Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task

Numerical tables are widely employed to communicate or report the classification performance of machine learning (ML) models with respect to a set of evaluation metrics. For non-experts, domain knowledge is required to fully understand and interpret the information presented by numerical tables. This paper proposes a new natural language generation (NLG) task where neural models are trained to generate textual explanations, analytically describing the classification performance of ML models based on the metrics’ scores reported in the tables. Presenting the generated texts along with the numerical tables will allow for a better understanding of the classification performance of ML models. We constructed a dataset comprising numerical tables paired with their corresponding textual explanations written by experts to facilitate this NLG task. Experiments on the dataset are conducted by fine-tuning pre-trained language models (T5 and BART) to generate analytical textual explanations conditioned on the information in the tables. Furthermore, we propose a neural module, Metrics Processing Unit (MPU), to improve the performance of the baselines in terms of correctly verbalising the information in the corresponding table. Evaluation and analysis conducted indicate, that exploring pre-trained models for data-to-text generation leads to better generalisation performance and can produce high-quality textual explanations.
Perennial Ryegrass Contains Gluten-Like Proteins That Could Contaminate Cereal Crops

Background: To ensure safe consumption of gluten-free products, there is a need to understand all sources of unintentional contamination with gluten in the food chain. In this study, ryegrass (Lolium perenne), a common weed infesting cereal crop, is analysed as a potential source of gluten-like peptide contamination. Materials and Methods: Ten ryegrass cultivars were analysed using shotgun proteomics for the presence of proteins from the prolamin superfamily. A relative quantitative assay was developed to detect ryegrass gluten-like peptides in comparison with those found in 10 common wheat cultivars. Results: A total of 19 protein accessions were found across 10 cultivars of ryegrass for the protein families of PF00234-Tryp_alpha_amyl, PF13016-Gliadin, and PF03157-Glutenin_HMW. Protein and peptide homology searches revealed that gliadin-like peptides were similar to avenin and gamma-gliadin peptides. A total of 20 peptides, characteristic of prolamin superfamily proteins, were selected for liquid chromatography mass spectrometry (LC-MS) with multiple reaction monitoring (MRM). Only two of the monitored peptides were detected with high abundance in wheat, and all others were detected in ryegrass. Glutenin and alpha-amylase/trypsin inhibitor peptides were reported for the first time in ryegrass and were noted to be conserved across the Poaceae family. Conclusion: A suite of gluten-like peptides were identified using proteomics that showed consistent abundance across ryegrass cultivars but were not detected in wheat cultivars. These peptides will be useful for differentiating wheat gluten contamination from ryegrass gluten contamination.
Unsupervised Machine Translation in Real-World Scenarios

In this work, we present the work that has been carried on in the MT4All CEF project and the resources that it has generated by leveraging recent research carried out in the field of unsupervised learning. In the course of the project 18 monolingual corpora for specific domains and languages have been collected, and 12 bilingual dictionaries and translation models have been generated. As part of the research, the unsupervised MT methodology based only on monolingual corpora (Artetxe et al., 2017) has been tested on a variety of languages and domains. Results show that in specialised domains, when there is enough monolingual in-domain data, unsupervised results are comparable to those of general domain supervised translation, and that, at any rate, unsupervised techniques can be used to boost results whenever very little data is available.
The C-Terminal Half of SARS-CoV-2 Nucleocapsid Protein, Industrially Produced in Plants, Is Valid as Antigen in COVID-19 Serological Tests

Background The fight against the current coronavirus disease 2019 (COVID-19) pandemic has created a huge demand of biotechnological, pharmaceutical, research and sanitary materials at unprecedented scales. One of the most urgent demands affects the diagnostic tests. The growing need for rapid and accurate laboratory diagnostic tests requires the development of biotechnological processes aimed at producing reagents able to cope with this demand in a scalable, cost-effective manner, with rapid turnaround times. This is particularly applicable to the antigens employed in serological tests. Recombinant protein expression using plants as biofactories is particularly suitable for mass production of protein antigens useful in serological diagnosis, with a neat advantage in economic terms. Methods We expressed a large portion of the nucleoprotein (N) derived from SARS-CoV-2 in Nicotiana benthamiana plants. After purification, the recombinant N protein obtained was used to develop an indirect enzyme-linked immunosorbent assay (ELISA) for detection of antibodies to SARS-CoV-2 in human sera. To validate the ELISA, a panel of 416 sera from exposed personnel at essential services in Madrid City Council were tested, and the results compared to those obtained by another ELISA, already validated, used as reference. Furthermore, a subset of samples for which RT-PCR results were available were used to confirm sensitivity and specificity of the test. Results The performance of the N protein expressed in plants as antigen in serologic test for SARS-CoV-2 antibody detection was shown to be highly satisfactory, with calculated diagnostic sensitivity of 96.41% (95% CI: 93.05–98.44) and diagnostic specificity of 96.37 (95% CI: 93.05–98.44) as compared to the reference ELISA, with a kappa (K) value of 0.928 (95% CI:0.892–0.964). Furthermore, the ELISA developed with plant-derived N antigen detected SARS-CoV-2 antibodies in 84 out of 93 sera from individuals showing RT-PCR positive results (86/93 for the reference ELISA). Conclusion This study demonstrates that the N protein part derived from SARS-CoV-2 expressed in plants performs as a perfectly valid antigen for use in COVID-19 diagnosis. Furthermore, our results support the use of this plant platform for expression of recombinant proteins as reagents for COVID-19 diagnosis. This platform stands out as a convenient and advantageous production system, fit-for-purpose to cope with the current demand of this type of biologicals in a cost-effective manner, making diagnostic kits more affordable.
A Corpus for Commonsense Inference in Story Cloze Test

The Story Cloze Test (SCT) is designed for training and evaluating machine learning algorithms for narrative understanding and inferences. The SOTA models can achieve over 90% accuracy on predicting the last sentence. However, it has been shown that high accuracy can be achieved by merely using surface-level features. We suspect these models may not truly understand the story. Based on the SCT dataset, we constructed a human-labeled and human-verified commonsense knowledge inference dataset. Given the first four sentences of a story, we asked crowd-source workers to choose from four types of narrative inference for deciding the ending sentence and which sentence contributes most to the inference. We accumulated data on 1871 stories, and three human workers labeled each story. Analysis of the intra-category and inter-category agreements show a high level of consensus. We present two new tasks for predicting the narrative inference categories and contributing sentences. Our results show that transformer-based models can reach SOTA performance on the original SCT task using transfer learning but don’t perform well on these new and more challenging tasks.
Making People Laugh like a Pro: Analysing Humor Through Stand-Up Comedy

The analysis of humor using computational tools has gained popularity in the past few years, and a lot of resources have been built for this purpose. However, most of these resources focus on standalone jokes or on occasional humorous sentences during presentations. In this paper I present a new dataset, SCRIPTS, built using stand-up comedy shows transcripts: the humor that this dataset collects is inserted in a larger narrative, composed of daily events made humorous by the ability of the comedian. This different perspective on the humor problem can allow us to think and study humor in a different way and possibly to open the path to new lines of research.
Polynomial chaos expansion for surrogate modelling: Theory and software

The paper is focused on the application of a surrogate model to reliability analysis. Despite recent advances in this field, the reliability analysis of complex non‐linear finite element models is still highly time‐consuming. Thus, the approximation of the non‐linear finite element model by a surrogate meta‐model is often the only choice if one wishes to perform a sufficient amount of simulations to enable reliability analysis. First, the basic theory of polynomial chaos expansion (PCE) is described, including the transformation of correlated random variables. The usage of the PCE for the estimation of statistical moments and sensitivity analysis is then presented. It can be done efficiently via the post‐processing of the employed surrogate model in explicit form without any additional computational demands. The possibility of utilizing the adaptive algorithm Least Angle Regression is also discussed. The implementation of the discussed theory into a software tool, and its application, are presented in the last part of the paper.
Exploring the Charge Nurse Role Transition A Qualitative Study of the Novice ’ s Perspective

The novice charge nurse role transition is not well understood. This article reports on a qualitative descriptive study of the novice charge nurse role transition. Rubin and Rubin ’ s responsive interviewing was used. Fourteen novice charge nurses participated in Zoom interviews. Thematic analysis revealed three phases: taxiing to the runway , takeoff , and reaching cruising altitude , each with distinct themes. Nursing professional development practitioners can use these findings to better understand and support new charge nurses through this transition.
An Open Challenge for Exact Job Scheduling with Reticle Batching in Photolithography

We consider scheduling solutions for photolithography, an important sub-task in semi-conductor production, where patterns are transferred to wafers using reticles. The problem can be modelled as job scheduling on unrelated parallel machines with sequence-dependent setup times and release dates. The reticles add auxiliary-resource constraints for processing jobs. Equipping machines with the right reticles using transport robots from stockers in time renders this problem extremely difficult for exact solvers that use a declarative model. The latter would be attractive as such models tend to be compact and easy to maintain. We present a solver-independent MiniZinc model and provide 500 new benchmark instances. However, only small instances can be solved with state-of-the-art MIP and CP solvers. Consequently, we present this problem as an open challenge with considerable potential for driving improvements towards industrial applications.
AIS Electronic Library (AISeL) AIS Electronic Library (AISeL)

Business and IT alignment continues to be a challenge for business which seeks to maximise value of and from the IT function. Research has covered the wider Business-IT alignment points from mainly a macro-level viewpoint, such as, structural, dynamic and functional alignment. However, both research and practice still consider Business and IT alignment to be a challenge. In this research, we seek to uncover part of the reasons why Business and IT alignment is challenging to organisations. We note the factors in the literature which emphasise Business-IT alignment, such as, shared understanding, communication, management commitment, IT investment evaluation, innovation and rewards, strategic planning of IS, and strategic agility. The results of this study show that even if organisations address these alignment factors, IT projects could still end up failing. We also note the opposing misalignment factors in practice, such as, human tensions and strained work relationships, knowledge silos, self-centred management, technology does not matter, organisational change resistance, technology as a burden, and resources inflexibility. We conclude that organisations need to address both alignment and misalignment factors.
Dynamic Linking in Trusted Execution Environments in RISC-V

Dynamic linking is an important feature for many applications, making it an integral function to provide in Trusted Execution Environments (TEEs) to improve the ease of use and encourage adoption of TEEs for security and privacy. However, the implementation of dynamic linking in enclaves poses many challenges, for example, the proper verification of code loaded after an enclave has begun execution and the efficient initialization of enclaves. Previous TEEs have included dynamic linking functionality but either fail to accommodate library sharing or require that libraries be manually loaded in advance. Our work draws upon previous studies to provide two methods of dynamic loading that allow for customization to users’ needs. First, we provide trusted loading after enclave execution as the default loading method that can automatically load in shared libraries by coordinating with an untrusted OS. Second, we provide library enclaves as an optimization for commonly used libraries to be pre-loaded and verified prior to enclave execution. These library enclaves allow for sharing between multiple enclave applications. While previous work has been done on proprietary software, we implement our design in Keystone, an open-source RISC-V framework for constructing customizable TEEs, making our work accessible for future research and study. Finally, we expand on previous dynamic library implementations by accounting for mitigations against side-channels attacks. We propose a cache tagging solution to defend against the Flush+Reload side-channel attack and analyze its performance tradeoffs through simulation.
Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization

The development of an automatic evaluation metric remains an open problem in text generation. Widely used evaluation metrics, like ROUGE and BLEU, are based on exact word matching and fail to capture semantic similarity. Recent works, such as BERTScore, MoverScore and, Sentence Mover’s Similarity, are an improvement over these standard metrics as they use the contextualized word or sentence embeddings to capture semantic similarity. We in this work, propose a novel evaluation metric, Sentence Pair EmbEDdings (SPEED) Score, for text generation which is based on semantic similarity between sentence pairs as opposed to earlier approaches. To find semantic similarity between a pair of sentences, we obtain sentence-level embeddings from multiple transformer models pre-trained specifically on various sentence pair tasks such as Paraphrase Detection (PD), Semantic Text Similarity (STS), and Natural Language Inference (NLI). As these sentence pair tasks involve capturing the semantic similarity between a pair of input texts, we leverage these models in our metric computation. Our proposed evaluation metric shows an impressive performance in evaluating both abstractive and extractive summarization models and achieves state-of-the-art results on the SummEval dataset, demonstrating the effectiveness of our approach. Also, we perform the run-time analysis to show that our proposed metric is faster than the current state-of-the-art.
Long term cost-effectiveness of resilient foods for global catastrophes compared to artificial general intelligence

Global agricultural catastrophes, which include nuclear winter and abrupt climate change, could have long-term consequences on humanity such as the collapse and nonrecovery of civilization. Using Monte Carlo (probabilistic) models, we analyze the long-term cost-effectiveness of resilient foods (alternative foods) - roughly those independent of sunlight such as mushrooms. One version of the model populated partly by a survey of global catastrophic risk researchers finds the confidence that resilient foods is more cost effective than artificial general intelligence safety is the 100 millionth on resilient the margin now, respectively. Another version of the model based on one of the authors produced and ∼ 99% confidence, respectively. Considering uncertainty represented within our models, our result is robust: reverting the conclusion required simultaneously changing the 3-5 most important parameters to the pessimistic ends. However, as predicting the long-run trajectory of human civilization is extremely difficult, and model and theory uncertainties are very large, this significantly reduces our overall confidence. Because the agricultural catastrophes could happen immediately and because existing expertise relevant to resilient foods could be co-opted by charitable giving, it is likely optimal to spend most of the money for resilient foods in the next few years. Both cause areas generally save expected current lives inexpensively and should attract greater investment.
Spatiotemporal Blue Noise Masks

Blue noise error patterns are well suited to human perception, and when applied to stochastic rendering techniques, blue noise masks can minimize unwanted low-frequency noise in the final image. Current methods of applying different blue noise masks to each rendered frame result in either white noise frequency spectra temporally, and thus poor convergence and stability, or lower quality spatially. We propose novel blue noise masks that retain high quality blue noise spatially, yet when animated produce values at each pixel that are well distributed over time. To do so, we create scalar valued masks by modifying the energy function of the void and cluster algorithm. To create uniform and nonuniform vector valued masks, we make the same modifications to the blue-noise dithered sampling algorithm. These masks exhibit blue noise frequency spectra in both the spatial and temporal domains , resulting in visually pleasing error patterns, rapid convergence speeds, and increased stability when filtered temporally. Since masks can be initialized with arbitrary sample sets, these improvements can be used on a large variety of problems, both uniformly and importance sampled. We demonstrate these improvements in volumetric rendering, ambient occlusion, and stochastic convolution. By extending spatial blue noise to spatiotemporal blue noise, we overcome the convergence limitations of prior blue noise works, enabling new applications for blue noise distributions. Usable masks and source code can be found at https://github.com/NVIDIAGameWorks/SpatiotemporalBlueNoiseSDK.
Computer Vision Based Indoor Rock Climbing Analysis

As personal fitness has risen in popularity, so too has the use of wearable fitness devices. There exist products for many sports, but few for Indoor Rock Climbing. Current products which provide post-climb reports utilize specialized hardware equipped with sensors. As such, they are limited in the types of information they can gather. We develop a novel tool which makes use of Computer Vision and Machine Learning to analyze video footage of the climber and produce post-climb reports that convey information which cannot be ascertained through sensor-only methods, such as the percentage of route completed and number of moves taken. To do so, we identify climb holds using Object Detection models such as YOLO, as well as the position of the climber using Keypoint Detection APIs. Our current approach produces post climb reports with an average RMSPE of 0.266 and we highlight specific areas of improvement to bolster the performance. Such a tool will provide a simple way for rock climbers to receive feedback on their climbs, with the use of a standard cell-phone camera.
Spanish Datasets for Sensitive Entity Detection in the Legal Domain

The de-identification of sensible data, also known as automatic textual anonymisation, is essential for data sharing and reuse, both for research and commercial purposes. The first step for data anonymisation is the detection of sensible entities. In this work, we present four new datasets for named entity detection in Spanish in the legal domain. These datasets have been generated in the framework of the MAPA project, three smaller datasets have been manually annotated and one large dataset has been automatically annotated, with an estimated error rate of around 14%. In order to assess the quality of the generated datasets, we have used them to fine-tune a battery of entity-detection models, using as foundation different pre-trained language models: one multilingual, two general-domain monolingual and one in-domain monolingual. We compare the results obtained, which validate the datasets as a valuable resource to fine-tune models for the task of named entity detection. We further explore the proposed methodology by applying it to a real use case scenario.
Animacy Denoting German Nouns: Annotation and Classification

In this paper, we introduce a gold standard for animacy detection comprising almost 14,500 German nouns that might be used to denote either animate entities or non-animate entities. We present inter-annotator agreement of our crowd-sourced seed annotations (9,000 nouns) and discuss the results of machine learning models applied to this data.
Estimating the Age of Toll-Like Receptor 5 Variants in N'Dama and Other Taurine Cattle

This study was conducted to assess the age of allele and linkage disequilibrium among the polymorphism detected in Toll-like receptor 5 of N’Dama and other taurine cattle. The results showed mutations that evolved around 10800 and 8256 generations ago and almost around the period of domestication of cattle before human selection for different traits of interest. Segregating and relatively new mutations (993A/G, and 2127C/T) emerged in N'Dama TLR5 at about 5121 generations ago. A great deal of non-random association existed between SNPs located on exonic position 470 and those on 1131, 1324, 2524, and 2542. Also, there was significant association between SNPs located on exonic position 1131 and those on 1324, 2524, and 2542. SNPs on 1975 and 2208 were also significantly associated, likewise, in the taurine cattle. that of other taurine cattle is different and may explain some of the differential ability of N’Dama cattle to better withstand harsh and disease endemic conditions compared to other taurine cattle.
Spillovers from extractive industries

: Extractive industries form an important part of the economy for many developing countries, but their impact on growth and welfare remains understudied. With global efforts to transition to net-zero carbon emissions in the coming decades, understanding the local impacts of the extractives sector is crucially important for regional economic development policy in the management of this transition. In this paper we use tax administrative data from South Africa to examine the local spillovers from mining activities, focusing on wages, firm profitability, and job creation. Utilizing exogenous changes in international mineral and metals prices, which had a heterogeneous impact across different local areas depending on the main minerals and metals being extracted, we find that, in the short term, a general contraction in the mining sector reduces the number of jobs in related service industries, as well as the wages received by local workers in those sectors. We also find evidence that a negative shock to the mining sector through vertical integration will have negative productivity impacts on the tradable sectors (especially manufacturing).