Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical strategies for detection of diazinon

Diazinon (DZN) was first registered as an insecticide in the U.S. However, it was categorized in the limited group of pesticides due to its high toxicity for birds, aquatic animals, and humans. Like other organophosphorus pesticides, this compound exhibits inhibitory effects on the acetylcholinesterase enzyme. The inhibition of the enzyme leads to the accumulation of acetylcholine and causes the death of insects. DZN is considered a toxic compound for humans due to its high adsorption via skin and inhalation, which leads to the emergence of different symptoms of toxicity. When DZN is used for plants, the compound residues in crops enter the food chain bringing about different health problems. Moreover, the compound is easily washed by surface water and enters the groundwater. Its entrance into aquatic environments can negatively affect a wide range of non-targeted organisms. Thus, researchers seek to find fast and precise methods for the analysis of DZN. The electrochemical method for recognizing the compound in real samples is preferable to other analytical methods. Because this method can be used without spending time preparing the sample, it is simple, fast, and cost-effective. Since such determinations may be made by using electrochemical sensors and biosensors, numerous researchers have developed such sensors for DZN detection, and different sensitive materials were used in order to improve the selectivity, sensitivity, and detection limit. The present study aims to present the main progress and performance characteristics of electrochemical sensors and biosensors used to detect DZN, as it is reported in a number of relevant scientific papers published mainly in the last decade.
Methods for Analyzing Attention Heads in Transformer-Based Predictive Models of Speech Representation Learning

Duygu Dogan: Methods for Analyzing Attention Heads in Transformer-Based Predictive Models of Speech Representation Learning Master of Science Thesis Tampere University Data Engineering and Machine Learning October 2021 Transformer has recently become one of the most popular deep learning models often utilized for processing sequential data. At the core of the Transformer, a mechanism called attention lies. The attention mechanism aims to learn the dependencies between different items in a sequence. Furthermore, Transformer applies multiple parallel attentions, called multi-head attention, in order to incorporate different types of dependencies behind the data. As Transformer became widely common, much literature has been devoted to the investigation of attention heads in natural language processing tasks. However, how they work in speech processing tasks is still under-explored. The present study analyzes multiple methodologies to analyze the attention heads in speech representation learning tasks. For this purpose, two different Transformer-based predictive coding models with different learning strategies, Autoregressive Predictive Coding and Contrastive Predictive Coding, are used. Furthermore, attentions are grouped into explainable categories by using temporal analysis along with correlation and linear regression methods with respect to the known characteristic of speech. Additionally, the contributions of individual heads to the performance on phoneme classification tasks is evaluated and analyzed by their designated categories using the aforementioned methods. The results of correlation and linear regression analyses show that individual attention heads have different functionalities and learn from varying speech features. Combining with temporal analysis, the findings further indicate that the heads learning from phonetic features tend to accumulate their attention in neighbor frames more consistently. On the other hand, the heads learning from other acoustic features spread their attention in temporally further past. In addition, the analyses indicate that instead of utilizing all heads, only a subset of heads can be used without seriously affecting the performance of the phoneme classification tasks. Although the choice of the subset is mainly related to the temporal behavior of the heads, it varies depending on the learning strategy of the model. As the model tries to predict further in the future, the best subset of heads consists of the ones that concentrate their attention on the more recent past.
ANTECEDENTS OF THE ADOPTION OF E-BANKING IN SOUTH PUNJAB, PAKISTAN; AN EXTENDED FRAMEWORK

Factors such as perceived ease of usefulness, the perceived ease of use, trust, Government support, and security and privacy are examined to determine whether variables impact the adoption of internet banking in Pakistan. A survey was distributed to 152 respondents in South Punjab, Pakistan. Data is being analyzed by using Pearson correlation and regression analysis. The study's outcomes confirmed that the perceived usefulness, ease of use, government support, and security all positively associated with the intention to use internet banking in Pakistan. On the contrary, trust is found to be insignificant in this study. These results demonstrate that the bankers make strategies to enhance the customer's trust on the security and privacy of internet banking. Finally, Government should provide a supporting role for the banks trying to adopt internet banking.
Recovery of phosphorus from

143 120 bed struvite This pilot showed that 94% precipitation of inflow soluble phosphate could be achieved at a pH of around 8.5 (addition of magnesium hydroxide). authors presented at the a business plan for the application of this process to pig farm liquid wastes. The authors suggest that the recovered struvite should achieve a market price as a fertiliser of at least 300 €/tonne and that the process could be attractive for over 100 pig farms (300,000 sows) and around 20 municipal sewage works in Australia.
A nominalização no português do Brasil

The morphological phenomenon of nominalization is a lexicalstandard which consists of the fomation of abstract substantivesfrom verbs (consagrar/consagracao, fingir/fingimento). Based onthe principles of Generative Morphology, this article aims to betterdefine the phenomenon, as well as to develop some aspects relatedto this linguistic mechanism, such as: the question of the derivationof the zero suffix (the problem of regressives), the study of whatmay be called the iterative -cao suffix and the relation betweennominalization and discourse type.
ID R&D team submission description for SASV Challenge 2022

In SASV challenge ID R&D team pursued two goals. The first included building the most precise fusion of systems minimizing the SASV EER. The second goal attempted to build a small single-model system providing a comparable to a big fusion system metrics. This report describes the details of our challenge submission. In particular, we cover up the training strategies for independently trained ASV and CM systems and present the results of a linear fusion of all scores together with QMFs. Our best fusion achieves 0.136 % EER on SASV-2022 evaluation set, while the smallest single-model system with 11.6M parameters achieves 0.223 % EER.
Galois images and modular curves (Algebraic Number Theory and Related Topics 2010)

This is a survey paper about Galois images, points on modular curves and Shimura curves, together with an application. The main topics are as follows. (1) The images of the Galois representations associated to elliptic curves and QM‐abelian surfaces. (2) Rational points, points over quadratic fields on modular curves and Shimura curves. (3) Application to a finiteness conjecture on abelian varieties with constrained prime power torsion.
Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori

This paper describes the process of data processing and training of an automatic speech recognition (ASR) system for Cook Islands Māori (CIM), an Indigenous language spoken by approximately 22,000 people in the South Pacific. We transcribed four hours of speech from adults and elderly speakers of the language and prepared two experiments. First, we trained three ASR systems: one statistical, Kaldi; and two based on Deep Learning, DeepSpeech and XLSR-Wav2Vec2. Wav2Vec2 tied with Kaldi for lowest character error rate (CER=6±1) and was slightly behind in word error rate (WER=23±2 versus WER=18±2 for Kaldi). This provides evidence that Deep Learning ASR systems are reaching the performance of statistical methods on small datasets, and that they can work effectively with extremely low-resource Indigenous languages like CIM. In the second experiment we used Wav2Vec2 to train models with held-out speakers. While the performance decreased (CER=15±7, WER=46±16), the system still showed considerable learning. We intend to use ASR to accelerate the documentation of CIM, using newly transcribed texts to improve the ASR and also generate teaching and language revitalization materials. The trained model is available under a license based on the Kaitiakitanga License, which provides for non-commercial use while retaining control of the model by the Indigenous community.
SUPERPOSITION'S PROPERTIES OF LOGARITHMIC GENERATING FUNCTIONS

AbstractThe paper discusses logarithmic generating functions and their properties. Thetheorem which is based on compositions of positive numbers and its conclusion areproved. Examples are given.Key words: Logarithmic generating functions, superposition ofgenerating functions,composition of positive number. Introduction Generating functions are a powerful tool for solving problems in number theory, combina-torics, algebra, probability theory. One of the advantages of the generating function is thatinﬁnite number sequence can be represented in the form of single expression. Generatingfunctions are divided into diﬀerent classes: ordinary, exponential, Dirichlet, Poisson, etc. Inthis paper one more class is considered - logarithmic generating functions. 1 Logarithmic generating functions Deﬁnition 1. Power seriesX ∞n=1 a(n)nx n , (1)where a(n) – is integer sequence, is a logarithmic generating function.Logarithmic generating function diﬀers from an ordinary one because elements a(n) di-vided by order number, i.e.
SHORT ONLINE COMPASSIONATE INTERVENTION BASED ON MINDFUL SELF-COMPASSION PROGRAM

J. Halamová, M. Kanovský, K. Jakubcová, N. Kupeli Objectives. The Mindful Self-Compassion (MSC) program is an empirically-developed group intervention aimed to cultivate self-com- passion. Sample and setting. A randomized control trial was conducted with pre-, post-measurements, and two-month follow-up. A total of 122 partici- pants were recruited from a general community by convenience sampling. They were randomly allocated to the Compassionate intervention (CI) based on MSC and to a control condition with no treatment. Hypotheses. The authors hypothesised that par- ticipation in the CI based on the MSC would decrease self-criticism and increase self-reas- surance and self-compassion. Statistical analysis. SPSS Statistics-20, program R, and the package nparLD for the statistical analysis. Non-parametric rank-based test for longitudinal data (pretest-postest design) was employed. Results. This version of the CI based on the MSC significantly increased levels of self-compassion and self-reassurance as reported immediately post intervention and at two-month follow-up. The CI based on the MSC was also effective at reducing self-uncompassionate responding, which was only present immediately post intervention. Self-compassion is responsive to improvement following a short-term online intervention of CI based on the MSC which suggests that interventions designed to increase self-compassion can be provided online to broader populations without direct involvement of mental health professionals. Study limitation. Participants to not to the full of the to a selected number of exercises from the MSC program.
A well‐balanced and entropy stable scheme for a reduced blood flow model

A well‐known reduced model of the flow of blood in arteries can be formulated as a strictly hyperbolic system of two scalar balance laws in one space dimension where the unknowns are the cross‐sectional area of the artery and the average blood flow velocity as functions of the axial coordinate and time. This system is endowed with an entropy pair such that solutions of the balance equations satisfy an entropy inequality in the distributional sense. It is demonstrated that this property can be utilized to construct an entropy stable finite difference scheme for the blood flow model based on the general framework by Tadmor (E. Tadmor, Math. Comput., 49 (1987), 91–103). Furthermore, a fourth‐order extension of the resulting entropy conservative flux and a fourth‐order sign‐preserving reconstruction of the scaled entropy variables are employed as well as a second‐order strong stability preserving Runge–Kutta method for time discretization. The result is a computationally inexpensive and easy‐to‐implement explicit entropy stable scheme for the blood flow model. It is proven that the scheme is well‐balanced (i.e., preserves certain steady solutions of the model) and numerical examples are presented.
Implementation Model of Community Based Tourism on Rural Tourism

One effort of increasing rural economy can be conducted by developing rural tourism. Tourism sector is considered to be capable to encourage the development of other economy sectors. Tourism also contributes in increasing job opportunity and income. The bottom up planning paradigm expects the community to take roles as both subject and object of development. Dieng Plateau as a tourist attraction has various kinds of attractions. Rural tourism is designed by the local community to the tourist attraction in Dieng. This research aims at identifying how the implementation model of Community Based Tourism has been conducted to the development of Dieng Kulon rural tourism. This research is a qualitative one, which uses the primary data taken from Observation Technique and Focus Group Discussion. The analysis used in this research is descriptive qualitative and Participatory Rural Appraisal (PRA). This research has found a model of Community-Based Tourism that has been conducted in the development of Dieng Kulon rural tourism through Pokdarwis (Tourism Awareness Group) and Pokja (Workgroup) establishment on eight fields. The Community Based Tourism model that has been tested and implemented in this research may complement the models in the previous research findings, which show the roles and functions of each actor of tourism development.
A Flight Level Analysis of Departure Delay and Arrival Delay Using Copula-based Joint

The main goal of the current study is to identify the factors affecting flight level airline delay by 2 jointly modeling departure and arrival delays. Towards this end, we develop a novel copula-based 3 group generalized ordered logit (GGOL) model system that accommodates for the influence of 4 common observed and unobserved effects on flight departure and arrival delays. The proposed 5 model is estimated using 2019 marketing carrier on time performance data compiled by BTS for 6 67 airports in the continental US. The delay data is augmented with a comprehensive set of 7 independent variables including traffic conditions at the origin and destination airports in the hours 8 preceding flight departure and arrival, trip level attributes, weather variables for the entire flight 9 duration, spatial, and temporal factors. The model estimation results highlight that Joe copula 10 model with parameterization provides the best data fit. The model performance is further 11 established to be excellent using a holdout sample. Finally, to illustrate the applicability of the 12 model for prediction and highlight the impact of independent variables, we perform a prediction 13 exercise under a host of hypothetical scenarios. The illustration provides a mechanism for 14 employing the proposed model as a tool for airline carrier level or airport level delay prediction 15 analysis using weather forecasts while controlling for a host of independent variables. 16
Estrus induction response and fertility performance in anestrus cows treated with Aegle marmelos (Bael) and Murraya koenigii (curry)

The current study uses Aegal marmelos (Bael) and Murraya koenigii (curry) to induce estrus in anestrus cattle and evaluate fertility performance. Selected cows are divided into 4 groups of 6 animals each, all of whom have smooth, dormant ovaries and no pathological conditions or a history of anestrus. Different doses of prepared curry powder at a dose rate of 400 mg/Kg, bael at a dose rate of 500 mg/Kg, and their combination at 200 mg/Kg of curry and 250 mg/kg of bael supplied orally for nine days. The combination of both Murraya and Aegal showed higher results in estrus induction, estrus grading, and also enhanced biochemical profile which is crucial for the fertility of cattle. It was discovered that combination treatment shows 83.33% of estrus induction followed by the 50% and 66.67% by other groups respectively. cold and respiratory infection, backache, abdominal disorder, vomiting, cut and wounds, dropsy, beriberi, weakness of heart, cholera, cardiac tonic, control blood nervous disorders, acute bronchitis, for wound healing, stimulation of respiration.
Short-Term Traffic Flow Prediction Based on Ga-Bp Neural Network

: In response to the short-term traffic flow prediction, it is more sensitive in the short-time traffic flow prediction, and a short-time traffic flow prediction method optimized by genetic algorithm optimizes BP Neural Network is proposed. The weight and threshold of the BP Neural Network are processed by the genetic algorithm, and the BP Neural Network optimization is applied to adjust the real-time data predictive value. The experimental results show that the average absolute error of the algorithm is reduced by 9.1998% compared to the BP Neural Network, and the mean square error is reduced by 0.092. The average absolute error of the algorithm is reduced by 3.7229% compared to the wavelet Neural Network algorithm, and the mean square error is reduced by 0.0573. Compared to the above two algorithms, the GA-BP Neural Network algorithm has a better predictive effect, and provides a certain reference value for short-term traffic flow forecasting.
Early detection of mental disorder via social media posts using deep learning models

Mental health, which has as equally important effects on people’s life as physical health, is receiving more and more attention nowadays, especially with a significant increase of pressure brought by the fast-paced evolution of technology and society. The diagnosis of mental health symptoms, however, mostly relies on the interpretation of languages and behaviors by experienced psychologists, who are not accessible for the great population. Depression causes cognitive and motor changes that affect speech production: reduction in verbal activity productivity, prosodic speech irregularities, and monotonous speech have all been shown to be symptomatic of depression. In this study, we aim to provide a deep learning based model that could give an initial diagnosis of mental health problems for individuals and screen the risk of developing mental health issues. This AI-driven model focuses on the understanding and analysis of people’s daily public comments/posts and captures the peoples’ mental health status embedded in the semantic and syntactic structure in those online posts.
Segmentacja obrazów kolorowych wzorcami nieregularnymi

: This paper considers the problem of object recognition in colour images, excepting the feature extraction process. On the pre-processing stage, the only preceding segmentation, a colour quantisation technique is applied to avoid the use of a whole RGB colour space. The generalized Hough transform is used as a tool of segmentation to identify irregular objects. The colour quantisation process is essential for the recognition reliability and received special attention. In a given computer vision system, the choice of a quantisation technique should be dictated by the nature of analyzed input images
Urban Sound Classiﬁcation

—Machine learning applications for image classiﬁ-cation and recognition is becoming the benchmark in artiﬁcial intelligence. Popular methods and tools developed for image related applications are soaring in both quantity and quality. In this paper we seek to examine the feasibility of several machine learning tools that were developed initially for image related applications and seek to adapt them to classify an image representation of sound. We represent sound as an image of their spectrograms, the merits of this representation is that we have both a temporal dimension of the data and a spatial dimension of our data that of the frequency band. In this way we can examine several methods that are best adapted to sequential and spatial data. The resulting image ﬁles produced from the sound ﬁles are thus seen to be smooth and characteristic of the environment that produced them, thereby constituting a good candidate to apply image classiﬁcation algorithms on them to learn relevant features and to classify them.
A study of the stretching vibrational spectra of fullerene dimers C 60 O and C 60 O 2 by U (2) Lie algebra

The vibrational energy levels of the fullerene dimers C 60 O and C 60 O 2 were calculated considering the local Hamiltonian of the Morse potential using algebra. Here, each bond of the molecules was replaced by a corresponding Lie algebra and finally the Hamiltonian was constructed considering the interacting Casimir and Majorana operators. The fundamental stretching modes of vibration of both dimers C 60 O and C 60 O 2 were then calculated using the algebraic Hamiltonian. This study leads to explore few fundamental hitherto unknown vibrational energy levels of both the fullerene dimers.
Effectiveness of Data Augmentation and Pretraining for Improving Neural Headline Generation in Low-Resource Settings

We tackle the problem of neural headline generation in a low-resource setting, where only limited amount of data is available to train a model. We compare the ideal high-resource scenario on English with results obtained on a smaller subset of the same data and also run experiments on two small news corpora covering low-resource languages, Croatian and Estonian. Two options for headline generation in a multilingual low-resource scenario are investigated: a pretrained multilingual encoder-decoder model and a combination of two pretrained language models, one used as an encoder and the other as a decoder, connected with a cross-attention layer that needs to be trained from scratch. The results show that the first approach outperforms the second one by a large margin. We explore several data augmentation and pretraining strategies in order to improve the performance of both models and show that while we can drastically improve the second approach using these strategies, they have little to no effect on the performance of the pretrained encoder-decoder model. Finally, we propose two new measures for evaluating the performance of the models besides the classic ROUGE scores.