaken to motivate learners to stay at school. It is unfortunate that most of the initiatives have not helped to avoid drop out of learners. The study is based on a mixed approached that was used targeting female learns from Oliver Tambo District …

Go to article

A Social Profile-Based E-Learning Model, Xola Ntlangula Sep 2023

A Social Profile-Based E-Learning Model, Xola Ntlangula

African Conference on Information Systems and Technology

Many High Education Institutions (HEIs) have migrated to blended or complete online learning to cater for less interruption with learning. As such, there is a growing demand for personalized e-learning to accommodate the diversity of students' needs. Personalization can be achieved using recommendation systems powered by artificial intelligence. Although using student data to personalize learning is not a new concept, collecting and identifying appropriate data is necessary to determine the best recommendations for students. By reviewing the existing data collection capabilities of the e-learning platforms deployed by public universities in South Africa, we were able to establish the readiness of …

Go to article

First Prev Next Last