Miracle-dz/UniFAR-FTUs huggingface dataset:
CO2 Supply during Photosynthesis of Sun and Shade Grown CAM Plants in Relation

Leaves of Kalancho#pinnata were exposed in the dark to air (allowing the fixation of C02 into malic acid) or 2% 02,0% CO2 (preventing malic acid accumulation). They were then exposed to bright light in the presence or absence of external CO2 and light dependent inhibition of photosynthetic properties assessed by changes in 77 K fluorescence from photosystem II (PSII), light response curves and quantum yields of 02 exchange, rates of electron transport from H20 through QB (secondary electron acceptor from the PSI1 reaction center) in isolated thylakoids, and numbers of functional PSII centers in intact leaf discs. Sun leaves of K. pinnata experienced greater photoinhibition when exposed to high light in the absence ofCO2 if malic acid accumulation had been prevented during the previous dark period. Shade leaves experienced a high degree of photoinhibition when exposed to high light regardless ofwhether malic acid had been allowed to accumulate in the previous dark period or not. Quantum yields were depressed to a greater degree than was 77 K fluorescence from PSII following photoinhibition.
Three-dimensional ultrasound studies of normal and abnormal ovaries

The aims of this work were: 1) to estimate the number of follicles, ovarian volume and power Doppler vascular indices and their changes with age using three-dimensional (3D) transvaginal grey-scale and power Doppler ultrasound in asymptomatic women of fertile age either with natural menstrual cycles or using combined oral contraceptives, and 2) to determine whether tumour vascularity, assessed by 3D power Doppler ultrasound, can help discriminate between benign and malignant ovarian tumours. A group of 303 gynaecologically asymptomatic women, aged 20–39 years with normal menstrual cycles (NMC) and another group of 213 women aged 20–39 years using combined oral contraceptives (COC) were examined using two-dimensional (2D) and 3D transvaginal ultrasound once between cycle day 4 and 8. Ovarian volume and the number of antral follicles were found to decrease with age, while no unequivocal changes in vascular indices were seen in women with NMC. In women using COC the number of antral follicles also decreased with age. The ovarian volume and the number of follicles were determined for different age groups, and these results can be used as reference data. A group of women with ovarian tumours (n=106) was examined using 2D and 3D transvaginal ultrasound. Tumour vascularization was assessed using 3D power Doppler ultrasound in the whole tumour and in a 5-cm3 spherical sample in the most vascularized part of the tumour. A logistic regression model with only grey-scale ultrasound variables (size of the largest solid component, wall irregularity, mean diameter of the lesion) was created to predict malignancy. The flow index in the 5-cm3 sample and the branching of vessels in the whole tumour provided additional information to the grey-scale model, but the diagnostic performance was only slightly improved.
Contextual Rephrase Detection for Reducing Friction in Dialogue Systems

For voice assistants like Alexa, Google Assistant, and Siri, correctly interpreting users’ intentions is of utmost importance. However, users sometimes experience friction with these assistants, caused by errors from different system components or user errors such as slips of the tongue. Users tend to rephrase their queries until they get a satisfactory response. Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e.g. users’ implicit feedback). To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multi-turn dialogues. We showcase how to leverage the dialogue context and user-agent interaction signals, including the user’s implicit feedback and the time gap between different turns, which can help significantly outperform the pairwise rephrase detection models.
Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks

A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.
Raﬃner l’heuristique CHS à l’aide de bandits ∗†

Résumé Récemment, une heuristique eﬃcace, appelée Conﬂict History Search (CHS), a été introduite pour la résolution du problème de satisfaction de contraintes (CSP). Elle repose sur une technique d’apprentissage par renforcement appelée Exponential Recency Weighted Average (ERWA) pour estimer la dureté des contraintes. CHS favorise les variables qui apparaissent souvent dans les échecs récents. Le paramètre de pas utilisé dans CHS est important car il contrôle l’estimation de la dureté des contraintes. Dans cet article, nous envisageons un raﬃnement de ce paramètre à l’aide d’un bandit manchot. Le bandit sélectionne une valeur appropriée de ce para-mètre lors des redémarrages eﬀectués par l’algorithme de recherche. Chaque bras correspond à l’heuristique CHS avec une valeur donnée pour le paramètre de pas et est récompensé selon sa capacité à mener une recherche eﬃ-cace. Une phase d’entraînement est introduite en amont de la recherche pour aider le bandit à choisir un bras pertinent. L’évaluation expérimentale montre que cette approche conduit à des améliorations signiﬁcatives.
Reaction of Bulgarian varieties of perennial ryegrass (Lolium perenne L.) to glyphosate

Marinov-Serafimov, P., A. , Golubinova, I. A. & Katova, A.I. (2021). Reaction of Bulgarian varieties of perennial ryegrass (Lolium perenne L.) to glyphosate. Bulg. J. Agri. Sci., 27 (5), 911–918 The aim of this study was to determine and compare the sensitivity of new high yield Bulgarian varieties of perennial ryegrass (Lolium perenne L.) – Harmoniya, Tetrany and Tetramis to glyphosate in laboratory conditions. The effect of three doses of glyphosate – 360, 720 and 1440 g a.i./ha applied in the growth stage tillering (BBCH – 22-23) on perennial ryegrass on the dynamics of plant growth and formed fresh biomass in g per plant was studied. Genotypic differences were observed between the studied varieties of perennial ryegrass to glyphosate. With a relatively high sensitivity to glyphosate conditionally can be determined diploid variety Harmoniya in which the depression coefficients (B) on the dynamics of growth and accumulation of fresh biomass are in the range from -5.7 to – 4.7, while for tetraploids Tetrany (B) (average) is in the range of -3.3 to -3.9 and for Tetramis (B) (average) is within limits from -3.2 to -3.9, they have relatively high tolerance to glyphosate and can be used as components in future programs.
Wasserstein-Optimal Bayesian System Identification for Domain Randomization

Domain Randomization (DR) has recently proven to be an effective approach for learning control policies from randomized simulations to increase robustness against the model-plant mismatch. In particular, adaptive methods that update domain parameter distributions using Likelihood-Free Inference (LFI) were shown to be efficient at sim-toreal transfer. However, current LFI methods either require manually designed distance metrics to distinguish simulated and real trajectories or learn a costly global surrogate inverse model of the simulator to enable amortized inference. In this thesis, we propose an algorithm termed Wasserstein-optimal Likelihood-Free Inference, that estimates the intractable likelihood of a simulator using Energy-Based Models (EBMs) in which the energy functional is represented by the Wasserstein distance over trajectories, thereby addressing the challenges of the current LFI approaches. For the proposed method, we develop Bayesian inference routines based on Sequential Monte Carlo Approximate Bayesian Computation (SMC-ABC) and Relative Entropy Policy Search (REPS) algorithms. We establish a theoretical connection between SMC-ABC and REPS using maximum entropy principles, demonstrating that both approaches optimize the same objective function. This connection can be leveraged to apply REPS to typical LFI problems, and in particular, Bayesian system identification. The proposed method is validated on sim-to-sim and sim-to-real tasks and are compared against SNPE-C, a state-of-the-art LFI algorithm that sequentially updates a neural model of the posterior. The sim-to-sim experiments confirm that both SNPE-C and REPS are able to capture the domain parameter correlations of the posterior. The sim-to-real experiments on the cart-pole and the Furuta pendulum swing-up tasks demonstrate that the proposed Wasserstein-optimal LFI algorithm finds the parameter distributions that match the real trajectories better than SNPE-C in terms of the Dynamic Time Warping (DTW) discrepancy and the Mean Squared Error (MSE) between the real and simulated trajectories.
Indonesian Journal of Electrical Engineering and Computer Science

Received Dec 11, 2020 Revised Aug 19, 2021 Accepted Sep 9, 2021 Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lot of attention in the data processing community. But, they inordinately affect the quality of the results obtained in case of popular clustering algorithms during the process of finding an optimal solution. In this work, we propose a novel method to classify the data points with grouping characteristics as either an outlier or not. We use both distance and density of a particular data point with respect to the rest of the data points for this process. Distances are used to find the points at the extremities while the densities are used to identify the data points at the sparsest spaces. Further, every data model has to take into account the aspect of generalization in order to work robustly even in out of the box situations. Hence, our approach provides a generalization aspect to the model. The accuracy of the proposed work is measured using area under curve (AUC) was found the highest for cardioto data set -AUC value-0.90 and second highest AUC value was obtained for Spambase data set -0.52 and several other datasets are used to demonstrate the usage of the model proposed.
Female engineering students: Career motivation and their learning experience

Engineering has remained one of the most male dominated professions across the globe and it is not until the latter half of the 20th century that engineering started opening its doors to women. The enrolments of female students in the Faculty of Engineering at the University of Sydney have been on a slow but steady rise over the last ten years, however, female engineering students form only about 17% of the total student population in the engineering faculty. This study is a part of a research project conducted at the Faculty of Engineering, in the University of Sydney, with about 250 engineering students, in order to highlight the learning experiences of the undergraduate female engineering students. It analyses the educational background of female engineering students and how this motivates them to choose engineering; furthermore it investigates their learning experiences in a male dominated faculty with an aim of identifying key issues that need to be addressed in order to improve the learning environment of female students, and to attract more female students to engineering.
Walking machines : an introduction to legged robots

Advantages of legs. - Terminology. - 1 A brief history of walking machines. - Rough-terrain transport. - Bipeds: medical applications and man-amplifiers. - The modern era: computer control. - Alternative mechanisms of locomotion. - 2 General principles of legged locomotion. - The theory of land locomotion in general. - The soil mechanics of wheels. - Locomotion using levers. - Soil mechanics and the difference between feet and wheels. - The energetics of legged locomotion. - Other properties of legs. - Mechanisms other than legs. - Aids to locomotion. - Non-locomotory uses of appendages. - Description and classification of gaits. - 3 The mechanics of legged vehicles. - Animal structures and their applications to robots. - Leg number and arrangement. - Leg design. - Structure. - Actuation methods. - 4 The analysis and control of legged systems. - Purpose of analysis and control. - The objectives of control in legged locomotion. - Dynamics. - Multipod control. - 5 Computing: hardware and software requirements. - Computer architectures. - Sensing methods. - The design of algorithms for multipod walking. - Programming languages for legged robots. - 6 Current developments. - The Ohio State University hexapod and adaptive suspension vehicle. - ReCUS: the Komatsu underwater octopod. - Sutherland's hexapod. - The Odetics ODEX I. - Quadruped research at the Tokyo Institute of Technology. - Walking robots in France. - Jurassic Robots. - Current research on bipeds. - Research on hopping at Carnegie-Mellon University. - Conclusions. - 7 Applications of walking machines. - Military transport. - Mining. - Nuclear engineering and other cases of remote handling. - Prosthetics and orthotics: walking chairs. - Planetary exploration. - Construction and related activities. - Agriculture and forestry. - Mobile robots in artificial intelligence. - Fire-fighting and rescue robots. - Applications unlikely to be suitable for walking machines. - The study of locomotion (zoology and engineering). - Education, art and entertainment. - References and bibliography.
A Causal Lens for Controllable Text Generation

Controllable text generation concerns two fundamental tasks of wide applications, namely generating text of given attributes (i.e., attribute-conditional generation), and minimally editing existing text to possess desired attributes (i.e., text attribute transfer). Extensive prior work has largely studied the two problems separately, and developed different conditional models which, however, are prone to producing biased text (e.g., various gender stereotypes). This paper proposes to formulate controllable text generation from a principled causal perspective which models the two tasks with a unified framework. A direct advantage of the causal formulation is the use of rich causality tools to mitigate generation biases and improve control. We treat the two tasks as interventional and counterfactual causal inference based on a structural causal model, respectively. We then apply the framework to the challenging practical setting where confounding factors (that induce spurious correlations) are observable only on a small fraction of data. Experiments show significant superiority of the causal approach over previous conditional models for improved control accuracy and reduced bias.
Bridging the gap between critical algorithm studies and the social sciences

Since the mid-2000s platforms and platform work have greatly proliferated. Although also research on platforms and platforms has expanded, there seems to be a great gulf between IT- and social research on these issues. In this article, I show, using sociologist Maurizio Lazzarato’s concepts, that the technical infrastructure of the platform, in particular the algorithm, has a direct impact on work and the agency of the platform worker. From this perspective, it is possible to ask critical questions about platforms and platform work from a multidisciplinary viewpoint. Methodologically, an ethnographic approach that sees algorithms as part of culture, with its own institutions, impact on people and intersection with different contexts, could support co-operation between different disciplines. When such an approach is combined with insights from algorithm studies, research is also relevant for existing political questions regarding platform workers’ labour market status and the functioning of platform firms.
Applying Grey Clustering and Shannon’s Entropy to Assess Sediment Quality from a Watershed

The evaluation of the quality of sediments is a complex issue in the Peruvian reality, mainly because there is no sampling protocol or norm for comparison, which leads to the assessment of sediments without a comprehensive analysis of their quality. In the present study, the quality of the sediments in the upper basin of the Huarmey river was evaluated in 30 monitoring points and 7 parameters, which are: arsenic, cadmium, copper, chromium, mercury, lead and zinc, which were compared according to the standards recommended by the Environmental Quality Guidelines for Sediments in freshwater bodies of Canada (Canadian Environmental Quality Guidelines CEQG, 2002. Sediment Quality Guidelines for Protection of Aquatic Life Fresh water according to Canadian Council of Ministers of the Environment (CCME)). The results of the evaluation, by grey clustering method and Shannon entropy, showed that 13 monitoring points resulted in good sediment quality, 1 monitoring point had moderate quality and 16 monitoring points presented poor quality; therefore, it can be concluded that the effluents and discharges of the mining activities that take place in the aforementioned location have a negative impact on environmental quality. Finally, the results obtained can be of great help for OEFA, the regional government, the municipalities and any other body that has oversight functions, since they will allow them to be more objective and precise decisions. Keywords—Grey clustering; sediment quality; Shannon entropy
Determinants and Magnitude of Neonatal Sepsis at Hiwot Fana Comprehensive Specialized University Hospital, Harar, Ethiopia: A Cross-Sectional Study

Introduction: Neonatal sepsis is a serious blood bacterial infection in neonates at the age of equal to or less than 28 days of life, and it's still the major significant cause of death and long-term morbidity in developing countries. Therefore, this study has assessed the prevalence and related factors with neonatal sepsis among new born admitted to the neonatal intensive care unit at Hiwot Fana Comprehensive Specialized University Hospital, Harar, Ethiopia. Methods: An institutional based retrospective cross-sectional study design was conducted among 386 neonates admitted to NICU from September 2017 to August 2019 G.C. A systematic random sampling method was used. Data was analyzed using SPSS V.26. Descriptive summary statistics was done. Bivariate analysis was computed to identify association between dependent and independent variables. Multivariate analysis was used to control possible confounder variables and variables with p-value <0.05 were declared as having statistically significant association. Result: The prevalence of neonatal sepsis was 53.1% and 59.5% were males. Among the total neonates who had sepsis, 67.8% had early neonatal sepsis. Among neonatal factors, preterm neonates (AOR: 8.1, 95%CI: 2.1, 31.2), birth asphyxia (AOR: 4.7, 95%CI: 1.6, 13.6); and among maternal factors, urban residence (AOR: 0.26, 95%CI: 0.1, 0.5), ANC attendance (AOR: 0.32, 95%CI: 0.2, 0.6), SVD (AOR: 0.047, 95%CI: 0.01, 0.2), Maternal antibiotic use (AOR: 0.39; 95%CI: 0.2, 0.8), duration of rupture of membrane < 12 hours (AOR: 0.11; 95%CI: 0.05, 0.2) were found to have significant association with neonatal sepsis. Conclusion: Overall, the magnitude of neonatal sepsis was high. Being preterm, low birth weight and having birth asphyxia were found to significantly increase the odds of neonatal sepsis. Urban residence, having ANC follow up, giving birth by SVD and CS, history of antibiotic use and having rupture of membrane < 18 hours were found to significantly decrease the odds of neonatal sepsis. Key words: Neonatal Sepsis, Intensive Care Unit, Harar
Field Experience With Point‐of‐Use Treatment Systems for Arsenic Removal

Point-of-use (POU) treatment devices can be effective for removing inorganic contaminants. This article describes the investigation of POU treatment systems used for arsenic removal in four homes in Alaska and Oregon. Small systems utilizing activated alumina, ion exchange, and reverse osmosis techniques were field-tested on waters naturally contaminated with arsenic. The waters contained arsenic in concentrations ranging from 0.1 to 1.0 mg/L, which were successfully lowered to below the 0.05mg/L maximum contaminant level.
Reconstructing Test Labels from Noisy Loss Scores (Extended Abstract)

Label inference was recently introduced as the problem of reconstructing the ground truth labels of a private dataset from just the (possibly perturbed) cross-entropy loss scores evaluated at carefully crafted prediction vectors. In this paper, we generalize this result to provide necessary and sufﬁcient conditions under which label inference is possible from a broad class of loss functions. We show that for many commonly used loss functions, including linearly decomposable losses, some Bregman divergence-based losses and when common activation functions are used, it is possible to design such attacks for arbitrary noise levels. We demonstrate that these attacks can also be carried out through a lightweight augmentation to any neural network model, enabling the adversary to make these attacks look benign. Our results call to attention these vulnerabilities which might be currently under silent exploitation. Armed with this information, individuals and organizations, which vend these seemingly innocuous aggregate metrics from their classiﬁcation models, can grasp the potential scope of the resulting information leakage.
Secure cluster-based routing using multi objective-trust centric artificial algae algorithm for wireless sensor network

Nowadays, wireless sensor network (WSN) is developed as a key technology to observe and track applications over a wide range. However, energy consumption and security are considered as important issues in the WSN. In this paper, the multi objective-trust centric artificial algae algorithm (M-TCAAA) is proposed to accomplish a secure broadcasting over the WSN. The proposed M-TCAAA is used to choose the secure cluster head (SCH) as well as routing path, based on the distinct fitness measures such as trust, communication cost, residual energy, and node degree. Hence, the M-TCAAA is used to ensure a secure data transmission while decreasing the energy consumed by the nodes. The performance of the M-TCAAA is analyzed by means of energy consumption, packet delivery ratio (PDR), throughput, end to end delay (EED), normalized routing load (NRL), and network lifetime. The existing researches namely energy aware trust and opportunity-based routing with mobile nodes (ETOR-MN), grey wolf updated whale optimization (GUWO), secure cluster-based routing protocol (SCBRP), secure routing protocol based on multi-objective ant-colony-optimization (SRPMA) and multi objective trust aware hybrid optimization (MOTAHO) are considered for evaluating the M-TCAAA. The PDR of the M-TCAAA for 100 nodes is 99.87%, which is larger than the ETOR-MN, GUWO, SRPMA and MOTAHO.