Miracle-dz/UniFAR-FTUs huggingface dataset:
SPECTROGRAM FLIPPING: A NEW TECHNIQUE FOR AUDIO AUGMENTATION

Data augmentation is a technique used to increase the amount and diversity of training data in deep learning models. In this paper, we propose a new audio data augmentation technique that combines traditional audio augmentation methods such as time-stretching, pitch-shifting, and noise injection with a novel technique called "spectrogram flipping. " Spectrogram flipping involves taking the spectrogram of an audio signal, flipping it horizontally, and then converting it back to a time-domain audio signal. This technique results in audio data that is both diverse and realistic. We evaluate our proposed technique on a repository diseases classification task using a deep neural network. Our experiments show that our technique improves the accuracy of the classification task model compared to traditional audio augmentation methods. We also show that our technique is computationally efficient and easy to implement. Overall, our proposed audio data augmentation technique is a valuable addition to the toolbox of deep learning researchers working with audio data. It has the potential to improve the performance of a wide range of audio-based deep learning models.
Türk Fen ve Sağlık Dergisi Turkish

: Purpose: This study's objective was to assess the effectiveness of neural mobilization on asymptomatic, healthy volunteers' pain, muscle strength, and reaction time. Material and Methods: Handgrip strength, reaction time, and pain thresholds of 50 participiants’ were evaluated using a dynamometer, the Nelson Hand Reaction Test, and a digital algometer, respectively. While the dominant extremities of the participants constituted the neural mobilization group, the non-dominant extremities constituted the control group. The same measurements were repeated in both extremities by the blinded assessor after median nerve mobilization was applied to the dominant upper extremities of participants. Results: When the measurements before and after mobilization were compared in the neural mobilization group, it was seen that the handgrip strength increased (p<0.01) and the reaction time decreased (p<0.001) after mobilization; The differences in pain threshold score were not statistically significant (p> 0.05). There was no statistically significant difference in handgrip strength between the control group before and after neural mobilization (p> 0.05); however, a statistically significant decrease was found in reaction time and pain threshold score (p <0.05). There was no statistically significant difference between the groups in parameters before and after mobilization (p> 0.05). Conclusion: Neural mobilization may increase grip strength in healthy individuals but has no effect on pain threshold. Its effect on reaction time can be explained by motor learning. New studies are needed in different disease groups.
The Response of the Tropical Western Pacific Region to a Dry Intrusion from the Perspective of Observations, A Cloud Resolving Model and a Single-Column Model

The appearance of extremely dry air over the Tropical Western Pacific (TWP) has received a great deal of attention in recent years (e.g., Parsons et al. 1994; Numaguti et al. 1995; Yoneyama and Fujitani 1995; Mapes and Zuidema 1996; Johnson et al. 1996; Sheu and Liu 1995; DeMott and Rutledge 1998; Yoneyama and Parsons 1999; Parsons et al. 1999). These dry air events are termed dry intrusions or dry tongues, since the dry air originates aloft at higher latitudes and subsides into the tropics in long filaments, several hundred kilometers in width. Recently, Yoneyama and Parsons (1999) showed that these events are associated with the remnant circulations of middle latitude baroclinic waves.
Getting Lost in the Matrix? On How the Characteristics and Arrangement of Linear Landscape Elements In�uence Ecological Connectivity

Context Linear landscape elements (LLEs) such as ditches and hedgerows can increase the ecological connectivity of habitat embedded within agricultural areas by acting as corridors for animal movement. However, we lack knowledge on how the spatial arrangement of LLEs in�uence dispersal, impeding our ability to offer robust advice on how best to add new LLEs to improve connectivity. Objectives To examine how the width and spatial orientations of LLEs composing an intersecting network might in�uence connectivity across landscapes. Methods We used an individual-based dispersal model to simulate the stochastic movement of small organisms through stylised LLEs of different characteristics. Landscapes were composed of two habitat patches separated by a grid-like network of LLEs composed of two types:
Spectrum Broadcast Structures from von Neumann type interaction Hamiltonians

In this paper, we contribute to the mathematical foundations of the recently established theory of Spectrum Broadcast Structures (SBS). These are multipartite quantum states, encoding an operational notion of objectivity and exhibiting a more advanced form of decoherence. We study SBS in the case of a central system interacting with N environments via the von Neumann-type measurement interactions, ubiquitous in the theory of open quantum systems. We state and prove a novel sufficient condition for SBS to arise dynamically for finite-dimensional systems. The condition is based on the Gram-Schmidt orthogonalization rather than on the Knill-Barnum error estimation used before.
Towards Flash Flood Modeling Using Gradient Resolving Representative Hillslopes

It is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub-daily and small-scale hydrological extreme events such as ﬂash ﬂoods. These events occur thereby at local scales within minutes to hours, typically in response to high-intensity rainfall events associated with convective storms. Despite their local scale and rapid onset, the eﬀects of ﬂash ﬂoods can be devastating, making their prediction and mitigation of critical importance. However, the modeling and analysis of such events in data-scarce regions present a unique set of challenges. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland ﬂow hydrology and hydraulics, we can get reliable simulations of ﬂash ﬂood response in small data-scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of ﬂood reservoirs downstream is employed to derive a target data set for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate datasets can provide reasonable uncalibrated estimates of the overland runoﬀ response in three of the four catchments considered. Given that ﬂash ﬂoods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational ﬂash ﬂood forecasting and the design of small and medium ﬂood retention basins around the world.
FUTURE HOST LOAD STATE DETECTION WITH HIDDEN MARKOV PREDICTION MODEL

In virtual machine(VM) live migration process, the VM selection and host detection which are overloaded or underload is place crucial role. host load is dynamic nature, so detection of overload or under load is very challenging take, the live migration process is done with current host loads state, we proposed method, it use future host state, the future loads detection by using Hidden markov model, by this it avoids intermediate live migration of VM. Our suggested techniques are tested using CloudSim simulations on a variety of PlanetLab actual and random workloads. The experimental results reveal that our suggested algorithms outperform the other competitive algorithms in terms of service-level agreement violations, number of VM migrations, and other metrics.
HYBRID CNN-LSTM MODEL: RAINFALL ANALYSIS AND PREDICTION FOR KARNATAKA REGION

Purpose Rainfall prediction is necessary for harvesting crops throughout the year, agriculture yields crops based on the farmer’s ability to work in a specific field for particular crop fertilization. This idea was not alone necessary to predict the crop’s yield. Seed firms regularly screen how efficiently plant varieties grow in a particular setup. Thirdly to predict agricultural produce is critical to solving emerging concerns for food security in the phase of global climatical changes. Accurate prediction of forecasts assists farmers so they can take more economical and cost-management decisions. They also enhance the prevention of famine. This results in protecting farmers’ efficiency and productivity to reduce the risks associated with environmental gain. Design/Methodology/Approach - This paper proposes a Hybrid machine learning model for efficient prediction of rainfall, this makes a solution that is used for encoding them to create solutions abruptly. A major part of this work here is focused on generating a solution for fitness to meet the highest accuracy. This algorithm works efficiently for the given input data. This algorithm tries to meet the necessary requirements until an optimal analysis is carried out to contribute to maximum accuracy for rainfall prediction. Findings-The performance of our proposed Hybrid Algorithm is compared with existing algorithms such as linear regression, logistic regression, and KNN. The proposed Hybrid Algorithm convolutional neural networks with LSTM (Long short-term memory model) with First-order optimization Algorithm which works along the gradient-descent algorithm based on different metrics like Accuracy, Sensitivity, Specificity, F-score, and found maximum performance. Originality
MIT Open Access Articles A multiple-predictor approach to human motion prediction

—The ability to accurately predict human motion is imperative for any human-robot interaction application in which the human and robot interact in close proximity to one another. Although a variety of human motion prediction approaches have already been developed, they are often designed for speciﬁc types of tasks or motions, and thus do not generalize well. Furthermore, it is not always obvious which of these methods is appropriate for a given task, making human motion prediction difﬁcult to implement in practice. We address this problem by introducing a multiple-predictor system (MPS) for human motion prediction. In our approach, the system learns directly from task data in order to determine the most favorable parameters for each implemented prediction method and which combination of these predictors to use. Our implementation consists of three complementary methods: velocity-based position projection, time series classiﬁcation, and sequence prediction. We describe the process of forming the MPS and our evaluation of its performance against the individual methods in terms of accuracy of predictions of human position over a range of look-ahead time values. We report that our method leads to a reduction in mean error of 18.5%, 28.9%, and 37.3% when compared with the three individual methods, respectively.
RECOMMENDATION OF A LIST OF ITEMS OF SEARCH RETRIEVAL FOR USER’S INTENT

Information retrieval systems aim to generate Search Engine Results Pages (SERP), which are web pages automatically generated by a search engine according to the keywords entered by the net surfers. The results are presented in a list where the most relevant data from the search engine are at the top. The main challenge about Information retrieval Systems is the gap between the intent of the Internet user and the appropriate keywords in their disposal. The emergence of such systems is motivated by the need of precise information and they may be different from Internet search engines like Google or Yahoo! WikiAnswers, Answers and domain-specific forums like Stack Overflow, on certain specific points. Although the idea of receiving a direct and targeted response to an issue seems very attractive and the quality of the question itself can have a significant effect on the likelihood of obtaining useful responses. Such an information retrieval paradigm is particularly appealing when the problem cannot be answered directly by the search engines due to the unavailability of relevant online content. A good understanding of the underlying purpose of an issue is important to better meet the information needed by the user. In this paper, we propose a new approach to detect the user's intent. This approach is based on the method of the recommendation of a list of items but without calculation of prediction. The method lies on the co-dissimilarity and the tree covering minimum weight based on the theory of graphs. Our approach improves the ranking of a website in organic search results to increase visibility and quality.
Efﬁcient Algorithm for the k -Means Problem with Must-Link and Cannot-Link Constraints

: Constrained clustering, such as k -means with instance-level Must-Link (ML) and Cannot-Link (CL) auxiliary information as the constraints, has been extensively studied recently, due to its broad applications in data science and AI. Despite some heuristic approaches, there has not been any algorithm providing a non-trivial approximation ratio to the constrained k -means problem. To address this issue, we propose an algorithm with a provable approximation ratio of O. log k/ when only ML constraints are considered. We also empirically evaluate the performance of our algorithm on real-world datasets having artiﬁcial ML and disjoint CL constraints. The experimental results show that our algorithm outperforms the existing greedy-based heuristic methods in clustering accuracy.
Excavations at Tall Arpachiyah, 1933

In 1932 the British School of Archaeology in Iraq made a grant of £600 towards an expedition to Arpachiyah, this being the first donation of the Gertrude Bell Memorial Fund to an archaeological expedition. The Trustees of the Percy Sladen Memorial (Linnean Society) also made a generous award of £400, and there were further munificent grants of £100 from Sir Charles Marston, £500 from an anonymous donor, and from many private subscribers. The Trustees of the British Museum undertook the scientific responsibility for the expedition.
Effects of throughfall manipulation on soil nutrient status: results of 12 years of sustained wet and dry treatments

To investigate the potential effects of changing precipitation on forest ecosystems, the Throughfall Displacement Experiment (TDE) was established on Walker Branch Watershed, Tennessee, in 1993. Three different throughfall amounts were tested: −33% (DRY); ambient (no change, AMB); and +33% (WET). Throughfall manipulations had no statistically significant effects on total C, N, exchangeable Ca2+, Mg2+, bicarbonate‐extractable P, or extractable SO42− in soils after 12 years of sustained treatments. Increased K+ inputs in the WET treatment resulted in relative increases in exchangeable K+compared with the AMB and DRY treatments. Soil C, N, and extractable P declined in all treatments over the 12‐year study, and the declines in N were inexplicably large. Field observations contrasted with earlier simulations from the Nutrient Cycling Model (NuCM), which predicted greater decreases in exchangeable K+, Ca2+, Mg2+, and extractable P in the order WET>AMB>DRY, and no change in C, N, and extractable SO42−. The failure of the NuCM model to accurately predict observed changes is attributed to the lack of mechanisms for deep rooting and the transfer of throughfall K+ from one plot to another in the model. Measurements of element availability using resin membranes during the final years showed higher values in wet and lower values in dry treatments compared with ambient conditions for mineral N, K, Mn, Zn, and Al, but the opposite for B, Ca, and Mg. In the cases of Ca and Mg, the patterns in resin values were similar to those at the soil exchange sites (greatest in the dry treatment) and appeared to reflect pretreatment differences. This study showed that while longer term changes in soil nutrients are likely to occur with changes in precipitation, potential changes over this 12‐year interval were buffered by ecosystem processes such as deep rooting.
Implications of Climate Science for Policy

Climate change presents the greatest challenge ever faced by our domestic and international institutions, and a great deal of the difficulty lies in the science of the issue. Because human influence on global climate differs in important ways from other environmental threats these peculiarities set the context for discussion of what can be done to reduce greenhouse gas emissions and to adapt to change that cannot be avoided. Following a brief summary of current understanding of how Earth’s climate works, five ways are presented by which the science of climate impinges on attempts to construct a policy response.
The Principle of Boycotting Number 1s: Enhancing Decision-Making Diversity and Balancing Market Power in a Complex World

This paper introduces the Principle of Boycotting Number 1s (also known as The Principle of Uncertainty in Prioritizing Number 1), a decision-making strategy devised to counteract the dominance of a single top option by redistributing probabilities associated with the Number 1 option to other alternatives. In today's complex world, individuals frequently encounter a vast array of choices, often defaulting to the number 1 option, whether it be the most luxurious car, the top-rated restaurant, or the highest-ranked university. This propensity, however, can result in arti�cially in�ated values for top-ranked options and substantial �nancial and non-nancial losses. To address this issue, we present the novel Principle of Boycotting Number 1s, accentuating the bene�ts of considering second or third choices. This pioneering approach to decision-making fosters more equitable pricing and accurate valuation of products and services. Renowned concepts, such as Status Quo Bias and the Bandwagon Effect, can be linked to the Principle of Boycotting Number 1s, which encourages diversity and uncertainty in decision-making and drives the exploration of alternative options. We investigate the implications of this principle in diverse decision-making contexts and establish connections to relevant concepts, including the Minority Game, the Exploration vs. Exploitation Trade-off, and the Wisdom of the Crowd. By understanding and implementing the Principle of Boycotting Number 1s, decision-makers can nurture more effective and diverse decision-making processes. Furthermore, we highlight the potential applications of this principle across an extensive array of �elds, such as mathematics, physics, logic, biology, genetics, evolutionary theory, politics, psychology, business, industry, social life, and computer science, particularly emphasizing its economic impact. This foundational principle generates a plethora of laws, lemmas, theorems, and concepts. Its utilization in achieving market price fairness exempli�es only a small portion of this principle's in�uence. With roots in the mathematical foundations of game theory, information theory, and topology, this principle bears signi�cance for numerous scienti�c domains and can affect global economic decisions made by governments and corporations.
Las metodologías activas de aprendizaje: la carpeta del estudiante y el uso de las nuevas tecnologías aplicadas a la asignatura de Teorías de la Comunicación

This paper explains the experience of introducing student’s portfolio as an active learning methodology in the field of “Theory of Communication” syllabus in Journalism studies degree. This is an example of how these methodologies together with the use of new technology (online virtual platform) let students’ learning and teaching quality to improve, as well as to adapt university teaching to changing times.
SUMA PSICOLÓGICA

Introduction: Food habits have been associated with positive outcomes during the COVID-19 pandemic. Family members may share protective factors, but each member may also respond differently to the pandemic. Aim: To examine food habits in different-sex dual-earner parents with adolescents, comparing reports from late 2019 before the pandemic (T1) and mid-2020 during the pandemic (T2) in Temuco, Chile. Method: A sample of 193 families composed of mother, father, and one adolescent aged 10 to 15 responded to a questionnaire at T1 and T2. Non-parametric tests were used to compare family food-related habits and the satisfaction with food-related life of each family member at T1 and T2. An Actor-Partner Interdependence Model was conducted to explore relations between T1 and T2 variables and between family members. Results: Frequency of family meals increased during the pandemic, and mothers increased their cooking hours. All family members improved their diet quality during the pandemic. Perceived family meal atmosphere and satisfaction with food-related life did not change. T1 food habits were not associated with T2 satisfaction with food-related life. Conclusion: These families changed their food-related habits during the pandemic, but their food-related life assessment was similar before and during the pandemic.
On distances and metrics in discrete ordered sets

Discrete partially ordered sets can be turned into distance spaces in several ways. The distance functions may or may not satisfy the triangle inequality, and restriction of the distance to finite chains may or may not coincide with the natural, difference-of-height distance measured in a chain. For semilattices, a semimodularity condition ensures the good behavior of the distances considered. This condition is trivially satisfied by trees, and in lattices it coincides with the usual semimodularity property. For a large class of distance functions the triangle inequality is equivalent to semimodularity.
Efficient depth localization of objects in a 3D space using computational integral imaging

Accurate localization and recognition of objects in the three dimensional (3D) space can be useful in security and defence applications such as scene monitoring and surveillance. A main challenge in 3D object localization is to find the depth location of objects. We demonstrate here the use of a camera array with computational integral imaging to estimate depth locations of objects detected and classified in a two-dimensional (2D) image. Following an initial 2D object detection in the scene using a pre-trained deep learning model, a computational integral imaging is employed within the detected objects’ bounding boxes, and by a straightforward blur measure analysis, we estimate the objects’ depth locations.