Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Performance of Cooperative Spectrum Sensing Under Binary Erasure Reporting Channels

The spectrum sensing based on single detector suffers from adverse effects such as fading, shadowing and hidden node problems. Cooperative spectrum sensing (CSS) is proposed to overcome these effects by utilizing the spatial diversity among the spatially located cooperating secondary users (CSUs). In traditional centralized CSS, the CSUs share their detection results with the fusion center (FC). Based on the information received from the CSUs, the FC analyze the information and takes the final decision on the occupancy of the primary user (PU) channel. Generally, in literature, the reporting channel between CSUs and the FC is assumed to be error free. However, these reporting channels are wireless channels which undergo fading and shadowing resulting in imperfect channels. In this work, we propose a model based on binary erasure channel (BEC) which is more suitable to model the reporting channel between the CSUs and the FC. We then, analyze the performance of the CSS under imperfect reporting channels. We then analyze the performance of repetition code based algorithm to improve the performance of CSS under imperfect reporting channels. It is found that the repetition code based algorithm significantly improves the performance. Also, by properly choosing the repetition length in the repetition based algorithm, the effects of reporting channel errors can be completely removed.
Sentiment Analysis of COVID-19 Vaccination Responses From Twitter Using Ensemble Learning

Despite the evidence that shows the benefits and safety of immunizations, the widespread vaccine-related misinformation and conspiracy theories online have fueled a general vaccine hesitancy, and coronavirus disease (COVID-19) vaccinations are no exception. COVID-19 vaccine hesitancy is considered a global threat to public health that undermines the efforts to control the COVID-19 pandemic. Twitter and other social media platforms allow people to exchange information and express concerns and emotions on COVID-19-related issues. This research aims to understand people’s sentiment on COVID-19 vaccines from data collected from Twitter. Analyzing the public attitude toward the vaccines helps the authorities to make better decisions and reach the intended herd immunity. In this paper, we utilize the state-of-the-art transformer-based classification models, RoBERTa and BERT, along with multiple task-specific versions, to classify people’s opinions about COVID-19 vaccinations into positive, negative, and neutral. A Twitter dataset that consists of people’s opinions about vaccines is used to train and evaluate the presented models. Two ensemble learning techniques that aggregate the individual classifiers are presented for further performance improvement: majority voting and stacking with Support Vector Machine (SVM) as meta-learner. The results also show that applying ensemble learning significantly outperforms the individual classifiers using all evaluation measures. We also found that ensembling with stacking has an advantage over simple majority voting.
Novel Graph-Based Features for Bearing Fault Diagnosis: Two Aspects of Time Series Structure

The feature-based methods for bearing fault diagnosis in prognostics and health management have been achieved satisfactory performances because of their robustness to noise and reduced dimension by pre-defined features. However, widely employed time- and frequency-domain features are insufficient to recognize the global pattern that indicates the structure of a time-series instance. In this paper, we propose two novel graph-based features which reflect the connection strength and degree of time series, respectively. First, we construct a graph of which an edge is defined as the Euclidean distance between each pair of time steps to measure the strengths of connections between the nodes. The other graph is constructed by the visibility algorithm, which converts a time series into a complex network to reflect the degrees of connections. Then, we calculate the Frobenius norms of the adjacency matrices of both graphs and use them as features for bearing fault diagnosis. To verify the proposed features, we performed several experiments with both synthetic and real datasets. From the synthetic datasets, it is observed that the changes in amplitudes and frequencies are detected by the features for the connection strength and degree, respectively. In addition, the proposed features also well-separate the distributions of each bearing state, including normal and several fault types, and show significant performance improvement as applied to the fault diagnosis task.
Raised Cosine Multicore Fibers For High-Density Space Division Multiplexing (H-DSDM) Systems

Space division multiplexing (SDM) over multicore few mode fibers (MC-FMFs) is considered among the latest technological drive towards high data rates in next generation optical communication systems. In this paper, we propose and design novel (MC-FMFs) that we refer to as raised cosine multicore fibers MC-RCFs and Trenched-raised cosine multicore Fibers (MC-TRCFs). the designed MCFs has seven cores each of which supports seven orthogonal spatial modes with large effective mode area (min AeJ J =96μm2) and with low differential mode delay (DMD) (max DMD =33 ps\\\\\\\\km). The intercore crosstalk of each designed fiber and their associated relative core multiplicity factor (RCMF) are assessed, discussed and compared with recently reported MCFs. The designed fibers are promising candidates for next generation short and medium haul interconnect dense-SDM systems.
EEG-based Mental Workload Assessment using a Graph Attention Network

Sustained high mental workload (MWL) experienced by operators in high-pressure jobs can compromise their performance, potentially endangering them as well as others. Using electroencephalograms (EEG) to gauge MWL levels is an approach that has been gaining prominence lately. Graph attention networks (GAT) have previously been used to great effect for traffic forecasting, citation networks, etc. In that context, we propose a GAT-based approach for improving the assessment of MWL using EEG signals. We focus on distinguishing EEGs corresponding to a high MWL from the EEGs corresponding to a low MWL and provide a comparative analysis of different features viz. band power, wavelet features, and autoregressive (AR) parameters. The obtained results show that this approach achieves an average accuracy of up to 95.66%, which is superior to that obtained using conventional multilayer perceptron (MLP) and several other recently used methods.
Communication in black spot using lora technology

The main theme of this paper is to develop an embedded based system to send messages from black spot area using LORA and GSM. Black spot is nothing but an area where we could not find any network signals. Even in cities also sometimes we may not have signals inside the room. Through such areas sending or calling is not possible. This black spot is used for security purposes. Military regiments are signal jammed areas where the communication is impossible, so the people there use this kind of communication. This project is also used in undercover operations where the communication takes place in this process. Here the project built is useful to create signals for sending messages in the black spot through the two different communication modules i.e., LORA and GSM. In the project work different frequencies for individual modules are likely to be used such as GSM (1800MHz) and LORA (2.4GHz). By using these two wireless communication devices we create communication link in between LORA and GSM by which a message will be sent in the form of SMS through the Bluetooth module.
Identification of Spliceogenic Variants beyond Canonical GT-AG Splice Sites in Hereditary Cancer Genes

Pathogenic/likely pathogenic variants in susceptibility genes that interrupt RNA splicing are a well-documented mechanism of hereditary cancer syndromes development. However, if RNA studies are not performed, most of the variants beyond the canonical GT-AG splice site are characterized as variants of uncertain significance (VUS). To decrease the VUS burden, we have bioinformatically evaluated all novel VUS detected in 732 consecutive patients tested in the routine genetic counseling process. Twelve VUS that were predicted to cause splicing defects were selected for mRNA analysis. Here, we report a functional characterization of 12 variants located beyond the first two intronic nucleotides using RNAseq in APC, ATM, FH, LZTR1, MSH6, PALB2, RAD51C, and TP53 genes. Based on the analysis of mRNA, we have successfully reclassified 50% of investigated variants. 25% of variants were downgraded to likely benign, whereas 25% were upgraded to likely pathogenic leading to improved clinical management of the patient and the family members.
Built-in Thermal Test as a Pre-Integrated Architecture

Built-in Thermal Test is a key method to assure reliable operation during the product life time. Pre-integrated architecture is a design concept for cyber-physical systems to reduce R&D efforts. In this work the built-in thermal test methodology was adapted to this design concept and its operation was demonstrated in a chemical flow-reaction system where the reactor’s thermal interface was qualified. The demonstrational measurements showed that the system was able to distinguish different reactor assemblies with intentionally degraded thermal interfaces with a resolution of 0.05 K/W.
IMPLICATIONS OF STOKES–CARTAN THEOREM TO TIME-HARMONIC ACOUSTIC BOUNDARY INTEGRAL EQUATION FORMULATIONS

Direct boundary integral equation (BIE) formalisms for wave radiation and scattering have been stable and universally accepted for decades. Yet, the classic separation of variables (SOV) solutions for acoustic radiation and scattering from spheres do not always agree with BEM results. For certain conditions, the boundary acoustic field predicted by low-frequency SOV and BEM methods match exactly and for other situations predicted fields by the two methods are complex-conjugates of each other. While this difference is subtle, modern BEM literature has not cited the transfer of known mathematics to this engineering application. Tracing signs within BEM code is daunting. To create a lucid and reproducible record of the issue and its resolution, this paper presents an analytical BIE solution for spherical geometry based on a Legendre polynomial simplex element and a power series of the spatial phase term of the Helmholtz operator Fundamental Solution. Optical theorem reasoning suggests the traditional BIE approach is the method in error. The core of this issue is the application of the divergence theorem (strictly true only for real-valued functions) to time-harmonic (complex-valued) formulations. The conjugation of spatial derivatives of a complex-valued field can be understood from Wirtinger derivatives and Dolbeault operators. This issue manifests itself when the Sommerfeld radiation condition is applied for unbounded domains. Exterior calculus ideas properly unite, generalize and extend a variety of related classical theorems including divergence, Cauchy’s integral theorem from complex analysis, and Green’s identities used in constructing a BIE. The resulting Stokes–Cartan theorem is properly applied to acoustic scattering in 3D within this paper and invokes corrections which match BIE and SOV solutions for the low frequency problems investigated.
Ways of coping in adolescents with schizophrenia

Accessible summary • Coping has been classified as emotion-focused coping and problem-focused coping. Adolescents with schizophrenia frequently used sleep and wishful thinking coping strategies as emotion-focused copings. • Adolescents with schizophrenia endorsed high levels of stress response when compared with the healthy controls. The stress made adolescents with schizophrenia feel anxious/fearful and irritable. For this reason, adolescents who experienced more emotional irritability and anxiety/fear symptoms were likely to mobilize emotion-focused coping. • Although emotion-focused coping may be effective for short periods of time as a temporary means of reducing the impact of stress, adolescents with schizophrenia should learn problem-focused coping as the primary method for long-term ways of coping. Abstract The purpose of this study was to explore ways of coping and its association with specific stress responses in adolescents with schizophrenia. Additionally, subjects and healthy controls were compared to identify stress responses. Forty subjects were drawn from a self-management therapy study for youth with schizophrenia. Thirty community-dwelling controls were selected. A revised Ways of Coping scale and the Symptom of Stress at baseline, 6, 30 and 54 weeks measured coping and stress response. Descriptive statistics, cluster analysis and Pearson correlation provided data analysis. Thirty-two subjects were male, and eight were female. Average age was 17.25 (SD = 1.37) years. Twenty-two (55%) were Caucasian; 18 (45%) were non-Caucasian. Seventeen (57%) of the 30 controls were female. The mean age was 17.10 years old (SD = 1.16). Adolescents with schizophrenia used emotion-focused coping more than problem-focused coping at baseline and 6 weeks (P < 0.01). Subjects reported higher stress than controls (t = 4.73, P < 0.01) and used emotion-focused coping with emotional stress responses (r = 0.34, P = 0.05). Adolescent coping strategies may persist into adulthood unless new skills are introduced. Developing effective coping skills for adolescents with schizophrenia is important for practice and future studies.
Epistemic Meaning and the LLL Tune in American English

In American English, the pitch pattern at the end of a prosodic phrase—the nuclear tune—conveys pragmatic meaning, which can be characterized in epistemic terms as relating to the mutual beliefs of the speaker and their addressee [1]. Epistemic accounts have been offered for the meaning of rising and falling nuclear tunes [2], or a mid-level pitch plateau [3], but the epistemic contribution for many other tunes has not been addressed. This paper addresses one such gap in the literature, examin-ing the meaning of the L*L-L% contour in American English through two experiments testing listeners’ preferences for H*L-L% vs. L*L-L% with declarative sentences as a function of whether the associated proposition is ruled in (congruent with speaker and addressee shared knowledge) or ruled out (incon-gruent). Our results show that listeners find L*L-L% to be more felicitous than H*L-L% in ruled-out contexts, despite prior claims that H*L-L% is the default tune for declarative assertions. A follow-up experiment shows that the preference for L*L-L% in ruled-out contexts remains even when the intonation is made redundant by explicit acknowledgement of the ruled-out status of the proposition. We conclude that L*L-L% makes an independent epistemic contribution to utterance meaning in American English and propose that a speaker uses L*L-L% to signal that they withhold their commitment to the propositional content of their utterance.
Local Surface Descriptor for Geometry and Feature Preserved Mesh Denoising

3D meshes are widely employed to represent geometry structure of 3D shapes. Due to limitation of scanning sensor precision and other issues, meshes are inevitably affected by noise, which hampers the subsequent applications. Convolultional neural networks (CNNs) achieve great success in image processing tasks, including 2D image denoising, and have been proven to own the capacity of modeling complex features at different scales, which is also particularly useful for mesh denoising. However, due to the nature of irregular structure, CNNs-based denosing strategies cannot be trivially applied for meshes. To circumvent this limitation, in the paper, we propose the local surface descriptor (LSD), which is able to transform the local deformable surface around a face into 2D grid representation and thus facilitates the deployment of CNNs to generate denoised face normals. To verify the superiority of LSD, we directly feed LSD into the classical Resnet without any complicated network design. The extensive experimental results show that, compared to the state-of-the-arts, our method achieves encouraging performance with respect to both objective and subjective evaluations.
Conflictos socioambientales en torno al turismo del tiburón ballena en la Bahía de la Paz, Baja California Sur, México

Desde el marco de la ecología política se analizan conflictos socioambientales producidos por el desarrollo del mercado turístico de avistamiento y nado con el tiburón ballena en la Bahía de La Paz. Se trata de entender un proceso de despojo legitimado dentro de un marco legal que beneficia a ciertos actores y vulnera a otros, así como analizar los mecanismos de resistencia oculta de pescadores ribereños frente a este proceso. La existencia de un plan de manejo vigente ha sido un detonante que legitima el despojo por la restricción de acceso a un espacio de uso común, afectando la pesca ribereña y al esparcimiento de habitantes locales; y lejos de ayudar a mejorar las condiciones ecológicas y de conservación del tiburón, este ha sido un instrumento que beneficia al sector turístico al construir un espacio exclusivo para su efectiva operación, a costa de impactos negativos directos hacia esta especie.
Synthetic Data Augmentation of Tomato Plant Leaf using Meta Intelligent Generative Adversarial Network: Milgan

—Agriculture is one of the most famous case studies in deep learning. Most researchers want to detect different diseases at the early stages of cultivation to save the farmer's economy. The deep learning technique needs more data to develop an accurate system. Researchers generated more synthetic data using basic image operations in traditional approaches, but these approaches are more complicated and expensive. In deep learning and computer vision, the system's accuracy is the crucial component for deciding the system's efficiency. The model's precision is based on the image's size and quality. Getting many images from the real-world environment in medicine and agriculture is difficult. The image augmentation technique helps the system generate more images that can replicate the physical circumstances by performing various operations. It also prevents overfitting, especially when the system has fewer images than required. Few researchers experimented using CNN and simple Generative Adversarial Network (GAN), but these approaches create images with more noise. The proposed research aims to develop more data using a Meta approach. The images are processed using kernel filters. Different geometric transformations are passed as input to the enhanced GANs to reduce the noise and create more fake images using latent points, acting as weights in the neural networks. The proposed system uses random sampling techniques, passes a few processed images to the generator component of GAN, and the system uses a discriminator component to classify the synthetic data created by the Meta-Learning Approach.
Liming alkaline clay soils: effects on soil structure, nutrients, barley growth and yield

ABSTRACT Liming before cultivation of sugar beets is favourable even on alkaline soils but knowledge of response in other crops is lacking. Therefore, effects of ground limestone (GL) and structure lime (SL1 slaked lime or SL2 mix of ground limestone and slaked lime) were evaluated in southern Sweden on soil structure, growth and nutrient concentration in barley under four fertilisation strategies 1.5–2 years after application. All lime products increased aggregate stability, but with variations between locations. A lower proportion of large aggregates was found in both limed treatments, and a higher proportion of small aggregates in SL. In barley, grain yield was unaffected while shoot numbers and biomass in first node stage increased for GL and biomass increased further for SL. Structure lime increased potassium concentration in plants in first node stage, due to more potassium in the product. Both lime types increased molybdenum concentration. Ground limestone reduced zinc concentration compared with no liming. Finer seedbed tilth and increased aggregate stability may explain increased biomass for GL. Higher potassium content in SL might be a further explanation. No interactions between liming and fertilisation were found. In conclusion, on the soil types studied, no change of fertilisation strategy is needed due to liming.
Reforming an Envy-Free Matching

We consider the problem of reforming an envy-free matching when each agent is assigned a single item. Given an envy-free matching, we consider an operation to exchange the item of an agent with an unassigned item preferred by the agent that results in another envy-free matching. We repeat this operation as long as we can. We prove that the resulting envy-free matching is uniquely determined up to the choice of an initial envy-free matching, and can be found in polynomial time. We call the resulting matching a reformist envy-free matching, and then we study a shortest sequence to obtain the reformist envy-free matching from an initial envy-free matching. We prove that a shortest sequence is computationally hard to obtain even when each agent accepts at most four items and each item is accepted by at most three agents. On the other hand, we give polynomial-time algorithms when each agent accepts at most three items or each item is accepted by at most two agents. Inapproximability and fixed-parameter (in)tractability are also discussed.
Do We Need a Specific Corpus and Multiple High-Performance GPUs for Training the BERT Model? An Experiment on COVID-19 Dataset

The COVID-19 pandemic has impacted daily lives around the globe. Since 2019, the amount of literature focusing on COVID-19 has risen exponentially. However, it is almost impossible for humans to read all of the studies and classify them. This article proposes a method of making an unsupervised model called a zero-shot classification model, based on the pre-trained BERT model. We used the CORD-19 dataset in conjunction with the LitCovid database to construct new vocabulary and prepare the test dataset. For NLI downstream task, we used three corpora: SNLI, MultiNLI, and MedNLI. We significantly reduced the training time by 98.2639% to build a task-specific machine learning model, using only one Nvidia Tesla V100. The final model can run faster and use fewer resources than its comparators. It has an accuracy of 27.84%, which is lower than the best-achieved accuracy by 6.73%, but it is comparable. Finally, we identified that the tokenizer and vocabulary more specific to COVID-19 could not outperform the generalized ones. Additionally, it was found that BART architecture affects the classification results.