Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Semantic Segmentation Models with and without Attention for White Blood Cells Segmentation

The identification of blood cancer or leukemia in its early stages poses a significant challenge within the medical field. Timely diagnosis of this disease is essential for the successful implementation of treatment. Hematologists frequently encounter a surplus of data that necessitates meticulous evaluation to make informed decisions. The utilization of deep learning methodologies has the potential to aid Hematologists in the comprehensive analysis and accurate diagnosis of white blood cells. This paper conducts a series of experiments on popular used segmentation models namely U-Net, SegNet, Attention U-Net, Swin-Unet, aiming to evaluate their efficacy in extracting white blood cell nuclei. The findings indicate that the SegNet model exhibits the least amount of loss and have the highest Dice score for the purpose of segmenting nuclei from the RAABIN-WBC dataset.
MLGAN: a Meta-Learning based Generative Adversarial Network adapter for rare disease differentiation tasks

Rare disease diagnosis is very challenging due to the rarity and lack of scientific knowledge. Many patients with rare diseases take years to get diagnosed and many stay misdiagnosed or are not diagnosed. Comparing with traditional diagnosis prediction task, rare disease detection has the unique challenges of low prevalence and label noise. In this paper, we propose Meta-Learning based Generative Adversarial Network module MLGAN, a rare disease detection enhancement module that can adapt any existing diagnosis prediction methods to rare disease detection task. We use generative adversarial network to generate synthetic positive em-beddings and we use Meta-Weight-Net to automatically assign weight to real data and synthetic data. MLGAN helps us to leverage the time-aware sequential modeling ability in diagnosis prediction methods, and also mitigate the low prevalence and label noise of rare disease dataset. We empirically show that MLGAN can greatly boost the prediction performance and have good robustness on four real-world rare disease datasets. We release our code at https://github.com/ruilialice/MLGAN.
Enabling the Informed Patient Paradigm with Secure and Personalized Medical Question Answering

Quality patient care is a complex and multifaceted problem requiring the integration of data from multiple sources. We propose Medicient, a knowledge-graph-based question answering system that processes heterogeneous data sources, including patient health records, drug databases, and medical literature, into a unified knowledge graph with zero training. The knowledge graph is then utilized to provide personalized recommendations for treatment or medication. The system leverages the power of large language models for question understanding and natural language response generation, while hiding sensitive patient information. We compare our system to a large language model (ChatGPT), which does not have access to patient health records, and show that our system provides better recommendations. This study contributes to a growing body of research on knowledge graphs and their applications in healthcare.
Arduino based smart home security design using biometric recognition

Abstract As a standard feature, smart homes have a lock door that requires a password to open. However, burglars can simply defeat this security measure, and it is impossible to identify the user or person who opens the lock door. The authors are interested in building a system with the name research design of an Arduino-based smart home security system employing biometric recognition based on the aforementioned issues. The purpose of this study is to identify the performance requirements and design of an Arduino-based biometric home security system. This study is an engineering study that tries to identify the performance and design requirements for an Arduino-based smart home security system. The mechanical shape of the tool was created using the performance specification. The properties of the sensor, as well as the accuracy and precision of the tool, serve as the basis for the design specifications. The precision value of each sensor, namely the fingerprint sensor, is extremely good, while the precision values of the PIR sensor and magnetic sensor are 99.56% and 97.87%, respectively. The accuracy value of the sensor on the tool, primarily the fingerprint sensor, is very good.
STEM integrated electronic student worksheet to promote conceptual understanding and literacy skills of students

Abstract The development of the Industrial Revolution 4.0 era requires students to have good literacy skills. The preliminary investigations at Senior High School 9 Padang showed that students’ literacy skills were still relatively low. This study investigated the effect of STEM integrated electronic student worksheet (STEM integrated ESW) to promote students’ conceptual understanding and literacy skills. We employed quasi-experimental research with a post-test-only design. A sample consist of 71 students was taken using the cluster random sampling technique. The post-test items and the literacy performance assessment sheet were used as data collection instruments. Data were analyzed using a comparison test of the two means of independent groups. The research revealed that the test on the conceptual understanding aspect obtained a t-test of 5.817. Aspects of data literacy skills obtained z test -6.085. In the aspect of technological literacy skills, the t-test value was obtained at 8.596. The analysis of students’ conceptual understanding and literacy skills showed that the experimental class average was superior to the control class. Thus, STEM integrated ESW has given significantly effects on students’ conceptual understanding and literacy skills. It is hoped that further researchers can develop similar products with different topics to optimize students’ literacy skills.
Validity of E-Book Based on Problem-Based Learning with Higher Order Thinking Skills Oriented for Senior High School

Abstract Human resources with a high level of thinking abilities, such as critical thinking, creativity, communication, teamwork, independence, cross-cultural understanding, and ICT literacy, are needed for the growth of the 21 st century. However, cultivating a high level of thinking abilities is still insufficient and does not fulfill the necessities of the 21 st century. An e-book based on problem-based learning and oriented toward higher-order thinking skills is developed as a result of this research. Measurement of the e-book’s validity is the aim of this study. This research used a descriptive methodology and a questionnaire for collecting data. The questionnaire instrument is a validation sheet with four e-book validation criteria consisting of several indicators. E-book validation has shown valid findings with an average of 0.87 valid categories. The results of the research show that the product can be used as a learning resource for students and teachers to achieve learning goals and meet the demands of the 21 st century.
A flood detection system and flood door control based on Arduino

Abstract Flooding has become one of the most serious problems for Indonesians, particularly in flood-prone areas. Floods can be hazardous to the community, particularly if they occur suddenly, at night, or at unpredictable times. The answer to these problems is the installation of a flood detection system and a flood barrier door control system. In this paper, a flood detection system and flood door control based on will be presented. Ultrasonic sensors monitor the air level, a SIM800L module delivers signals, a stepper motor operates the door, an LCD shows the air level, and a buzzer offers an immediate warning. The accuracy and precision of the ultrasonic sensor are 98.83% and 99.40%, respectively. Based on the accuracy and precision of the system, it can be concluded that the system can be used for flood control systems.
Unleashing Stable Diffusion for Multi-Modal Facial Image Augmentation

In this paper, we propose a novel method that combines Stable Diffusion (SD), ControlNet, and ChatGPT to enhance the FFHQ-Aging dataset. Our approach leverages Stable Diffusion to generate realistic and high-quality synthetic images, while ChatGPT prompts enable the generation of diverse facial expressions, ethnicities, and lighting variations. The ControlNet plays a crucial role in controlling facial pose variations during the augmentation process. We carefully design the parameters of the diffusion model and ControlNet, and provide appropriate prompts to preserve desired facial attributes while introducing pose variations. Furthermore, we introduce an advanced model that extends our previous work by simultaneously performing age transformation and face reenactment. This model enables comprehensive facial transformation and provides a versatile tool for various applications. Our experimental results demonstrate the effectiveness of our proposed method in improving age transformation and face reenactment tasks.
Sensor Fusion Estimation for Omni-Directional Vehicle with Mecanum Wheel

In this research, an omni-directional vehicle using mecanum wheel is estimated for its current states, through fusion of its measured motor rotation speed, and its acquired velocity from accelerometer. Simulation utilizing MATLAB, as the two sensor measurements are fused using a Kalman filter. Both the omnidirectional vehicle’s kinematic and dynamic model is used for the sensor fusion. The fusion process considers both the rotational speed of each wheel as well as the measured speed of the center of mass of the vehicle, and taken into account of all the kinematics model and the dynamics model, to estimate a reliable omnidirectional vehicle state.
Drone Control through Deep Learning based on Pose Recognition

This research introduces an innovative approach to personal video capture using Unmanned Aerial Vehicles (UAVs) with pose recognition. The primary aim is to automate the video capture process for high-quality, self-captured videos. A deep learning algorithm was employed to develop a pose recognition model for real-time detection of an individual’s pose. This model was integrated into the UAV’s control system, enabling autonomous navigation. The trained model showed significant pose prediction accuracy (80.35%) within the training location with minor background changes. However, performance diminished under different perspectives (50.02%), locations (62.39%), and low-light conditions (29.44%). A prediction filter was implemented to enhance performance, enabling drone movement only after consecutive identical pose prediction. The findings demonstrate the effectiveness of pose recognition in capturing high-quality video footage autonomously. The integration of this technology in UAVs presents several advantages for self-capturing video, including unique perspectives and distinct angles without the need for an operator, leading to superior footage quality. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
THE USE OF BOOK CREATOR IN THE BLENDED LEARNING MODEL IN SOCIAL STUDIES LEARNING

This research used a descriptive qualitative approach. The instruments of collecting data in this research included observation, interview, and documentation. Observation was used to gain the data including students ‘condition in participating social science teaching and learning process as data. The population of this research was the seventh grade students including and all of the number of the population was 298 students, then the researcher took only two classes (67 students) as the purposive sampling because it represented 20%-25% from all the population. Then observation and documentation was used to gain the planning and the steps of implementing of teaching model of blended learning using book creator in the materials the role of information and technology in the economics activities in order to increase the students’ learning activity of the seventh grade of Senior High School 3 Kedungwaru. Next, the interview was used the students’ responses on the implementation of teaching model of blended learning using book creator in the materials of the role of information and technology in the economics activities in order to increase the students’ learning activity of the seventh grade of Senior High School 3 Kedungwaru. The result of study showed that theimplementation of teaching using blended learning based on book creator of the role of information and technology in the economics activities could create a new learning in teaching social science. Instead of that condition, the students were involved actively in discussion activity and presentation, and then the result of discussion of the role of information and technology in the economics activities could be conducted by altogether with the classmates smoothly. Furthermore, they supposed that teaching and learning of social science using teaching model blended learning based on book creator could ease the students to understand the material of teaching learning social science.
An Enhanced Side Channel Secured PRESENT Light Weight Cipher for Smart Devices

The Internet of Things (IoT) is a community of interconnected gadgets that could alternate records over the Internet without human intervention. The smart life did have many advantages, but it also had security vulnerabilities which are exploited by code breakers to violate security standards. Side channel attacks are dangerous attacks that work mainly using device operation characteristics like power used, noise, magnetic fields, etc. IoT devices commonly use lightweight cryptographic algorithms because of their less complexity, which makes them an obvious target for attacks. PRESENT, a lightweight block cipher algorithm was primarily created for these IoT applications which have resource limitations. Several Countermeasures have been Identified at various levels of Abstraction. The Masked-Tdpl (m-Tdpl)is one such new gate-level technique resistive against DPA and fault attacks. In order to maintain the algorithm as lightweight, the security overhead on the implementation of PRESENT using the proposed design is reduced by various optimization techniques. The PRESENT algorithm is implemented using the proposed technique using Cadence 180 nm Technology and the results were analyzed. The Number of Gates consumed is reduced by 3.5 % respectively. In addition to that, Using the proposed technique flexible key scheduling structure for Overall Physical Hardware Implementation of PRESENT with less gate Equivalent has been proposed.
Advancements in Speech Recognition Technology: A Cutting-Edge Tool for Improved Speech Analysis and Interaction

The concept of controlling devices through voice commands has always fascinated humans, and after years of research, speech recognition systems have become widely adopted across various industries. Achieving high accuracy in voice recognition is a key focus of ongoing research, addressing factors such as background noise, linguistic and speaker diversity, vocabulary size, and domain specificity. This white paper explores the main challenges associated with developing speech recognition systems, including language classes and representations, language modeling techniques, feature extraction methods, databases, and performance evaluation. Additionally, it delineates the front-end and back-end components of the system for better comprehension and presentation. With an increasing number of people accessing the internet, a significant percentage, approximately 50% globally, face difficulties due to various impairments. These challenges go beyond data limitations and extend to actual usability issues. This paper discusses the existing problems with the website's modules, particularly in content segmentation and font accessibility for users with vision impairments. As an alternative, a continuous scrolling page format is proposed to improve information presentation and user experience.
Crime Scene Object Detection from Surveillance Video by using Tiny YOLO Algorithm

In recent years, law enforcement agencies and security professionals have rewarded much attention to using artificial intelligence for criminal detection based on video surveillance. The ability of deep learning (DL) models to automatically detect and follow prospective offenders saves time and money for law enforcement organization signs by allowing them to understand complicated patterns from data. This helps them conduct in-depth probes and direct their search efforts more precisely. Bladed crimes, such as swords, daggers, knives, and bayonets, and portable firearms, such as pistols, Hand gun or carbines, rifles, Kinfe and machine guns, are often found at crime scenes. In this research, a deep learning based surveillance system is proposed that is capable of identifying the presence of traced objects, such as handguns and knives, and potentially warning authorities of impending danger. Compared to DL-based object identification algorithms like the Enhanced single shot detector (ESSD) ImageNet and FRCNN (Faster Region-based convolutional neural networks), Tiny YOLO has the best real-time detection mean average precision and inference speed. Thus, proposed solution will incorporate YOLO.
Image Understanding of GUI Widgets for Test Reuse

Testing of UI after its development is an essential activity. Due to the complexity of the task of developing new tests for UI, it is an expensive one. Different approaches for reusing tests in similar scenarios have been created. These different methods employ different information available in the GUI to decide the appropriate transfer of tests from one UI to another. Similar GUI events are related together by these systems to decide the transferability of tests. The usage of semantic knowledge during the matching of GUI events has proven to be an important asset in understanding their similarities. The usage of textual information available in GUI and their semantic meanings in this task have promising results. However, the usage of semantic information that can be obtained from images and icons has remained unexplored. This study presents the integration of semantic annotation of icons, widgets and images for semantic matching of GUI events. Proposed findings demonstrate the importance of image understanding of GUI widgets in test reuse and offer practical insights for software developers and researchers. In addition to improving semantic matching, the integration of semantic annotation of icons, widgets, and images can also enhance the interpretability and maintainability of UI tests.
An Adaptive Thresholding Framework for Detecting the Subsurface Movements in Landslide-prone Sites

Landslides are mainly caused by heavy rainfall, soil erosion, changes in groundwater, earthquakes, and human activities such as deforestation, shifting cultivation, mining, and urbanization. Understanding surface and subsurface ground movements is crucial for developing effective landslide monitoring and early warning systems. One of the major challenges in developing such systems is the identification of the signal patterns that trigger landslides. Due to the weak nature of the landslide signals and the high interference of external noises, accurately detecting these signals can be difficult. Existing systems often rely on decision models with fixed thresholds, for event detection which may not be suitable for different landslide-prone areas. To address this issue, this paper proposes an adaptive thresholding mechanism that uses a Linear Energy-Based Detector and Gaussian Kernel to detect subsurface activities in landslide-prone sites. The onset arrivals of the subsurface activities are calculated using a recursive STA/LTA algorithm. This adaptive strategy can be used in sites with different environmental and climatic conditions and offers greater flexibility than fixed threshold systems. Using geophone sensors, the proposed methodology is tested by conducting experiments that capture subsurface ground movements caused by human footsteps and vehicular vibrations.
Role of Artificial Intelligence in Input Tax Credit Reconciliation

The Goods and Services Tax has significantly changed the economic structure of India and replaced the previous indirect taxation system. With the intention of reducing tax evasion, this new law simplifies the implementation procedure and makes tax credit technology more readily available. Prior to this time, there were several taxes, which encouraged tax evasion, increased compliance with government regulations, and burdened consumers. New statistical and analytical tools that offer speed and simplicity are now available to tax professionals. AI can help with fraud identification, which will help with governmental oversight and control. Taxes are a significant source of public finance in a growing nation like India. Taxpayers view about using artificial intelligence to power tax assessment. This study emphasizes how AI can simplify input tax credit reconciliation and future scope.
Bio-Inspired Optimization with Transfer Learning Based Crowd Density Detection on Sparse Environment

Crowd density estimation is a major importance for applications including crowd control, public space planning, autonomous driving, visual surveillance, and warning visually distrait drivers previous accident. With strong scale, reflective, and translational symmetry, techniques to estimate the density of the crowd yield a promising outcome. But dynamic scenes with constantly evolving spatial and temporal domains and perspective distortion yet have difficulties. The dynamic nature of scenes and the complexity of demonstrating and integrating the feature space of objects of different magnitudes as predictive prototypes are the primary reason for this. This manuscript presents a Red Fox Algorithm with Transfer Learning based Crowd Density Detection (RFOTL-CDD) technique in Sparse Environment. The purpose of the RFOTL-CDD system lies in the effectual identification and classification of distinct types of crowds in a sparse environment. To achieve this, the presented RFOTL-CDD method uses a ResNet prototype for feature vector generation. For the identification and classification of a crowd, the RFOTL-CDD technique applies Naive Bayes (NB) classifiers. In this work, the RFO algorithm is utilized for boosting the performance of the ResNet method. The stimulation outcomes of the RFOTL-CDD technique can be well studied on a crowd dataset and the outcomes confirmed the supremacy of the RFOTL-CDD technique on crowd detection.
Efficient Multi Authority Attribute Based Signcryption

The security of data kept in the cloud has been an issue of great concern in recent years. The data is taken from the system regardless of how robust the protection is. One of the most significant areas of study in the cloud storage industry has been the development of efficient fine-grained access methods. Confidentiality and authenticity for any type of data are provided via Attribute Based Encryption (ABE) and Attribute Based Signature (ABS). Attribute-Based Sign-Cryptography is a synthesis of these methods (ABSC). Many proposed fixes, however, fall short when applied to actual problems. This method has a high computational and communication cost and uses fewer third-party services. As a result, this research study develops a unique ABSC method to safeguard both the signcryptor's and the ciphertext's privacy while allowing public verification of the cipher's authenticity.