A Generative Approach to Person Reidentification

Abstract: Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.


Keyboard Data Protection Technique Using GAN in Password-Based User Authentication: Based on C/D Bit Vulnerability

Abstract: In computer systems, user authentication technology is required to identify users who use computers. In modern times, various user authentication technologies, including strong security features based on ownership, such as certificates and security cards, have been introduced. Nevertheless, password-based authentication technology is currently mainly used due to its convenience of use and ease of implementation. However, according to Verizon’s “2022 Data Breach Investigations Report”, among all security incidents, security incidents caused by password exposures accounted for 82%. Hence, the security of password authentication technology is important. Consequently, this article analyzes prior research on keyboard data attacks and defense techniques to draw the fundamental reasons for keyboard data attacks and derive countermeasures. The first prior research is about stealing keyboard data, an attack that uses machine learning to steal keyboard data to overcome the limitations of a C/D bit attack. The second prior research is an attack technique that steals keyboard data more efficiently by expanding the features of machine learning used in the first prior research. In this article, based on previous research findings, we proposed a keyboard data protection technique using GAN, a Generative Adversarial Network, and verified its feasibility. To summarize the results of performance evaluation with previous research, the machine learning-based keyboard data attack based on the prior research exhibited a 96.7% attack success rate, while the study’s proposed method significantly decreased the attack success rate by approximately 13%. Notably, in all experiments, the average decrease in the keyboard data classification performance ranged from a minimum of −29% to a maximum of 52%. When evaluating performance based on maximum performance, all performance indicators were found to decrease by more than 50%.


Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships

Abstract: In the pursuit of optimal quantitative structure–activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed filter-induced genetic programming 2 (FIGP2), as an extension of our previously proposed SR method. FIGP2 is designed to improve the generalizability of SR models and to be applicable to data sets in which cost-intensive descriptors are employed. The FIGP2 method incorporates two major improvements: a modified domain filter to eradicate diverging expressions based on optimal calculation and the introduction of a stability metric to penalize expressions that would lead to overfitting. Our retrospective comparative analysis using 12 structure–activity relationship data sets revealed that FIGP2 surpassed the previously proposed SR method and conventional modeling methods, such as support vector regression and multivariate linear regression in terms of predictive performance. Generated mathematical expressions by FIGP2 were relatively simple and not divergent in the domain of function. Taken together, FIGP2 can be used for making interpretable regression models with predictive ability.


Novel neurodigital interface reduces motion sickness in virtual reality

Abstract: Virtual reality (VR) is a computer-created 3D environment with a focus on realistic scenes and pictures created for entertainment, medical and/or educational and training purposes. One of the major side effects of VR immersion reported in the scientific literature, media and social media is Visually Induced Motion Sickness (VIMS), with clinical symptoms such as disorientation, nausea, and oculomotor discomfort. VIMS is mostly caused by the discrepancy between the visual and vestibular systems and can lead to dizziness, nausea, and disorientation. In this study, we present one potential novel solution to combat motion sickness in VR, showcasing a significant reduction of nausea in VR users employing the META Quest 2 headsets in conjunction with a whole-body controller. Using a neurodigital approach, we facilitate a more immersive and comfortable VR experience. Our findings indicate a marked reduction in VR-induced nausea, paving the way to promote VR technology for broader applications across various fields.


Searching near and far: The attentional template incorporates viewing distance.

Abstract: According to theories of visual search, observers generate a visual representation of the search target (the "attentional template") that guides spatial attention toward target-like visual input. In real-world vision, however, objects produce vastly different visual input depending on their location: your car produces a retinal image that is 10 times smaller when it is parked 50 compared to 5 m away. Across four experiments, we investigated whether the attentional template incorporates viewing distance when observers search for familiar object categories. On each trial, participants were precued to search for a car or person in the near or far plane of an outdoor scene. In "search trials," the scene reappeared and participants had to indicate whether the search target was present or absent. In intermixed "catch-trials," two silhouettes were briefly presented on either side of fixation (matching the shape and/or predicted size of the search target), one of which was followed by a probe-stimulus. We found that participants were more accurate at reporting the location (Experiments 1 and 2) and orientation (Experiment 3) of probe stimuli when they were presented at the location of size-matching silhouettes. Thus, attentional templates incorporate the predicted size of an object based on the current viewing distance. This was only the case, however, when silhouettes also matched the shape of the search target (Experiment 2). We conclude that attentional templates for finding objects in scenes are shaped by a combination of category-specific attributes (shape) and context-dependent expectations about the likely appearance (size) of these objects at the current viewing location. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Optimizing freeform lenses for extended sourceswith algorithmic differentiable ray tracing andtruncated hierarchical B-splines

Abstract: We propose a method for optimizing the geometry of a freeform lens to redirect the light emitted from an extended source into a desired irradiance distribution. We utilize a gradient-based optimization approach with MITSUBA 3, an algorithmic differentiable non-sequential ray tracer that allows us to obtain the gradients of the freeform surface parameters with respect to the produced irradiance distribution. To prevent the optimizer from getting trapped in local minima, we gradually increase the number of degrees of freedom of the surface by using Truncated Hierarchical B-splines (THB-splines) during optimization. The refinement locations are determined by analyzing the gradients of the surface vertices. We first design a freeform using a collimated beam (zero-etendue source) for a complex target distribution to demonstrate the method's effectiveness. Then, we demonstrate the ability of this approach to create a freeform that can project the light of an extended Lambertian source into a prescribed target distribution.


A machine learning-based hybrid recommender framework for smart medical systems

Abstract: This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy. The second method implements the term frequency and inverse document frequency (TF-IDF) algorithm to construct a model based on the patient’s symptom vector space, incorporating score weighting, modified cosine similarity, and K-means clustering. Then, the alternating least squares (ALS) matrix decomposition and user collaborative filtering algorithm are applied to calculate patients’ predicted scores for doctors and recommend top-performing doctors. Experimental results show significant improvements in metrics called precision and recall rates compared to conventional methods, making the proposed approach a practical solution for department triage and doctor recommendation in medical appointment platforms.


Architectural Design for Data Security in Cloud-based Big Data Systems

Abstract: Architecture design of system is creative process after all. Non-functional requirements and software architecture have a strong relationship. Security of system is one of great concern and it is a non-functional need and requirement. The architectural style and structure chosen for a system should be based on the non-functional system requirements. Big Data technologies are used to store handles huge amounts of operational data in the public domain. Structure and behavior diagrams of the system is proposed that use a secure architectural design for a system that works over the cloud. Different components of Big Data have been investigated in this research work due to the seriousness of security issues focus on privacy and confidentiality. The security of data over the cloud being outsourced is a growing concern. There are outsider and insider malicious database administrators who can gain access and modify private and sensitive data therefore database service provider can be deceitful. Security mechanisms are necessarily required to protect sensitive data residing on public clouds. At the application level, the proposed solution ensures privacy and confidentiality of data at rest over the cloud. Extensive tests have been carried out, and the results of this research work are as follows that the recorded results revealed statistically significant variations in plaintext and multimedia contents (i.e., audio and video) with time measured in milliseconds for small, medium, and large datasets..


Diffractive optical computing in free space

Abstract: Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.


Tracking hand movements with a smart glove

Abstract: Dynamic hand movements could be detected in real time using machine learning and a smart textile glove.


The Role of Artificial Intelligence in Shaping the Future of Education at Higher Secondary Level

Abstract: This study examines the implications of Artificial Intelligence (AI) in enhancing educational methodologies at the higher secondary level, focusing on its impact on student engagement and learning outcomes, the experiences and challenges faced by educators in integrating AI tools, and the broader ethical and privacy considerations. Utilizing a qualitative methodology based on interviews with educators and students, the research aims to explore the nuanced perspectives on the role of AI in education, identifying both the potential benefits and drawbacks. Initial findings highlight the positive effects of AI on personalized learning experiences alongside significant challenges related to resource limitations and the need for comprehensive support for educators. Moreover, concerns about ethical implications and data privacy emerge as critical issues requiring careful management. The study concludes that while AI offers transformative potential for educational practices, ensuring effective integration, addressing ethical concerns, and preparing students for future technological challenges is essential for leveraging AI's full benefits in the educational sector.


MCMC algorithm based on Markov random field in image segmentation

Abstract: In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.


Evaluation of an artificial intelligence-based clinical trial matching system in Chinese patients with hepatocellular carcinoma: a retrospective study

Abstract: Abstract Background Artificial intelligence (AI)-assisted clinical trial screening is a promising prospect, although previous matching systems were developed in English, and relevant studies have only been conducted in Western countries. Therefore, we evaluated an AI-based clinical trial matching system (CTMS) that extracts medical data from the electronic health record system and matches them to clinical trials automatically. Methods This study included 1,053 consecutive inpatients primarily diagnosed with hepatocellular carcinoma who were referred to the liver tumor center of an academic medical center in China between January and December 2019. The eligibility criteria extracted from two clinical trials, patient attributes, and gold standard were decided manually. We evaluated the performance of the CTMS against the established gold standard by measuring the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and run time required. Results The manual reviewers demonstrated acceptable interrater reliability (Cohen’s kappa 0.65–0.88). The performance results for the CTMS were as follows: accuracy, 92.9–98.0%; sensitivity, 51.9–83.5%; specificity, 99.0–99.1%; PPV, 75.7–85.1%; and NPV, 97.4–98.9%. The time required for eligibility determination by the CTMS and manual reviewers was 2 and 150 h, respectively. Conclusions We found that the CTMS is particularly reliable in excluding ineligible patients in a significantly reduced amount of time. The CTMS excluded ineligible patients for clinical trials with good performance, reducing 98.7% of the work time. Thus, such AI-based systems with natural language processing and machine learning have potential utility in Chinese clinical trials.


Laboratory experiments of model-based reinforcement learning for adaptive optics control

Abstract: Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, making their detection difficult. Consequently, the adaptive optics (AO) system’s control algorithm must be carefully designed to distinguish the exoplanet from the residual light produced by the host star. A promising avenue of research to improve AO control builds on data-driven control methods, such as reinforcement learning (RL). RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment. Thus, RL can be seen as an automated approach to AO control, where its usage is entirely a turnkey operation. In particular, model-based RL has been shown to cope with temporal and misregistration errors. Similarly, it has been demonstrated to adapt to nonlinear wavefront sensing while being efficient in training and execution. In this work, we implement and adapt an RL method called policy optimization for AO (PO4AO) to the GPU-based high-order adaptive optics testbench (GHOST) test bench at ESO headquarters, where we demonstrate a strong performance of the method in a laboratory environment. Our implementation allows the training to be performed parallel to inference, which is crucial for on-sky operation. In particular, we study the predictive and self-calibrating aspects of the method. The new implementation on GHOST running PyTorch introduces only around 700 μs of in addition to hardware, pipeline, and Python interface latency. We open-source well-documented code for the implementation and specify the requirements for the RTC pipeline. We also discuss the important hyperparameters of the method and how they affect the method. Further, the paper discusses the source of the latency and the possible paths for a lower latency implementation.


Breast Cancer Image Classification based on Adaptive Interpolation Approach Using Clinical Dataset

Abstract: In the healthcare and bioinformatics disciplines, the categorization of breast cancer has become an emerging paradigm due to the second most common cause of cancer-related mortality in women. A biopsy is a procedure in which tissue has been examined to determine whether or not it is breast cancer through histopathologists that may lead to a mistaken diagnosis. The research is mainly focused on the patient data (884 case reports of patients) is acquired from the American Oncology Institute implemented with preprocessing techniques consists of missing values and those values are recovered with Novel Modified Interpolation (MI) Method. Deep learning networks effectively detect and assess the pattern for annotating histological data based on the labelling, which preserves time, system cost and enhances the system accuracy. This framework addressed feature acquisition and missing analysis strategies based on entropy confidence weight factor. First, the iterative patterns have been treated as a potential diagnostic rule, and the attention-based rule combination formulates the classification issue based on integrating convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches. Second, the key part of label construction is carried out with an entropy confidence-weight factor assessment which detects and predict different patterns to construct the rule for classification. Third, optimization of clustering data by assessing missing parameters based on mean square error and the concept of interpolation to reduce data loss by around 20% and enhance the system accuracy. Simulation results show that the proposed system achieves 91.3% accuracy to state-of-art approaches, potentially allied in clinical applications. Modified Interpolation (MI) method recovered missing values with least mean square error and less data loss of 0.0123 and 1.38% respectively. This method also compared with existing Linear Interpolation (LI) method which is able to recover least men square error and data loss of 3.295 and 18.925% respectively. Comparatively the modified interpolation method recovered the missing values with less mean square error and less data loss.


Fourier domain filtering analysis for color polarization camera demosaicking

Abstract: We review Fourier-domain methods for demosaicking Bayer-filter color cameras and monochrome polarization cameras, and then generalize the approach for the quad-Bayer-filter mosaic and for color-polarization cameras. For each of these four mosaic filter types, we provide theoretical expressions for the sampling functions, the Fourier-domain channels, and the linear combination of reconstructed channels (the demosaicking algorithm) needed to estimate the input (presampled) image. A useful advantage of the Fourier-domain approach is that it provides a direct means of visualizing and quantifying when aliasing is likely or unlikely to be present. For the Bayer and quad-Bayer-filter types, we provide simulated images, while for the polarization camera types we provide experimental images and videos to illustrate the algorithm and analyze crosstalk error.


Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Abstract: In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.


Hourglass, a compass navigating global and regional heterogeneity of pancreatic cancer<sup>†</sup>

Abstract: Advances in the digital pathology field have facilitated the characterization of histology samples for both clinical and preclinical research. However, uncovering subtle correlations between bioimaging, clinical and molecular parameters requires extensive statistical analysis. As a user-friendly software, Hourglass, simplifies multiparametric dataset analysis through intuitive data visualization and statistical tools. Systemic analysis of interleukin-6 (IL-6)/pStat3 signaling pathway through Hourglass revealed differences in regional immune cell composition within tumors. Moreover, these regional disparities were partially mediated by sex. Overall, Hourglass simplifies information extraction from complex datasets, resolving overlooked regional and global spatial tumor differences. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.


Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection

Abstract: Time series anomaly detection is very important to ensure the security of industrial control systems (ICSs). Many algorithms have performed well in anomaly detection. However, the performance of most of these algorithms decreases sharply with the increase in feature dimension. This paper proposes an anomaly detection scheme based on Graph Attention Network (GAT) and Informer. GAT learns sequential characteristics effectively, and Informer performs excellently in long time series prediction. In addition, long-time forecasting loss and short-time forecasting loss are used to detect multivariate time series anomalies. Short-time forecasting is used to predict the next time value, and long-time forecasting is employed to assist the short-time prediction. We conduct a large number of experiments on industrial control system datasets SWaT and WADI. Compared with most advanced methods, we achieve competitive results, especially on higher-dimensional datasets. Moreover, the proposed method can accurately locate anomalies and realize interpretability.


Lightweight Meter Pointer Recognition Method Based on Improved YOLOv5

Abstract: In substation lightning rod meter reading data taking, the classical object detection model is not suitable for deployment in substation monitoring hardware devices due to its large size, large number of parameters, and slow detection speed, while is difficult to balance detection accuracy and real-time requirements with the existing lightweight object detection model. To address this problem, this paper constructs a lightweight object detection algorithm, YOLOv5-Meter Reading Lighting (YOLOv5-MRL), based on the improved YOLOv5 model's speed while maintaining accuracy. Then, the YOLOv5s are pruned based on the convolutional kernel channel soft pruning algorithm, which greatly reduces the number of parameters in the YOLOv5-MRL model while maintaining a certain accuracy loss. Finally, in order to facilitate the dial reading, the dial external circle fitting method is proposed to calculate the dial reading using the circular angle algorithm. The experimental results on the self-built dataset show that the YOLOv5-MRL object detection model achieves a mean average precision of 96.9%, a detection speed of 5 ms/frame, and a model weight size of 5.5 MB, making it better than other advanced dial reading models.


A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Abstract: Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.


Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization

Abstract: The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it's called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.


Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet++

Abstract: Abstract Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1.


Self-Localization of Guide Robots Through Image Classification

Abstract: The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots. To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accurate solution for indoor robot navigation. The more accurate solution of the guide robotic system opens a new window of the self-localization system and solves the more complex problem of indoor robot navigation. It makes a reliable interface between humans and robots. This study successfully demonstrated how a robot finds its initial position inside a room. A deep learning system, such as a convolutional neural network, trains the self-localization system as an image classification problem. The robot was placed inside the room to collect images using a panoramic camera. Two datasets were created from the room images based on the height above and below the chest. The above-mentioned method achieved a localization accuracy of 98.98%.


Pixel-wise calibration method for telecentric structured light system

Abstract: This paper introduces a pixelwise calibration method designed for a structured light system utilizing a camera attached with a telecentric lens. In the calibration process, a white flat surface and a flat surface with circle dots serve as the calibration targets. After deriving the properties of the pinhole projector through a conventional camera calibration method using circle dots and determining the camera's attributes via 3D feature points estimation through iterative optimizations, the white surface calibration target was positioned at various poses and reconstructed with initial camera and projector calibration data. Each 3D reconstruction was fitted with a virtual ideal plane that was further used to create the pixelwise phase-to-coordinate mapping. To optimize the calibration accuracy, various angled poses of the calibration target are employed to refine the initial results. Experimental findings show that the proposed approach offers high calibration accuracy for a structured light system using a telecentric lens.


Unsupervised underwater imaging based on polarization and binocular depth estimation

Abstract: Scattering caused by suspended particles in the water severely reduces the radiance of the scene. This paper proposes an unsupervised underwater restoration method based on binocular estimation and polarization. Based on the correlation between the underwater transmission process and depth, this method combines the depth information and polarization information in the scene, uses the neural network to perform global optimization and the depth information is recalculated and updated in the network during the optimization process, and reduces the error generated by using the polarization image to calculate parameters, so that detailed parts of the image are restored. Furthermore, the method reduces the requirement for rigorous pairing of data compared to previous approaches for underwater imaging using neural networks. Experimental results show that this method can effectively reduce the noise in the original image and effectively preserve the detailed information in the scene.


Analisis Dampak Kegiatan Pasar Numerasi terhadap Peningkatan Hasil Belajar Siswa Kelas V UPTD Satdik SDN Curahtakir 4

Abstract: Numeracy markets are activities that imitate market situations that are closely related to everyday life. Students are given the opportunity to design, manufacture, market, and sell goods. This research aims to help students improve their numeracy skills, increase their understanding of basic mathematical concepts, and improve their social skills and creativity through numeracy market activities. This type of research is descriptive with a qualitative approach. The research subjects consisted of fifth grade students at UPTD satdik SDN Curahtakir 4. The stages in this research included observing placement schools and their problems, designing activities as solutions to problem solving, carrying out AKM pretests, carrying out numeracy market activities, carrying out AKM posttests, and then analyzing and evaluating . The research results show that it is very suitable for elementary school (SD) students because it offers new experiences in interpreting mathematical concepts. This numeracy market activity also has a positive impact on improving student learning outcomes in the context of mathematics. Apart from that, the findings from this research are that the AKM numeracy post-test scores for class V students have increased compared to the previous AKM numeracy pre-test scores.


Security-oriented steganographic payload allocation for multi-remote sensing images

Abstract: Multi-image steganography, a technique for concealing information within multiple carrier mediums, finds remote sensing images to be particularly apt carriers due to their complex structures and abundant texture data. These characteristics bolster the resilience against steganalysis and enhance steganographic capacity. The efficacy of multi-image steganography hinges on the diplomatic strategy of cover selection and the meticulous allocation of the payload. Nevertheless, the majority of current methods, which are empirically formulated, predominantly focus on the texture complexity of individual images, thereby potentially undermining overall security. This paper introduces a security-oriented approach to steganographic payload allocation for multiple remote sensing images aimed at fortifying the security of multi-image steganography. Our primary contributions include employing a steganalysis pre-trained network to quantify texture complexity in remote sensing cover images, directly correlating it with security. Additionally, we have developed an adaptive payload allocation strategy for multiple images, which embeds a payload proximate to each image's maximal steganographic capacity while concurrently ensuring the security of the embedding process. Experimental results corroborate that our methodology excels in cover selection and payload allocation and achieves better undetectability against modern steganalysis tools.


Assessment of inference accuracy and memory capacity of computation-in-memory enabled neural network due to quantized weights, gradients, input and output signals, and memory non-idealities

Abstract: Abstract This paper proposes an approach to enhance the efficiency of computation-in-memory enabled neural networks. The proposed methods involve partial quantization of learning and inference processes within the neural network to increase the training and inference speed while reducing energy and memory consumption. The impact of quantization due to the usage of computation-in-memory is evaluated based on inference accuracy. The effect of non-idealities incurred due to the employment of different memories such as ReRAM on the network accuracy is documented and reported. The results indicate that a certain quantization bit precision threshold is necessary for weights, input/output data, and gradients to maintain an acceptable inference accuracy level. Notably, the experiments demonstrate a modest degradation of approximately 2.8% in inference accuracy compared to the neural network trained without using computation-in-memory, this accuracy trade-off is accompanied by a substantial memory capacity improvement, with best-case memory usage reductions of 62% and 93% during the training and inference phase respectively.


WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound

Abstract: Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks, it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates and attention gates between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Two datasets are utilized for the analysis: the public 'Breast Ultrasound Images' dataset of 780 images and a private VSI dataset of 3818 images, captured at the University of Rochester by the authors. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Moreover, our model significantly outperformed other models in McNemar's test with false discovery rate correction on a 381-image VSI set. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.


Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach

Abstract: Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival rates, early and accurate detection is crucial. Machine learning techniques, particularly deep learning, have demonstrated impressive success in various image recognition tasks, including breast cancer classification. However, the reliance on large labeled datasets poses challenges in the medical domain due to privacy issues and data silos. This study proposes a novel transfer learning approach integrated into a federated learning framework to solve the limitations of limited labeled data and data privacy in collaborative healthcare settings. For breast cancer classification, the mammography and MRO images were gathered from three different medical centers. Federated learning, an emerging privacy-preserving paradigm, empowers multiple medical institutions to jointly train the global model while maintaining data decentralization. Our proposed methodology capitalizes on the power of pre-trained ResNet, a deep neural network architecture, as a feature extractor. By fine-tuning the higher layers of ResNet using breast cancer datasets from diverse medical centers, we enable the model to learn specialized features relevant to different domains while leveraging the comprehensive image representations acquired from large-scale datasets like ImageNet. To overcome domain shift challenges caused by variations in data distributions across medical centers, we introduce domain adversarial training. The model learns to minimize the domain discrepancy while maximizing classification accuracy, facilitating the acquisition of domain-invariant features. We conducted extensive experiments on diverse breast cancer datasets obtained from multiple medical centers. Comparative analysis was performed to evaluate the proposed approach against traditional standalone training and federated learning without domain adaptation. When compared with traditional models, our proposed model showed a classification accuracy of 98.8% and a computational time of 12.22 s. The results showcase promising enhancements in classification accuracy and model generalization, underscoring the potential of our method in improving breast cancer classification performance while upholding data privacy in a federated healthcare environment.


Assessing spontaneous categorical processing of visual shapes via frequency-tagging EEG

Abstract: Categorization is an essential cognitive and perceptual process, which happens spontaneously. However, earlier research often neglected the spontaneous nature of this process by mainly adopting explicit tasks in behavioural or neuroimaging paradigms. Here, we use frequency-tagging (FT) during electroencephalography (EEG) in 22 healthy human participants (both male and female) as a direct approach to pinpoint spontaneous visual categorical processing. Starting from schematic natural visual stimuli, we created morph sequences comprising 11 equal steps. Mirroring a behavioural categorical perception discrimination paradigm, we administered an FT-EEG oddball paradigm, assessing neural sensitivity for equally sized differences within and between stimulus categories. Likewise, mirroring a behavioural category classification paradigm, we administered a sweep FT-EEG oddball paradigm, sweeping from one end of the morph sequence to the other, thereby allowing us to objectively pinpoint the neural category boundary. We found that FT-EEG can implicitly measure categorical processing and discrimination. More specifically, we could derive an objective neural index of the required level to differentiate between the two categories, and this neural index showed the typical marker of categorical perception (i.e., stronger discrimination across as compared to within categories). The neural findings of the implicit paradigms were also validated using an explicit behavioural task. These results provide evidence that FT-EEG can be used as an objective tool to measure discrimination and categorization and that the human brain inherently and spontaneously (without any conscious or decisional processes) uses higher-level meaningful categorization information to interpret ambiguous (morph) shapes. Significance Statement Every time when we encounter a new image or object, we will automatically relate it to previously stored categories. This categorization process allows us to efficiently react on new information and it influences our perception. The behavioural hallmark of categorical perception entails that we perceive differences between categories more distinctly than within a category. Previous research has mainly investigated categorical processing using explicit tasks. Here, we use carefully controlled morphs with implicit, neural electroencephalography measures to assess spontaneous categorical processing. We found higher neural amplitudes for “between”- than “within”-category morph pairs, establishing a neural correlate of categorical perception. This provides evidence that the brain inherently and automatically uses higher-level meaningful categorization information to interpret ambiguous shapes.


On the timing of overt attention deployment: Eye-movement evidence for the priority accumulation framework.

Abstract: Most visual-search theories assume that our attention is automatically allocated to the location with the highest priority at any given moment. The Priority Accumulation Framework (PAF) challenges this assumption. It suggests that the priority weight at each location accumulates across sequential events and that evidence for the presence of action-relevant information contributes to determining when attention is deployed to the location with the highest accumulated priority. Here, we tested these hypotheses for overt attention by recording first saccades in a free-viewing spatial-cueing task. We manipulated search difficulty (Experiments 1 and 2) and cue salience (Experiment 2). Standard theories posit that when oculomotor capture by the cue occurs, it is initiated before the search display appears; therefore, these theories predict that the cue's impact on the distribution of first saccades should be independent of search difficulty but influenced by the cue's saliency. By contrast, PAF posits that the cue can bias competition later, after processing of the search display has already started, and therefore predicts that such late impact should increase with both search difficulty and cue salience. The results fully supported PAF's predictions. Our account suggests a distinction between attentional capture and attentional-priority bias that resolves enduring inconsistencies in the attentional-capture literature. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Special issue on analysis and mining of social media data

Abstract: This Editorial introduces the PeerJ Computer Science Special Issue on Analysis and Mining of Social Media Data. The special issue called for submissions with a primary focus on the use of social media data, for a variety of fields including natural language processing, computational social science, data mining, information retrieval and recommender systems. Of the 48 abstract submissions that were deemed within the scope of the special issue and were invited to submit a full article, 17 were ultimately accepted. These included a diverse set of articles covering, inter alia, sentiment analysis, detection and mitigation of online harms, analytical studies focused on societal issues and analysis of images surrounding news. The articles primarily use Twitter, Facebook and Reddit as data sources; English, Arabic, Italian, Russian, Indonesian and Javanese as languages; and over a third of the articles revolve around COVID-19 as the main topic of study. This article discusses the motivation for launching such a special issue and provides an overview of the articles published in the issue.


CuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence?

Abstract: In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of "tales with a message" is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.


A discrete component in visual working memory encoding.

Abstract: Working memory (WM) is a central cognitive bottleneck, which has primarily been attributed to its well-known storage limit. However, relatively little is known about the processing limit during the initial memory encoding stage, which may also constrain various cognitive processes. The present study introduces a novel method using dynamic stimulus presentation and hierarchical Bayesian modeling to quantitatively estimate visual WM encoding speed. Participants performed a delayed-estimation task with two memory items continuously changing color hues in perceptually unnoticeable steps. Across three experiments, the recall errors systematically shifted toward the direction of color change, providing a proxy measure of encoding speed. Importantly, the observed shifts were best characterized by a temporal lag during the encoding of different items, supported by a mixture of two distributions with credibly distinct encoding times. A supplementary model-free analysis further confirmed the discrete encoding component in visual WM for multiple items. These findings shed light on the temporal dynamics of WM encoding processes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


METHODICAL SYSTEM FOR THE PEDAGOGICAL EXCELLENCE DEVELOPMENT OF GENERAL EDUCATION TEACHERS OF VOCATIONAL EDUCATION INSTITUTIONS

Abstract: The relevance. The issue of developing teacher's pedagogical excellence is one of the most significant in pedagogical theory and practice, especially at the current stage of overcoming the consequences of the pandemic and martial law. The article proves the importance of process studying of the pedagogical excellence developing of general education teachers of vocational education institutions as a goal-oriented, dynamic system. The aim is to substantiate the methodical system for the development of pedagogical excellence of general education teachers of vocational education institutions. Methods: the study used theoretical methods, which provided an opportunity to examine the state of the research problem in scientific sources, compare different views, analyse the process of development of pedagogical excellence of general education teachers of vocational education institutions as a methodical system. This study used the methods of analysis and synthesis, systematisation, abstraction, generalisation, and modelling, which resulted in building a structure of functional components of the system of teachers' pedagogical excellence development and clarifying the interconnection of the selected components. Results: the article deals with the concept of "system" as a structure that combines naturally arranged elements, components grouped by some common feature; the principles of functioning and development of pedagogical systems are highlighted, the types and structural components of pedagogical systems are analysed; the design of a system for the development of pedagogical excellence of general education teachers as a certain type of pedagogical system is considered. The main structural components of the designed system (target, conceptual and methodological, subjective, content, technological, diagnostic and resultant) are characterised; the functional components of the system for developing pedagogical excellence of general education teachers in a vocational education institution are allocated; the place of the proposed system for developing pedagogical excellence of general education teachers among the systems of vocational education is determined. Conclusions: the designed system for the development of pedagogical excellence of general education teachers in a vocational education institution, as a certain type of pedagogical system, combines certain structural components (target, conceptual and methodological, subjective, content, technological, diagnostic and resultant, and functional components: design, constructive, gnostic, prognostic, organisational, communicative, evaluative and corrective); the general pedagogical system of a vocational education institution includes a system of pedagogical staff professional development, which in turn combines the system of pedagogical staff advanced training and the pedagogical excellence development system of general education teachers of a vocational education institution as a subsystem; prospects for further research will be devoted to substantiating the goals and designing the content of the development of pedagogical excellence of general education teachers.


Pendidikan Karakter dalam Anime: Analisis Psikologi Tokoh Naruto pada Serial Anime Naruto

Abstract: Naruto anime is one of the anime that can be used as an educational watch that supports us in teaching character education to the younger generation. The purpose of this study is to analyze the psychology of the main characters of Naruto anime and find out the influence of Naruto anime in shaping characters and values in the audience. This study used a qualitative approach with data collection techniques, watch-listen-record and read-record. From this research, it can be found that there are several psychological aspects of Naruto characters, namely 1) loneliness and rejection, 2) social care, 3) motivation and determination, 4) self-development, 5) courage and confidence, 6) empathy, and the influence of the anime in shaping the character and values in the audience, namely 1) inspiration to overcome obstacles, 2) the importance of friendship, 3) face conflict peacefully, 4) courage to oppose injustice. The conclusion obtained is that the character Naruto shows a rich journey in terms of psychology, from a difficult childhood to strong self-development. In addition, Naruto Anime can inspire and shape characters and values in its audience.


Restoration of UAV-Based Backlit Images for Geological Mapping of a High-Steep Slope

Abstract: Unmanned aerial vehicle (UAV)-based geological mapping is significant for understanding the geological structure in the high-steep slopes, but the images obtained in these areas are inevitably influenced by the backlit effect because of the undulating terrain and the viewpoint change of the camera mounted on the UAV. To handle this concern, a novel backlit image restoration method is proposed that takes the real-world application into account and addresses the color distortion issue existing in backlit images captured in high-steep slope scenes. Specifically, there are two main steps in the proposed method, which consist of the backlit removal and the color and detail enhancement. The backlit removal first eliminates the backlit effect using the Retinex strategy, and then the color and detail enhancement step improves the image color and sharpness. The author designs extensive comparison experiments from multiple angles and applies the proposed method to different engineering applications. The experimental results show that the proposed method has potential compared to other main-stream methods both in qualitative visual effects and universal quantitative evaluation metrics. The backlit images processed by the proposed method are significantly improved by the process of feature key point matching, which is very conducive to the fine construction of 3D geological models of the high-steep slope.


EXPRESS: Processing Wh-filler-gap Dependencies

Abstract: We present experimental evidence showing that different wh-filler-gap dependencies are processed differently, depending on their syntactic licensors. Our studies compared the active storage profiles for why, how, and who (serving as subject or object of the verb). The results of offline and online experiments revealed that these wh-fillers are stored in memory for different durations, and predictably so based on the hypothesized structural distance between each wh-filler and the licensor which determines its grammatical and interpretive functions. Furthermore, the results showed that once the wh-filler is licensed, it is integrated to the current structure, and no longer engenders additional memory costs. Based on these findings, we argue that the mechanism of online sentence processing may employ both storage and integration components in memory.


Evaluation of a Deep Learning Model for Metastatic Squamous Cell Carcinoma Prediction From Whole Slide Images

Abstract: Squamous cell carcinoma (SCC) is a histologic type of cancer that exhibits various degrees of keratinization. Identifying lymph node metastasis in SCC is crucial for prognosis and treatment strategies. Although artificial intelligence (AI) has shown promise in cancer prediction, applications specifically targeting SCC are limited.To design and validate a deep learning model tailored to predict metastatic SCC in radical lymph node dissection specimens, using whole slide images (WSIs).Using the EfficientNetB1 architecture, a model was trained on 6587 WSIs (2413 SCC and 4174 nonneoplastic) from several hospitals, encompassing esophagus, head and neck, lung, and skin specimens. The training exclusively relied on WSI-level labels without annotations. We evaluated the model on a test set consisting of 541 WSIs (41 SCC and 500 nonneoplastic) of radical lymph node dissection specimens.The model exhibited high performance, with receiver operating characteristic curve areas under the curve between 0.880 and 0.987 in detecting SCC metastases in lymph nodes. Although true positives and negatives were accurately identified, certain limitations were observed. These included false positives due to germinal centers, dust cell aggregations, and specimen-handling artifacts, as well as false negatives due to poor differentiation.The developed artificial intelligence model presents significant potential in enhancing SCC lymph node detection, offering workload reduction for pathologists and increasing diagnostic efficiency. Continuous refinement is needed to overcome existing challenges, making the model more robust and clinically relevant.


Identification of Fencing Athletes Based on Anthropometric Measurements Using MediaPipe Pose

Abstract: Over time, numerous developments in digital technology have benefited people, including anthropometric measurements that provide information on an athlete’s ability in sports. The use of digital technology in sports must continue, particularly in the National Sports Committee of Indonesia (Komite Olahraga Nasional Indonesia, KONI) of Bandung City. This study proposed a technique for classifying and identifying fencing athletes’ talents. This work developed a methodology for evaluating sports talent based on anthropometric measurements of athletes’ bodies using the posture detection approach. Fencing and nonfencing athletes in KONI Bandung City were categorized using this talent identification. This study used 36 datasets of body posture images from various skills of the sport. These images were in JPEG or JPG format with a resolution of 3,024 × 4,032 and were acquired using a Canon EOS 1300D camera. This study utilized four points landmarks, which are usually used as measurement components in KONI, to categorize fencing athletes. The four points are shoulder (S), elbow (E), index (I), and hip (H) landmarks. The testing was done using three different dataset settings. According to the test results of all scenarios, scenario 2 had the highest accuracy. This scenario was able to categorize fencing and nonfencing athletes with an accuracy rate of 89% and an average processing time of less than 3 s per image.


Methods and means of developing the pedagogical skill of teachers of general education disciplines in vocational education institutions

Abstract:
The problem of developing the professionalism of teachers today belongs to the key directions of research in the field of modern pedagogical science. The high quality of teaching in the vocational education system depends on the level of knowledge, pedagogical skill of the teacher, and his/her professional maturity. On the basis of the conducted analysis, it is substantiated that the effectiveness of the development of the pedagogical skill of teachers of general education disciplines of vocational education institutions depends both on the correct definition of goals and content, and on the methods of achieving the goal - teaching methods.
 The purpose of the article is the theoretical justification of the main methods and means of developing the pedagogical skill of teachers of general education disciplines of vocational education institutions.
 The work characterizes the main functions of teaching methods, analyzes their main classifications, examines familiar, traditional methods (lecture method, story, explanation, training exercises, explanatory conversation, discussion, independent work, instruction, etc.), as well as innovative: simulation and game methods, brainstorming method, problem methods, project method, case method, master class, work in pairs, work in groups, consultations, portfolio, method of situational analysis, coaching, tutoring, etc.
 The study revealed that improving the professional skill of teachers of general education disciplines in vocational education institutions is a complex process. The professional skill of a teacher is an individual style, combined with high professional competence, which determines the ability to apply traditional and innovative technologies, to carry out professional activities regardless of the conditions of the social environment and the characteristics of students. That is why explanatory-illustrative, reproductive, and problem-based teaching methods (traditional and innovative) should gradually and purposefully implement cognitive activity, develop and improve professional skills within the framework of formal, non-formal, and informal continuous pedagogical education of teachers of general education disciplines of vocational education institutions


THE SPECIALIST AND APPLICABLE REQUIREMENTS IN THE CRIMINAL-PROCEDURAL LEGISLATION OF THE REPUBLIC OF UZBEKISTAN

Abstract: It is perceptible that a number of requirements are stipulated regarding the specialist in the process of proof by virtue of analyzing the Criminal-procedural Code of the Republic of Uzbekistan. The disinterestedness and worthwhile elements of the profession in performing the tasks assigned to the specialists as indispensable part of these requirements are vitally important in the criminal procedural relations. Ultimately, the lack of special knowledge of the specialists involved in the case shows theirincompetence for their profession and inhibits them to participate in the proceeding.


Validasi Dokumen Pengajuan Kredit Nasabah Menggunakan Metode Simple Additiveweighting dan Rapid Application Development

Abstract: Applying for credit to purchase goods is now very easy, just attach it personal and supporting data documents, your credit application can be approved in a matter of hours. These submission documents must have valid criteria and information prospective customer data, and fast and accurate validation methods are carried out in doing so document verification and validation. The Simple Additive Weighting method is used for validating documents and verification is carried out by checking the source of the document issued. This method, out of 100 credit applications that are approved, has a level success reached 97% and only 3% of customers experienced credit problems.


THE ROLE OF COPYRIGHT IN THE CONTEXT OF SOCIAL MEDIA

Abstract: This article examines important aspects of the interaction between copyright and social networks in contemporary information society. The article emphasizes key elements of agreements between users and platforms, defining rules for content usage. The Digital Millennium Copyright Act (DMCA) is highlighted as an effective mechanism for protecting copyright in the online environment. Court decisions on copyright infringement in social networks serve as examples for understanding the complexities of balancing between freedom of information exchange and intellectual property protection. In conclusion, the importance of effective collaboration between authors and social platforms is emphasized to ensure fairness, diversity, and quality of content in the online space.


MEASURES TO ENSURE THE EFFECTIVENESS OF THE PREPARATION FOR THE INSPECTION OF THE CRIME SCENE

Abstract: In this article, the nature of the investigative action of inspecting the scene of the incident, the concept of the stage of preparation for examining the scene of the incident. There has also been an attempt to improve the preparation for the survey in order to improve the effectiveness of the survey of the scene. To this end, in the practice of Investigation, measures have been developed that will be carried out precisely on the issue under consideration, in preparation for consideration in any situation. It has been proposed to strengthen the issue of preparing for the incident in the legislation in order to increase the responsibility of the participants in the survey of the scene, to ensure the effectiveness of the investigative action.


GENESIS OF CYBERCRIME AND ITS CONCEPT PECULIARITIES

Abstract: The article deals with the history of the origin of cybercrime. It describes in detail the essence and peculiarities of cybercrimeterminology. At the same time, the article reveals types of cybercrime, as well as its goals and objects of influence.


THE SIGNIFICANCE OF AL'GOFLORA IN OPTIMIZING WATER TREATMENT PROCESSES

Abstract: Water treatment facilities play a vital role in ensuring the availability of safe and clean water for human consumption and various industrial purposes. Among the innovative approaches in water treatment, the integration of Al'GOFLORA stands out as a promising method for optimizing the treatment process. Al'GOFLORA, a long water treatment facility utilizing algae and other flora, offers a sustainable and effective solution for water purification. This article explores the importance of Al'GOFLORA in optimizing water treatment processes, highlighting its benefits, mechanisms, and potential applications.


Groups of Non-Conventional Means Referring to Time in Literary Texts.

Abstract: The paper is an extensive analysis of literary texts spanning different eras, genres, and languages (including the epic of Gilgamesh, Russian works by A. P. Chekhov, S. Dovlatov, G. G. Demidov, as well as English works by W. Shakespeare, R. P. Warren, W. Faulkner, K. Vonnegut). Through this analysis, the author identified a common poetic feature shared by these works: the utilization of both conventional and non-conventional means to convey the passage of time. The objective of the study was to examine and categorize these non-conventional means according to their respective referential spheres, as well as to elucidate their functions within literary texts. Four main groups were identified, encompassing references to food, human physiology, events or objects in the characters’ living spaces, and processes in nature. The study reveals that non-conventional methods of conveying time in literature are considered archaic; however, they are still widely employed to personalize characters’ perception of time and highlight its psychological aspect. The temporality of these methods is generally reinforced through the use of adverbs, prepositions, or grammatical structures that indicate time. In conclusion, it is established that due to the directional nature of time, its fluidity, and the inevitability of approaching death, authors strive to find ways to capture and define time, giving it tangible forms, and in doing so, the past becomes alive once more.


Penerapan Artificial Intelligence dalam Mendeteksi Batu Ginjal secara Otomatis pada Citra CT Scan

Abstract: Background: Kidney stones are a clinical condition with the presence of stones along the urinary tract of varying sizes. The aim of this research is the need for a system to automatically detect kidney stones so that it can help radiologists in diagnosing kidney stones accurately, effectively and efficiently, and patients can immediately undergo further action to cure kidney stones.Methods: The difference in research carried out by researchers is the use of artificial intelligence which uses deep learning with a convolutional neural network (CNN) algorithm. This research uses images obtained from CT scan results from public data (Kaggle) and primary hospital data. The number of images used in the Augmentation training data was 2338 normal images and 2390 kidney stone images. The augmentation testing data used 540 normal images and 446 kidney stone images. The research also involved experts, namely radiology specialists, in determining images with abnormal and normal stone tones.Results: research obtained from CT Scan images of kidney stones with augmentation and original using public data/Kaggle images, obtained using augmentation obtained a high accuracy value of 99.69%. Meanwhile, in testing data using primary/hospital data images, augmented data obtained accuracy values that were still low at 45.43% and 45.23%, respectively.Conclusions: The use of deep learning with the CNN model in training data augmentation obtained high accuracy values, however in testing data using hospital CT scan images the accuracy value was still low, but it was able to recognize images of kidney stones, so it could help in automatically diagnosing kidney stones. For future work could involve refining the model to handle variations in hospital data or exploring additional features to improve generalizability.


Website-Based Employee Administration System at Tebing Tinggi City Regional Personnel Agency

Abstract: Employee administration is a system that contains related information consisting of elements that are guided towards the target in order to achieve ideal results. For that, the system gets input containing information about employee needs. Often this staffing administration is done manually as written in a book. The problem makes the author interested in creating an employee administration site that is useful for making it easier to provide information related to employees. This administration system runs on localhost using the PHP programming language and MySQL as a database. With this administration system application, it can facilitate and speed up employees in getting information wherever and whenever just by accessing the employee administration system site.


The Internet of Things (IoT) in World Practice: Review and Analysis

Abstract:
This article explores the contemporary realm of the Internet of Things (IoT) and its influence on society and the economy. The primary emphasis lies in examining the principal patterns within the realm of IoT, encompassing the escalation of connected device quantities and the utilisation of 5G networks. The article encompasses various facets, including an overview of the evolution and possibilities of IoT, encompassing the advancement of intelligent urban areas and the influence of 5G networks. The analysis of threats and vulnerabilities related to this technology focuses on detecting common flaws, including weak passwords, unsecured network services, and inadequate privacy measures.
 Furthermore, the essay presents concrete hazards in diverse sectors that employ IoT technology. Particular emphasis is given to potential cybersecurity vulnerabilities in healthcare, manufacturing, agriculture, retail, transport and logistics, energy, and smart cities. These businesses have distinct challenges and hazards due to incorporating IoT technologies.
 The authors also analyse the substantial financial commitment to IoT and its profound influence on the worldwide economy, including data on the expansion of the IoT market and investments in IoT technologies and smart cities. The findings emphasise that the Internet of Things (IoT) substantially influences society and the economy. However, it necessitates meticulous consideration of cybersecurity and privacy concerns. The authors underscore the significance of creating efficacious cyber security policies to safeguard against the possible hazards presented by IoT technology.


ELECTRIC VEHICLE BLOCKCHAIN: PROBLEMS AND OPPORTUNITIES

Abstract: These days, we are observing a very rapid spread of the electric vehicleindustry. This means a significant increase in the data and energy exchanged betweenthese vehicles. The existing centralized approach is less secure and more vulnerableto data destruction and manipulation by intruders. Therefore, it became necessary tosearch for an alternative that provides excellent protection for this massive amountof data and energy. Although blockchain technology and cryptocurrencies are closelyassociated, they also have many other potential applications in fields including energyand sustainability, the Internet of Things (IoT), smart cities, smart mobility, andmore. In the Internet of Vehicles (IoV) idea, blockchain can provide security forelectric vehicle (EV) transactions, enabling electricity trading to be carried out ina decentralized, transparent, and secure manner. . This paper will explain the use ofblockchain in this field and how it can handle the trade of transmitted and receivedenergy between electric vehicles. The advantages of using blockchain with electriccars and how it can secure the transactions of energy trading will be shown too. Agroup of researchers in this field and the challenges that face this technology in energytrading will be discussed too; the studies will be looked at, and recommendations forinvestments and security will be made. Additionally, the future implications of variousblockchain technologies will be highlighted.


Survey of MCDM-Based Software Engineering Method

Abstract: Multi-criteria decision making (MCDM) is an operation research subfield (OR). Decision-making is primarily a process involving various actors, including individuals, groups of individuals, institutions, and the state. As a discipline, multi-criteria decision-making is relatively new Many researchers have devoted their time to developing new MCDM models and techniques since the 1950s and 1960s, when the foundations of modern multi-criteria decision-making methods were laid. Research and development in the area have intensified recently and appear to be developing tremendously. Few attempts have been made to systematically convey the theoretical foundations and advancements of multi-criteria decision-making systems, despite the widespread and intense development. The framework for evaluating judgments and the methodological options are still up for debate. In this paper reviews of MCDM/MADM technologies will be included describing their scenarios. Making a decision that is more appropriate than others to solve a particular problem is the goal of decision-making (DM), so this study presents a review of the advantages and disadvantages of different decision-making techniques, specifically in the field of software engineering.


Understanding the Impact of Chatbot Technology in Learning: Analysis of Utilization at SMA Negeri 5 Binjai

Abstract: This research aims to comprehend the impact of chatbot technology on learning, with a specific focus on analyzing its utilization at SMA Negeri 5 Binjai. Employing both qualitative and quantitative approaches, this study explores the implementation of chatbots in an educational context, including their applications in language learning, mental health support, and the teaching process. A comprehensive literature review was conducted to identify potential benefits and challenges associated with chatbot implementation in schools. The analysis results highlight the evolution of chatbot roles, ranging from basic functionalities to sophisticated capabilities in supporting the learning process. The research also encompasses a chatbot development model presented by Author and the findings from action research, providing practical insights into the use of chatbots in online learning media. Through surveys, interviews, and observations, research participants were engaged to provide a comprehensive perspective on the impact and acceptance of chatbot technology in the educational environment of SMA Negeri 5 Binjai. The outcomes of this research are anticipated to offer in-depth insights into how chatbots influence the learning process and lay the foundation for developing more effective strategies for chatbot utilization in educational institutions. The conclusions and recommendations from this study may assist policymakers, educators, and researchers in understanding the role of chatbots in enhancing the learning experience at SMA Negeri 5 Binjai and similar educational contexts.


Symmetric Cryptography Modification Using Diffie Hellman On RDBMS

Abstract: One of the most widely used and freely available RDBMS is MySQL. MySQL users generally do not care about the confidentiality of the contents stored in MySQL. The application of data confidentiality in MySQL generally only uses a username and password to protect MySQL but not its contents. Therefore, it is necessary to apply the confidentiality of the data content in MySQL so that thieves cannot obtain information from the data contained in MySQL by converting the contents of the text stored in MySQL into secret text. This research uses one of the symmetric cryptography, namely vigenere cipher to keep the text stored in MySQL secret. The disadvantage of symmetric cryptography compared to asymmetric is the same encryption and decryption keys. Therefore, this research modifies the key in symmetric cryptography using Diffie Hellman so that the key used becomes asymmetric. So that with symmetric cryptography using Diffie Hellman on the contents of the data stored in MySQL, MySQl gets better confidentiality of its data content


A Study on the Dong-A Ilbo’s “Romance in History” Series to Expand Minority Readership

Abstract: This paper is a study that reveals the literary historical significance of the planned serialization “Romance in History” of the Dong-A Ilbo and the subsequent serialization “Children’s Chosun.” In this regard, based on a series of surveys on newspaper serials, including “Romance in History,” Kang Mi-Jung tried to reveal the function of private painting during this period and link it with the development of historical literature theory. Taking the achievements and limitations of the preceding studies as a starting point for discussion, this paper attempted to reconsider the literary and historical status of the series “Romance in History.” In order to derive the practical basis, this paper analyzed the text from the background of the appearance of “Romance in History,” the actual status of the series, and the level of formal aesthetics. As a result, it was verified that “Romance in History,” which was greatly welcomed by the public, was a genealogical precedent that suggested a motif for the creation of historical novels in the newspaper series entering the prosperity of the 1930s. It was also confirmed that it was the background and test that inspired the creation of historical novels.


Meningkatan Kemampuan Komunikasi Siswa Vokasi Menggunakan Flipbook dengan Augmented Reality

Abstract: The era of society 5.0 today plays an important role in everyday life, especially in education, which requires learning innovation. This research aims to develop learning innovations for an effective society in the 5.0 era. The method used to test the validity of flipbook learning media with AR is ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The results of the study showed that the educational media is valid, with a score of 80,5% for the material experts and 80,8% for the media experts.


The Impact of Digitalization on Economic Growth in ASEAN

Abstract: Purpose – The study aims to confirm a positive relationship between digitalization and economic growth in ASEAN. It seeks to provide insights into the challenges and future directions for ASEAN’s economic growth by examining the impact of digitalization through broadband penetration. Design/Methodology/Approach – This study employs regression analysis using a fixed-effect model with data sourced from World Development Indicators (WDI) and the United Nations Development Programme (UNDP), covering the period from 2008 to 2020. Its primary objectives are to offer a comprehensive understanding of the relationship between digital technologies and the economic development of ASEAN, and to identify factors contributing to economic growth in the region. The focus is on Southeast Asian countries, namely Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam Findings – The findings yield compelling evidence in support of the hypothesis, emphasizing the pivotal role of expanding broadband infrastructure in fostering economic growth across ASEAN countries. Furthermore, the study underscores the importance of enhancing broadband infrastructure and highlights the potential impact on the economic development of developing nations. It emphasizes the crucial role played by fixed-broadband, mobile-broadband, and fixedtelephone subscriptions in contributing to the economic growth of ASEAN countries. Research Implications – This study proves a significant positive relationship between digitalization and economic growth in the region. It offers a preliminary understanding of the impact of digitalization on economic growth in ASEAN, laying the groundwork for future research.


A Case Study of University Reading Education Using Intertextual Reading: focused on utilizing Lee Kwang-soo’s “Moojeong” & Kim Keum-hee’s “Too Bright Midday for Romance”

Abstract: Tcs.


Design and Implementation of Behavior Analysis and Risk Identification for Stroke Patients under Medical Technology Intelligentization.

Abstract: This study aimed to explore behavior analysis and risk identification for stroke patients under medical technology intelligentization, to establish a design and implementation of risk control and post-event recovery. A total of 80 stroke patients from 2019 to 2021 were selected using service design thinking combined with patient behavior as the guiding principle. The study used smart device monitoring modules and balance monitoring applications to monitor the daily behavior and post-event behavior of stroke patients and used intelligent recognition and analysis of risk behavior to develop interventions for post-event recovery services. The conclusion is that by combining service design thinking with patient behavior as the guiding principle, medical technology advancements in the field of intelligentization have the potential to greatly impact stroke patient care by enabling behavior analysis and risk identification. By utilizing smart device monitoring modules and balance monitoring applications, healthcare professionals can gather valuable data to understand better the behaviors and risks associated with stroke patients. This approach can provide a forward-looking reminder for risky behaviors and intervene in stroke patients' behaviors, reducing the risk of stroke recurrence.


Security in cloud computing: Methods for ensuring privacy and integration in modern applications

Abstract: Cloud computing has become a necessary component for data storage and processing and is becoming more widespread. However, there are threats to the security and privacy of user data, which is why it is important to find out the most effective methods for ensuring data security in the cloud. The purpose of the study was to develop methods aimed at ensuring privacy and security in cloud environments and in modern applications. The method of analysis was used to review other publications on the topic, and the method of experiment was used for practical implementation. The main results of the study include the development of a security monitoring programme. It analyses event logs and determines the number of failed login attempts, which indicates the detection or absence of suspicious activity. Access to resources is checked, and the necessary information is displayed on the console. A comparison table of cloud platforms has been created, considering their advantages and disadvantages in the context of data security and privacy. It specifies the criteria for delivering services to the selected services. A block diagram of ways to provide security in cloud computing is developed, illustrating the relationship between various aspects of providing security in cloud systems. It contains parameters and strategies for encrypting data, protecting sensitive data, and countering attacks. Various aspects of security and methods of ensuring privacy in cloud computing are considered, namely authorisation, intrusion detection, regulatory requirements, integration with modern applications, monitoring and logging, user identification and authentication. The practical significance of the study lies in the creation of innovative ways to help improve security and privacy in cloud computing. They will allow cloud developers and administrators to effectively protect user data and ensure their privacy in modern applications


Motivational and semantic relations of a person in professional activity

Abstract: The object of this research is the motivational and semantic relations of the individual. The subject of the research is the motivational and semantic relations of employees with different professional affiliations and specifics of performing functional duties. The purpose of the research is to study the structure of motivational and semantic relations of a person and to identify patterns of influence of professional affiliation and functional duties. The methodological basis for writing the article is the research of domestic and foreign scientists. Theoretical (comparative analysis) and empirical (interview, questionnaire) research methods were used in the study. The orientation of motivational and semantic relations of a person does not depend on the professional affiliation and the specifics of the performance of functional duties. Since the motivational and semantic relations of a person in professional activity are characterized by the presence of motivation based on the personal meaning, which motivates a person to activity and depend on the psychological state of a person who is in certain life circumstances. Motivational and semantic relations of a person are based on motivational and semantic processes that accompany and support a person throughout his or her working life. The aim of the work is to establish the regularities of the influence of professional affiliation and the specifics of functional duties on the motivational and semantic relations of employees to professional activities. In order to increase the efficiency of professional activities and improve the motivation of employees to the heads of organizations, the authors give practical recommendations for improving psychological support in organizations.


Development of Basic Geology Mobile Learning Based on Android Indonesia Geodiversity Area

Abstract: Basic Geology is a prior knowledge that needs to be supported by a flexible learning environment through the use of mobile technology. Through the supports of mobile technology, students will gain freedom of access because they can be used anytime and anywhere. The progress and speed of technological development, the ease and practicality of using smartphone devices are the potential, opportunities and challenges for presenting mobile technology in learning. The presence of generation Z who are already familiar with technology has reinforced the need to utilize technology to accommodate them in learning better. The aim of this development research is to produce a learning product in the form of a Basic Geology mobile learning application to support learning anywhere and anytime and to determine the feasibility of the product in terms of media and materials, as well as responses from users. This research uses the Lee & Owens model in developing mobile learning applications. Data was collected using a questionnaire filled out by media experts, material experts and service users in the Indonesian Geodiversity Area. The results of this research are (1) user needs for mobile learning to support Basic Geology learning by providing learning resources and building learning communities, (2) producing Basic Geology mobile learning products, (3) results of feasibility tests by media experts, material experts and Individual, small group and field trials show that mobile learning is suitable for use in learning.Keywords: geodiversity; basic geology; mobile learning


Problems of crime prevention by operational-investigative units in Kazakhstan

Abstract: Relevance. Crime prevention by operational-investigative units is an integral part of effective law enforcement, yet it is increasingly being overshadowed by a punitive approach focused on arrests rather than proactive prevention measures. This reactive approach results in significant societal costs and fails to address the root causes of criminal behavior. Purpose. This study aims to examine the challenges faced in organizing effective crime prevention efforts by operational-investigative units in Kazakhstan and propose clear criteria to evaluate and improve the efficacy of such preventive measures. Methodology. An integrated approach combining theoretical principles from related legal branches and practical insights from the activities of operational-investigative units in Kazakhstan is employed. Various research methods, including system-structural, comparative, logical, and transition from general to particular concepts, are utilized. Results. The study identifies several key issues hindering preventive efforts, such as a lack of conceptual understanding, a prevailing punitive mindset among law enforcement officers, and an absence of clear evaluation criteria. To address these challenges, the authors propose specific criteria focusing on early detection of criminal intent, timely intervention to prevent crime completion, and retrospective analysis of preventive measures' effectiveness. Conclusions. Implementing the proposed criteria consistently can reorient the operational-investigative units' approach toward a stronger emphasis on prevention. This shift is crucial for addressing the root causes of crime, reducing societal costs, and enhancing the overall effectiveness of law enforcement efforts in Kazakhstan.


Analisis Pembentukan dan Perubahan Makna Wasei Eigo pada Situs Jejaring Sosial Ameba Blog

Abstract: This study aims to identify the formation, change of meaning, and types of wasei-eigo found in the Ameba blog. The underlying theories are Tsujimura's (2014: 150) Japanese word formation, Irwin's (2011: 143) compound word shortening theory, Chaer's (2013: 140) meaning change theory, and wasei-eigo types according to Shibasaki, et al (2007: 90). The method used is qualitative descriptive method. The data analysis technique applied is translational pairing. The base technique is the technique of sorting out the determining elements (PUP) with the advanced technique of comparative relationship to distinguish (HBB). The analysis yielded 14 wasei-eigo words in the fashion category of Ameba blog. The data consisted of 9 compounds, 3 words experienced shortening type compound truncation, 1-word experienced shortening type morpho-orthographic truncation, and 1-word experienced shortening type compound truncation and ellipsis middle truncation. In the change of meaning, 4 data experienced narrowing, 1 data experienced expansion, and 9 data experienced total change of meaning. Additionally, out of the total data, 5 data involved imizuregata, 4 data involved eigohyougenfuzaigata, and 5 data involved tanshukugata and eigohyougenfuzaigata types


The use of SMART technologies in the development of transliteracy of future educational psychologists

Abstract: Relevance. The relevance of the research is conditioned by the fact that in the educational activities of future educational psychologists, it is possible to acquire modern professional competencies in the development of transliteracy using SMART technologies. Intensive penetration of these tools into the educational environment updates the requirements and compliance of students, which leads to the optimisation of the current process. The ability to access high-quality resources increases the role of individual work and self-education, which are aimed at high independence level. Purpose. The purpose of the study is to highlight the features of the application of information and communication aspects in a specific educational system of the Republic of Kazakhstan, to consider innovative SMART technologies in the development of transliteracy to improve the level of education and qualifications of future educational psychologists so that they can use information, skills, and knowledge effectively. Methodology. The following methods were used: prognostic, psychological-semantic, system-structural. Results. The need to improve the quality of education through the use of modern technologies actualises the development of a creative and active personality as a subject of upcoming professional activity. The future educational psychologist needs to be able to rationally solve specific tasks, be competitive in the labour market, continuously improve their competence, transliteracy, and efficiency by using the latest mechanisms. In addition, the task of the teaching staff is the ability to navigate and determine modern prospects for the successful introduction of SMART technologies in the educational process to improve the quality of training of future educational psychologists. Conclusions. The practical value of the research lies in providing an innovative approach to the educational process with the introduction of SMART technologies for the development of transliteracy of future educational psychologists.