Miracle-dz/UniFAR-FTUs huggingface dataset:
Governing AI and Autonomous Systems

– Algorithms are increasingly entrenched in how decisions are made in society today, particularly in autonomous systems – In transportation, algorithms drive the operation of autonomous vehicles (AVs) through its environmental perception, decision-making and decision execution to yield AVs’ manifold societal benefits, particularly road safety. However, algorithmic decision-making is plagued by concerns over bias, ethical considerations, perverse incentives of stakeholders and technical issues that shape the programming and implementation of these algorithms. These issues stemming from algorithmic decision-making can translate into greater safety risks as well as potential societal discrimination imposed by AVs. Due to ambiguities in the delineation of responsibilities for the design, manufacture and operation of AVs, stakeholders in the supply chain are also exposed to new liability risks for accidents involving AVs. This paper first identifies how biases, ethical issues, perverse incentives, and technical issues can affect AVs and then explores how these issues affect algorithmic decision-making in AVs. The paper then examines the potential safety risks, discriminatory outcomes and the factors contributing to liability risks of AV stakeholders stemming from algorithmic decision-making in AVs, discusses the steps taken by several governments to tackle these risks and highlights the outstanding issues that need to be addressed before concluding. The results from our analysis indicate that most of the surveyed governments have begun addressing safety risks. However, while recognising bias and ethical issues arising from the application of AI in AVs, with a few exceptions less has been done to address discriminatory effects and liability concerns.
Cultivation of Caenorhabditis elegans on new cheap monoxenic media without peptone

Abstract The study of species biodiversity within the Caenorhabditis genus of nematodes would be facilitated by the isolation of as many species as possible. So far, over 50 species have been found, usually associated with decaying vegetation or soil samples, with many from Africa, South America and Southeast Asia. Scientists based in these regions can contribute to Caenorhabditis sampling and their proximity would allow intensive sampling, which would be useful for understanding the natural history of these species. However, severely limited research budgets are often a constraint for these local scientists. In this study, we aimed to find a more economical, alternative growth media to rear Caenorhabditis and related species. We tested 25 media permutations using cheaper substitutes for the reagents found in the standard nematode growth media (NGM) and found three media combinations that performed comparably to NGM with respect to the reproduction and longevity of C. elegans. These new media should facilitate the isolation and characterization of Caenorhabditis and other free-living nematodes for the researchers in the poorer regions such as Africa, South America, and Southeast Asia where nematode diversity appears high.
The K Primer (version 3.3) http://k-framework.org

This paper serves as a brief introduction to the K tool, a system for formally deﬁning programming languages. It is shown how sequential or concurrent languages can be deﬁned in K simply and modularly. These formal deﬁnitions automatically yield an interpreter for the language, as well as program analysis tools such as a state-space explorer.
High-Level Synthesis Tools should be Proven Correct

With hardware designs becoming ever more complex, and demand for custom accelerators ever growing, high-level synthesis (HLS) is increasingly being relied upon. However, HLS is known to be quite flaky, with each tool supporting subtly different fragments of the input language and sometimes even generating incorrect designs. We argue that a formally verified HLS tool could solve this issue by dramatically reducing the amount of trusted code, and providing a formal description of the input language that is supported. To this end, we are developing Vericert, a formally verified HLS tool, based on CompCert, a formally verified C compiler.
TAX KNOWLEDGE AND TAX COMPLIANCE OF SMALL AND MEDIUM ENTERPRISES IN GHANA

The importance of taxes in every economy cannot be underestimated. This study recognises that efforts are being made to use tax knowledge to improve the taxes generated to promote national development. The need to promote tax paying behaviour in subSaharan Africa is needed to ensure the development of countries within the region. Tax knowledge has been acknowledged as one of the factors that promote tax compliance. This study is aimed at examining the effect of dimensions of tax knowledge on tax compliance in the Ghanaian context using small and medium enterprises. Tax knowledge is about knowledge of tax responsibilities and rights, knowledge about employment income, knowledge about business income, and awareness of sanctions. The survey includes 130 managers of small business enterprises (SMEs) in Ghana, who had been part of a tax education campaign by the Ghana Revenue Authority in the Nkawkaw Municipality. The data was analysed using a structural equation modelling approach. The results of the study suggest that knowledge of tax rights and responsibilities, knowledge of employment income, and awareness of sanctions were found to have a positive and significant relationship with tax compliance. Knowledge of business income was not statistically related to tax compliance. The findings of the study suggest that knowledge of tax rights and responsibilities, and awareness of sanctions are the strongest determinants of tax compliance in the SME sector. Despite the insignificant relationship between knowledge of business income and tax compliance, this study considers this factor as important in ensuring SME conduct self-assessment. Self-assessment must be pursued to encourage SMEs to pay their taxes. This study recommends the use of tax education to enhance tax knowledge in an attempt to improve compliance among SMEs in the developing country context. The study also contributes to the literature on the use of tax knowledge to enhance tax compliance using SMEs in Ghana.
WHAT DRIVES THE INTENTION TO PATRONIZE GREEN-THEMED HOTELS IN MALAYSIA?

In line with the thriving growth in the tourism industry and with the emergence of a vigorous group of patrons seeking for green consumption, going green is a vital move to hoteliers to ensure sustainability in the industry. Hoteliers are actively repositioning themselves from the conventional service providers that emphasize on quality and ratings, to becoming green partakers that emphasize not only on quality, but on their obligations towards greener environment by instigating green-efforts and -activities. In addition, the enforcement from regulated and non-regulated stakeholders to comply with environment-friendly requirements have also contributed to the growing numbers of green-themed hotels. The objective of this study is to investigate into the drivers of the intention of guests to patronize green-themed hotels in Malaysia by applying the theoretical framework of the theory of planned behaviour (TPB). As an extension to TPB, this study examines the influence of environmental knowledge and past behaviour on green consumption on the intention of generations X and Y to patronize green-themed hotels in Malaysia. A total of 243 questionnaires are processed and analysed with the statistical program Statistical Analysis System 5.1, using reliability, correlation and inferential analysis. Findings indicate that environmental knowledge, past behaviour on green consumption, environmental attitude, subjective norms and perceived behavioural control are positively and significantly influencing patron’s intention to choose a green-themed hotel in Malaysia. This study delivers great contribution to policy makers and hoteliers from both theoretical and practical viewpoint.
Is there a link between the gut microbiome and arterial hypertension?

The human microbiota consists of a variety of bacterial species which populate the mucosal surfaces and contribute a great deal to the education of our immune system. In terms of metabolism, the microbiota plays an important role by decomposing nutrients that we consume in our food and degrading environmental toxicants and environmental chemicals and drugs. Changing the state of eubiosis to dysbiosis represents an area of extreme interest nowadays, as researchers try to understand the pathophysiology behind it fully. Blood pressure can be influenced by changes in ratios of gut microbiota categories, short-chain fatty acids, specific kidney and blood vessel receptors, gut metabolites trimethylamine (TMA), and trimethylamine N-oxide (TMAO). This minireview aims to reveal some of the major aspects that support the hypothesis that gut microbiota contributes to the regulation of blood pressure. The purpose of all research is to discover the possibility of future personalized microbiome targeted therapy in order to lower the prevalence and incidence of some of the most common afflictions of our time, especially arterial hypertension.
Development and sensory evaluation of yoghurt flavoured with solar dried fruits

A study of developing solar dried fruit (5%) flavoured yoghurts using three types of fruits was conducted to determine acceptability and shelf life of developed products. A total of six samples namely banana, mango, pineapple, banana/mango, banana/pineapple and mango/pineapple were used as flavours in yoghurts. They were added either as fruit pieces or as powders. Shelf life projection study suggested that developed yoghurt products could be stored for up to 21 days at 4 °C without undergoing spoilage. Plain yoghourt (control) was the most liked sample and scored highest mean values for all attributes, which was significantly different from all other samples (p<0.05). Among the fruit flavoured yoghourts, mango was the most preferred sample in terms of flavor, aroma and general acceptability whereas the pineapple was the least acceptable. Although yoghurts flavoured with fruits pieces scored higher mean values in all sensory attributes than yoghurts flavoured with powdered fruits, no significant differences (p>0.05) between the two forms of fruit flavours was found. This needs to be further investigated. Thus, although the control was the most liked sample, solar dried fruit can be successfully used as yoghurt flavour, which may improve the texture and nutritional quality of the formulated yoghurts, making them available throughout the year, hence increasing farmer’s income.
AJPH202033097_Verbeek 820..825

Jos Verbeek, Jan Hoving, and Julitta Boschman are with the Coronel Institute of Occupational Health, University Medical Center Amsterdam, Amsterdam, the Netherlands. Lee-Yee Chong is with the Nuffield Department of Surgical Sciences, Cochrane Public Health and Health Systems Network, University of Oxford, Oxford, England. Jonathan Livingstone-Banks is with the Nuffield Department of Primary Care Health Sciences, University of Oxford. Lisa Bero is with the Center for Bioethics and Humanities, University of Colorado Anschutz Medical Campus, Aurora.
Sensitivity of productivity to precipitation amount and pattern varies by topographic position in a semiarid grassland

Aboveground net primary productivity (ANPP) in grasslands is an important integrator of terrestrial ecosystem function, a key driver of global biogeochemical cycles, and a critical source of food for wild and domesticated herbivores. ANPP exhibits high spatial and temporal variability, driven by a suite of factors including precipitation amount and pattern, biotic and abiotic legacies, and topographic heterogeneity. Global climate models forecast an altered hydrological cycle due to climate change, including higher precipitation variability and more extreme events, which may further increase spatiotemporal variability in ANPP. Therefore, it is essential to understand the sensitivity of this central ecosystem function to various precipitation metrics, legacies, and topographic positions to better inform sustainable grassland management. In this study, we analyzed long-term (36-yr) ANPP data collected across a topographic sequence in the semiarid shortgrass steppe of North America to examine patterns and drivers of spatiotemporal variability in ANPP. We observed that (1) ANPP varied substantially by topographic position, with greater divergence during years with high production, (2) ANPP variability was higher temporally (16-fold maximum difference across years) than spatially (4-fold maximum difference across topographic positions), (3) warm-season perennial grasses were the dominant plant functional type across all topographic positions and strongly influenced total ANPP dynamics, and (4) ANPP had strong sensitivities to current year precipitation amount and pattern that varied by plant functional type, as well as weaker sensitivities to precipitation and productivity legacies. Overall, the lowest topographic position had the highest sensitivity to precipitation, likely due to higher resource availability via the downhill movement of water and nutrients during years with high precipitation and large rainfall events. These results suggest that temporal and spatial ANPP variability in shortgrass steppe is primarily driven by the combined effects of precipitation amount and pattern during the current year, with the dominant warm-season perennial grasses governing these responses.
Insulin-resistance conditions as risk factors for Acanthosis nigricans: A systematic review and meta-analysis

Background: Acanthosis nigricans (AN), a dermatological condition, is characterized by dark velvety discoloration and thickening of the skin. It has been known to be associated with various insulin-resistant conditions; however, risk factors of AN are not fully established. Methods: We conducted a systematic search of the literature using various combinations of specific keywords. The odds ratio (OR) and their 95% CI were used to draw forest plots. Results: In total, 37 studies were included in this meta-analysis. The combined meta-analysis yielded an OR of 0.35 with a 95% CI of 0.19–0.64 and a P value of . 0006. The type 2 diabetes mellitus (T2DM)-related studies had an overall effect size of OR 0.24 with a 95% CI of 0.10–0.59 and a P value of . 002. For obesity-related studies, the overall effect size was OR 0.62 with a 95% CI of 0.23–1.63 and a P value of . 33. PCOS-related studies yielded an OR of 0.34 with a 95% CI of 0.07–1.59 and a P value of . 17. Finally, the studies on acromegaly had an effect size of -0.17 with a 95% CI varying from -0.42 to 0.008 and a P value of . 19. Conclusion: Insulin-resistance conditions such as T2DM, obesity, PCOS, and acromegaly are important risk factors for the development of AN when their effect size is combined with strong statistical significance. However, only T2DM shows a strong statistical significance; as a risk factor for AM when these conditions are taken individually.
Sigma-UPM ASR Systems for the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge

This paper describes the Sigma-UPM Automatic Speech Recognition (ASR) systems submitted to IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge. Deep Neural Networks (DNNs) are becoming the most promising technology for ASR at present. Since last few years, traditional hybrid models are being evaluated and compared to other end-to-end ASR systems in terms of accuracy and efﬁciency. In this challenge, we contribute with two different approaches: a primary hybrid ASR system based on DNN-HMM and two contrastive state-of-the-art end-to-end ASR systems, based on lattice-free maximum mutual information (LF-MMI). Our analysis of the results from the last edition led us to conclude that some adaptation should be accomplished to improve the performance of the systems. In particular, data augmentation techniques and Domain Adversarial Training (DAT) have been applied to the aforemen-tioned approaches. Multi-condition data augmentation applied to our hybrid DNN-HMM models has demonstrated WER improvements in noisy scenarios (about 10% relative). In contrast, results obtained using an end-to-end Pychain-based ASR system are far from our expectations. Nevertheless, we found that when including DAT techniques a relative WER improvement of 2.87% was obtained as compared to the Pychain-based system.
Impedance assays and the bacteriological testing of milk and milk products

Recherche des correlations entre l'activite metabolique et les modifications de la conductivite. Etude du signal de conductance de cultures bacteriennes pures. Influence de la temperature d'incubation sur la conductivite et examen de l'aptitude des mesures de conductance a l'evaluation de la recontamination
Atomistic Insights into Chemical Interface Damping of Surface Plasmon Excitations in Silver Nanoclusters

A detailed description of the mechanism underlying chemical interface damping (CID) in silver nanoclusters is presented. The effect of adsorbates on the surface plasmon excitation in silver nanoclusters is explored by means of a method based on time-dependent self-consistent charge density functional tight binding (TD-SCC-DFTB). By using this tool, we have calculated the homogeneous line width of the surface plasmon resonance (SPR) band for both naked and capped silver nanoclusters. A new picture explaining the decreased lifetime of the surface plasmon excitations is provided, in which coupling between particles states via adsorbate states enhances the natural dephasing mechanism of the surface plasmon excitation. To the best of our knowledge, this is the first report that addresses this topic from a fully atomistic time-dependent approach considering nanosized particles.
Evaluating Saliency Methods for Neural Language Models

Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these interpretations trustworthy enough to be used in analyses? To address this question, we conduct a comprehensive and quantitative evaluation of saliency methods on a fundamental category of NLP models: neural language models. We evaluate the quality of prediction interpretations from two perspectives that each represents a desirable property of these interpretations: plausibility and faithfulness. Our evaluation is conducted on four different datasets constructed from the existing human annotation of syntactic and semantic agreements, on both sentence-level and document-level. Through our evaluation, we identified various ways saliency methods could yield interpretations of low quality. We recommend that future work deploying such methods to neural language models should carefully validate their interpretations before drawing insights.
Enhancing User' s Income Estimation with Super-App Alternative Data

This paper presents the advantages of alternative data from Super-Apps to enhance user' s income estimation models. It compares the performance of these alternative data sources with the performance of industry-accepted bureau income estimators that takes into account only financial system information; successfully showing that the alternative data manage to capture information that bureau income estimators do not. By implementing the TreeSHAP method for Stochastic Gradient Boosting Interpretation, this paper highlights which of the customer' s behavioral and transactional patterns within a Super-App have a stronger predictive power when estimating user' s income. Ultimately, this paper shows the incentive for financial institutions to seek to incorporate alternative data into constructing their risk profiles.
No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks

This paper applies machine learning to optimize the transmission policies of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks, where time-division multiple access (TDMA) is used to serve multiple primary users and an energy-constrained secondary user is admitted to the primary users’ time slots via NOMA. During each time slot, the secondary user performs the two tasks: data transmission and energy harvesting based on the signals received from the primary users. The goal of the paper is to maximize the secondary user’s long-term throughput, by optimizing its transmit power and the time-sharing coefficient for its two tasks. The long-term throughput maximization problem is challenging due to the need for making decisions that yield long-term gains but might result in short-term losses. For example, when in a given time slot, a primary user with large channel gains transmits, intuition suggests that the secondary user should not carry out data transmission due to the strong interference from the primary user but perform energy harvesting only, which results in zero data rate for this time slot but yields potential long-term benefits. In this paper, a deep reinforcement learning (DRL) approach is applied to emulate this intuition, where the deep deterministic policy gradient (DDPG) algorithm is employed together with convex optimization. Our simulation results demonstrate that the proposed DRL assisted NOMA transmission scheme can yield significant performance gains over two benchmark schemes.
Weak topology and Opial property in Wasserstein spaces, with applications to gradient flows and proximal point algorithms of geodesically convex functionals

In this paper we discuss how to define an appropriate notion of weak topology in the Wasserstein space $(\mathcal{P}_2(H),W_2)$ of Borel probability measures with finite quadratic moment on a separable Hilbert space $H$. We will show that such a topology inherits many features of the usual weak topology in Hilbert spaces, in particular the weak closedness of geodesically convex closed sets and the Opial property characterizing weakly convergent sequences. We apply this notion to the approximation of fixed points for a non-expansive map in a weakly closed subset of $\mathcal{P}_2(H)$ and of minimizers of a lower semicontinuous and geodesically convex functional $\phi:\mathcal{P}_2(H)\to(-\infty,+\infty]$ attaining its minimum. In particular, we will show that every solution to the Wasserstein gradient flow of $\phi$ weakly converge to a minimizer of $\phi$ as the time goes to $+\infty$. Similarly, if $\phi$ is also convex along generalized geodesics, every sequence generated by the proximal point algorithm converges to a minimizer of $\phi$ with respect to the weak topology of $\mathcal{P}_2(H)$.