Miracle-dz/UniFAR-FTUs huggingface dataset:
Never trust, always verify : a roadmap for Trustworthy AI?

Artificial Intelligence (AI) is becoming the corner stone of many systems used in our daily lives such as autonomous vehicles, healthcare systems, and unmanned aircraft systems. Machine Learning is a field of AI that enables systems to learn from data and make decisions on new data based on models to achieve a given goal. The stochastic nature of AI models makes verification and validation tasks challenging. Moreover, there are intrinsic biaises in AI models such as reproductibility bias, selection bias (e.g., races, genders, color), and reporting bias (i.e., results that do not reflect the reality). Increasingly, there is also a particular attention to the ethical, legal, and societal impacts of AI. AI systems are difficult to audit and certify because of their black-box nature. They also appear to be vulnerable to threats; AI systems can misbehave when untrusted data are given, making them insecure and unsafe. Governments, national and international organizations have proposed several principles to overcome these challenges but their applications in practice are limited and there are different interpretations in the principles that can bias implementations. In this paper, we examine trust in the context of AI-based systems to understand what it means for an AI system to be trustworthy and identify actions that need to be undertaken to ensure that AI systems are trustworthy. To achieve this goal, we first review existing approaches proposed for ensuring the trustworthiness of AI systems, in order to identify potential conceptual gaps in understanding what trustworthy AI is. Then, we suggest a trust (resp. zero-trust) model for AI and suggest a set of properties that should be satisfied to ensure the trustworthiness of AI systems.
How Does Automation Shape the Process of Narrative Visualization: A Survey on Tools

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.
Effect of corrosion on the buckling capacity of tubular members

Offshore installations are subjected to harsh marine environment and often have damages from corrosion. Several experimental and numerical studies were performed in the past to estimate buckling capacity of corroded tubular members. However, these studies were either based on limited experimental tests or numerical analyses of few cases resulting in semi-empirical relations. Also, there are no guidelines and recommendations in the currently available design standards. To fulfil this research gap, a new formula is proposed to estimate the residual strength of tubular members considering corrosion and initial geometrical imperfections. The proposed formula is verified with results from finite element analyses performed on several members and for varying corrosion patch parameters. The members are selected to represent the most relevant Eurocode buckling curve for tubular members. It is concluded that corrosion reduces the buckling capacity significantly and the proposed formula can be easily applied by practicing engineers without performing detailed numerical analyses.
Computational Complexity Evaluation of Neural Network Applications in Signal Processing

—In this paper, we provide a systematic approach for assessing and comparing the computational complexity of neural network layers in digital signal processing. We provide and link four software-to-hardware complexity measures, deﬁning how the different complexity metrics relate to the layers’ hyper- parameters. This paper explains how to compute these four metrics for feed-forward and recurrent layers, and deﬁnes in which case we ought to use a particular metric depending on whether we characterize a more soft- or hardware-oriented ap- plication. One of the four metrics, called ‘the number of additions and bit shifts (NABS)’, is newly introduced for heterogeneous quantization. NABS characterizes the impact of not only the bitwidth used in the operation but also the type of quantization used in the arithmetical operations. We intend this work to serve as a baseline for the different levels (purposes) of complexity estimation related to the neural networks’ application in real-time digital signal processing, aiming at unifying the computational complexity estimation.
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms

BACKGROUND Accurate extraction of coronary arteries from invasive coronary angiography (ICA) images is essential for the diagnosis and risk stratification of coronary artery disease (CAD). OBJECTIVE In this study, a novel deep learning (DL) method is proposed for automatically extracting coronary arteries from ICA images. METHODS A convolutional neural network (CNN) was developed with full-scale skip connections and full-scale deep supervisions. The encoder architecture was based on the residual and inception modules to obtain multi-scale features from multiple convolutional layers with different window shapes. Transfer learning was utilized to improve both the initial performance and learning efficiency. A hybrid loss function was employed to further optimize the segmentation model. RESULTS The model was tested on a data set of 616 ICAs obtained from 210 patients, composed of 437 images for training, 49 images for validation, and 130 images for testing. The segmentation model achieved a Dice score of 0.8942, a sensitivity of 0.8735, a specificity of 0.9954, and a Hausdorff distance of 6.0794 mm; it could predict arteries for a single ICA frame in 0.2114 seconds. CONCLUSIONS The results showed that our model outperformed the state-of-the-art deep-learning models. Our new method has great potential for clinical use.
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution

—Federated learning is an emerging concept in the do- main of distributed machine learning. This concept has enabled GANs to beneﬁt from the rich distributed training data while preserving privacy. However, in a non-iid setting, current federated GAN architectures are unstable, struggling to learn the distinct features and vulnerable to mode collapse. In this paper, we propose a novel architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse and instability for non-iid datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e. high inception score) on average over 20 clients compared to baseline FLGAN.
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank

Click-based learning to rank (LTR) tackles the mismatch between click frequencies on items and their actual relevance. The approach of previous work has been to assume a model of click behavior and to subsequently introduce a method for unbiasedly estimating preferences under that assumed model. The success of this approach is evident in that unbiased methods have been found for an increasing number of behavior models and types of bias. This work aims to uncover the implicit limitations of the high-level prevalent approach in the counterfactual LTR field. Thus, in contrast with limitations that follow from explicit assumptions, our aim is to recognize limitations that the field is currently unaware of. We do this by inverting the existing approach: we start by capturing existing methods in generic terms, and subsequently, from these generic descriptions we derive the click behavior for which these methods can be unbiased. Our inverted approach reveals that there are indeed implicit limitations to the counterfactual LTR approach: we find counterfactual estimation can only produce unbiased methods for click behavior based on affine transformations. In addition, we also recognize previously undiscussed limitations of click-modelling and pairwise approaches to click-based LTR. Our findings reveal that it is impossible for existing approaches to provide unbiasedness guarantees for all plausible click behavior models.
Looking for related discussions on GitHub Discussions

Software teams are increasingly adopting diﬀerent tools and communication channels to aid the software collaborative development model and coordinate tasks. Among such resources, Programming Community-based Question Answering (PCQA) forums have become widely used by developers. Such environments enable developers to get and share technical information. Interested in supporting the development and management of Open Source Software (OSS) projects, GitHub announced GitHub Discussions — a native forum to facilitate collaborative discussions between users and members of communities hosted on the platform. As GitHub Discussions resembles PCQA forums, it faces challenges similar to those faced by such environments, which include the occurrence of related discussions (duplicates or near-duplicated posts). While duplicate posts have the same content—and may be exact copies—near-duplicates share similar topics and information. Both can introduce noise to the platform and compromise project knowledge sharing. In this paper, we address the problem of detecting related posts in GitHub Discussions. To do so, we propose an approach based on a Sentence-BERT pre-trained model: the RD-Detector . We evaluated RD-Detector using data from diﬀerent OSS communities. OSS maintainers and Software Engineering (SE) researchers manually evaluated the RD-Detector results, which achieved 75% to 100% in terms of precision. In addition, maintainers pointed out practical applications of the approach, such as merging the discussions’ threads and making discussions as comments on one another. OSS maintainers can beneﬁt from RD-Detector to address the labor-intensive task of manually detecting related discussions and answering the same question multiple times.
Agriculture-Vision Challenge 2022 - The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models

The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images. In this paper, we propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582, accomplish-ing the 2nd place in this challenge.
A Literature Review on Serverless Computing

Serverless computing is an emerging cloud computing paradigm. Moreover, it has become an attractive development option for cloud-based applications for software developers. The most significant advantage of serverless computing is to free software developers from the burden of complex underlying management tasks and allow them to focus on only the application logic implementation. Based on its benign characteristics and bright prospect, it has been an increasingly hot topic in various scenarios, such as machine learning, scientific computing, video processing, and the Internet of Things. However, none of the studies focuses on a comprehensive analysis of the current research state of the art of serverless computing from the research scope and depth. Such an analysis is a foundation for understanding an evolving research area. It can provide a global and detailed overview for researchers and practitioners and help them understand and propose promising strategies and best software practices for serverless application engineering. To fill this knowledge gap, we present a comprehensive literature review to summarize the current research state of the art of serverless computing. This review is based on selected 164 research papers to answer three key aspects, i.e., research directions (What), existing solutions (How), and platforms and venues (Where). Specifically, first, we construct a taxonomy linked to research directions about the serverless computing literature. Our taxonomy has 18 research categories covering performance optimization, programming framework, application migration, multi-cloud development, cost, testing, debugging, etc. Second, we classify the related studies of each research direction and elaborate on existing solutions. Third, we investigate the distributions of experimental serverless platforms for existing techniques and publication venues for selected research papers. Finally, based on our analysis, we discuss some key challenges and envision promising opportunities for future research on the serverless platform side, serverless application side, and serverless computing community side. Our study’s summary can give a quick overview of the current state-of-the-art research on serverless computing for researchers and practitioners. Meanwhile, it will inspire researchers and practitioners to venture into new research topics, novel solutions, and best software practices and significantly contribute to serverless application engineering.
A METHOD FOR ASSESSING CREATIVITY AND INNOVATION OF INDUSTRIAL PRODUCTS BY ENGINEERING STUDENTS

In this paper experiences about the implementation of creative activities in industrial engineering degrees are related. These activities were used in engineering courses to improve the creative skills of the students. Thus, the main goal was to make them capable to design innovative products using ideation methods and working in groups. Although there are some works in the literature about the implementation of creative methods in engineering degrees none of them have established well defined approaches which are able to assess the creativity and innovation level of products in an effective way. Indeed, one of the main problems that have been found by instructors during the classes is how students can evaluate their own creative solutions and the degree of innovation of the designed products. There are no many works in the literature focusing in this topic and all of them have been developed to be applied by engineers with long experience in design. These methods measure the innovation of products by the quantification of a number of factors. However, these factors present some difficulties for students who are not used to work with them. In fact, one of the main problems found by instructors in implementing creativity procedures at university level is that students do not have enough experience about the assessment of engineering products and their degree of innovation. This paper describes a new procedure for the measurement of the level of creativity that new ideas provide. It can be also used for the measurement of the innovation of products. A case study is presented to illustrate the method and evaluate its effectiveness. In this activity an engineering design problem is proposed to the students and they have to find the best solution. Brainstorming is used as idea generation method and they have to measure their creativity using the proposed approach. The main advantage of the method is that it can be easily used by students and instructors. In this way the students can evaluate their own ideas in order to sort them and select the most creative ones. The method works with a three dimensional scale which are level, scope and goals. The comparison of the proposed method with other existing approaches allows highlighting the easier understanding of the factors to be evaluated.
A Manifold-Based Airfoil Geometric-Feature Extraction and Discrepant Data Fusion Learning Method

The geometrical shape of airfoils and the corresponding flight conditions are crucial factors for aerodynamic coefficient prediction. The obtained geometric-features of airfoils in most existing approaches (e.g., geometrical parameter extraction, polynomial description, and deep learning) are in Euclidean space. State-of-the-art studies have shown that the curves or surfaces of an airfoil form a manifold in Riemannian space. Therefore, the features extracted by existing methods are not sufficient to reflect the geometric-features of airfoils. Meanwhile, flight conditions and geometric-features are greatly discrepant with different types. The discrepancy between these two factors must be considered and evaluated to improve the aerodynamic coefficient accuracy. Motivated by the advantages of manifold theory and multitask learning (MTL), we propose a manifold-based airfoil geometric-feature extraction and discrepant data fusion learning method (MDF) to extract geometric-features of airfoils in Riemannian space (we call them manifold-features) and further fuse the manifold-features with flight conditions to predict aerodynamic coefficients. Experimental results show that our method can extract geometric-features of airfoils more accurately than existing methods, that the average mean square error (MSE) of airfoils rebuilt based on geometric-features is reduced by 41.30%, and that while keeping the same prediction accuracy level for the lift coefficient $C_{L}$, the MSE of the drag coefficient $C_{D}$ predicted by MDF is further reduced by 54.56%.
Unsupervised Learning Algorithms for Keyword Extraction in an Undergraduate Thesis

Abstract: The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process. Information and communication technologies, such as the platform for integrated research and academic work with responsibility (PILAR), which records information about research projects, such as titles, summaries, and keywords in their various modalities, have gained relevance and importance in the management of these. We proved algorithms with these records of research projects that have been analysed in this study, and predictions were made for each of the nine (09) models of unsupervised machine learning algorithms that were implemented for each of the 7430 records from the dataset. The most efficient way of extracting keywords for this dataset was the TF-IDF method, obtaining 72% accuracy and [0.4786, SD 0.0501] in average extraction time for each thesis file processed by this model. Keywords: Keyword extraction, Machine learning, thesis. university students, unsupervised learning
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines

Online travel agencies (OTA's) advertise their website offers on meta-search bidding engines. The problem of predicting the number of clicks a hotel would receive for a given bid amount is an important step in the management of an OTA's advertisement campaign on a meta-search engine, because bid times number of clicks defines the cost to be generated. Various regressors are ensembled in this work to improve click prediction performance. Following the preprocessing procedures, the feature set is divided into train and test groups depending on the logging date of the samples. The data collection is then subjected to feature elimination via utilizing XGBoost, which significantly reduces the dimension of features. The optimum hyper-parameters are then found by applying Bayesian hyperparameter optimization to XGBoost, LightGBM, and SGD models. The different trained models are tested separately as well as combined to form ensemble models. Four alternative ensemble solutions have been suggested. The same test set is used to test both individual and ensemble models, and the results of 46 model combinations demonstrate that stack ensemble models yield the desired R2 score of all. In conclusion, the ensemble model improves the prediction performance by about 10%.
Findings from a

: Transgender issues are under-explored and marginalised within mainstream social work and social care professional practice. The experience of gender transition has a profound impact on the individuals who have diverse gender identities and their family members. We present findings from a systematic review of studies concerning the experiences of transgender parenting conducted January – September 2017. We took a lifecourse approach, examining the research studies that investigated the experience of people identifying as transgender who were already parents at the time of their transition, or who wished to be parents following transition. The review evaluated existing findings from empirical research on transgender parenting and grandparenting to establish how trans people negotiate their relationships with children following transition, and sought to consider the implications for professional practice with trans people in relation to how best to support them with their family caring roles. We used the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method. Empirical studies published from 1 January 1990 to 31 April 2017 in the English language, and which had transgender parenting as a significant focus, were included in the review. Twenty-six studies met the criteria. Key themes reported are: how trans people negotiate their relationships with children following disclosure and transition; the impact of parental transitioning on children; relationships with wider families; trans people’s desires to be parents; and the role of professional practice to support trans families. We discuss how the material from the review can inform social work education and practice, including to help identify future research, education and practice priorities in this area.
Integrating Reinforcement Learning into Behavior Trees by Hierarchical Composition

This thesis investigates ways to extend the use of Reinforcement Learning (RL) to Behavior Trees (BTs). BTs are used in the field of Artificial Intelligence (AI) in order to create modular and reactive planning agents. While human designed BTs are capable of reacting to changes in an environment as foreseen by an expert, they are not capable of adapting to new scenarios. The focus of the thesis is on using existing general-purpose RL methods within the framework of BTs. Deep Q-Networks (DQN) and Proximal Policy Optimisation (PPO) were embedded into BTs, using RL implementations from an open-source RL library. The experimental part of the thesis uses these nodes in a variety of scenarios of increasing complexity, demonstrating some of the benefits of combining RL and BTs. The experiments show that there are benefits to using BTs to control a set of hierarchically decomposed RL sub-tasks for solving a larger problem. Such decomposition allows for reuse of generic behaviors in different parts of a BT. By decomposing the RL problem using a BT, it is also possible to identify and replace problematic parts of a policy, as opposed to retraining the entire policy.