Reviewerly/exHarmony huggingface dataset:
Light Automatic Authentication of Data Transmission in 6G/IoT Healthcare System

IoT healthcare systems aim to help patients to monitor their own condition and connect and interact with healthcare professionals. However, patients' privacy and sensitive data security are big issues which should be considered. Healthcare systems absolutely depend on 6G technologies due to its important characteristics. This paper provides a light mutual authentication and a secure data transmission in 6G/IoT healthcare system. Our proposal takes into account the emergency cases and non-emergency ones. We introduce a light authentication and a secure health data transmission between health service provider and health user. Then, we proceed a mutual authentication algorithm between health user and health collaborator followed by a light key agreement establishment. We prove the security of the protocol against several knowns' attacks. Then, we compare our proposed solution with other related works, the findings show the performance in terms of communication and computational.
Weather Monitoring App

In this digital era, integrating Internet of Things (IoT) technology with mobile applications has revolutionised various industries, including weather monitoring. This project presents the development of a weather monitoring app that harnesses the power of IoT and Kotlin programming language. The app utilizes a weather API to obtain real-time weather data and seamlessly communicates with an IoT device, enabling users to access and display weather information conveniently. project begins with a detailed analysis of requirements, outlining the desired features and functionalities of the app. The weather API selection process is discussed, and the chosen API is integrated into the Kotlin app to fetch weather data. Additionally, location services are implemented to offer location-based weather updates. An IoT device, such as a Raspberry Pi or Arduino, is selected for displaying the weather data. The IoT device is connected to the internet and programmed to fetch weather data from the app. Communication protocols between the Android app and the IoT device are established, ensuring smooth data exchange. The app's user interface is designed to provide an intuitive experience for users, with weather data and location information prominently displayed. Error handling mechanisms are implemented to deal with potential API downtimes or communication failures between the app and the IoT device. Through comprehensive testing, the app's functionality, performance, and reliability are assessed. Security measures are implemented to safeguard user data and privacy during data transmission. The result is a robust and user-friendly weather-monitoring app that seamlessly integrates IoT technology with Kotlin's efficiency and flexibility. The app empowers users to access real-time weather updates, enhancing their preparedness for weather changes and enabling informed decision-making for various outdoor activities. With the proliferation of IoT and its impact on various sectors, this project serves as a valuable example of leveraging IoT and Kotlin to develop innovative mobile applications for everyday use. Keywords – Weather Monitoring App, Kotlin, IoT, Real- time Weather Data, Weather API
Understanding User Needs, Persona Scenarios for Privacy-Preserving Visual System Development

As the world’s population ages, the demand for active and assisted living technologies that can support older adults maintain their independence, health, and quality of life is increasing. Video monitoring cameras can provide a sense of safety and peace of mind for both older adults and their caregivers. However, these visual sensing systems come with major privacy concerns. Researchers have developed various visual privacy preservation filters that can be used for video-based monitoring technology, such as blurring, pixelation, silhouette, or avatar. To understand the user’s needs and fine-tune the system to their preferences, the persona scenario method was employed in this study. The goal-directed approach to persona design was followed. This scenario-based technique involves creating fictitious persona archetypes that represent the unique characteristics, needs, and goals of the target user group and other stakeholders involved in the process of care provision. A set of eight personas were created based on the qualitative data collected through interviews and focus groups in Spain. Data from 62 participants were analyzed, which represented different contributor groups such as older adults, direct caregivers, healthcare experts, and other stakeholders. The final personas are accessible to the public on a Blueprint persona repository.
Hole Detection in Plastic Mulch Using Template Matching and Machine Learning Algorithms

Mulch is a ground cover material to maintain soil moisture and temperature stability as a plant medium. Mulch also helps prevent weed growth for better plant growth. For planting with plastic mulch, farmers need to make holes in the mulch the day before planting. Precision agriculture is needed because it can obtain savings in input financing, labor, and better yields, so this research aims to identify holes in mulch based on Unmanned Aerial Vehicle images. The advantage of this research is that it can monitor each plant based on the mulch holes, and the number of holes identified can be used as a parameter to estimate the amount of crop production. This research combines Template Matching Algorithm and Machine Learning Algorithm to improve accuracy in predicting holes in mulch. Three machine learning algorithms are used, namely the Random Forest, Support Vector Machine, and XGBoost. The data used is an orthophoto mosaic from aerial photographs. Nine areas were taken from orthophotos to be used as research samples. The results of this study obtained the highest average recall, precision, and f-measure values using the Support Vector Machine algorithm with a recall value of 87.7%, precision of 97.5%, and f-score of 92.3%. This research focuses on reducing detected commission errors. Therefore, omission errors were still detected in the damaged or leaf-covered holes.
An xAI Approach for Data-to-Text Processing with ASP

The generation of natural language text from data series gained renewed interest among AI research goals.Not surprisingly, the few proposals in the state of the art are based on training some system, in order to produce a text that describes and that is coherent to the data provided as input.Main challenges of such approaches are the proper identification of what to say (the key descriptive elements to be addressed in the data) and how to say: the correspondence and accuracy between data and text, the presence of contradictions/redundancy in the text, the control of the amount of synthesis.This paper presents a framework that is compliant with xAI requirements.In particular we model ASP/Python programs that enable an explicit control of accuracy errors and amount of synthesis, with proven optimal solutions.The text description is hierarchically organized, in a top-down structure where text is enriched with further details, according to logic rules.The generation of natural language descriptions' structure is also managed by logic rules.
Lightweight Video Frame Interpolation Based on Bidirectional Attention Module

Video frame interpolation can enhance the frame rate and improve video quality by synthesizing non-existing intermediate frames between two consecutive frames. Recently, remarkable advances have been made due to the employment of convolutional neural networks. However, most existing methods suffer from motion blur and artifacts when handling the case of large motion and occlusion. To solve the problem, we propose a lightweight but effective deep neural network which is trained end-to-end. Specifically, the bidirectional attention module is first devised to enhance motion-related features representation in both channel and spatial dimensions. Then the synthesis network estimates kernel weights, visibility map and offset vectors to finally generate the interpolation results. Moreover, to compress the model, we introduce the Ghost module to the synthesis network which is verified to be highly effective. Experiment results demonstrate that our proposed architecture performs favorably against state-of-the-art frame interpolation methods on various public video datasets.
Detecting Ethereum Phishing Scams with Temporal Motif Features of Subgraph

In recent years, Ethereum has become a hotspot for criminal activities such as phishing scams that seriously compromise Ethereum transaction security. However, existing methods cannot accurately model Ethereum transaction data and make full use of the temporal structure information and basic account features. In this paper, we propose an Ethereum phishing detection framework based on temporal motif features. By designing a sampling method, we convert labeled Ethereum addresses into multi-directed transaction subgraphs with time and amount to avoid losing structure and attribute information. To learn representations for subgraphs, we define and extract the temporal motif features and general transaction features. Extensive experiments on Support Vector Machine, Random Forest, Logistic Regression, and XGBoost demonstrate that our method significantly outperforms all baselines and provides an effective phishing scams detection for Ethereum.
Demonstrating (Hybrid) Active Logic Documents and the Ciao Prolog Playground, and an Application to Verification Tutorials

We propose a demonstration of the Active Logic Documents (ALDs) approach and the Ciao Playground, as well as a recent extension to ALDs to facilitate the integration of other tools into the system for creating Hybrid Active Logic Documents (HALD), and a concrete application of these technologies.
An Efficient Polarization Code-Multiplexed 3-D Shape Measurement Method for High-Dynamic Range Objects

Fringe Projection Profilometry (FPP) method is intensively studied and widely used for its high precision and good anti-noise ability. In practical 3D measurement applications, most metal products do not have the ideal Lambertian surfaces. In addition, too many projected patterns complicate the application of such strategies while improving robustness. We propose a polarization code-multiplexed phase shift (PMPS) method to overcome the above challenges. Our method uses the polarization state of light to assist encoding. The phase is robustly unwrapped with complementary gray code information in the polarization fringe patterns. Compared to the traditional methods, the PMPS method can efficiently and robustly reconstruct metallic objects under the influence of ambient light with half the number of projected patterns.
SunScreen: Visual Fault Detection for Solar-Thermal Systems

Fault detection is essential to ensure the proper operation of solar-thermal plants. Hence, monitoring personnel frequently analyze the data to detect unusual behavior. While visualization approaches may considerably support the monitoring of personnel during their work, no existing application can yet deal with the multivariate and time-dependent sensor data, or does not fully support the users' workflow. Thus, this work introduces the visual framework SunScreen. It allows users to explore the sensor data, automatically detected anomalies, and system events (e.g., already detected faults and services). The feedback from the users shows that they appreciate the tool and especially its annotation functionality. However, the system-usability-scale (SUS) results indicate that it does not meet all requirements yet.
Distortion-Independent Pairwise Underwater Image Perceptual Quality Comparison

Ranking underwater images according to their quality is a key indicator comparing the performance of different methodologies and therefore is critical in the field of instrumentation and measurement. Perceiving differences in the quality of underwater images is a challenging task that has received relatively less attention, primarily due to mixed distortion, diversified image content, and the absence of high-quality reference images. Thus, based on the pairwise underwater image quality voting data, we develop a novel ternary classification transformer to predict a quality comparison of underwater images without reference. This is the first attempt to model the quality discrimination of an image pair. The proposed model combines the perception of convolutional neural networks and Transformer encoder to explore local quality features and visual perceptual connections between different patch tokens. Experimental results reveal that the proposed pairwise underwater image quality comparison (PUIQC) scheme predicts noticeable quality differences correlating well with subjective perception. The quantification of complex distortions in underwater images compared to other learning-based methods is a compelling feature of this technology. It delivers competitive results in ranking the different enhancement outputs. In addition, we reveal the self-attention of local quality features within the two images and capture their responsive contribution to the quality decision, which explains the underlying subjective quality-sensitive mechanism during image quality comparison.
Deep Learning Based Parameter Estimation of Frequency Shift Keying LPI Radars

LPI radars aim to both perform their own functions and not be detected by their targets by transmitting at low power. For this purpose, they usually perform frequency modulation (linear incremental, triangular, frequency shift keying, etc.) within the signal. Due to the development of cognitive electronic warfare systems, it is essential to characterize the behavior of these radars automatically. We propose three different deep learning models to solve the less studied automatic parameter estimation problem compared to the LPI radar frequency modulation classification problem, which has been extensively researched in the literature. We designed these models as convolutional, recursive, and hybrid to predict frequency jump points and frequency values in the signal. In the numerical analysis, we evaluated the network performances in terms of correct detection, false alarm, and mean square error metrics. As a result, we saw that the hybrid model, which uses convolutional and recursive networks together, achieved the highest performance and we determined that the main contribution to this success came from the convolutional network.
Extreme Learning Machines for Detecting the Water Quality for Human Consumption

Determining the potability of water for consumption is crucial for human health. To assess the water quality, levels of minerals and ions are measured, such as pH, hardness, sodium, chloramines, sulfate, conductivity, organic carbon, tri-halomethanes, and turbidity. To achieve this more efficiently and accurately, techniques of Machine Learning (ML) and Deep Learning (DL) have been applied, with deep neural networks being one of the most popular methods. In this study, Extreme Learning Machines (ELM) are evaluated for the first time, including the standard ELM, the Regularized ELM, the weighted ELMs in configurations 1 and 2, and the multi-layer ELM. Accuracy and the G-mean were used to extensively compare the results and it was found that the weighted ELM 1 is the most recommended algorithm for the binary classification of the potability of water for human consumption, with an accuracy of 75.8% and a G-mean of 80.6%. The feasibility of using ELMs to determine the potability of water is thus demonstrated, as they offer acceptable performance and low computational cost for training.
Evaluation of Extreme Learning Machines for Detecting Heart Diseases

Currently, cardiovascular diseases are the leading cause of human death according to the World Health Organization. Their prediction allows doctors to indicate preventive measures to their patients and perform procedures on time. In this research, the performance of different Extreme Learning Machine (ELM)-based algorithms applied to the binary classification problem of the heart's state (healthy or sick) was evaluated. The following ELMs were used: the basic model, regularized, weighted, and multi-layer. The experiments were carried out in a MATLAB programming environment and a mid-range laptop. To evaluate the models' performance, the accuracy (Acc), the geometric mean (G-mean), and the execution time of the algorithms were used, comparing the results with other classifiers reported in the literature. In this research, it is proposed to use a Weighted ELM (W1-ELM) due to its acceptable accuracy of 0.81 and its low training complexity compared to deeper models such as Convolutional Neural Networks.
Radar Intrapulse Modulation Classification with Deep Learning

Identification of threat radar signals is extremely important for electronic support systems. Radar intrapulse modulations are used by electronic support systems to identify radar systems. In this work, radar intrapulse modulations are classified using signal processing and deep learning techniques. Short Time Fourier Transform, Fourier Synchrosqueezed Transform and Smoothed Pseudo Wigner Ville Distrubution are used to extract time-frequency images of radar signals. After feature extraction, convolutional neural network is used as classifier and %98 modulation classification performance is achieved at 0 dB signal to noise ratio.
LDA Topic Modeling on Twitter Data Concerning Immigrants and Refugees

In this study, the attitudes and opinions of Twitter users in Turkey towards immigrants have been examined to see how people express their thoughts and opinions about immigrants in Turkey and whether there are any dominant and interpretable topics that emerge. After a comprehensive pre-preprocessing, latent themes in the tweets are discovered using the Latent Dirichlet Allocation (LDA) topic modeling methodology. As the result of this analysis, 14 topics have emerged as meaningful and interpretable. The study is done over a small dataset and is somewhat limited; however, the results can shed light on the perspectives of Twitter users towards immigrants and refugees.
Gaze Estimation by Attention Using a Two-Stream Regression Network

Determining the point of view of people is an important human-computer interaction problem that has been studied for a long time. This subject, which has many applications, is used in different fields such as marketing, automotive, medical, games and entertainment. In this study, we propose a remote eye tracking method that makes gaze estimation using convolutional neural network based on regression. The proposed method uses a two-stream deep learning architecture that utilizes eye images and iris segmentation masks obtained through segmentation neural network. The architecture employed selective attention-based mechanisms to enhance its performance. Experimental results demonstrate that the attention-based two-stream architecture outperforms both single-stream deep learning architectures and architectures without attention mechanisms.
Single or Double: On Number of Classifier Layers for Small Language Models

Pretrained transformer language models such as BERT have become very popular among NLP practitioners, and as such have been applied to a variety of tasks. BERT's strength derives from producing contextual vector representations for text, which are then traditionally used with a linear classifier (a single fully connected layer). One of the less famous NLP tasks that BERT has been applied to is address parsing, which deals with breaking an address into its various components, such as street name or door number. In an earlier study on address parsing it was observed that replacing the traditional single layer linear classifier with a double layer MLP (Multi-Layer Perceptron) could yield an increase in performance metrics if the language model providing the underlying representation had low capacity (as assumed by its low number of parameters). In this study, we examine whether this observation carries to the much better known NLP task of text classification.
Classification in dynamic videos via a slow-fast network

For a long time, classification in dynamic videos on public datasets have been a hot research topic. However, in medical image processing, many methods still use traditional machine learning methods to address the problem of cellular dynamics classification. In this study, we paid more attention in the morphological changes of lymphocytes in healthy mice after skin transplantation. We applied a typical deep learning network Slow-Fast into cellular dynamics classification task, in which we incorporated data augmentation techniques such as rotation and reverse ordering to alleviate the overfitting problem to a great extent. Our method achieved better performance on the live-cell video dataset compared to previous approaches.
Application of Construct on Scaffold Concept Map in Mobile Programming Learning with Flutter Layout Topic

Flutter is a framework for making mobile applications cross-platform made by Google. From 2019 to 2021 the popularity of flutter is increasing. Flutter use declarative writing style to create layouts. This makes the layout in flutter immutable, and a light blueprint. This research proposes a construct on scaffold concept map method to help students understand the concept of widget arrangement in flutter layout. Construct on scaffold will provide the learner with a framework from an incomplete expert concept map. Some of the nodes and connecting relationships in the framework have been removed, so students must fill in the missing parts with several available answer choices to complete the concept map. To prove the impact of the application of this method, the study was conducted using a pre-post-test group experimental design. Students will do a pre-test, use the EasyFlutter application, and post-test. The results of the pre-test and post-test obtained were tested for normality first, then tested to find out whether there was an average difference between the pre-test and post-test scores. The results of the normality test show that the pre-test data are not normally distributed, and the post-test data are normally distributed, so the next test will use a non-parametric test, namely the Wilcoxon test. The test results show that the post-test mean score is higher than the pre-test mean. Wilcoxon test results also show that the Asymp value. Sig. (2-tailed) of 0.01, so it can be concluded that there is a significant difference between the pre-test and post-test scores. The application of the construct on scaffold method has a significant positive impact on the post-test scores of students related to the concept of widget arrangement in flutter.