Miracle-dz/UniFAR-FTUs huggingface dataset:
Elucidation of validity of emotion model on EEG and facial expression

There have been many attempts to recognize emotions based on biometric information such as EEG. In these attempts, the emotion model used has a significant impact on the estimation of emotion. In this study, we examined the validity of the models by comparing the estimation of internal emotions by Plutchik’s Emotion Wheel and Ekman’s Basic Emotion Model, which are frequently used psychological emotion models, with a self-model created for each participant based on the classification of facial expression and the estimation of pleasant feelings by EEG. The results showed that Ekman’s model was more consistent with the self-model than Plutchik’s model. The result suggests that a model that does not include complex emotions, such as Ekman’s model, is more intuitively consistent with human emotions. In addition, the fluctuation of alpha band EEG was used as an EEG index to evaluate pleasantness and unpleasantness, but no significant difference was found, suggesting that this index may not be appropriate for the estimation of the unintentionally occurring emotion.
Solid Waste Management using Equal Sized Clustering and Capacitated Vehicle Routing Problem

The production of solid waste is increasing every year. With this increasing waste production comes a challenge to managing it. The initial step in managing this waste is an efficient collection plan to collect the waste from its locations and transport it to the treatment plant/dumping area. This paper considers a solid waste management problem in which solid waste from a particular area or city is collected using vehicles responsible for collecting and transporting the waste from bins to the treatment plant. The problem aims to optimize the solution by minimizing vehicle expenditure during travel. It is achieved by finding the best possible route and minimizing the vehicle's distance traveled. This paper proposes a solution with a two-tier approach based on first clustering using an equal-sized clustering using a modified k-means algorithm and then reducing the path to a low-cost path using simulated annealing.
A Systematic Mapping of Computer Vision-Based Sign Language Recognition

Sign languages are the primary mean of communication for the majority of deaf people. Unfortunately, there are multiple barriers due to the lack of assistive technologies, this causes problems in the communication between the Deaf and their environment, access to services, education, information, and employment. Automatic sign language recognition using machine learning techniques has gained special attention in the last years to help reduce such barriers. Different lines of investigation are being followed for isolated and continuous sign language recognition, and multiple models exist that attempt to translate from sign language to a written one. We present a systematic mapping of the papers that have been published relevant to this area between January 2019 and March 2022. The main focus of the study is on technologies used to recognize isolated signs and sentences in different sign languages from RGB videos and without specialized hardware or sensors. A total of 1061 articles are reviewed and classified showing that: (1) There is a majority of publications which use external hardware or recognize alphabet signs from static images, (2) LSTM and different variants of CNNs are the main methods used for isolated sign language recognition and Transformer models are predominant for continuous sign language recognition, (3) Different types of CNNs are the primary feature extraction methods followed by skeleton representations. Finally, we highlight the use of negative vocabulary and labels in research papers to refer to the Deaf community and call for the eradication of this practice.1
Portable Air Quality Monitoring System in ANN Using Combination Hidden Layer Hyperparameters

The transportation and industrial sectors are growing rapidly, with positive and negative consequences in the form of air pollution. According to the Global Alliance on Health and Pollution (GAHP), 3.4 million people died from air pollution-related causes worldwide in 2017, with 123,700 of them dying as a result of air pollution. As a result, a portable system was built in this study to monitor air quality and categorize it using the Artificial Neural Network (ANN), with the classification results displayed on an Android application. Air quality classification is accomplished by varying the hyperparameters of the Artificial Neural Network (ANN), such as the number of hidden layer neurons, dropout, and batch size, while utilizing the gas parameters PM10, PM2.5, NO2, SO2, CO, and 03. The classification results will also be classified into five categories: good, moderate, satisfactory, poor, and very poor air quality. The system is intended to give accurate results.
Effect of intervening galaxies on quasar counts and colors

Ostriker and Heisler (1984) suggested that optically thick dust in galaxies could reduce the number of high-redshift quasars without reddening the observed quasars, because the only observed quasars would be those seen through no intervening galaxies. However, Ostriker and Heisler assumed that galaxies were hard-edged uniform disks, and the fuzzy edges of real galaxies significantly change this conclusion. The quasar distribution observed through more realistic galaxy models is calculated, and it is found that dust cannot significantly reduce the high-redshift quasar counts without also reddening the observed sample. But this reddening could be consistent with past studies of quasar reddening; improved studies of quasar reddening using new UV and IR data are needed.
Automatic recognition of fetal facial ultrasound standard planes based on improved YOLOv4

Accurate acquisition of standard planes of fetal facial ultrasound is essential for subsequent biometry and disease diagnosis. Foreign scholars have extensively researched algorithms for the automatic acquisition of ultrasound standard planes. We view standard plane identification as a detection task, unlike previous classification studies. This study proposes a lightweight target detection network for identifying fetal facial ultrasound standard planes. Methods: Firstly, the model is based on the YOLOv4 algorithm, and given the storage resource limitations of the ultrasound device, we used a lightweight network (GhostNet) to replace the YOLOv4 backbone feature extraction network (CSPDarkNet53). Results: The experimental results show that the average accuracy of the improved YOLOv4 algorithm is 98.06%. The model size is 42.7 MB, a reduction of 85% compared to the original YOLOv4. It takes only 0.07 seconds to detect an ultrasound image, which can fully meet the real-time clinical requirements. It has high detection speed and accuracy, and the model's size is reduced substantially. The algorithm can assist young ultrasonographers in better acquiring high-quality ultrasound images and, to some extent, can address the limitations of the traditional manual approach to acquiring standard planes.
Transfer Training of a Neural Network to Improve the Efficiency of Automatic Recognition of Children Using NVIDIA Jetson Nano

this work is devoted to the transfer training of a model for automatic detection of children. The main goal of the work was to increase the accuracy of detecting children in comparison with the MobileNet V1 model taken as a basis, with which a comparative analysis will be carried out in the future. Our own data set was used for transfer training. The training itself was carried out in the free cloud service Google Colab. Both models were launched and tested on a single-board NVIDIA Jetson Nano microcomputer.
Monitoring Sagittarius A* in the MIR with the VLT

The ISAAC and NAOS/CONICA systems on the ESO VLT UT1 and UT4 telescopes have been used to monitor the MIR L and M band (3.3 μm and 4.6 μm) flux density at the position of Sgr A* and to obtain new information on the stellar populations within the central cluster. The monitoring program was carried out simultaneously to Chandra observations and resulted in upper limits on the flux density of Sgr A* during a newly detected out-burst of the X-ray source at the position of Sgr A*.
Segformer-Mobilenetv3 fusion coordinate attention mechanism: A lite portrait segmentation network

Aiming at the problem of low accuracy of portrait segmentation, we proposes a lite mobile image segmentation algorithm, SegFormer MoibleNetv3(SMN), based on SegFormer and fusion coordinate attention mechanism MoblieNetv3 to achieve portrait segmentation. The improved MobileNetv3 is used as the backbone network for feature extraction, which can effectively reduce the size of the model and facilitate the migration and training of the model. SMN network structure can effectively learn portrait features, so as to separate portrait from background. The network structure can be selected to take portrait as input and output the corresponding image mask. Experiments were conducted on Matting, EG1800, and P3M-10K public datasets, The results show that the SMN algorithm outperforms the current mainstream lite portrait segmentation methods in terms of Mean Intersection over Union (MIoU), accuracy (Acc), model consistency (Kappa) and segmentation coefficient (Dice). MIoU index can reach 97.61% (Matting), 94.31%(EG1800), 95.64%(P3M-10K), accuracy (Acc) can reach 98.22%(Matting), 97.27%(EG1800), 97.97% (P3M-10K). The accuracy has been significantly improved, and the target person can be clearly separated from the background. The final model size is only 3.1M, which is smaller than SegFormer-B0 11.1M. The model in this paper can be applied to mobile phone and other mobile devices. Each image can reach more than 30 fps when processed on the test dataset on the experimental platform.
Design and Testing on Migration of Remiss-Supply in Banking System to Microservice Architecture

The architectural migration of the banking service system from a monolithic architecture to a microservices architecture is now comprehensive. However, service applications that adapt to a monolithic architecture have many shortcomings at the time of development. This paper analyses, migration, and testing microservices architecture to meet the needs of banking services at PT. Bank Negara Indonesia with the scrum method. The Scrum method focuses on migration analysis, data inquiry, details inquiry, remis-supply, deployment, and testing. The test results on migrating banking services to microservices can be applied and have non-constant performance.
Quality Enhancement of Abstractive Text Summaries with Content Selection

Abstractive text summarization focuses on generating summaries by preserving the vital information from the source document. The content selector module proposed in this paper takes the source document's top $n$ sentences and feeds them into the abstractive text summarization model to produce quality summaries. When combined with the content selector module, the abstractive text summarization model produces summaries that are semantically equivalent to the reference summaries. We use the ROUGE evaluation metric and human evaluation to assess the abstractive text summarization model's summaries with and without the content selector module. We found that the abstractive text summarization models using the content selector module provide summaries on par with those generated by humans.
Evaluation of Associative Learning Based on Functional Connectivity in a Cultured Neuronal Network

In the brain, a large number of neurons interact with each other, and learning and memory take place while changing their functional structure. Therefore, in order to elucidate the mechanism of information processing in the brain, analysis focusing on functional connectivity between cells is necessary. In this study, we attempted to extract functional connections in the cultured neuronal network by repeated stimulation. In order to improve the accuracy of the extraction, we used a culture medium with a higher concentration of MgC1. As a result, the graph constructed by the extracted functional connections had small-world characteristics, indicating that this method may be effective for analyzing functional connections. In addition, we attempted to express the basic learning, associative learning, in the neuronal network. It was confirmed that repeated application of simultaneous stimuli enhanced the similarity of evoked responses by the two stimuli. These results indicate that learning is expressed by changes in functional connections, and that analysis of the specific changes in connections may help to elucidate the mechanism of information representation.
Ergodic Secrecy Capacity Performance in RIS-Enabled Wireless Communications

In this paper, we investigate the secrecy performance of a secure wireless communication system configured with reconfigurable intelligent surfaces (RIS). In particular, a legitimate source sends its private message to a legitimate destination with the aid of an RIS, while a passive eavesdropper intercepts this private information from both source and RIS. Considering this system setup, we first study the statistical analysis of the instantaneous signal-to-noise ratios (SNRs) at the destination and eavesdropper by exploiting the method of moments. Then, we deduce the closed-form expression of the ergodic secrecy capacity (ESC) over independent and identically distributed Rayleigh fading channels. Further, we provide the asymptotic ESC analysis in the high SNR regime to reveal some insightful information. Using the asymptotic ESC analysis, we also analytically present the impact of number of RIS elements on the ESC performance. Lastly, the analytical findings are corroborated via numerical and simulation studies, and show the impact of various involved parameters on the ESC performance.
RRAM-based Neuromorphic Computing: Data Representation, Architecture, Logic, and Programming

RRAM crossbars provide a promising hardware plat-form to accelerate matrix-vector multiplication in deep neural networks (DNNs). To exploit the efficiency of RRAM crossbars, extensive research ex-amining architecture, data representation, logic de-sign as well as device programming should be conducted. This extensive scope of research aspects is enabled and required by the versatility of RRAM cells and their organization in a computing system. These research aspects affect or benefit each other. Therefore, they should be considered systematically to achieve an efficient design in terms of design complexity and computational performance in accelerating DNNs. In this paper, we illustrate study exam-ples on these perspectives on RRAM crossbars, in-cluding data representation with pulse widths, archi-tecture improvement, implementation of logic functions using RRAM cells, and efficient programming of RRAM devices for accelerating DNNs.
Modular Wearable Textile Antenna with Pattern-Interchangeability using Snap-on Buttons

A dual-band wearable textile patch antenna with modular pattern interchangeability is proposed in this paper. The antenna simultaneously covers the 2.45 and 5.8 GHz Industrial, Scientific and Medical (ISM) radio bands. By utilizing detachable shorting connections made of metallic snap-on buttons, the proposed antenna can be set up to operate either with broadside or omnidirectional radiation patterns at 2.45 GHz, while the radiation characteristics at 5.8 GHz remain unchanged. The proposed modular antenna is fabricated and measured to validate the concept.
Network-based Intrusion Detection Datasets: A Survey

The increasing reliance on computers and the internet has increased the need for protection against attacks and security threats. A network-based intrusion detection system (NIDS) is one of the promising research fields that could highly contribute to cyber-attacks detection and mitigation. Datasets are necessary for NIDS training and evaluation. This paper provides a comprehensive survey of publicly available NIDS datasets. The study covers the most used NIDS datasets, each dataset is briefly described along with its main properties and shortcomings. Moreover, the paper highlights the most important dataset characteristics and provides a clear datasets comparison accordingly. Finally, the paper discusses observations and provides some recommendations to help researchers identify future work.
An open-source framework for cross-platform testing in agile projects

Agile methodologies have become popular in the software development industry because they improve the interaction among stakeholders, developers and project managers by achieving development results through shorter delivery times. One of the main problem with agile methodologies is the quality and the quantity of testing tasks to be performed, which in many cases are focused on unitary testing. This causes different issues for agile projects at the integration of sub-systems or the acceptance of the products developed. Continuous integration (CI) technique is commonly used to avoid some integration problems because it improves the validation of the software products by means of the automatic execution of different test cases at the moment of updating a software product located in a version control platform. Nevertheless, these test cases are manly focused on unitary testing and they are created for specific platforms. This paper presents an open source framework to improve continuous integration in order to execute acceptance test cases automatically. In particular, we use Behavioral Driven Development (BDD) to create the test cases that can be used with web and mobile drivers for different platforms. The framework proposed has been evaluated obtaining promising results.
Finite conductivity uniform GTD versus knife edge diffraction in prediction of propagation path loss

Diffraction propagation over hills and ridges at VHF and UHF is commonly estimated using Fresnel knife edge diffraction. This approach has the advantage of simplicity, and for many geometries yields accurate results. However, since it neglects the shape and composition of the diffracting surface, it can in some cases yield results which are in serious disagreement with measurements. To remedy this, attempts have been made to approximate the diffracting hill or ridge by other shapes, most notably cylinders. These approaches have not been widely adopted, due in large part to their greater numerical complexity. In this paper it is proposed to apply wedge diffraction in the format of the geometrical theory of diffraction (GTD), modified to include finite conductivity and local surface roughness effects. It is shown that, for geometries with grazing incidence and/or diffraction angles, significant improvement in accuracy is obtained. Further, the GTD wedge diffraction form used is based on the Fresnel integral, so that it is only slightly more complex numerically than knife edge diffraction. Finally, the GTD includes reflections from the sides of the ridge (wedge faces), and can be extended to multiple ridge diffraction and three-dimensional terrain variations.
Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins.
Non-Contact Breathing Signal Classification Using Hybrid Scalogram Image Representation Feature

When monitoring human vital signs, breathing is one of the most critical physiological metrics. In areas with limited resources and a shortage of trained medical professionals, automated analysis of abnormal breathing patterns may prove advantageous to healthcare systems. In this paper, we implemented the architecture of five transfer learning models to classify individuals’ breathing patterns using our proposed method which uses hybrid scalogram image-based features. We implemented the Sleep Breathing Detection Algorithm (SBDA) for extracting the actual breathing signals from ultra-wideband (UWB) radar for the pre-processing method. Later, the signals were converted to hybrid scalogram image-based representations before being classified using the VGG16, DenseNet, Xception, ResNet, and MobileNet models. The performance of the proposed method was validated using two other image representations: a standard image and a spectrogram image. The overall result showed that the proposed method obtained the highest classification accuracy on the test set for all pre-trained models.
Glycan Immunogenicity Prediction with Efficient Automatic Graph Neural Network

Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of glycans, previous research treats the glycans graph structure as a topological graph to represent glycans data effectively. Graph neural networks (GNNs) are an efficient graph mining method and have many applications in bioinformatics. Therefore, researchers have successfully used handcrafted GNNs to predict glycan immunogenicity. However, a GNN architecture contains many different components, and designing GNN architectures for specific graphs in the bioinformatics field is time-consuming and expert-dependent. To address this challenge, we propose an efficient automatic graph neural network method called EAGNN that can efficiently and automatically construct GNN architecture for glycan immunogenicity prediction. We design an effective graph attention pooling (GAP) search space. We use differential architecture search to efficiently create the optimal GNN architecture in the search space to build the GNN model for glycan immunogenicity prediction. We test EAGNN on the data set SugarBase based on the glycan immunogenicity prediction task. The experiment results show that EAGNN can work more superiorly than the baseline model and achieve the best performance.
A QSPI Interface Supporting ‘Bits-Decoding’ for High-Speed Access to Flash

A QSPI interface is proposed for accessing QSPI flash in this paper. This proposed QSPI interface transmission information for the master supports access to 3 types of flash by using the Bit-decoding algorithm, that is, the data is received from MSB to LSB in order into the master. This QSPI interface includes SPI mode, DSPI mode and QSPI mode. The SPI mode has 4 clock modes to switch, for receiving and transmitting the information. The DSPI mode and QSPI mode is utilized when needed for high-speed access to flash. Based on the QSPI transmission protocol, a finite state machine is used in this QSPI interface design, which to control the transmission timing. The design is verified through RTL simulation. The simulation result shows the correct functions and transports stable data.