Miracle-dz/UniFAR-FTUs huggingface dataset:
Design UWB antenna with notch band for WiMAX application

During the last two decades, radar, remote sensing, and imaging applications have all made use of ultra-wide band (UWB) technology. UWB systems are susceptible to interference from narrowband signals, hence this work provides a single-notch antenna for the UWB system. There are two stages to the design process. After creating the baseband antenna, it is necessary to create a notched band UWB antenna by carving a slot into patch antenna. In the UWB range (3.1-10.6) GHz, the UWB antenna has the dimensions of 20x30 mm with substrate thickness 1.6 mm made from FR4 lossy. The design relative permittivity was 4.3, a rectangular patch with a portion of the ground is used in the design. A typical slot-shaped resonator is connected to the patch to reject a frequency band (3.273-3.81) GHz which is a world interoperability for microwave access (WiMAX) to solve the problem of the interference with other bands in UWB system For WiMAX applications. The suggested UWB filter will achieve notch band response centered at the resonance frequency of 3,4 GHz. Analysis CSTS v2020 software was used to carry out the simulation. Priority should be given to what has been learned rather than what has been accomplished.
Bulletin of Electrical Engineering and Informatics

Received Dec 4, 2022 Revised Feb 23, 2023 Accepted Mar 7, 2023 Nowadays, depression is a common mental illness. Failure to recognize depression early or guarantee that a depressed individual receives prompt counseling can lead to serious issues. Social media allow us to monitor people's thoughts, daily activities, and emotions, including persons with mental illnesses. This study suggested novel hybrid models that combine one of the deep learning techniques with one of the machine learning approaches. This paper used a dataset from the Kaggle website to predict depression. Two deep learning techniques were chosen to conduct the experiments: bidirectional long short-term memory (Bi-LSTM), and convolutional neural network (CNN). Three machine learning techniques were also selected, which are support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBOOST). Deep learning methods were applied to extract important features from input data and training, and then machine learning was utilized to predict the class. The performance of the hybrid models was compared against that of five single models. The results showed that Bi-LSTM-XGBOOST is better than single models and achieve the highest performance, with 94% for all evaluation metrics. The proposed model can improve the performance of machine learning techniques and increase the detection rate of depression.
Dactylogyrids (Platyhelminthes, Monogenea) from the gill lamellae of doradids (Siluriformes) with description of five new species of Cosmetocleithrum and new geographical distribution for known species from the Neotropical Region, Brazil

Five new species of Cosmetocleithrum were described parasitizing the gill filaments of neotropical doradid fishes. Cosmetocleithrum undulatum n. sp. , Cosmetocleithrum brachylecis n. sp. and Cosmetocleithrum ludovicense n. sp. are described from Platydoras brachylecis from a market-place of São Luís, State of Maranhão, Brazil. Cosmetocleithrum sacciforme n. sp. and Cosmetocleithrum basicomplexum n. sp. are described from Oxydoras niger from Juruá River, State of Acre, Brazil. Cosmetocleithrum undulatum and Cosmetocleithrum brachylecis resemble Cosmetocleithrum falsunilatum Feronato, Razzolini, Morey & Boeger, 2022 mainly by the unique male copulatory organ (MCO) morphology but differ from these and all congeneric species mainly by the morphology of the MCO, accessory piece and hooks pairs. Cosmetocleithrum ludovicense is closer to Cosmetocleithrum confusus Kritsky, Thatcher & Boeger, 1986 and to Cosmetocleithrum akuanduba Soares, Santos Neto & Domingues, 2018 but differs from those mainly by the morphology of the accessory piece. Cosmetocleithrum sacciforme differs from all congeneric species mainly by the morphology of the accessory piece formed by a single plate of saccular appearance. Cosmetocleithrum basicomplexum also shares morphological characters with Cosmetocleithrum gigas Morey, Cachique & Babilonia, 2019 considering the size of the body and shape of the anchors, but differs mainly in the morphology of the bars and hooks. Besides the new species, new data are presented for Cosmetocleithrum leandroi Soares, Neto & Domingues, 2018, C. akuanduba and C. confusus regarding morphological characteristics and biogeography.
Design and Analysis of a Quad Band BPF Using Ring Resonator

A flexible quad-band BPF was designed by utilizing a ring resonator and an I shaped resonator is etched from a circular resonator to obtain better return loss, transmission co-efficient and group delay. This I shaped resonator etched from the circular ring can also support the multiple band applications. By utilizing this circular ring resonator, a quad band BPF was designed and simulated by utilizing An-soft HFSS v13. The designed filter results shows that the quad band BPF resonates at 11.2GHz, 14.2GHz, 16.5GHz and 19.6GHz with a transmission co-efficient of 0.2dB at all the resonating frequencies and return loss of -27dB, - 47.3dB, -15.7dB and -36.1dB with a fractional bandwidth of 4.1 %, 4.15%, 10.9% and 2.3% respectively.
An Approach to Clean Architecture for Microservices Using Python

Microservices architecture (MSA) is a popular approach to create maintainable and independent units of large systems. Recently many works have been carried out in migrating monolithic systems into MSA as well as using it in upcoming areas of development like Internet of Things(IoT). Most of the existing work emphasizes MSA for the system, little attention is paid to the architecture followed in the implementation of the underlying services. Clean architecture (CA) is a type of Domain Driven Design (DDD) approach that separates business logic so that it can be tested independently. Clean architecture is often used in Android systems. Introducing clean architecture in MSA can further ensure Separation of Concerns (SoC) and testability of business logic in the system. Through this paper an optimal solution to clean architecture in microservices is proposed. The system under consideration is a financial system that uses microservices based on Python and GraphQL.
Speech Emotion Recognition Using ML

Due to its numerous uses in industries including human-computer interaction, healthcare, and entertainment, speech emotion recognition (SER) has attracted a lot of interest recently. The use of Convolutional Neural Networks (CNN) in combination with the Librosa library for SER is used to recognise emotions from audio data. The capacity to automatically recognise and categorise emotions from spoken words can revolutionise many applications since emotions are a crucial component of human communication. Here, CNNs-known for their skill in image analysis-are adapted to the spectrogram representation of audio, efficiently identifying distinguishing characteristics. To improve the reliability of emotion identification, Librosa, a Python library for music and audio analysis, offers crucial capabilities for preprocessing and feature extraction from audio data. The architecture of the CNN model, which includes of convolutional layers for feature extraction and pooling layers for dimensionality reduction, is required to build the model. To increase model generality, methods for data augmentation and transfer learning are employed. The two feature extraction capabilities of Librosa, chroma features and mel-frequency cepstral coefficients (MFCCs), are emphasised for their effectiveness in extracting emotional cues from audio data. It has been proved via considerable research that CNNs and Librosa together produce state-of-the-art results in a variety of emotion identification tasks, considerably improving SER accuracy. This study has broad repercussions, from enhancing the emotional intelligence of virtual assistants to helping mental health practitioners monitor patients' emotional health through speech analysis. In summary, the combination of CNNs and Librosa in SER offers a potential path for developing the discipline of emotional analysis in speech, resulting in more emotionally intelligent technology and a deeper comprehension of human emotions.
Insights from 3D Device Modeling of Nanowall-Integrated n-CdS/p-CdTe Solar Cell

Utilizing TCAD's Silvaco software, an extensive three-dimensional device modeling of an n-CdS/p-CdTe solar cell based on the Nanowall architecture has been conducted. The device performance metrics such as open circuit voltage (Voc), fill factor (FF), power conversion efficiency (Eff), and quantum efficiency have been studied as a function of substrate thickness (CdTe Layer). The results not only unveil optimal geometries, such as a 100 nm CdTe layer thickness under the given design parameters, for achieving superior photovoltaic performance but also provide a deeper understanding of the underlying mechanisms driving the observed effects. This analysis is quite beneficial in the design of determining the appropriate substrate thickness for the solar cell at the nanoscale level.
An Arms Race in Theory-of-Mind: Deception Drives the Emergence of Higher-level Theory-of-Mind in Agent Societies

It has been argued that using Theory-of-Mind (ToM) is a fundamental cognitive ability that underpins effective deceptive behaviour. However, we still do not have a clear understanding of the effect ToM has on deception under evolutionary pressure in self-organising multi-agent systems (MAS). To better understand it, in this paper we introduce the Evolutionary Deception Game (EDG) to model deception and investigation of deception in societies of agents with different levels of ToM. We show that the impact of higher levels of ToM is different in deceptive agents compared to agents that investigate deception. We also show the creation of a dominance cycle, i.e., an arms race in ToM where investigators need to be one level higher to dominate deceivers. Additionally, we provide new insight into the effect that important societal factors in MAS such as social learning and population size have on the dominance of strategies in the EDG. We conclude that ToM is both the solution to agent deception, as well as its defence.
Examining the Realities and Nuances of ‘Low-stakes’ Interest-Driven Learning Environments

The push to develop low-stakes and personally meaningful computer science experiences is creating novel opportunities to broaden participation in CS. These opportunities have become increasingly present across contexts and have expanded the possibilities for introducing and sustaining student participation in computing. However, while these experiences tend to be effective ways for engaging new participants and new forms of participation, we must be careful to not overlook how ’high-stakes’ these experiences might be for learners. To explore this tension, this paper describes two case studies of students engaging in coding and computational thinking with Minecraft Education Edition. The first case study involves a 7-year-old Black and Latina girl who experiences significant frustration when her computer program destroys significant portions of her project. The second is from a Latino boy who avoids using the coding capabilities in Minecraft EDU out of fear that the code might not work properly. Building on these case studies, this paper suggests that the field take steps to ensure that the language and actions associated with low-stakes and high-stakes are reflective of learner perceptions, and that we design learning experiences that appropriately reflect this nuance.
A Technology Quality Improvement Approach Based on the Scopus Engineering and Computer Science Information Registered in the USA Since 2018 to 2022

Technology Quality Improvement is characterized by the use of technology. According to the literature review, Mexico has a great gap of information in this global topic, that is why this article aims to review in depth the advances that exist in the United States of America that have already been implemented and that can be developed and operated in Mexico. Based on the literature searched during recent years 2018 to 2022.
Real Time Speech Emotion Recognition Using LSTM and Raspberry Pi

Detecting and treating depression early is vital for achieving remission, preventing relapse, and alleviating the emotional burden of the illness. However, the current diagnostic methods are predominantly subjective, lack consistency across professionals, and can be financially burdensome for individuals urgently requiring assistance. This paper proposes an effective approach of feature extraction from speech signals of patients to diagnose fluctuations in emotions and mood of people. These observed repetitive emotion patterns are indicative of a patient's mental health and helps in the treatment of depression. The acoustic feature used for analysis and decision-making in this work is the MFCC (Mel Frequency Cepstral Coefficient). A detailed spectral analysis of the audio files in the DAIC_WOZ database has been carried out using MATLAB to bring out the differences in spectral features of healthy versus depressed individuals. Further, a Long Short-Term Memory (LSTM) has been built with an accuracy score of 86%, to predict the emotions from voices of different people using the RAVDESS dataset. Validation of the working of our machine learning model has been done using real time voice signals of people which have been collected and processed using Raspberry Pi 3 and this results in a similar accuracy score as the RAVDESS dataset. Hence, speech signal analysis using machine learning to predict depression in patients and a detailed spectral analysis makes the case for our proposed methodology.
Recurrent replenishment of Labrador Sea Water and associated decadal‐scale variability

Winter convective overturning in the Labrador Sea reached an “aggregate” maximum depth of 1700m in 2015 – the deepest since 1994 – with the resulting Labrador Sea Water (LSW) “year class” being one of the deepest and thickest observed outside of the early 1990s. Argo float, annual survey and moored measurements in recent decades provide an unprecedented view of important seasonal, interannual and longer-term LSW variability in the Labrador Sea region. During the 2002-2015 “Argo” era, the average winter LSW pycnostad volume was about 70% larger in relatively strong convection years than in relatively weak ones. However, the winter-to-fall LSW disappearance volume was 180% larger, pointing to a factor of 2.8 difference in the potential LSW export rates from the region between relatively strong and weak convection years. Intermittently recurrent deep convection is contributing to predominant decadal-scale variations in intermediate-depth temperature, salinity and density in the LS, with implications for decadal-scale variability across the subpolar North Atlantic and potentially in the Atlantic Meridional Overturning Circulation. Comparison of the LS ocean heat content changes and cumulative surface heat losses during the fall-winter cooling seasons indicates that anomalously strong winter atmospheric cooling, associated at least in part with the North Atlantic Oscillation, is continuing to be a major forcing of the recurrent convection. This article is protected by copyright. All rights reserved.
Study of the K-Nearest Neighbors Method with Various Features for Text Classification in Machine Learning

The aim of this publication is to compare the accuracy of the K-Nearest Neighbors method using various distance calculation functions between two objects from the training sample in text classification within machine learning. The study involves classifying English language texts representing movie reviews. The compared functions are: LP metric (Minkowski), Manhattan metric (L1), Euclidean distance (L2), and cosine similarity. The results indicate that the highest accuracy in text classification using the K-Nearest Neighbors method was achieved when employing the cosine similarity function, with a difference of approximately 19 percent compared to the other functions, especially with larger data sets. When classifying opinions from user comments in English using the k-nearest neighbors method with the cosine similarity function, the examined parameters of accuracy, precision, and recall increased as the data volume expanded.
UvA-DARE (Digital Academic Repository) Relating ASD symptoms to well-being: moving across different construct levels

Background. Little is known about the specific factors that contribute to the well-being (WB) of individuals with autism spectrum disorder (ASD). A plausible hypothesis is that ASD symptomatology has a direct negative effect on WB. In the current study, the emerging tools of network analysis allow to explore the functional interdependencies between specific symptoms of ASD and domains of WB in a multivariate framework. We illustrate how studying both higher-order (total score) and lower-order (subscale) representations of ASD symptomatology can clarify the interrelations of factors relevant for domains of WB. Methods. We estimated network structures on three different construct levels for ASD symptomatology, as assessed with the Adult Social Behavior Questionnaire (item, subscale, total score), relating them to daily functioning (DF) and subjective WB in 323 adult individuals with clinically identified ASD (aged 17 – 70 years). For these networks, we assessed
Master Computer Science The effect of robot errors on children in the context of education

This master thesis focus is a social experiment conducted to determine to what extent robot errors affect children in the context of education. A total of 60 children of age 10-12 from a public school in Leiden participated in the experiments in which they interacted with a Nao robot two by two. During the study, the robot simulated some speech recognition, voice localization and gaze errors based on a literature review conducted, which lead to the robot ignoring one of the two participants during the interaction. The human-robot interaction consisted of an interactive vocabulary game, where Nao acted as a peer to the children, playing the game together. The robot recognizes it is faulty and explains its behaviour either before the experiment or after the completion of a questionnaire that follows the experiments. The effect on children based on these two conditions is examined, i.e. how children are affected by being ignored by a robot and if the moment the robot explains its errors matter in how it is perceived by them. Apart from the questionnaire, the affective outcomes of the study are further examined with video recordings that are used within a framework to estimate head direction. Overall, neither the ignored condition or the moment of explanation one seem to have a big significance on children. Being ignored only slightly affects the closeness to the robot, where we observed children being ignored ranking less close to it. Furthermore, the moment of explanation affects the trustworthiness of the robot, where it is generally more trusted when it apologizes for its faults in advance. Lastly, in terms of children’s attention, they are focused on either the robot or the screen of the game for the majority of the task irrespective of the experimental condition they are in.
Toward a Cost-Effective Smart Crop Health Monitoring System

In this research we have investigated and imple-mented smart-connected and cost-effective technologies that can aid with the crop management. In particular, we have installed nodes with various sensors in several parts of a Green house to supervise environmental and lighting conditions as well as to monitor the growth of plants. Furthermore, the overall monitoring system further includes a central access node for accumulating the sensory data that can also be accessed remotely for further analysis. The data collected from this research will be used in determining the optimal placement of sensors within the Green house, their performance as well as the strategies to be followed for minimizing the growth rate of plannts while at the same time maximizing their health. Finally, we have integrated all sensors input data into a Graphical User Interface.
An Emotion Aware Music Recommendation System Using Flask and Convolutional Neural Network

Music has been instrumental in influencing the emotions of a person. Every person develops a choice of music depending on varied factors like- philosophy, situations and personal emotions. Many organizations have been providing different ways to recommend a good playlist of music to their users based on their previous choices and emotion of user but couldn't bridge the gap of personalization and emotion driven recommendation. This paper emphasizes on building this gap by providing a recommendation of music based on user's choices and their current emotions. The proposed model is an innovative approach by harnessing the power of Convolutional Neural Networks (CNN) and music therapy approaches to ensure to provide people with an apt recommendation that enhances the user's experience of music. This model has achieved an accuracy of 71%. It can easily be integrated with music platforms to enhance the user experience and provide better services.
LFTransNet: Light Field Salient Object Detection via a Learnable Weight Descriptor

Light Field Salient Object Detection (LF SOD) aims to segment the visually distinctive objects out of surroundings. Since light field images provide a multi-focus stack (many focal slices in different depth levels) and an all-focus image for the same scene, they record comprehensive but redundant information. Existing methods exploit the useful cue by long short-term memory with attention mechanism, 3D convolution, and graph learning. However, the importance of intra-slice and inter-slice in the focal stack is not well investigated. In the paper, we propose a learnable weight descriptor to simultaneously exploit different weights in slice, spatial region, and channel dimensions, and therefore propose an LF SOD method based on the learnable descriptor. The method extracts slice features and all-focus features from a weight-shared backbone and another backbone, respectively. A transformer decoder is used to learn the weight descriptor which both emphasizes the importance of each slice (inter-slice) and discriminates the spatial and channel importance of each slice (intra-slice). The learnt descriptor serves as the weight to make slice features attend to important slices, regions, and channels. Furthermore, we propose the hierarchical multi-modal fusion which aggregates high-layer features by modelling the long-range dependency to fully excavate common salient semantics and combines low-layer features by spatial constraint to eliminate the blurring effect of slice features. The experimental result exceeds the state-of-the-art methods at least 25% in terms of mean absolute error evaluation metric. It demonstrates a significant improvement in LF SOD performance via the designed learnable weight descriptor. https://github.com/liuzywen/LFTransNet
Occlusion-Aware 3D Priors for Deep Learning-Based Applications

This paper investigates the benefits of incorporating point visibility information of 3D point clouds within a deep learning framework, using occlusion-aware 3D priors. The presented methods for deriving the visibility of each point rely on ray-casting techniques, making the proposed solution generic and sensor independent. We demonstrate the benefits of integrating point visibility using two real-world applications. In a first application, a novel data augmentation technique is proposed leveraging occlusion-aware CAD 3D priors, resulting in state-of-the-art 3D vehicle detection. In a second application, we integrate visibility information into a vehicle pose estimation pipeline based on 3D priors. The presented techniques achieve state-of-the-art performance, significantly improving both translation and rotation accuracy on the Apollo3DCar dataset.