Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Secure and Verifiable Hybrid Federated Learning

Reducing computation cost and ensuring update integrity, are key challenges in federated learning (FL). In this paper, we present a secure and verifiable hybrid FL system for training, namely SVHFL. SVHFL enables training models on both plaintext and encrypted data simultaneously. Furthermore, we propose a mutual verification scheme for the integrity of updates in FL. It is a general and efficient scheme that can eliminate malformed updates from clients and enforce the integrity checks of the aggregation results from the server. The training and verification schemes of SVHFL have reduced the computation cost from a quadratic cost to a linear cost. The experimental results demonstrate the practicality of SVHFL.
An evaluation of the vulnerability of Uzumba Maramba Pfungwe (UMP) district of Zimbabwe to agrometeorological hazards and extreme events for improved farmer resilience

Increased climate variability due to climate change has always results in the occurrence of varied meteorological hazards world-wide. These hazards and extreme events have the potential to decimate completely a peopleâ€™s livelihood if adequate adaptation measures are not taken. In this study, a vulnerability assessment and mapping of agrometeorological hazards and extreme events that occur in Uzumba Maramba Pfungwe (UMP) district was carried out. Household surveys were used to document the hazards and to identify the communityâ€™s adaptation strategies. Climatological data analysis was used to verify occurrence of hazards to assess and map vulnerability. Satellite derived and ground based measurements were used to characterise the agroclimate of the district and to identify extreme events. Comparison of the maximum temperature from ground based and satellite derived measurements showed a high coefficient of determination (r2 = 0.9664). Start of season, end of season, length of growing period and length of dry spells were analysed using Instat+ 3.37. ArcGis 10.1 was used to map agroclimatic parameters such as temperature, rainfall, potential evapotranspiration and length of growing period over the district. The survey results showed that the districts is vulnerable to droughts, dry spells, waterlogging and extreme hot days more so over lower UMP. The seasons start late (7 December or later) and terminate early (4 March or earlier) resulting in short Length of growing season. Mapping of the hazards showed lower UMP being most vulnerable to the hazards and extreme events. Not all the components of vulnerability were considered in this study, therefore further studies should include such factors as socio-economic status of the smallholder farmers. Similar approaches can be used to carry out vulnerability assessment at national level.
Advancements in Financial Document Structure Extraction: Insights from Five Years of FinTOC (2019-2023)

In this comprehensive paper, we present a detailed overview of the Financial Table Of Content extraction shared task series, FinTOC, conducted over a span of five years from 2019 to 2023. This paper serves as a retrospective analysis of the key developments in the field of financial document structure extraction. The FinTOC series, hosted within the framework of the Financial Narrative Processing (FNP) workshop, has been instrumental in shaping the landscape of Natural Language Processing (NLP) in the financial domain. Our analysis delves into the diverse methodologies proposed by participants across all editions, shedding light on the innovative strategies employed to tackle the intricate challenge of extracting structured information from financial documents. We explore the evolution of techniques, from traditional rule-based approaches to cutting-edge deep learning models, showcasing the dynamic nature of NLP advancements. Furthermore, our study investigates the introduction of multilingual datasets by the organizers, highlighting the importance of cross-lingual analysis in financial document processing. We also examine the contributions made by participants in augmenting the training data with external sources, showcasing the collaborative spirit of the NLP community in enhancing the quality and size of the shared training dataset.
Diversity of floristic composition of midfield baulks under different farming systems implemented on the outskirts of the Białowieża Forest

: A study was conducted on changes in the abundance and diversity of floristic composition in baulks and cultivated agrocenoses in the area of the village of Nowokornino, located on the outskirts of the Białowieża Forest. Within the geodesic area of the village, which covers approximately 1,100 ha, agricultural crops are grown under three farming systems: conventional farming, integrated farming and organic farming. In the study area, there are unique marginal ecosystems in the form of mid-field baulks on which various weed species of ecological importance occur. Three study plots were designated on the baulks, and three plots on field crops Floristic surveys were carried out in 2016, 2020 and 2022. The cover of individual species was estimated according to the 7-level Braun-Blanquette scale. A total of 91 plant species belonging to 6 phytosociological groups were found, with the Stellarietea mediae group having the highest species richness. The significance of differences in floristic richness between study and control plots was tested by one-way analysis of variance (ANOVA). Statistical analysis showed that the factors differentiating the floristic composition on the baulks were their location within the cultivated agrocenoses, natural soil abundance and interaction with fertiliser factors. The highest species abundance of plants defined as weeds, occurred in fields under the integrated farming system (58 species), the lowest in fields under the conventional system (39 species). The vegetation of the baulks and adjacent arable fields was dominated by plants representing mainly three types of ecological strategies: C, R and C-R.
Arrhenius Equation for Calculating Viscosity in Assessing the Dilution Level of Lubricating Oil with Diesel Oil—A Case Study of SAE 30 and SAE 40 Grade Marine Lubricating Oils

This article proposes using the Arrhenius model for estimating the viscosity of a mixture of two liquids in the quantitative assessment of the physicochemical properties of lubricating oils in the context of assessing the level of dilution of lubricating oil with diesel oil. Dynamic are made of mixtures of lubricating oil and diesel oil with known concentrations of 0, 1, 2, 5, 10, 20, 50, and 100% m/m of the diesel oil content in the mixture. Mixtures of the most popular oils (viscosity classes SAE 30 and SAE 40) with diesel oil that meet the requirements of the DMX of the marine distillate fuels category are prepared and tested. Viscosity measurements are performed at 40, 50, 60, 70, 80, 90, and 100 °C temperatures. The Arrhenius model is used to estimate the viscosity of the mixtures with an assumed diesel oil content and to estimate the diesel oil concentration in the mixtures with a known measured viscosity value. In both cases, the absolute estimation error is determined, and the accuracy of the estimation is assessed against the known concentration of diesel oil in the mixture with lubricating oil and the temperature at which the viscosity is measured. The estimated concentrations of diesel oil in mixtures with lubricating oil are useful to assess the condition of the lubricating oil (for concentrations of diesel oil lower than 5% m/m). The method is proposed to be used in practice to support standard laboratory oil analysis.
Violent Video Recognition Based on Global-Local Visual and Audio Contrastive Learning

The aim of the violent recognition task is to determine whether a video contains violent behaviors. Given that violent behavior often comes with visual and audio anomalies, multimodal approaches have always played an important role in this field. However, existing methods have been limited by the insufficient utilization of audio-visual self-supervised semantic cues and correlation, resulting in a restricted representational capacity of the network and low generalization due to the scarcity of available violent video datasets. To address this issue, we propose a violent action recognition model based on global-local visual and audio contrastive learning. Our model introduces global and local contrastive objectives to achieve audio-visual multi-grained semantic alignment and leverage the correlation for violent video recognition. Experimental results demonstrate that our proposed model improves state-of-the-art by 2.31% on the VSD dataset, 0.71% on the Violent-Flows dataset, and 1.43% on the VCD dataset.
Intuitive Cell Manipulation Microscope System with Haptic Device for Intracytoplasmic Sperm Injection Simplification

In recent years, the demand for effective intracytoplasmic sperm injection (ICSI) for the treatment of male infertility has increased. The ICSI operation is complicated as it involves delicate organs and requires a high level of skill. Several cell manipulation systems that do not require such skills have been proposed; notably, several automated methods are available for cell rotation. However, these methods are unfeasible for the delicate ICSI medical procedure because of safety issues. Thus, this study proposes a microscopic system that enables intuitive micropipette manipulation using a haptic device that safely and efficiently performs the entire ICSI procedure. The proposed system switches between field-of-view expansion and three-dimensional image presentation to present images according to the operational stage. In addition, the system enables intuitive pipette manipulation using a haptic device. Experiments were conducted on microbeads instead of oocytes. The results confirmed that the time required for the experimental task was improved by 52.6%, and the injection error was improved by 75.3% compared to those observed in the conventional system.
Construction Industry Causes of Injuries, Management of EMS and Its Impact on Public Health

Introduction: Injuries in construction are not a rare occurrence and cause a strain on the health care service. By having education on workplace hazards, trained first aiders, and an effective accident emergency response system, the occurrence of illness/injuries will reduce and hence the number of fatalities. The purpose of this research is the evaluation, epidemiology, management, diagnosis, treatment, systematization, and the results obtained about the construction industry for injury in life-threatening cases with trauma, reducing morbidity, disability, and mortality while increasing survival. Solving problems for severe traumas in the health system of Kosovo, such as with professional staff, doctors and nurses, dedicated spaces, medical equipment, drugs, consumables, poor diagnostics, non-decisional consultations, as well as their timely and timely delivery. seeks political health solutions to solve this problem. Material and Methods; In this retrospective study, we researched and analyzed the data of patients with construction-related injuries in ED treated from January-December 2021 Results; During the study period, we analyzed 75,899 ED patients. Of these, 420 cases, or 0.06% were injured. In the research, 420 cases of injuries were investigated, investigating all age groups arising from construction-related activities for 2021. Conclusion; To reduce workplace injuries an organization must carry out regular risk assessments, conduct physical assessments for demanding roles, provide safety and wellness training regularly, train and appoint first aid marshals, hire qualified workers, hire enough workers, keep workspaces clean and walkways clear, post proper signage, Provide adequate lighting, education and training on staff emergency response systems via courses BLS-D BTLS, PHTLS ATLS.
The complete chloroplast genome sequence of Crepidiastrum lanceolatum (Asteraceae)

Abstract The characteristic of complete chloroplast (cp) genome sequence of Crepidiastrum lanceolatum, a member of the subfamily Cichorioideae with unresolved phylogenetic relationship among the related taxa, was firstly reported in this study. It was 152,748 bp and contained a large-single copy region (SSC) (84,022 bp) and a small-single copy region (LSC) (18,568 bp) which were separated by two inverted repeat (IR) regions (25,079 bp). In total, 133 genes were identified and they were consisted of 86 coding genes, 8 rRNA genes, 37 tRNA genes, and 2 pseudogenes. Molecular phylogenetic analysis showed that the genus Crepidiastrum was a sister group of the genus Taraxacum in subfamily Cichorioideae.
Lessons Learned from Translating Genome Sequencing to Clinical Routine: Understanding the Accuracy of a Diagnostic Pipeline

The potential of genome sequencing (GS), which allows detection of almost all types of genetic variation across nearly the entire genome of an individual, greatly expands the possibility for diagnosing genetic disorders. The opportunities provided with this single test are enticing to researchers and clinicians worldwide for human genetic research as well as clinical application. Multiple studies have highlighted the advantages of GS for genetic variant discovery, emphasizing its added value for routine clinical use. We have implemented GS as first-line genetic testing for patients with rare diseases. Here, we report on our experiences in establishing GS as a reliable diagnostic method for almost all types of genetic disorders, from validating diagnostic accuracy of sequencing pipelines to clinical implementation in routine practice.
The effect of banks’ cost efficiency and competition on liquidity creation

This study examines the role of a bank’s cost efficiency and competition when creating liquidity. It also investigates the different abilities to create liquidity between conventional banks and Islamic banks. This study employs data from annual reports for 117 banks, including 103 conventional banks and 14 Islamic banks from the Association of Southeast Asian Nations 4 (ASEAN-4). Using the dynamic panel regression with the GMM system, this study finds that cost-efficient banks have a higher ability to create liquidity, while high banking competition deteriorates that ability. However, these effects decrease as banks manage their costs more efficiently. The findings imply that banks’ ability to create liquidity is impacted by their market power to win the competition. Additionally, this study found that Islamic banks create more liquidity than conventional banks. This phenomenon indicates that by being more focused on activities using on-balance sheet items, Islamic banks are spared from risky off-balance sheet commitments. Furthermore, efficient banks are more able to generate liquidity in competitive markets.
A novel incomplete hesitant fuzzy information supplement and clustering method for large-scale group decision-making

Clustering is an effective means to reduce the scaling of large-scale group decision-making (LSGDM). However, there are many problems with clustering methods, such as incomplete or ambiguous information usually provided by different decision makers. Traditional clustering methods may not be able to handle these situations effectively, resulting in incomplete decision-making information. Calculating the clustering centers may become very complex and time-consuming. Inappropriate distance weights may also lead to incorrect cluster assignments, and these problems will seriously affect the clustering results. This research provides a novel incomplete hesitant fuzzy information supplement and clustering approach for large-scale group decision-making in order to address the aforementioned difficulties. First, the approach takes into account the trust degradation and the inhibition of relationships of distrust in the process of trust propagation, and then it builds a global and local network of trust. A novel supplemental formula is provided that takes into account the decision-preference maker’s as well as the trust-neighbor’s information, allowing the decision-neighbor maker’s recommendation to be realized. Therefore, an improved distance function can be proposed to calculate the weights by combining the relative standard deviation theory and selecting the selected clustering centers by using the density peaks in order to optimize the selection of clustering centers and reduce the complexity and scaling of the decision. Finally, an example is presented to demonstrate how the proposed method can be applied. The consistency index and comparison experiments are used to evaluate if the suggested approach is effective and reliable.
Refactoring ETL Flows in The Wild

In modern data-driven ecosystems, Extract, Transform, Load (ETL) flows serve as the backbone of data integration pipelines. These flows facilitate the seamless movement of data across disparate systems and formats, streamlining processes that range from data acquisition to preparation for analysis. However, the pervasive use of ETL flows introduces a pressing challenge-how to bound the maintenance cost of an ever-expanding number of flows. In this paper, we describe an end-to-end prototype for ETL flow refactoring, aimed at reducing the maintenance cost, which keeps the human in the loop for refactoring decisions. Our prototype adopts and significantly extends the gSpan Frequent Subgraph Mining (FSM) algorithm to apply it to real-world ETL use cases in the context of the IBM DataStage™ data integration tool. We report on real customer workloads, share their statistics and evaluate the performance of our prototype. We found potential for up to 32% maintenance cost reduction on the use cases we analyzed after removing duplicate flows. We also share an anonymized version of the workloads with the research community.
Eurozone inflation: fresh projections from global factors

This study identifies the contributions of global factors in determining eurozone inflation. The analysis makes use of an augmented Phillips curve model of inflation, as well as panel data estimates. The findings document the importance of such factors in forecasting inflation, thus, confirming the substantial role of external factors in eurozone inflation. The findings survived certain model specifications. Therefore, monetary, and fiscal policymakers should explicitly consider both domestic and global conditions to anchor inflation expectations in forming specific strategies for taming inflation expectations.
Effect of blue - green algae on chemical and nutritional properties of salt-affected soil of mulberry garden, productivity of mulberry and its economics

L'efficacite des algues bleu - vert dans la regeneration d'un sol salin, en ameliorant les caracteristiques nutritionnelles, ainsi qu'en augmentant la productivite et la rentabilite du murier, a ete etudiee a la plantation de murier du C. S. R. & T. I. Mysore. L'application d'algues bleues reduit considerablement le pH du sol, la conductivite electrique et le sodium echangeable et a pour resultat, une amelioration du statut nutritionnel du sol en ce qui concerne le carbone organique, l'azote total, le phosphore et le potassium disponibles. Ceci augmente egalement le rendement en feuilles de murier, ce qui a pour resultat une reduction des couts de production.
Analysis of Direct Torque Control of Induction Motor with Regenerative Braking for an Electric Vehicle

In this paper, a meticulous MATLAB-based modeling of an electric vehicle (EV) has been performed with a suitable selection of its subsystems to achieve a more reliable and robust induction motor-based drive system. Various dynamics of EVs have been analyzed based on practical driving experiences to achieve the desired speed and torque results. A hysteresis band-based Direct Torque Control (DTC) system has been implemented that generates switching pulses for a three-phase inverter powered by the lithium-ion battery pack. DTC is a simple control technique of Induction motors (IM) that allows us to enhance its transient response with greater torque accuracy by directly controlling its torque and flux parameters without complex coordinate transformations. DTC seamlessly integrates with the regenerative braking systems of EVs that enable energy recovery during deceleration, thus extending the vehicle’s range and overall drive efficiency. The regenerative braking system, along with the four-quadrant operation of the IM has been validated by the results of this model by comparing battery state of charge (SOC) with speed, torque, and power requirements.
Reavaliação das Glossopterídeas do Permiano Inferior do Afloramento Cambaí Grande, Bacia do Paraná, RS

O Afloramento Cambai Grande esta localizado no Municipio de Sao Gabriel, no Estado do Rio Grande do Sul, Brasil, sendo conhecido pela ocorrencia de uma associacao impar, composta de conchas de pelecipodes marinhos associados a restos vegetais de alguns elementos da “Flora Glossopteris ”. O presente trabalho teve como principal objetivo a reavaliacao da paleoflora de Cambai Grande, bem como a reanalise do posicionamento estratigrafico do afloramento. Para tanto, foi realizada a revisao do material paleobotânico existente em colecoes e uma redescricao da secao aflorante. A sequencia aflorante esta dividida em duas secoes bem definidas (uma inferior e outra superior), sendo que os megafosseis vegetais estudados encontram-se na parte basal da secao superior. Diferentemente do que se encontra estabelecido na literatura, as correlacoes com sondagens indicaram que o Afloramento Cambai Grande posiciona-se estratigraficamente no intervalo correspondente a porcao medio-superior da Formacao Rio Bonito e nao na porcao superior do Grupo Itarare. Alem disso, o conteudo paleobotânico levantado por diversos autores compreendia, ate o presente trabalho, a presenca dos generos Gangamopteris, Samaropsis, Cordaicarpus e Cordaites , afora restos nao identificados atribuidos a algas e a um cone de gimnosperma. Este estudo, no entanto, confirmou a presenca do primeiro registro de folhas do tipo Glossopteris na paleoflora de Cambai Grande, alem da ocorrencia, pela primeira vez, da especie indiana Gangamopteris cyclopteroides Feistmantel, o que aumenta a possibilidade de correlacao desta associacao com as das paleofloras mais jovens, pertencentes a Formacao Rio Bonito, e com aquelas dos estagios floristicos indianos.
Image Generation and Feedback Based on Deep Learning in Visual Communication Design

The feedback process plays an important role in visual communication design and has a profound impact on the effect of visual information transmission. This paper takes image generation as the main line, and focuses on the analysis of feedback behavior in two ways: real-time interaction and data synchronization acquisition. The study found that with the help of deep learning methods, it can effectively promote the expression of the relationship between the outputter and the viewpoint and the position in the graphical memory task, and improve the ability to map attributes such as images. This paper also conducts a functional test analysis of the platform, and the test results show that the processing time of the platform is between 2 seconds and 4 seconds. The flexible control of the time required for the feedback process is realized in the form of pictures, which further improves the effect of visual communication design. By learning a large amount of image data, these models can automatically identify and repair defects in images, such as blurring, noise, and missing parts. Research has found that deep learning based image restoration and enhancement techniques have made significant improvements in restoring image quality.