OpenRaiser/Intern-Atlas huggingface dataset:
The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Values of Blockchain Technologies, assessing the Opportunities, and defining the Financial and Cybersecurity Risks of the Metaverse
Venue: arXiv cs.CY 2023
Year: 2023

This paper contextualises the common queries of "why is crypto crashing?" and "why is crypto down?", the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency. The study examines blockchain technologies and their pivotal role in the evolving Metaverse, shedding light on topics such as how to invest in cryptocurrency, the mechanics behind crypto mining, and strategies to effectively buy and trade cryptocurrencies. Through an interdisciplinary approach, the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse. Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies, the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies. Moreover, it probes into both enduring and dubious crypto projects, drawing a distinct line between genuine blockchain applications and Ponzi-like schemes. The conclusion resolutely affirms the continuing dominance of blockchain technologies, underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors.
Aviation Safety Risk Analysis and Flight Technology Assessment Issues
Venue: arXiv cs.CY 2023
Year: 2023

This text highlights the significance of flight safety in China's civil aviation industry and emphasizes the need for comprehensive research. It focuses on two main areas: analyzing exceedance events and statistically evaluating non-exceedance data. The challenges of current approaches lie in insufficient cause analysis for exceedances. The proposed solutions involve data preprocessing, reliability assessment, quantifying flight control using neural networks, exploratory data analysis, flight personnel skill evaluation with machine learning, and establishing real-time automated warnings. These endeavors aim to enhance flight safety, personnel assessment, and warning mechanisms, contributing to a safer and more efficient civil aviation sector.
Enhancing interoperability among health information systems in low- and middle- income countries: a review of challenges and strategies
Venue: arXiv cs.CY 2023
Year: 2023

The review article aims to provide an overview of the challenges and strategies for enhancing interoperability among health information systems in low- and middle- income countries (LMICs). Achieving interoperability in LMICs presents unique challenges due to various factors, such as limited resources, fragmented health information systems, and diverse health IT infrastructure. The methodology involves conducting a comprehensive literature review, synthesising findings, identifying challenges and strategies, analysing and interpreting results, and writing and finalising the article. The article highlights that the interoperability challenges include a lack of standardisation, fragmented systems, limited resources, and data privacy concerns. The article proposes strategies to enhance interoperability in LMICs, such as standardisation of data formats and protocols, consolidation of health information systems, investment in health IT infrastructure, and capacity building of health IT professionals in LMICs. The article aims to provide insights into the current state and potential strategies for enhancing interoperability among health information systems in LMICs, intending to improve healthcare delivery and outcomes in these KEYWORDS Interoperability, Health information systems, low and middle-income countries (LMICs), challenges, strategies, standardisation
Education in the age of Generative AI: Context and Recent Developments
Venue: arXiv cs.CY 2023
Year: 2023

With the emergence of generative artificial intelligence, an increasing number of individuals and organizations have begun exploring its potential to enhance productivity and improve product quality across various sectors. The field of education is no exception. However, it is vital to notice that artificial intelligence adoption in education dates back to the 1960s. In light of this historical context, this white paper serves as the inaugural piece in a four-part series that elucidates the role of AI in education. The series delves into topics such as its potential, successful applications, limitations, ethical considerations, and future trends. This initial article provides a comprehensive overview of the field, highlighting the recent developments within the generative artificial intelligence sphere.
On the culture of open access: the Sci-hub paradox
Venue: arXiv cs.CY 2023
Year: 2023

Shadow libraries, also known as ''pirate libraries'', are online collections of copyrighted publications that have been made available for free without the permission of the copyright holders. They have gradually become key players of scientific knowledge dissemination, despite their illegality in most countries of the world. Many publishers and scientist-editors decry such libraries for their copyright infringement and loss of publication usage information, while some scholars and institutions support them, sometimes in a roundabout way, for their role in reducing inequalities of access to knowledge, particularly in low-income countries. Although there is a wealth of literature on shadow libraries, none of this have focused on its potential role in knowledge dissemination, through the open access movement. Here we analyze how shadow libraries can affect researchers' citation practices, highlighting some counter-intuitive findings about their impact on the Open Access Citation Advantage (OACA). Based on a large randomized sample, this study first shows that OA publications, including those in fully OA journals, receive more citations than their subscription-based counterparts do. However, the OACA has slightly decreased over the seven last years. The introduction of a distinction between those accessible or not via the Scihub platform among subscription-based suggest that the generalization of its use cancels the positive effect of OA publishing. The results show that publications in fully OA journals are victims of the success of Sci-hub. Thus, paradoxically, although Sci-hub may seem to facilitate access to scientific knowledge, it negatively affects the OA movement as a whole, by reducing the comparative advantage of OA publications in terms of visibility for researchers. The democratization of the use of Sci-hub may therefore lead to a vicious cycle, hindering efforts to develop full OA strategies without proposing a credible and sustainable alternative model for the dissemination of scientific knowledge.
How Beaufort, Neumann and Gates met? Subject integration with spreadsheeting
Venue: arXiv cs.CY 2023
Year: 2023

Computational thinking should be the fourth fundamental skill, along with reading, writing, and arithmetic (3R). To reach the level where computational thinking skills, especially digital problem solving have their own schemata, there is a long way to go. In the present paper, a novel approach is detailed to support subject integration and building digital schemata, on the well-known Beaufort scale. The conversion of a traditional, paper-based problem and a data retrieval process are presented within the frame of a Grade 8 action research study. It is found that both students content knowledge and their digital skills developed more efficiently than in traditional course book and decontextualized digital environments. Furthermore, the method presented here can be adapted to any paper-based problems whose solutions would be more effective in a digital environment and which offer various forms for building schemata both in the subject matter and informatics.
Role of ICT Innovation in Perpetuating the Myth of Techno-Solutionism
Venue: arXiv cs.CY 2023
Year: 2023

Innovation in Information and Communication Technology has become one of the key economic drivers of our technology dependent world. In popular notion, the tech industry or how ICT is often known has become synonymous to all technologies that drive modernity. Digital technologies have become so pervasive that it is hard to imagine new technology developments that are not totally or partially influenced by ICT innovations. Furthermore, the pace of innovation in ICT sector over the last few decades has been unprecedented in human history. In this paper we argue that, not only ICT had a tremendous impact on the way we communicate and produce but this innovation paradigm has crucially shaped collective expectations and imagination about what technology more broadly can actually deliver. These expectations have often crystalised into a widespread acceptance, among general public and policy makers, of technosolutionism. This is a belief that technology not restricted to ICT alone can solve all problems humanity is facing from poverty and inequality to ecosystem loss and climate change. In this paper we show the many impacts of relentless ICT innovation. The spectacular advances in this sector, coupled with corporate power that benefits from them have facilitated the uptake by governments and industries of an uncritical narrative of techno-optimist that neglects the complexity of the wicked problems that affect the present and future of humanity.
Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
Venue: arXiv cs.CV 2023
Year: 2023

The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing. The 2022 IEEE VIP Cup asked undergraduate students to develop a system capable of distinguishing pristine images from generated ones. The interest in this topic stems from the incredible advances in the AI-based generation of visual data, with tools that allows the synthesis of highly realistic images and videos. While this opens up a large number of new opportunities, it also undermines the trustworthiness of media content and fosters the spread of disinformation on the internet. Recently there was strong concern about the generation of extremely realistic images by means of editing software that includes the recent technology on diffusion models. In this context, there is a need to develop robust and automatic tools for synthetic image detection.
Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling
Venue: arXiv cs.SE 2023
Year: 2023

Modern agile software projects are subject to constant change, making it essential to re-asses overall delay risk throughout the project life cycle. Existing effort estimation models are static and not able to incorporate changes occurring during project execution. In this paper, we propose a dynamic model for continuously predicting overall delay using delay patterns and Bayesian modeling. The model incorporates the context of the project phase and learns from changes in team performance over time. We apply the approach to real-world data from 4,040 epics and 270 teams at ING. An empirical evaluation of our approach and comparison to the state-of-the-art demonstrate significant improvements in predictive accuracy. The dynamic model consistently outperforms static approaches and the state-of-the-art, even during early project phases.
Generativism: the new hybrid
Venue: arXiv cs.CY 2023
Year: 2023

Generative Artificial Intelligence (GenAI) in Education has in a few short months moved from being the topic of discussion around speculative education futures to a very concrete reality. It is clear that the future of education, as all industries, is collaboration with GenAI. GenAI attributes make it well suited for social and constructivist approaches to learning that value collaboration, community and the construction of knowledge and skills through active learning. This article presents an approach to designing education in collaboration with GenAI, based on digital education frameworks adapted for this new hybrid of the AI age.
Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

With recent advancements in the field of communications and the Internet of Things, vehicles are becoming more aware of their environment and are evolving towards full autonomy. Vehicular communication opens up the possibility for vehicle-to-infrastructure interaction, where vehicles could share information with components such as cameras, traffic lights, and signage that support a countrys road system. As a result, vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data used to make driving safer and more convenient. With 5G cellular networks and beyond, there is going to be more data bandwidth available on our roads, but it may be heterogeneous because of limitations like line of sight, infrastructure, and heterogeneous traffic on the road. This paper addresses the problem of route planning for autonomous vehicles in urban areas accounting for both driving time and data transfer needs. We propose a novel reinforcement learning solution that prioritizes high bandwidth roads to meet a vehicles data transfer requirement, while also minimizing driving time. We compare this approach to traffic-unaware and bandwidth-unaware baselines to show how much better it performs under heterogeneous traffic. This solution could be used as a starting point to understand what good policies look like, which could potentially yield faster, more efficient heuristics in the future.
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

To alleviate the expensive human labeling, semi-supervised semantic segmentation employs a few labeled images and an abundant of unlabeled images to predict the pixel-level label map with the same size. Previous methods often adopt co-training using two convolutional networks with the same architecture but different initialization, which fails to capture the sufficiently diverse features. This motivates us to use tri-training and develop the triple-view encoder to utilize the encoders with different architectures to derive diverse features, and exploit the knowledge distillation skill to learn the complementary semantics among these encoders. Moreover, existing methods simply concatenate the features from both encoder and decoder, resulting in redundant features that require large memory cost. This inspires us to devise a dual-frequency decoder that selects those important features by projecting the features from the spatial domain to the frequency domain, where the dual-frequency channel attention mechanism is introduced to model the feature importance. Therefore, we propose a Triple-view Knowledge Distillation framework, termed TriKD, for semi-supervised semantic segmentation, including the triple-view encoder and the dual-frequency decoder. Extensive experiments were conducted on two benchmarks, \ie, Pascal VOC 2012 and Cityscapes, whose results verify the superiority of the proposed method with a good tradeoff between precision and inference speed.
From Text to Trends: A Unique Garden Analytics Perspective on the Future of Modern Agriculture
Venue: arXiv cs.AI 2023
Year: 2023

Data-driven insights are essential for modern agriculture. This research paper introduces a machine learning framework designed to improve how we educate and reach out to people in the field of horticulture. The framework relies on data from the Horticulture Online Help Desk (HOHD), which is like a big collection of questions from people who love gardening and are part of the Extension Master Gardener Program (EMGP). This framework has two main parts. First, it uses special computer programs (machine learning models) to sort questions into categories. This helps us quickly send each question to the right expert, so we can answer it faster. Second, it looks at when questions are asked and uses that information to guess how many questions we might get in the future and what they will be about. This helps us plan on topics that will be really important. It's like knowing what questions will be popular in the coming months. We also take into account where the questions come from by looking at the Zip Code. This helps us make research that fits the challenges faced by gardeners in different places. In this paper, we demonstrate the potential of machine learning techniques to predict trends in horticulture by analyzing textual queries from homeowners. We show that NLP, classification, and time series analysis can be used to identify patterns in homeowners' queries and predict future trends in horticulture. Our results suggest that machine learning could be used to predict trends in other agricultural sectors as well. If large-scale agriculture industries curate and maintain a comparable repository of textual data, the potential for trend prediction and strategic agricultural planning could be revolutionized. This convergence of technology and agriculture offers a promising pathway for the future of sustainable farming and data-informed agricultural practices
ChainsFormer: A Chain Latency-aware Resource Provisioning Approach for Microservices Cluster
Venue: arXiv cs.DC 2023
Year: 2023

The trend towards transitioning from monolithic applications to microservices has been widely embraced in modern distributed systems and applications. This shift has resulted in the creation of lightweight, fine-grained, and self-contained microservices. Multiple microservices can be linked together via calls and inter-dependencies to form complex functions. One of the challenges in managing microservices is provisioning the optimal amount of resources for microservices in the chain to ensure application performance while improving resource usage efficiency. This paper presents ChainsFormer, a framework that analyzes microservice inter-dependencies to identify critical chains and nodes, and provision resources based on reinforcement learning. To analyze chains, ChainsFormer utilizes light-weight machine learning techniques to address the dynamic nature of microservice chains and workloads. For resource provisioning, a reinforcement learning approach is used that combines vertical and horizontal scaling to determine the amount of allocated resources and the number of replicates. We evaluate the effectiveness of ChainsFormer using realistic applications and traces on a real testbed based on Kubernetes. Our experimental results demonstrate that ChainsFormer can reduce response time by up to 26% and improve processed requests per second by 8% compared with state-of-the-art techniques.
Improving Machine Learning Robustness via Adversarial Training
Venue: arXiv cs.LG 2023
Year: 2023

As Machine Learning (ML) is increasingly used in solving various tasks in real-world applications, it is crucial to ensure that ML algorithms are robust to any potential worst-case noises, adversarial attacks, and highly unusual situations when they are designed. Studying ML robustness will significantly help in the design of ML algorithms. In this paper, we investigate ML robustness using adversarial training in centralized and decentralized environments, where ML training and testing are conducted in one or multiple computers. In the centralized environment, we achieve a test accuracy of 65.41% and 83.0% when classifying adversarial examples generated by Fast Gradient Sign Method and DeepFool, respectively. Comparing to existing studies, these results demonstrate an improvement of 18.41% for FGSM and 47% for DeepFool. In the decentralized environment, we study Federated learning (FL) robustness by using adversarial training with independent and identically distributed (IID) and non-IID data, respectively, where CIFAR-10 is used in this research. In the IID data case, our experimental results demonstrate that we can achieve such a robust accuracy that it is comparable to the one obtained in the centralized environment. Moreover, in the non-IID data case, the natural accuracy drops from 66.23% to 57.82%, and the robust accuracy decreases by 25% and 23.4% in C&W and Projected Gradient Descent (PGD) attacks, compared to the IID data case, respectively. We further propose an IID data-sharing approach, which allows for increasing the natural accuracy to 85.04% and the robust accuracy from 57% to 72% in C&W attacks and from 59% to 67% in PGD attacks.
Unlocking Model Insights: A Dataset for Automated Model Card Generation
Venue: arXiv cs.CL 2023
Year: 2023

Language models (LMs) are no longer restricted to ML community, and instruction-tuned LMs have led to a rise in autonomous AI agents. As the accessibility of LMs grows, it is imperative that an understanding of their capabilities, intended usage, and development cycle also improves. Model cards are a popular practice for documenting detailed information about an ML model. To automate model card generation, we introduce a dataset of 500 question-answer pairs for 25 ML models that cover crucial aspects of the model, such as its training configurations, datasets, biases, architecture details, and training resources. We employ annotators to extract the answers from the original paper. Further, we explore the capabilities of LMs in generating model cards by answering questions. Our initial experiments with ChatGPT-3.5, LLaMa, and Galactica showcase a significant gap in the understanding of research papers by these aforementioned LMs as well as generating factual textual responses. We posit that our dataset can be used to train models to automate the generation of model cards from paper text and reduce human effort in the model card curation process. The complete dataset is available on https://osf.io/hqt7p/?view_only=3b9114e3904c4443bcd9f5c270158d37
Construction contract risk identification based on knowledge-augmented language model
Venue: arXiv cs.AI 2023
Year: 2023

Contract review is an essential step in construction projects to prevent potential losses. However, the current methods for reviewing construction contracts lack effectiveness and reliability, leading to time-consuming and error-prone processes. While large language models (LLMs) have shown promise in revolutionizing natural language processing (NLP) tasks, they struggle with domain-specific knowledge and addressing specialized issues. This paper presents a novel approach that leverages LLMs with construction contract knowledge to emulate the process of contract review by human experts. Our tuning-free approach incorporates construction contract domain knowledge to enhance language models for identifying construction contract risks. The use of a natural language when building the domain knowledge base facilitates practical implementation. We evaluated our method on real construction contracts and achieved solid performance. Additionally, we investigated how large language models employ logical thinking during the task and provide insights and recommendations for future research.