Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Mechanisms Acting on the Atlantic Meridional Overturning Circulation in CESM2 for the 1pctCO 2 Experiment

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial component of the Earth’s climate system due to its fundamental role in heat distribution, carbon and oxygen transport, and the weather. Other climate components, such as the atmosphere and sea ice, influence the AMOC. Evaluating the physical mechanisms of those interactions is paramount to increasing knowledge about AMOC’s functioning. In this study, the authors used outputs from the Community Earth System Model version 2 and observational data to investigate changes in the AMOC and the associated physical processes. Two DECK experiments were evaluated: piControl and 1pctCO 2 , with an annual increase of 1% of atmospheric CO 2 . The analysis revealed a significant decrease in the AMOC, associated with changes in mixed layer depth and buoyancy in high latitudes of the North Atlantic, resulting in the shutdown of deep convection and potentially affecting the formation of North Atlantic Deep Water and Antarctic Bottom Water. A vital aspect observed in this study is the association between increased runoff and reduced water evaporation, giving rise to a positive feedback process. Consequently, the rates of freshwater spreading have intensified during this period, which could lead to an accelerated disruption of the AMOC beyond the projections of existing models.
Fuzzy and Spiking Neural Network-based Secure Data Exchange Framework for Autonomous Vehicle

Autonomous vehicles (AVs) represent a revolutionary step in intelligent transportation (ITS), which uses artificial intelligence (AI) models to navigate without human intervention. However, the reliance on sensor networks for operational functionality introduces significant vulnerabilities as inadequate training of these sensors can lead to sub-optimal path selection or, more critically, accidents. The main security issue addressed in this study is the lack of strong security mechanisms for the operation of sensors in AVs, which makes them vulnerable to counter-revolutionary actions. These attacks can degrade performance and compromise the security of ITS. Thus, we propose a novel intrusion detection system integrated with fuzzy logic and AI, which is designed to validate and sanitize the communication between the sensors to improve the performance of AVs. For that, a sensor communication dataset is utilized comprised of malicious and non-malicious data. Furthermore, a spiking neural network (SNN) is employed to get trained on the dataset for efficient binary classification tasks. Our implementation of the SNN model has demonstrated a remarkable accuracy of 97.76% in identifying non-malicious data. The performance of our framework is further validated by comprehensive evaluations using indicators such as recall, accuracy, precision, and F1 score.
Advanced Deep Learning Method for Thyroid Nodule Detection and Evaluation in Ultrasound Images

Thyroid nodule detection in its earliest stages is a difficult endeavor. Thyroid nodule diagnosis requires a lot of time and effort if done manually. The original ultrasound imaging of thyroid nodules is very variable due to differences in equipment and technicians. The issue with nodules is determining whether or not they are cancerous. Thyroid nodule evaluation through ultrasonography is presently advised as the first line of treatment. Ultrasound imaging can be helpful in making a diagnosis; But this relies heavily on the proficiency of the radiologist. There is a great need for faster and more efficient automated techniques of screening ultrasound pictures. This research study presents a deep learning method to detect thyroid nodules quickly and accurately. Some image processing techniques are required for optimal utilization of ultrasound imaging data of thyroid nodules. In this study, authors used deep neural networks InceptionResnet V2 and image augmentation to create a system for autonomous thyroid nodule categorization. For testing, neural networks with four-layer network nodes and the Local Laplacian Filtering technique are used to improve the image quality. The proposed method's reliability is evaluated by using the Thyroid Digital Image Database (TDID), a freely available dataset. The comparative findings demonstrate that the proposed method is superior to the current state of advancement in machine learning. Based on experiments, it is evident that the proposed technique has an Accuracy 0.99, Precision 0.98, Recall 0.96, Fl-Score 0.98, AVC-ROC 0.99.
Emotional Reactivity Classification Using Artificial Intelligence Based on Twitter in Bahasa Indonesia

Twitter is one of Indonesia's most widely used social media to express positive and negative emotions. Negative emotions are referred to as emotional reactivity, an example of which is depression. To detect emotional reactivity, Artificial Intelligence (AI) was used. With deep learning pretrained model methods (IndoBERT and IndoBERTweet), AI successfully classified emotional reactivity. AI satisfactorily detected emotional reactivity on Twitter.
Unlocking the Emotional States of High-Risk Suicide Callers through Speech Analysis

Suicide remains a major public health concern worldwide, and early detection of suicidal ideation is crucial for prevention. One promising approach for monitoring symptoms is through the prediction of suicidal speech, as speech can be passively collected and may provide insight into changes in risk. However, identifying suicidal speech is challenging due to the rapid variability in speech characteristics and the association of suicidal ideation with emotion dysregulation. In light of these challenges, we present a novel end-to-end (E2E) method for speech emotion recognition (SER) as a mean of detecting changes in emotional state, that may indicate a high risk of suicide. Our method incorporates the use of Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to analyze raw waveform signals. Firstly, our model is designed to train the CNN component to identify higher-level speech representations directly from raw waveform data, rather than relying on manually crafted features or spectrograms. This enables the network to effectively capture specific emotion-related features within a narrow frequency range, while also handling speech of varying lengths without the need for segmentation. Secondly, the GRU component is capable of learning temporal patterns, enhancing the network’s ability to capture time-dependent features in signal. Furthermore, we validate our approach on the NSPLCRISE emotion dataset that we recently created. This dataset contains phone call recordings from lifeline frequent callers with psychological problems, and potentially with a history of suicidal ideation and previous attempts. Our experimental results show that our method outperforms other state-of-the-art techniques for SER. The source code of this paper is provided on the Git repository Unlocking the Emotional States of High-Risk Suicide Callers through Speech Analysis.
On the Efficiency of Multivariable TS Fuzzy Modeling

Studies on the efficiency of multivariable Takagi-Sugeno fuzzy modeling is described. From the interpretability viewpoint fuzzy modeling becomes complex especially in multidimensional cases due to decomposition of multidimensional fuzzy sets for each dimension. Even in one dimensional case, appropriate selection of fuzzy sets in number and shape is essential for satisfactory models. This general observation in mind, this research aims to make a study on the efficiency of fuzzy modeling taking two exemplary cases. Both cases are considered to be representing a dynamic system in two dimensional space. They are distinguished by the degree of nonlinearity they have. Both systems are modeled by fuzzy logic and the outcomes are comparatively studied to show explicitly how the modeling performance varies being dependent on the degree of nonlinearity involved.
Placing Multi-Component Applications in the Multi-Access Edge Computing

Multi-access Edge Computing resolves the problem of the transportation of enormous amounts of data via the Internet by localizing the computation and the storage of data near the user. The offloading of applications to the MEC Servers must be done in a way that minimizes the latency while considering their limited resources. Additionally, there is a trend for applications to be separated into microservices that run on different MEC Servers. This adds complexity to the placement of applications on the MEC Servers because it has to take into consideration the dependencies of its microservices. This problem is defined in the literature as the multi-component application placement problem. This paper proposes an algorithm to tackle this problem efficiently, while the dependencies of services are linear, i.e., a chain of microservices. The algorithm starts with the allocation of the 1-medlan of the backhaul network. Then, the proposed algorithm extends outwards and moves around until the score function stops improving. In order to evaluate the proposed algorithm a Mixed-integer Quadratic Program is formulated. The results showed that the proposed algorithm achieved great performance in various cases.
SemanticPaint: interactive segmentation and learning of 3D worlds

We present a real-time, interactive system for the geometric reconstruction, object-class segmentation and learning of 3D scenes [Valentin et al. 2015]. Using our system, a user can walk into a room wearing a depth camera and a virtual reality headset, and both densely reconstruct the 3D scene [Newcombe et al. 2011; Nießner et al. 2013; Prisacariu et al. 2014]) and interactively segment the environment into object classes such as 'chair', 'floor' and 'table'. The user interacts physically with the real-world scene, touching objects and using voice commands to assign them appropriate labels. These user-generated labels are leveraged by an online random forest-based machine learning algorithm, which is used to predict labels for previously unseen parts of the scene. The predicted labels, together with those provided directly by the user, are incorporated into a dense 3D conditional random field model, over which we perform mean-field inference to filter out label inconsistencies. The entire pipeline runs in real time, and the user stays 'in the loop' throughout the process, receiving immediate feedback about the progress of the labelling and interacting with the scene as necessary to refine the predicted segmentation.
Logs2Vul: Vulnerability Detection from Logs for CSPM

Cloud Security Posture Management (CSPM) is gaining importance with the increase in the adoption of multicloud environments. CSPM focuses not only on visualization and monitoring security postures but also on automating tasks to ensure minimal interference. NIST and MITRE Corporation have invested a tremendous effort in collecting, classifying, and publishing vulnerability incidences as CVE (Common Vulnerabilities and Exposures) datasets. They have also been publishing CWE (Common Weakness Enumerations) data year after year, strengthening Vulnerability Classification by including new Weakness Enumerations. To exploit this wealth of information in the CVE/CWE database in real-time contexts such as CSPM, the outcome of vulnerability classification should respond to some real-time signals or alerts. Although many research efforts use CWE/CVE databases for vulnerability classification, none of them use real-time alerts/signals as input. The research work presented here exploits CVE and CWE databases for the classification of cloud error logs and identifies vulnerabilities in real-time. Cloud error logs being the real-time indicators of the system health, vulnerability classification based on cloud error logs can go far in the automation of CSPM. The framework not only indicates the vulnerability classes corresponding to cloud error logs but also computes a CVS Score (Common Vulnerabilities Severity Score) against each error log. CVS Score indicates the seriousness of threats and thus provides key information for CSPM. The framework uses Deep Learning algorithms such as CNN, LSTM and transformers to build Machine Learning models that are trained on Cloud error logs augmented with CVE descriptions.
Multimodal depression detection using deep learning in the workplace

Stress, followed by depression has become the most common phenomenon in modern work environment. Early detection of depression is important to avoid health degradation and prevent suicide tendencies. Noninvasive monitoring of stress level is effective at screening stage. Many methods based on visual cues, audio feeds and text messages have been used for noninvasive monitoring. In individual modalities, the accuracy is low and false positives are high. This study proposed a multimodal depression classification system based on deep learning. The proposed solution integrates visual, speech and text feeds and extracts deep learning features from these feeds. The features are classified into emotions and temporal emotion variability, and the depression level is classified.
Wnt/β-catenin signaling controls development of the blood–brain barrier

The blood–brain barrier (BBB) is confined to the endothelium of brain capillaries and is indispensable for fluid homeostasis and neuronal function. In this study, we show that endothelial Wnt/β-catenin (β-cat) signaling regulates induction and maintenance of BBB characteristics during embryonic and postnatal development. Endothelial specific stabilization of β-cat in vivo enhances barrier maturation, whereas inactivation of β-cat causes significant down-regulation of claudin3 (Cldn3), up-regulation of plamalemma vesicle-associated protein, and BBB breakdown. Stabilization of β-cat in primary brain endothelial cells (ECs) in vitro by N-terminal truncation or Wnt3a treatment increases Cldn3 expression, BBB-type tight junction formation, and a BBB characteristic gene signature. Loss of β-cat or inhibition of its signaling abrogates this effect. Furthermore, stabilization of β-cat also increased Cldn3 and barrier properties in nonbrain-derived ECs. These findings may open new therapeutic avenues to modulate endothelial barrier function and to limit the devastating effects of BBB breakdown.
Inbreeding Avoidance Drives Consistent Variation of Fine-Scale Genetic Structure Caused by Dispersal in the Seasonal Mating System of Brandt’s Voles

Inbreeding depression is a major evolutionary and ecological force influencing population dynamics and the evolution of inbreeding-avoidance traits such as mating systems and dispersal. Mating systems and dispersal are fundamental determinants of population genetic structure. Resolving the relationships among genetic structure, seasonal breeding-related mating systems and dispersal will facilitate our understanding of the evolution of inbreeding avoidance. The goals of this study were as follows: (i) to determine whether females actively avoided mating with relatives in a group-living rodent species, Brandt’s voles (Lasiopodomys brandtii), by combined analysis of their mating system, dispersal and genetic structure; and (ii) to analyze the relationships among the variation in fine-genetic structure, inbreeding avoidance, season-dependent mating strategies and individual dispersal. Using both individual- and population-level analyses, we found that the majority of Brandt’s vole groups consisted of close relatives. However, both group-specific FISs, an inbreeding coefficient that expresses the expected percentage rate of homozygosity arising from a given breeding system, and relatedness of mates showed no sign of inbreeding. Using group pedigrees and paternity analysis, we show that the mating system of Brandt’s voles consists of a type of polygyny for males and extra-group polyandry for females, which may decrease inbreeding by increasing the frequency of mating among distantly-related individuals. The consistent variation in within-group relatedness, among-group relatedness and fine-scale genetic structures was mostly due to dispersal, which primarily occurred during the breeding season. Biologically relevant variation in the fine-scale genetic structure suggests that dispersal during the mating season may be a strategy to avoid inbreeding and drive the polygynous and extra-group polyandrous mating system of this species.
Antiviral Activity of a Novel Compound CW-33 against Japanese Encephalitis Virus through Inhibiting Intracellular Calcium Overload

Japanese encephalitis virus (JEV), a mosquito-borne flavivirus, has five genotypes (I, II, III, IV, and V). JEV genotype I circulates widely in some Asian countries. However, current JEV vaccines based on genotype III strains show low neutralizing capacities against genotype I variants. In addition, JE has no specific treatment, except a few supportive treatments. Compound CW-33, an intermediate synthesized derivative of furoquinolines, was investigated for its antiviral activities against JEV in this study. CW-33 exhibited the less cytotoxicity to Syrian baby hamster kidney (BHK-21) and human medulloblastoma (TE761) cells. CW-33 dose-dependently reduced the cytopathic effect and apoptosis of JEV-infected cells. Supernatant virus yield assay pinpointed CW-33 as having potential anti-JEV activity with IC50 values ranging from 12.7 to 38.5 μM. Time-of-addition assay with CW-33 indicated that simultaneous and post-treatment had no plaque reduction activity, but continuous and simultaneous treatments proved to have highly effective antiviral activity, with IC50 values of 32.7 and 48.5 μM, respectively. CW-33 significantly moderated JEV-triggered Ca2+ overload, which correlated with the recovery of mitochondria membrane potential as well as the activation of Akt/mTOR and Jak/STAT1 signals in treated infected cells. Phosphopeptide profiling by LC-MS/MS revealed that CW-33 upregulated proteins from the enzyme modulator category, such as protein phosphatase inhibitor 2 (I-2), Rho GTPase-activating protein 35, ARF GTPase-activating protein GIT2, and putative 3-phosphoinositide-dependent protein kinase 2. These enzyme modulators identified were associated with the activation of Akt/mTOR and Jak/STAT1 signals. Meanwhile, I-2 treatment substantially inhibited the apoptosis of JEV-infected cells. The results demonstrated that CW-33 exhibited a significant potential in the development of anti-JEV agents.
Research on Modulation Signal Detection Method based on Deep Learning

Facing the complex electromagnetic environment, the modulation mode of communication signal is becoming increasingly complex. The existing detection methods of modulation mode of communication signal can not detect the modulation mode of communication signal accurately and quickly. In order to facilitate the presentation, we represent the digital signal on the complex plane, form the constellation map according to the mapping formula, analyze the difference of the characteristics of the constellation map, and train and test the constellation map. It can be found that when the signal-to-noise ratio is lower than 20dB, the classification accuracy of the characteristics of the constellation map is greatly affected for the 64QAM signal with the largest number of points and the smallest radius. To solve this problem, A method of signal constellation de-noising using VMD is proposed. Compared with the pre-de-noising method, the average accuracy of VGGNet-16 classification is increased by 7.76%; The average accuracy rate of ResNet-18 classification increased by 9.77%; The average accuracy rate of ResNet-50 classification increased by 7.57%. This method improves the accuracy of constellation classification detection, which is difficult to improve, and lays a good foundation for the research of modulation signal detection methods.
Enhancing Efficiency in Livestock Monitoring and Management with IoT Solutions ’

This technical abstract makes a specialty of an Iota-based strategy to enhance the performance of livestock monitoring and management. The proposed answer involves the usage of wireless sensors and mobile gadgets to gather records regarding the environment and the health of the animals, which are then processed and transmitted to the cloud for similar analysis. The evaluation from the cloud can then be used to detect modifications in the livestock's health or surroundings, trigger indicators, and inform endorsed moves. This solution reduces the time requirements for facts collection and evaluation and consequently decreases the ability for diseases to occur or unfold many of the farm animals. Moreover, the solution can offer vital statistics for farmers and ranchers to make higher selections to enhance the productivity of their herds and streamline operational processes.
Polynomial time approximation schemes for geometric k-clustering

We deal with the problem of clustering data points. Given n points in a larger set (for example, R/sup d/) endowed with a distance function (for example, L/sup 2/ distance), we would like to partition the data set into k disjoint clusters, each with a "cluster center", so as to minimize the sum over all data points of the distance between the point and the center of the cluster containing the point. The problem is provably NP-hard in some high dimensional geometric settings, even for k=2. We give polynomial time approximation schemes for this problem in several settings, including the binary cube (0, 1)/sup d/ with Hamming distance, and R/sup d/ either with L/sup 1/ distance, or with L/sup 2/ distance, or with the square of L/sup 2/ distance. In all these settings, the best previous results were constant factor approximation guarantees. We note that our problem is similar in flavor to the k-median problem (and the related facility location problem), which has been considered in graph-theoretic and fixed dimensional geometric settings, where it becomes hard when k is part of the input. In contrast, we study the problem when k is fixed, but the dimension is part of the input. Our algorithms are based on a dimension reduction construction for the Hamming cube, which may be of independent interest.
Performing Text Classification and Categorization through Deep Learning and Semi-Supervised Learning

Text classification is a truly mandatory field in the world of Natural Language Processing (NLP). It involves real-life applications in domains such as engineering and computer science. It is also successful in addressing complications with labeled data. There are two particular Deep Learning (DL) algorithms that are effectively productive in text classification: Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) as well as the semi-supervised learning algorithm. This research provides a much better overview of the text in each online document being classified and categorized into an almost infinite number of different sets by the RNN, CNN, and semi-supervised learning techniques. Furthermore, it determines which method has the best and the highest accuracy. It can also identify and resolve any problems the DL and semi-supervised learning methods face using text classification/categorization.