Reviewerly/exHarmony huggingface dataset:
GoSage: Heterogeneous Graph Neural Network Using Hierarchical Attention for Collusion Fraud Detection

We propose a graph learning mechanism in a unique but very practical heterogeneous setting, where multiple types of relations can exist between pair of nodes of same or different type. Such problem setting is ubiquitous in digital product/service platform. For example, food delivery or digital payment platform where customer and merchant can be connected by multiple relations such as transactions, shared device and or source/sync of fund etc. Capturing the node representation which encapsulates the multiple facets of information represented by each relation type is vital to detect fraudulent and collusive interaction pattern in the interconnected digital world. We at Gojek, design and deploy a multi-level attention based GNN, which we name as GoSage to intelligently capture the inter-node and inter-relational dependencies in the connected world of our consumers and merchants. We train GoSage in a semi-supervised fashion to detect collusion and fraud in our service and payment network. Through performance comparison as well as ablation study, we establish that our framework is able to outperform state-of-the-art GNN. Multiple experiments and evaluation also establish superior performance even at industrial application scale.
THEORETICAL ANALYSIS OF PROFESSIONAL BURNOUT PROBLEMS

The aim of the research is to analyze the essence, components and symptoms of the professional burnout problem, documented in the scientific and methodological literature. The analysis of the scientific literature shows that burnout is a complex phenomenon that affects employees and has a adverse consequences on their well-being and organizational performance. Thus, by studying and classifying theories, we can develop a comprehensive image of burnout which includes personal characteristics, organizational factors, and their complex interactions. This comprehensive understanding is crucial for developing effective strategies and measures to prevent and manage burnout in the workplace.
A few little steps beyond Knuth’s Boolean Logic Table with Neutrosophic Logic: A Paradigm Shift in Uncertain Computation

The present article delves into the extension of Knuth’s fundamental Boolean logic table to accommodate the complexities of indeterminate truth values through the integration of neutrosophic logic (Smarandache Christianto, 2008). Neutrosophic logic, rooted in Florentin Smarandache’s groundbreaking work on Neutrosophic Logic (cf. Smarandache, 2005, and his other works), introduces an additional truth value, ‘indeterminate,’ enabling a more comprehensive framework to analyze uncertainties inherent in computational systems. By bridging the gap between traditional boolean operations and the indeterminacy present in various real-world scenarios, this extension redefines logic tables, introducing neutrosophic operators that capture nuances beyond the binary realm. Through a thorough exploration of neutrosophic logic's principles and its implications in computational paradigms, this study proposes a novel approach to logic design that accommodates uncertain, imprecise, and incomplete information. This paradigm shift in logic tables not only broadens the spectrum of computing methodologies but also holds promise in fields such as decision-making systems and data analytics. This article amalgamates insights from over twelve key references encompassing seminal works in boolean logic, neutrosophic logic, and their applications in diverse scientific and computational domains, aiming to pave the way for a more robust and adaptable logic framework in computation.
‘Truthmaker Semantics for Relevance Logic’: Response to ‘Fine’s Semantics for Relevance Logic and its Relevance’ by Katalin Bimbó and J. Michael Dunn

I outline a truthmaker semantics for relevanced logic.
Knowledge Management Orientation as SME Resilience in Disruption Era

This article explores the challenges posed by the disruptive era in the business environment and the importance of adapting to change. It emphasizes the role of Knowledge Management Orientation (KMO) in improving company performance and resilience. The article discusses key components of KMO, including organizational memory, knowledge sharing, knowledge absorption, and knowledge receptivity. Practical strategies for implementing KMO are provided, highlighting the significance of knowledge retention for Micro, Small, and Medium Enterprises (MSMEs) to navigate the era of disruption successfully.
‘The Ghost of Impossibility’: Response to Peter Verdée’s ‘Truthmakers and Relevance for FDE, LP, K3 and CL’

I how, in contrast to Verdée, that one may think of classical consequence as a form of inexact entailment within a standard modalized state space.
Fine on the Possibility of Vagueness

In his paper ‘The possibility of vagueness’ (Fine in Synthese 194(10):3699–3725, 2017), Kit Fine proposes a new logic of vagueness, CL, that promises to provide both a solution to the sorites paradox and a way to avoid the impossibility result from Fine (Philos Perspect 22(1):111–136, 2008). The present paper presents a challenge to his new theory of vagueness. I argue that the possibility theorem stated in Fine (Synthese 194(10):3699–3725, 2017), as well as his solution to the sorites paradox, fail in certain reasonable extensions of the language of CL. More specifically, I show that if we extend the language with any negation operator that obeys reductio ad absurdum, we can prove a new impossibility result that makes the kind of indeterminacy that Fine takes to be a hallmark of vagueness impossible. I show that such negation operators can be conservatively added to CL and examine some of the philosophical consequences of this result. Moreover, I demonstrate that we can define a particular negation operator that behaves exactly like intuitionistic negation in a natural and unobjectionable propositionally quantified extension of CL. Since intuitionistic negation obeys reductio, the new impossibility result holds in this propositionally quantified extension of CL. In addition, the sorites paradox resurfaces for the new negation.
Truthmaker Semantics for Epistemic Logic

We explore some possibilities for developing epistemic logic using truthmaker semantics. We identify three possible targets of analysis for the epistemic logician. We then list some candidate epistemic principles and review the arguments that render some controversial. We then present the classic Hintikkan approach to epistemic logic and note—as per the ‘problem of logical omniscience’—that it validates all of the aforementioned principles, controversial or otherwise. We then lay out a truthmaker framework in the style of Kit Fine and present six different ways of extending this semantics with a conditional knowledge operator, drawing on notions of implication and content that are prominent in Fine’s work. We demonstrate that different logics are thereby generated, bearing on the aforementioned epistemic principles. Finally, we offer preliminary observations about the prospects for each logic.
A New Semantic Framework for the Logic of Worldly Grounding (and Beyond)

I compare the semantic approach to the logic of worldly grounding put forward in my “Grounding and Truth-Functions” (2010) with the approach developed by Fine in “Guide to Ground” (2012a) and “The Pure Logic of Ground” (2012b). I argue that both are defective in some respects, and offer an alternative approach in the same spirit but based on a new semantic framework, which combines the best aspects of the frameworks within which the previous approaches were developed.
Bibliometric Analysis of Customers’ Purchase Intention in e-commerce

This study aims to conduct a bibliometric analysis of purchase intentions in e-commerce published in 2001 to 2022. This study shows publication trends by utilizing scientific mapping and analyzing publication performance. This study also identified the contributions of authors, journals, countries, and articles using bibliometric analysis. Several countries have contributed to publications of purchase intention in e-commerce. China and the United States have the largest contribution in scientific publications around the world. In addition, "The Journal of retailing and customer services" published the most articles in this field. According to the citation analysis of the author, Gefen D., Karahanna. E, Straub D.W. has a total of 5,315 citations. Another result found that some keywords have large nodes related to their frequency of occurrence and represent information about the relationship between keywords. A visualization of publication trends is presented to serve as a reference and guidance for researchers in future studies.
Robust Environmental Perception of Semantic Segmentation

Semantic segmentation is a fundamental computer vision task and has a wide range of applications [1–3].
Robust Environmental Perception of Monocular 3D Object Detection

The main role of monocular vision based 3D object detection in intelligent vehicles is to extract obstacle information from the environment, including the category of object, location and orientation. Utilizing monocular cameras for this purpose has several advantages, such as being cost-effective and easily accessible compared to other types of sensors. The realization of 3D object detection through monocular vision alone has considerable potential for both commercial and research purposes. In the context of intelligent vehicles, perception systems play a key role by accurately assessing the surrounding environmental conditions. These systems generate reliable observations, which are essential for tasks such as prediction and planning. Within this framework, 3D object detection is a key feature that allows systems to predict the location, size and class of important 3D objects in the vicinity of intelligent vehicles. This chapter of the book is dedicated to this task. In order to address the different aspects of accuracy and robustness, two approaches, FANet and AMNet, are introduced.
GAME-BASED INSTRUCTION: ENHANCING THE LEARNER'S RETENTION IN WORLD HISTORY SUBJECT

This research aims to determine the influence of game-based instruction in improving students' learning retention. Ascertain the difference in the students' learning retention of the group and individual game-based instruction. The researchers utilized Action Research Design, specifically Collaborative Action Research Design. The data collection was done through both quantitative and qualitative approaches. A game-based instruction and interview were employed to collect the required data. Based on the results the respondents found the 5Ps as an assessment tool that helps retain information. This indicates that collaborative game-based activity is effective for the students in acquiring knowledge and skills. The results indicate that the student’s performance in Group game-based obtained better scores than the Individual game-based. Results indicated that group game-based instruction is highly beneficial and significantly contributes to the enhanced learning retention of the students.
Focus on Text Producers: Plain and Easy Language in the Swiss Multilingual Institutional Context

This paper was carried out as part of the MACSI project (Multilingual Accessible Communication in Swiss Institutions) and focuses on a case study carried out with the Federal Statistical Office (FSO). The broad aim of this cooperation with the FSO is to evaluate ways of implementing multilingual accessible expert-lay communication on statistics. Switzerland boasts a long-established tradition of clear writing at institutional level. Being a multilingual country, accessible communication is institutionally mandatory in all its official languages.&#x0D; While most of the research on accessible communication is text and recipient-based, in this study we focused primarily on drafters, who are specialized experts in different domains, as well as on translators, who have to face the challenges of interlinguistic transfer of both specialized and plain communication. This paper presents the results from a questionnaire on accessibility submitted to drafters and translators within the FSO. The answers show that drafters address several target groups with the same text and or they produce different texts for different target groups. Moreover, both drafters and translators have little knowledge and experience with plain and easy language, although most of them are interested in putting more effort into writing lay-friendly texts. Thus, there is a need for sensitising and training drafters and translators so that they are able to adapt content, level of technicality and language to the different levels of expertise when producing different multilingual accessible texts.
The Effectiveness of Artificial Intelligence Teaching Methods in Art Subject Classrooms

The subject of art is highly subjective, and each student has his or her own unique way of creation and expression. This makes teachers face certain challenges in the teaching process and need to flexibly respond to the individual needs of different students. This study explores the effectiveness of artificial intelligence teaching methods in art subject classrooms and evaluates its impact on students' academic performance and satisfaction. This study adopted an experimental design and randomly divided students into experimental groups and control groups. The experimental group used artificial intelligence-assisted teaching methods, including personalized learning support, real-time feedback, and independent learning opportunities; the control group used traditional teaching methods. The effectiveness of the artificial intelligence teaching method was evaluated by comparing the academic performance and satisfaction of the two groups of students. Experimental results showed that the effective teaching method of using artificial intelligence into art subject classrooms had a positive impact on students' academic performance. 80% of students in the experimental group expressed satisfaction with the artificial intelligence teaching method, which showed that students had a positive attitude towards it and believed that this teaching method could provide better learning experience and learning results.
Deep Learning Framework for the Detection of Invasive Ductal Carcinoma

Invasive ductal carcinoma (IDC) is the type of breast cancer that occurs most frequently. IDC detection is a laborious process and important task. Furthermore, accurate IDC detection is necessary to provide patients with appropriate medical care. Deep learning technique is far more reliable in these kinds of medical scenarios. This study proposes a deep learning model for identifying IDC in breast cancer histopathology images as a tumor is malignant or benign. Residual Network (ResNet) is a component of our deep learning model. Extract the similar features and train to identify future images; this residual network is given tagged BH image data. As a result, it can recognize the presence of IDC in images of breast tissue lesions. The proposed model produces higher accuracy than the existing models.
Object Localization in Emoji-Based Social Networks Using Deep Learning Techniques

Contrary to picture classification, object localization is a computer vision issue for which, despite the use of deep learning, solutions have not yet reached human level performance. Moving sliding windows over the image, teaching neural architecture to forecast bounding boxes, and applying venerable image processing methods like Scale Invariant Feature Transform (SIFT) and region proposals are common ways to approach this issue in the framework of deep learning. The above-mentioned techniques locate images by using the structure of the categorization neural network. Emojis (such as smileys, memes, and hearts) are frequently used by members of online or chat social networks to express the emotions behind their textual interactions in an effort to improve interpretability, particularly for brief polysemous words. Any chat or social network that uses semantic-based context recognition technologies may understand text-based emoticons (i.e., emojis made up of a combination of symbols and characters) and convert them into audio data (e.g., text-to-speech readers for individuals with vision impairment). For the purpose of object localization, the proposed method operates under the presumption that there is only one object present in every given image. The proposed Convolution Neural Networks’ (CNNs) model can successfully categorize and localize that object. The objective of the proposed study is to (i) create synthetic data for model training, (ii) create custom metrics and callbacks in Keras, (iii) create and train a multi-output neural network to perform object localization. The outcome of the work is to locate the emoji, that can be used for sentiment analysis.
Analysis of Various Video-Based Human Action Recognition Techniques Using Deep Learning Techniques

Human action recognition is the ability to identify and naming activities using Artificial Intelligence (AI) from the collected movement raw information through variety of resources. Distinguishing human activities from images or video sequences is a challenging task because of problems, including background untidiness, biased occlusion, and scale changes. In this survey, a complete reassess of modern and high-tech research advances in the field of human motion categorization is explicated. In particular, human activity recognition methods are classified into four categories according to the methods used. Moreover, the review is prepared based on the published year of the article, the method used for research, and performance metrics. Finally, the research gaps and concerns of systems are explained for raising an efficient practice for human action recognition techniques using deep learning approaches.
Fusion of Variational Autoencoder-Generative Adversarial Networks and Siamese Neural Networks for Face Matching

In this research paper, we propose a new methodology for content-based image retrieval, wherein the user queries the dataset by a visual example and our proposed model efficiently retrieves similar images from the dataset. Our method consists of a modified variational autoencoder for global feature extraction and a Siamese Network as a similarity measure between the query image and the dataset, renovating the current models still in practice that use autoencoders and traditional feature vectors for image enhancing and image retrieval respectively. With this new model, we not only are able to retrieve images at higher efficiency but also, we have gone a step closer toward identifying a person’s face even if it is disguised. We plan to evaluate the functioning ability of our framework using the CelebFaces Attributes Dataset, which contains more than 200,000 images with 40 binary attribute annotations. We expect to see decent improvements in accuracy and precision compared to the traditional image retrieval methods.
A Cloud Secure Storage Mechanism Based on Data Dispersion and Encryption

Abstract: This work is entitled “A Cloud Secure Storage Mechanism Based on Data Dispersion and Encryption”. In today's digital era, the need for secure storage mechanisms is paramount due to the increasing amount of sensitive data being generated and shared. Cloud storage has emerged as a popular solution for storing and accessing data remotely. However, concerns about data privacy and security have arisen, leading to the development of innovative approaches to ensure the confidentiality and integrity of stored data. Cloud computing provides a solution to reduce the adverse environmental impacts and saves energy
Augmented Reality in Education

Abstract: Augmented reality-based education platforms are a new initiative that aims to change the way students interact with the curriculum. The program uses advanced computer vision to reconcile the physical world of books, photographs and educational models with a rich digital content system, multimedia language. By doing this, it creates an interactive and interactive learning experience that encourages students to understand complex concepts. The main goal of the program is to bridge the gap between traditional education and the dynamic digital age. The platform transforms static learning materials into interactive three-dimensional learning tools by integrating augmented reality (AR). Users simply point their mobile phone or AR-equipped glasses at a physical object andthe app will quickly find it and provide important details suchas 3D models, videos and detailed descriptions. Key elementsof an AR-based learning platform include a powerful computer vision system for object recognition, a user-friendly mobile appor AR glasses interface, a comprehensive library of educational content, and user experience management. Integration of these elements allows students to explore complex concepts on their own in an engaging way. Teachers can also use this platform to develop learning content and customize classes to meet individuallearning needs. The program incorporates educational innova- tions and remaining traditional issues for understanding and collaboration. It has the potential to improve classroom learning, distance learning, and lifelong learning. Augmented realitybased education platforms represent a major breakthrough in education technology, integrating physical and digital spaces to empower students of all ages.
An assessment of the impact of the banking sector on the economic growth of sub-Saharan African countries in the post financial crises

This study investigated the influence of banking sector on economic growth in sub-Saharan Africa (SSA) during the post financial crises of 2007/2008. In the last one and the half decades, most SSA countries have made remarkable strides in terms of the development of their banking sectors as well as enacting policies that will boost Africa’s economic growth as documented in various reports of the World Bank and International Monetary Fund (IMF. Despite all these positive scores and developments made by the banking sectors in Africa, growth has been hampered in the last one and the half decades. Moreover, most African countries witnessed unsteady and hampered growth in their economies during the period after the crises despite the adjudged weak linkages of banking sectors across Africa to the sub-prime mortgage market and asset-backed securities, and limited exposure to complex financial instruments among others. Thus, we utilized the Pooled Mean Group Autoregressive Distributed Lag (PMG/ARDL) estimation technique to analyze the secondary data spanning from 2009 to 2020. The empirical result revealed, among others, that the level of development of the banking sectors in the region influenced economic growth in the region during the aforesaid period. Furthermore, any short run deviation or shock is normally corrected in the long run at the speed of 82.85%. Curiously, this study revealed that financial deepening and the degree of monetization in the economies are the main drivers of short and long run growth in the post crises period. Thus, this study recommended that policy makers in the region should pay great attention to deepening their economies as this study has shown that it is the main driver of growth in the post financial crises economies of sub Saharan Africa.
What Makes LLMs Large?

By now you should have a high-level understanding of the concepts of natural language processing and how the transformer architecture and attention mechanisms revolutionized the NLP field and how it changed the way we look at language modeling. Now we are ready to step into our main topic: large language models. You might be wondering what makes a large language model. Is an LLM the same as a transformer? And, more importantly, why do we call them “large” language models? Let’s find out.