Reviewerly/exHarmony huggingface dataset:
Unleashing the Power of SVD and Louvain Community Detection for Enhanced Recommendations

Recommendation systems play a vital role in delivering personalized content to users, thereby enhancing their overall experiences across diverse applications. Collaborative filtering based recommendation systems have demonstrated success through the application of matrix factorization techniques. However, the incessant growth in dataset size and complexity presents challenges regarding the scalability of recommendation algorithms. Consequently, addressing these scalability concerns becomes imperative to ensure the seamless functioning of recommendation systems in handling increasingly large and diverse datasets. This research introduces an innovative method that seamlessly integrates matrix factorization techniques and community detection algorithms to effectively tackle the scalability issue in recommendation systems. Through numerous experiments utilizing real-world datasets, the proposed method's efficiency is thoroughly assessed. These compelling findings underscore the method's potential as a promising solution for constructing robust and scalable recommendation systems effectively. Ultimately, the overarching objective is to enhance user experiences by providing personalized and relevant content recommendations that cater to the evolving needs of modern recommendation systems. By optimizing scalability and recommendation accuracy, this innovative approach seeks to elevate the efficacy and user satisfaction of recommendation systems across various domains.
Evaluating the Sentiment Analysis from Auto-Generated Summary Text Using IndoBERT Fine-Tuning Model in Indonesian News Text

Recently, online news has replaced conventional magazines and physical newspapers because of their intuitiveness and timeliness. News sites provide a comprehensive overview of important current events, serving as a valuable source for learning about a country's latest social, political, and economic issues. The government utilizes news channels to get an overview of the specific problems with sentiment analysis. However, the current system only reads news headlines to determine sentiment, so it does not thoroughly measure the opinion in the news content. This situation causes errors in sentiment reading, which should be negatively interpreted as positive or vice versa. This research tests the auto-generated summary text using the IndoBERT fine-tuning model to label the sentiment of news text. This research shows that fine-tuning IndoBERT using the human-made summaries dataset achieves the optimal outcome, with an F1-score of 75% compared to the 65% F1-Score of the auto-generated summary testing dataset. This study shows that the sentiment analysis prediction using the human-made summary dataset scores better than the sentiment analysis resulting from the Autogenerated summary testing dataset.
VietLegalQA: Unsupervised Legal Question Answering for Vietnamese Using Cloze Translation Approach

The acquisition of training data for AI modeling, especially in Natural Language Processing (NLP) and Question Answering (QA), is well-known as laborious and time-consuming process. However, the accessibility of such datasets is typically limited to specific domains and languages. In this paper, we introduce a framework designed for generating training data for such scenario, especially in the Vietnamese language and legal domain. Our approach only requires a source of document as input data, then it can generate a high quality dataset that can be used for model training from that data. We first extract named entities (NE) and part-of-speech (POS) components from these contexts, serves them as the basis for the QA pairs dataset. Subsequently, we transform these contexts into fill-in-the-blank (cloze) question formats and serve them as natural questions. Combining the answer and question pairs, we then construct a synthetic dataset. Finally, a simple filter was applied to eliminate the noise data from the synthetic dataset. In our experiments, the model using our method achieved a state-of-the-art performance on ALQAC dataset, a 68.52 in F1 score and 39.80 on exact match (EM) score.
Deep Learning and Search-Based Approach for Duplicate Bug Report Detection in Industrial Datasets

The large-scale production of software naturally produces releases, which in turn are susceptible to bugs. These bugs are then reported in bug tracking systems, leading to occurrence reports that reference the same underlying defect, known as duplicate bug reports. Identifying these duplicates poses a significant challenge, particularly in the context of global-scale software development, where a substantial volume of bug reports are reported. To address this problem, existing literature has explored the use of pairs of bugs, treating them as either duplicates or non-duplicates and leveraging classification techniques. However, when considering the practical implementation of such approaches in a production environment, the process of comparing a new bug report to the entire collection of prior bug reports becomes time-consuming and resource-intensive. Consequently, applying them in industrial environments becomes unfeasible. To overcome this challenge, this paper proposes a method that combines deep learning and search-based techniques, making the applicability of such method feasible for large datasets. It is based on a real industry case, and the results proved the approach to be useful in industrial environments.
Robust Fake Review Detection Using Uncertainty-Aware LSTM and BERT

In a web-based world driven by e-commerce, customers are quick to turn to online shopping services. However, the products available for purchase cannot be personally inspected so buyers turn to online product reviews. Consumers trust these reviews and are likely to spend more at stores with good evaluations. Sellers are well aware of this phenomenon and are not averse to using unethical methods to boost the reputation of their own products, or demerit the products of their rivals. In other words, fake reviews are quite rampant and are responsible for heavily affecting consumer purchasing decisions and business profits. In response, many fake review detection models have been extensively explored in the last decade. However, there is still a lack of robustness in these approaches. Our research addresses this gap in the field of fake review detection. We deploy the BERT and LSTM models coupled with the Monte Carlo Dropout (MCD) technique, on the Yelp Labelled Dataset comprising 10,000 hotel reviews from North America. MCD provides a representation of uncertainty by randomly dropping neurons in multiple predictions of the network. This gives us an approximation of the uncertainty. Since, fake review detection is a risky task we employ MCD to make our system robust and reliable. Our study yields an accuracy of 91.75% using the MCD-embedded BERT model. It outperforms the LSTM model overall.
A Reversible Data Hiding Approach in Medical Images Using Difference Expansion

The use of digital multimedia has become a promising way to protect sensitive information by hiding it within these media files. This technique, known as steganography, has gained popularity because digital images are readily available on public networks. However, current research in steganography faces a challenge: when a substantial amount of secret data is hidden within these images, it can lead to noticeable distortions in the image quality. This distortion makes most steganographic schemes not considered to be used for protecting sensitive data, such as the ones from medical applications. In this research, we present a new steganographic algorithm to optimize image quality after using it as a carrier of secret data. In our method, we use the spaces between the adjacent pixels to improve the imperceptibility of the image. The experimental results of our method show a significant improvement compared to the existing ones.
A Steganographic Approach to Secure Data Using Pairs-Based Difference Expansion

Within the realm of security systems, the concept of information hiding is a vast and encompassing field that spans across a wide spectrum of diverse research domains. The technological advancement in data transmission over the public network made the need for data security a field to focus on for information protection. Several research works have been proposed to use various data-hiding paradigms to improve the confidentiality of the secret data transmitted over the public network. However, the previously proposed solution did not achieve promising performance and efficiency in protecting the personal information of companies and individuals. To address this challenge, this research proposes a steganographic algorithm to enhance the security of the bitstreams of the secret data using pairs-based difference expansion in the neighboring pixels of a digital image. The experimental results of our method demonstrated a superior performance over the existing method. It is important to note that our method improved the payload capacity from 0.0585 bpp to 0.1370 bpp in the existing and proposed methods, respectively.
Examining the Impact of Cloud Computing on Indian Education

This article explores the job of distributed computing in changing and computerizing exercises in Indian advanced education associations. Distributed computing, with its adaptability, versatility, and cost-viability, holds colossal potential for these organizations, as per the report. One of the possible purposes of distributed computing in schooling is the arrangement of “Education and Learning as a Service” (ELaaS), which permits organizations to give their understudies superior grades, and customized growth opportunities. Nonetheless, distributed computing in schooling is not without its hindrances, remarkably regarding security. Another subject that requires extra investigation is the utilization of “Software as a Service” (SaaS) in advanced education. As per the report, the arrangement of distributed computing in Indian advanced education organizations has colossal possibilities for helping understudy results. The review reasons that the reception of distributed computing in Indian advanced education establishments presents huge open doors for working on the conveyance and nature of training. In any case, it additionally requires cautious preparation and the executives to address possible difficulties and dangers. Future exploration ought to investigate these issues in more detail to give an extensive comprehension of the ramifications of SaaS in training.
Pre-trained Deep Learning Networks for Advanced Visible Imagery Drone Detection and Recognition

In this paper, we introduce a state-of-the-art deep learning technique designed to accurately differentiate between drones and birds. This technique is particularly effective in reducing hazards associated with unauthorized drones, especially in airport environments where such drones can cause significant flight disruptions. Our approach involves the utilization of a meticulously compiled image dataset for testing, yielding results that surpass previous detection methods outlined in existing literature. Among the models evaluated, ResNet18 emerges as a standout, achieving an impressive average precision (AP) of 0.739 in medium area ratios. A key feature of our method is its ability not only to detect drones but also to precisely distinguish them from birds. The dataset employed in this research is derived from the publicly accessible real-world data of the 2020 Drone vs. Bird Detection Challenge.
The Future of Document Retrieval: Harnessing the Power of OpenAI and AWS Kendra

In an era inundated with data, the quest for efficient and accurate information retrieval has become paramount for organizations. Traditional enterprise search methodologies, while foundational, grapple with challenges ranging from relevance issues to the complexities of everevolving linguistic nuances. This research delves into a transformative approach to enterprise search by integrating OpenAI's Foundation Model, a state-of-the-art AI model renowned for its profound linguistic understanding, with Amazon Web Services' Kendra, a machine learning-powered search platform. Our integrated system, showcased through a meticulously designed architecture, demonstrates a remarkable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$92 \%$</tex> accuracy in document retrieval, outshining the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$78{{\% }}$</tex> achieved by traditional methods. Furthermore, user feedback sessions accentuated the system's adeptness in natural language query processing and the heightened relevance of search results. This paper not only presents the fruits of melding advanced AI with robust search platforms but also paves the way for the future of intelligent enterprise search, emphasizing adaptability, continuous learning, and the promise of a search realm that's not just efficient but profoundly insightful.
"\"Remembering” as a Decolonial Praxis in African HCI and Design"

The fourth wave of HCI sought to engage with the ethics, politics, and values of design as an engine of modernity/coloniality. In doing so, we've witnessed a series of critiques and reflections on how the broadening of context and application in the third wave does not equate to any substantial structural changes in the ontologies and epistemologies informing HCI research and design. This can be attributed to the inevitable chaos of multiplicity inherent in HCI - and issues that we reckon could further implicate the efforts towards politicizing design as applied in the context of Africa. In this conceptual paper, we take the politics of design to another level by integrating distinct religious and sociopolitical practices across Africa in showing the colonizing dimension of the entirety of modern design enterprise. We argue that remembering the onomatopoeic dynamic of Amun-RA and the Ekumeku - both as theological thoughts and political praxis that are situated in African traditions - could provide a powerful instrument for 'asking questions' and 'forming conversation' on how technology can be developed and evaluated in/from African communities. Building on this year's theme of 'beyond limit', this study showcases how decolonizing African design entails, on an abstract level, a decolonisation of the imagination. To go beyond colonially imposed limits in computing research and design, we ought to remember our histories and cultures clearly, there is a designerly power in remembering.
Usability and User Experience of Mobile Applications: A Case of Functional Illiterates in Nigeria

Despite the challenges faced by functional illiterates (FXI) when using smartphones, usability studies of mobile applications for FXI in Nigeria are still lacking in Human-Computer Interaction (HCI) research. To address this gap, we conducted a mixed (quantitative and qualitative) user study with FXI users to explore their experiences using current mobile applications for banking and shopping. Our study uses a digital skills framework (DLGF) with usability and user experience (UX) metrics to investigate user interaction, providing a novel approach to understanding users' capabilities in HCI research. Triangulating interview data with both applications' user interface (UI) data, we found that FXI users encountered challenges in certain tasks but not all. Our findings revealed below-average UX ratings of the current mobile applications compared to established benchmarks. These findings contribute to an emerging discussion about designing more inclusive and accessible UIs. We propose that using the DLGF in usability studies can provide a more comprehensive view to all stakeholders in accessibility research.
Towards Adoption of Sentence Computation Collaborative Mobile App (Scapp) for Effective Correctional/Offender Management

Recently, correctional facilities meant for offender reformation have been left out of technological advances pertaining to mobile application development and human computer interaction. Designed to basically aid offender management, the sentence computation application (ScApp) is essentially a mobile collaborative platform for sentence calculation, offender information collection and storage, processing, searching, and notification. Therefore, the study is aimed at evaluating the behavioral aspects of correctional staff's adoption of ScApp using the unified theory of acceptance and use of technology (UTAUT). The study comprised 69 participants who are officers of the Nigerian Correctional Service, selected through a multi-stage sampling technique. Based on five-point rated responses of correctional officers, the internal consistency of the ScApp was assessed using Cronbach's Alpha reliability analysis after deriving the zero-order inter-item correlation. The results showed that the independent contributions of anxiety, facilitating conditions, and attitude towards technology are predictors of participants' behavioural intentions to use the ScApp.
Cultural Considerations in AI Systems for the Global South: A Systematic Review

The field of Artificial Intelligence (AI) is leading transformative impacts across different sectors. However, these advancements are often developed with a Western-centric focus, neglecting the cultural diversity in regions such as the Global South. In this paper, we synthesize twelve research papers focusing on cultural considerations in designing AI systems for the Global South. Our findings revealed a significant focus on domains like healthcare and intelligent assistants and challenges, including usability, AI transparency, and data availability, that can hinder the design and deployment of culturally sensitive AI systems. Moreover, the results demonstrated the need to integrate cultural values to increase the acceptance and usefulness of AI systems for regions in the Global South. This paper guides future research towards developing culturally and contextually relevant AI systems for Africa, highlighting the need for increased visibility and representation of African researchers in HCI and AI domains.
Co-Design of a Patient Fall Risk Prevention Service Powered by Machine Learning

In hospitals, patients often experience falls at night, especially when they're under the influence of painkillers and anesthesia. Instead of seeking assistance, these patients, in their confused state, might impulsively try to get up, endangering their safety. Such incidents can lead to severe injuries, even fatalities, or cases where patients wander away from their rooms. These occurrences not only impact the health of the patients but also increase insurance costs for hospitals. Centro Chirurgico Toscano (CCT - Tuscany Surgery Center) and the Interaction Design Lab of the University of Siena have explored a fall prevention system using Machine Learning (ML) and image recognition. This system, installed in a CCT room, monitors patients' positions in real-time (whether they're lying, sitting, or standing) and raises an alert for any risky postures. The project favored a human-centered approach to meet the specific needs of stakeholders. The setting of the fall prevention system, and four dashboards to view, control, manage and modify the system, were co-designed for and with the CCT. The tested system has shown reliability, leading to considerations for its broader application. The goal is to promote a preventive culture in health care by leveraging technologies such as image recognition to minimize fall risks.
Leveraging Human-Computer Interaction (HCI) for Enhancing Military Operations in Nigeria

The Nigerian Military interaction with the HCI-based system in daily operation shows why they can further be assisted in enhancing the HCI-based system managing their activities. The panel discussion is on how they can further leverage the use of the HCI-based system in improving their operations in Nigeria.
Designing persuasive technology for schools to save energy

Energy efficiency is essential in overcoming environmental, economic, and energy security challenges. Public buildings, from a global perspective, are a significant source of electrical energy waste and are currently considered dominant objects in energy efficiency studies. Solutions to reduce electricity consumption in public buildings point directly to technological transformation (use of more efficient equipment and sensors) leaving a gap in the behavioral component. In this research, a persuasive technology is designed and implemented to promote energy efficiency practices in school buildings. A case study was carried out in 2 Mozambican schools to analyze the context of persuasion and then a persuasive technology was built based on Fogg's behavioral model and persuasive systems design model. The results reveal that systems that offer immediate visual and audible feedback on the energy usage are highly persuasive, promoting behavioral change and a consequent reduction in electrical energy consumption.
Baraza II: Expanding African HCI Communities and Curriculum

Even with the chaos of multiplicity and fragmentation, the inter-discipline of Human-Computer Interaction (HCI) is becoming a household name in African academia, industries, and communities. We propose this workshop to foreground the future dimension of HCI education in Africa, in terms of subject matters, philosophies, methodologies, theories, principles, and curriculum. The workshop is meant to be an expanded Baraza and/or a meeting point where pathways for living curriculums and pedagogies are identified and discussed. We aim to bring together researchers and practitioners doing HCI work in Africa and for African communities to reflect on the experiences of adopting (and translating) HCI methods and procedures, leading to the possibilities for the transfer of expertise and knowledge that could widen the pedagogical practices of HCI in Africa.
Bridging Spaces: A Student Perspective on Designing for Remote Presence with 3D Holograms

We recognize the conceptual and technical challenges of orchestrating remote presence and determining spatial features within mixed reality educational contexts. We maintain that involving stakeholders, such as primary students in early stage development of 3D immersive video technology is important to provide guidance for further technical developments. In this paper we present two explorative sessions, which were held with 8 primary students to explore specifically spatial constraints and features in remote presence scenarios. We identified remote object interactions, filtering distractions, movement, and the distinction of augmented and extended spaces as research topics of relevance to future developments of mixed reality technologies in an educational setting.
Decolonizing the Computing Curriculum: Aligning Stakeholders for an African-Centric Education

Western ideologies have largely influenced the computing curriculum in Africa at the expense of African students and their cultural heritage. This paper argues for the decolonisation of the computing curriculum to reflect the needs and interests of African students and prepare them for success in the 21st century. Decolonising the computing curriculum is important for several reasons. First, it will help to foster cultural pride and identity in African students. Second, it will bridge the generational gap between African youth and their elders. Third, it will ensure that African youth are well-equipped to lead the continent into a future steeped in authenticity. Human-centred computing (HCC) is a decolonial approach to computing that emphasises the importance of culture, community, and sustainability. HCC can be used to create computing curricula that are more relevant and accessible to African students. The paper concludes by calling for stakeholders to develop a decolonised computing curriculum for Africa. This curriculum should be based on the principles of HCC and reflect African students' needs and interests.