Miracle-dz/UniFAR-FTUs huggingface dataset:
FIRST RECORD OF Promops davisoni (THOMAS, 1921)(CHIROPTERA, MOLOSSIDAE) FROM CHILE AND A DESCRIPTION OF ITS ECHOLOCATION CALLS

Few studies have been conducted on the bat fauna of the Arica and Parinacota region, northern Chile. The genus Promops (Gervais, 1855) is a poorly known group of molossid bats, with three species widely distributed in Central and South America. We report for the first time the species Promops davisoni in Chile. Identification was based on echolocation calls obtained at the Azapa and Lluta valleys and compared to reference calls from Tacna, Peru. We report the species as far as 127 km south of the previous published southernmost record in Pampa Alta, Peru. In addition we obtained an individual found at the Anzota caves, near the city of Arica. The species is found on the coast and in the fluvial valleys of Northern Chile, with altitudes ranging from sea level to 822 m a.s. l. We propose that the distribution of P. davisoni may extend further south, to the locality of Tana in the Tarapacá region, based on the capacity of the species to cross distances of over 100 km over large desert areas. With this species, we increase the known bat fauna of Chile to 14 species, and the bat fauna of the Arica and Parinacota region to nine species. RESUMEN. Primer registro de Promops davisoni (Thomas, 1921) (Chiroptera, Molossidae) en Chile, y descripción de sus llamadas de ecolocación. Pocos estudios sobre la fauna de murciélagos se han realizado en el la región de Arica y Parinacota, extremo norte de Chile. El género Promops (Gerais, 1855) corresponde a un grupo poco conocido de especies de la familia molossidae, con tres especies ampliamente distribuidas en América Central y Sur. Reportamos por primera vez la especie Promops davisoni en Chile, basados en el análisis de sus llamadas de ecolocación obtenidas en los valles de Azapa y Lluta, y comparados con llamadas de referencia de individuos registrados en Tacna, Perú. Se registró la especie a 127 km al sur del registro publicado anteriormente en Pampa Alta, Perú. Además obtuvimos un individuo encontrado en las cuevas de Anzota, cerca de la ciudad de Arica. La especie utiliza la costa y los valles del norte de Chile, desde el nivel del mar hasta los 822 m s.n. m. Consideramos que la distribución de P. davisoni se puede extender hasta la localidad de Tana, en la región de Tarapacá, dada la capacidad de la especie de cruzar extensas áreas desérticas de más de 100 km de distancia. Mastozoología Neotropical, en prensa, Mendoza, 2018 http://www.sarem. org.ar http://www.sbmz. com.br G. Ossa et al.
B2-C2: Blockchain-based Flow Control Consistency for Multi-Controller SDN Architecture

Software-defined networking (SDN) is transforming network management, yet it grapples with performance bottlenecks in large-scale deployments. Multi-controller solutions have been proposed to address this issue. However, they introduce the challenge of maintaining consistent controller states across the network. Past efforts to employ blockchain for controller state consistency were hindered by latency issues due to custom consensus algorithms. This research introduces B2-C2, an integration of blockchain and digital signatures into SDN infrastructure to ensure flow control consistency within multi-controller SDN environment. In contrast to previous approaches, our framework enhances security by protecting SDN infrastructure against compromised controllers and switches, simultaneously holding controllers accountable for maintaining network state consistency. In practical tests simulating controller downtime, we achieved rapid replacement of an unresponsive controller in just 1.5 seconds. B2-C2 represents a significant advancement in enhancing the reliability and efficiency of multi-controller SDN architectures, promising a robust solution to network management challenges and ensuring secure control in modern IT environments. B2-C2 stands poised to address the evolving needs of multi-controller SDN, making networks more resilient and adaptable.
Target Distance Estimation Model Based on Deformable Convolution and Attention Mechanism

Environment perception tasks, including target detection, distance estimation, and other charges, are essential components of autonomous driving systems or automation systems. Among them, target distance value estimation is used to achieve tasks such as collision avoidance in automated systems. Although the target distance estimation task is indispensable, few related studies exist. This paper proposes a model combined with a deformable convolution kernel attention mechanism for monocular ranging tasks. Compared with previous distance estimation deep learning models, introducing the attention mechanism can better grasp the relationship between the target and the global situation. At the same time, deformable convolution can better capture the characteristics of different targets and reduce the impact of the background on the results. Experiments have proven that distance estimation and other models based on attention mechanisms perform better. Based on the basic model, we introduce a multi-task tool and conduct joint training through joint pixel-level tasks, which can better combine the global information of the target and allow the model to obtain better expressive capabilities. Through ablation experiment comparison, we can find that multi-task joint is after training, and the performance of the basic model can be significantly improved.
Behavior Estimation System for Elderly Individuals based on 3D LiDAR and Self-Learning Function

In recent years, population is aging rapidly in Japan, and the shortage of caregivers in the welfare field is a serious problem. Therefore, a monitoring system is being considered to observe the lives of elderly individuals in group homes on behalf of their family members living in the remote location. In the existing studies, the system for estimating human behaviors using cameras and handheld sensors are proposed and developed. However, these systems have problems related with invasion of privacy and discomfort when the user carries the sensor. Therefore, in this study, we propose a new system to observe the behavior of residents by utilizing 3D LiDAR (Light Detection and Ranging) that can accurately measure the three-dimensional structure of target objects as point clouds. The proposed system estimates positions and postures of residents by applying a deep learning technology to point clouds measured using the 3D LiDAR. In addition, generation of training data for building machine learning models requires an annotation process on 3D point clouds for setting a correct label to each part of the point clouds, which takes significant time and effort. Therefore, in this study, we propose a method for supporting to generate training data by automatically setting the correct labels to appropriate parts of the point clouds by linking image analysis and machine learning technologies.
A Multi-Functional Drone for Agriculture Maintenance and Monitoring in Small-Scale Farming

Agriculture covers almost 40% of the Earth’s land surface and is essential to human survival. Farmers, however, face numerous challenges to maintain sustainable production. This work introduces an innovative, low cost and comprehensive solution to address some of these challenges. We designed and implemented a multi-functional agricultural drone with monitoring tasks to ensure crops are adequately watered, protected from pests, and monitored for fires. Our results show the potential to improve the effectiveness of small-scale farming operations.
On the natural history of the Neotropical spider Enoploctenus cyclothorax (Araneae, Ctenidae)

Summary Enoploctenus cyclothorax (Bertkau, 1880) is a Neotropical spider which inhabits rainforests. We studied the relationship between the meteorological conditions and the use of retreats and their surroundings by males and females. We also report on feeding and defensive behaviour. Movement of individuals was studied by marking all the individuals seen in the study area. Two kinds of observations were carried out: (1) inter-night: during 11 consecutive nights in winter and 11 consecutive nights in spring we searched one hour per day between 20:00–23:00, and noted the exact location of each individual seen; (2) intra-night: on individual nights, we regularly monitored the marked individuals, at 30 min intervals. Enoploctenus cyclothorax used a variety of crevices as retreats during the day and occasionally left these places after dusk. The number of individuals seen out of the retreats at night was correlated with humidity and/or temperature in winter but not in spring. Adult males were the wandering sex, while females spent most of the time at rest. Enoploctenus cyclothorax preyed on a variety of arthropods, including conspeciﬁcs, but harvestmen (Arachnida, Opiliones) were rejected in three out of four attacks. The most common defensive behaviour observed was ﬂeeing, although biting and the ejection of hindgut contents towards the observer were also seen.
Human-Computer Interaction: A Systematic Review

This paper presents an overview of the significance of Human-Computer Interaction (HCI) in modern technology and its influence on different fields. The study of HCI involves understanding how people interact with technology and the various techniques and methodologies used to make technology more user-friendly. The paper aims to explore the applications of HCI in different areas such as education, entertainment, and healthcare. The field of HCI is crucial in the development of technology that is not only innovative but also user-friendly. With the advancement of technology, HCI has become increasingly important in ensuring that technology is designed to meet the needs of users. HCI techniques and methodologies, such as user-centered design and usability testing, have become essential in developing technology that is designed with users in mind. The applications of HCI in different fields. In education, for example, HCI can be used to develop learning management systems that are engaging and interactive for students. By incorporating HCI principles in the design of these systems, educators can create a more effective learning environment that is tailored to the needs of students.
Bone Marrow Classification Using Hematologic Malignancies Dataset

Morphological analysis of bone marrow cells is considered the most reliable method for diagnosing leukemia. However, due to diverse cell appearances, it requires extensive expertise and patience. An automated diagnostic system combining image analysis and pattern recognition technology is needed to reduce workload, minimize errors, and enhance overall work efficiency. This paper proposes a deep learning-based strategy for classifying bone marrow samples using ResNet50, DenseNet121, and EfficientNet models. After training and assessment on a large dataset, DenseNet121 was the most accurate (98%), followed by ResNet50 (97%). EfficientNet, with a lower accuracy of 69%, still provided insightful information. These results demonstrate the effectiveness of deep learning models in classifying bone marrow, paving the way for improved leukemia diagnosis and treatment.
Upper Limb and Back Rehabilitation Exoskeleton

The advent of exoskeleton-based rehabilitation technologies has brought benefits to patients with musculoskeletal disorders, bringing more effective rehabilitation treatment aids to patients with upper limb and low back disorders, as well as to people with disabilities. However, there are still many issues that need to be addressed, such as better ergonomics of mechanical design, development of better control schemes, and more systematic evaluation and testing schemes. Based on the recent literature, this paper will illustrate the current research status of rehabilitation exoskeletons for the upper limb and back in terms of mechanical design and exoskeleton control strategies, and finally provide some outlook on the development trend of exoskeletons and propose the key to the combination of exoskeletons for the upper limb and back.
A Fine Tuned Ensemble Approach to Classify Requirement from User Story

The manual classification process in software requirement Specification (SRS) has several challenges, including subjectivity, time commitment, and the possibility of mistakes and discrepancies. There is a chance of inconsistencies and classification errors when requirements are manually classified because of subjectivity and anomalies. Furthermore, the corresponding manual effort grows increasingly difficult and unfeasible as the volume of requirements increases. This study explores the field of automated classification, highlighting the crucial role of automation in distinguishing between functional and non-functional requirements. Utilizing the PROMISE repository with 969 requirements, the study deploys an ensemble of machine learning classifiers—Random Forest, Multinomial Naive Bayes, Gradient Boosting, XGBoost, and AdaBoost. Based on the test dataset, the empirical results show that the suggested ensemble method achieves a 98% accuracy in binary classification of Functional Requirements (FR) and Non-Functional Requirements (NFR). Moreover, Grid search and hyperparameter optimization methods refine the ensemble’s performance, highlighting its usefulness in enhancing the accuracy and consistency of software requirement analysis in the binary classification framework.
Ensembles of climate change models for risk assessment of nuclear power plants

Climate change affects technical systems, structures and infrastructures, changing the environmental context for which systems, structures and infrastructure were originally designed. In order to prevent any risk growth beyond acceptable levels, the climate change effects must be accounted for into risk assessment models. Climate models can provide future climate data, such as air temperature and pressure. However, the reliability of climate models is a major concern due to the uncertainty in the temperature and pressure future projections. In this work, we consider five climate change models (individually unable to accurately provide historical recorded temperatures and, thus, also future projections) and ensemble their projections for integration in a probabilistic safety assessment, conditional on climate projections. As case study, we consider the passive containment cooling system of two AP1000 nuclear power plants. Results provided by the different ensembles are compared. Finally, a risk-based classification approach is performed to identify critical future temperatures, which may lead to passive containment cooling system risks beyond acceptable levels.
Go to the river: Understanding and experiencing the Liard watershed.

This study contributes to an emerging space of interdisciplinary literature that explores the cultural dynamics people and rivers and the associated contestations. A network of rivers in northern British Columbia, all within the Liard River watershed, provides a relevant case study to examine such topics. Data and analysis are presented using a phenomenological approach that employs archival and participatory fieldwork. Through this research, I ask: why do people go to the river ? In attempting to understand the significance of rivers in people's lives, Go to the River addresses questions concerning the Liard watershed, including: how interpretations of rivers are represented in historic maps the significant transitions during the nineteenth and twentieth century that redefined human-river relations and how rivers are still experienced through direct lived engagements. I argue that past and present direct experiences with rivers are essential in reframing the dialogue about the future of rivers in western Canada. --P. ii.
Regional Economic Issues--Special Report 25 Years of Transition::Post-Communist Europe and the IMF

The past 25 years have seen a dramatic transformation in Europe's former communist countries, resulting in their reintegration with the global economy, and, in most cases, major improvements in living standards. But the task of building full market economies has been difficult and protracted. Liberalisation of trade and prices came quickly, but institutional reforms - such as governance reform, competition policy, privatisation and enterprise restructuring - often faced opposition from vested interests. The results of the first years of transition were uneven. All countries suffered high inflation and major recessions as prices were freed and old economic linkages broke down. But the scale of output losses and the time taken for growth to return and inflation to be brought under control varied widely. Initial conditions and external factors played a role, but policies were critical too. Countries that undertook more front-loaded and bold reforms were rewarded with faster recovery and income convergence. Others were more vulnerable to the crises that swept the region in the wake of the 1997 Asia crisis.
A Conceptual Framework of an Automated Mosquito Control in Drainage Systems for Combating Dengue in Bangladesh

In recent times, Dengue fever has been causing widespread suffering among the people of Bangladesh. Despite Dengue outbreaks occurring every year, the current endemic has surpassed the records of previous years in terms of the number of deaths and hospitalizations. Dengue is mainly spread through the Aedes mosquito, which is prevalent in Bangladesh. Although many initiatives have been taken to combat and prevent the spread of Dengue fever in most households, the spread could not be controlled since the main breeding grounds of mosquitoes, the drainage systems, remain mostly unmaintained and unclean. The local authorities usually carry out the traditional fogging method at certain intervals to keep the mosquitoes under control. However, this method is manual, ineffective, and harmful to the environment and human lives. As a result, there are more risks than rewards in such methods. Thus, the contribution of this paper lies in proposing a conceptual framework of an automated, cost-effective, simple, and efficient mosquito-controlling system that can be employed in drainage systems of Bangladesh to limit mosquito breeding and prevent the spread of Dengue fever. The detection process of this system showed success of more than 80% on all performance parameters.
Design of an Ultra Low-Power Tunable Ring VCO in 65 nm CMOS Technology for 2.9-4.5 GHz

The design of a tunable ring voltage-controlled oscillator (VCO) using TSMC 65 nm CMOS technology and a 1 V power supply is presented. The design consists of three inverter stages and capacitive loads for adjusting the frequency. Capacitive loads are implemented as MOS varactors connected to the output of the inverter stages. It is shown that the resulting design has a power dissipation of 0.65 mW, provides a tunable frequency range from 2.9 GHz to 4.5 GHz for -1.5 V to 1.5 V control voltage and phase noise of -90dBc/Hz is adequate for tuner application. The proposed VCO provides the frequency control in a wide tuning range and to adjust the oscillation amplitude without increasing the power consumption during the tuning.
Online Multi-User Scheduling for Extended Reality Transmissions with Hard-Latency Constraint

In the forthcoming 6G era, Extended reality (XR) is an emerging application with unique traffic characteristics requirements, calling for innovative Ultra-Reliable and Low-Latency Communication (URLLC) technologies. In this paper, we investigate multi-user scheduling to meet hard-latency constraints for XR services. Specifically, we focus on a periodical XR traffic model, where the latency constraint for transmitting each XR frame is less than the inter-arrival time. To find an optimal multi-user scheduling scheme, we first describe the system as a periodic Markov Decision Process (MDP), where the scheduling performance is expressed as the probability of successful transmission within the latency constraint. Then, we obtain the maximum success probability and the optimal scheduling based on the optimal value function. Inspired by the properties of the optimal value function, we construct a lower bound of it and propose an online multi-user scheduling scheme. In particular, scheduling decisions under the proposed scheme are determined by solving a series of nonlinear Knapsack Problem (KP) in polynomial time. Finally, simulation results show that the proposed scheduler achieves nearly optimal performance and outperforms other benchmark schedulers.
Grothendieck Graph Neural Network (GGNN): A Path-Based Framework For Network Modelling

We introduce a new framework for applying machine learning to graph data. Recently, graphs have emerged for modeling complex relations between components and giving a big picture of a system. The existence of paths, in addition to nodes and edges, helps represent some non-directed relations between nodes at different levels of importance. Understanding paths and interactions between them reveals more information about the entire graph. Most representation tools in graph theory are based on neighborhoods and lack special tools to represent paths. Therefore, it is very challenging to replace neighborhoods with paths in studying a graph. In this paper, we propose a matrix representation of paths and a binary operation to get a monoid. This algebraic point of view benefits the graph neural network (GNN) and can be seen as an alternative for neighborhoods in GNN. We apply this monoidal representation of graphs to introduce a new type of GNN called Grothendieck Graph Neural Network (GGNN), inspired by the Grothendieck Topology concept [1]. To evaluate our approach, we build a model to estimate path delays in networks based on GGNN. The results (MRE=0.0004) show the eligibility of applying GGNN in this kind of problem compared with RouteNet (MRE=0.022).
A Study on the Comparison and Measurement of Improved CartoonGAN Performance for High-Quality Image Generation

With the rapid growth of computer vision, among them, generative artificial intelligence is being used in various industries. CartoonGAN, one of the generating artificial intelligence, can create new images by changing the original image into a cartoon style, which can be used in various fields such as webtoons and animations. To this end, a process of improving the performance of CartoonGAN is necessary, and research for this should be conducted. In this study, to solve the performance degradation that occurs when CartoonGAN is applied, we would like to suggest a direction to improve by applying technologies such as SRCNN and Tiling.
New Methods for Lunar Impact Crater Detection Based on YOLO v7 with Deformable ConvNets

The Lunar impact crater is one of the most remarkable geographic features on the surface of the Lunar. The size and shape of the Lunar impact crater are important for future Lunar landings and resource development on the Lunar surface. Hence, detecting impact craters is an essential research orientation. In this paper, we introduced two novel convolutional neural networks that combine YOLO v7 and deformable convolution of Deformable Convolutional Networks, called YOLOv7_DCN and YOLOv7_DCNv2. With the global Lunar CCD data provided by NASA, the experiment has been conducted under the same settings. The result of the experiment showed that the Precision of YOLOv7_DCN is 77.36%, and the Precision of YOLOv7_DCNv2 is 77.19%. Both values exceed the Precision of YOLO v7, which is 75.86%. Improvements have been found in several other key metrics. Demonstrating that YOLOv7_DCN and YOLOv7_DCNv2 have better performance than YOLO V7 in detecting Lunar impact crater.